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Evaluation of an eco-driving support system
Transportation Research Part F 27 (2014) 11–21
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Transportation Research Part F journal homepage: www.elsevier.com/locate/trf
Evaluation of an eco-driving support system Maria Staubach a,⇑, Norbert Schebitz a, Frank Köster a, Detlef Kuck b a b
Institute of Transportation Systems, German Aerospace Center (DLR), Germany Ford Forschungszentrum Aachen GmbH, Süsterfeldstr. 200, 52072 Aachen, Germany
a r t i c l e
i n f o
Article history: Received 3 December 2013 Received in revised form 6 September 2014 Accepted 6 September 2014
Keywords: Eco driving support User acceptance Driving simulator Evaluation Fuel consumption
a b s t r a c t In this study an eco-driving support system was evaluated within a driving simulator study. The presented system was able to communicate with traffic lights and to detect traffic signs. On the basis of this information the system gave recommendations to the participants concerning fuel efficient gear shifting and acceleration/deceleration behavior. Thirty participants experienced three different driving situations (traffic light approach, curves, and stop signs) with and without the eco-driving support system in an urban and a rural scenario. As a result with the systems mean fuel savings between 15.9 and 18.4 percent could be realized because the participants shifted up earlier and used more coasting strategies than in the condition without the system. Thus the number of stops decreased. At the same time travel time increased significantly only in the rural scenario (+5.4%) and there were no negative effects on safety. Further, the eco-driving support system was highly accepted by the participants. The high potential of the system for fuel saving should be researched further with a focus on interactions between vehicle drivers. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction According to a press release of the International Transport Forum (2010) global CO2-emissions from transport grew by 45% from 1990 to 2007 and will rise further by another 40% until 2030. Since CO2 emissions seem to be closely connected to the global greenhouse effect it is necessary to develop measures in order to reduce CO2 emissions. Therefore the European Union strives for fuel savings and the reduction of CO2 emissions in transport by at least 60% compared to the year 1990. Until 2020 it is the goal to limit the increase of CO2 emissions to 8% (European Commission, 2011). One possibility to pursue this goal is to promote eco-driving among car drivers. Thus, for example within the ECOWILL project (Jellinek, 2010) ecodriving training programs in Europe are researched and promoted. Beusen et al. (2009) used on board devices to study longer-term impacts of an ecodriving course via GPS-tracking and found reductions of 5.8% after four months. Other research programs focus on the change of driving behaviors using advanced driver assistance and cooperative systems. For example within the eCoMove project (Cooperative Mobility Systems and services for energy efficiency, Castermans, Brusselmans, & Pandazis, 2010) a lot of technologies and applications were developed which altogether aim to reduce fuel consumption and CO2 emissions by up to 20%. The Ford eco-driving support system is one of those systems. Since it is able to detect road signs and to communicate with infrastructural components such as traffic lights it can recommend the drivers how and when to accelerate and decelerate fuel-efficiently and which gear to choose. Therefore an HMI was realized with a visual display and a haptic force feedback pedal which signalizes the drivers when to decelerate and when to shift gears.
⇑ Corresponding author at: Lilienthalplatz 7, 38108 Braunschweig, Germany. Tel.: +49 531 2953478. E-mail address: [email protected] (M. Staubach). http://dx.doi.org/10.1016/j.trf.2014.09.006 1369-8478/Ó 2014 Elsevier Ltd. All rights reserved.
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Both modalities were implemented because in an earlier study three different advice modalities (visual, haptic, visual-haptic) were researched regarding their fuel saving potential and acceptance issues and it was found that the combination of visual and haptic recommendations lead to the best gear shifting and acceleration behavior for fuel-efficient driving (Staubach, Fricke, Schießl, Brockmann, & Kuck, 2014). Consequently in the present study a combination of visual and haptic recommendations was used. In the past some authors have already researched the impact of eco-driving support systems on the reduction of fuel consumption and emissions. Várhelyi, Hjälmdahl, Hydén, and Draskócszy (2004), for example, reported significant effects of an active acceleration pedal on the reduction of speed and emissions especially in urban surroundings. Birrel, Young, and Weldon (2013) as well as Larsson and Ericsson (2009) found that a haptic force feedback on the acceleration pedal led to reduced maxima of acceleration, which is one important precondition for the realization of fuel consumption reductions. Further, Gonder, Earleywine, & Sparks, 2011 compared the effectiveness of different advice categories towards their fuel reduction potential and found that acceleration/deceleration recommendations do have a potential to save 6% of fuel. Wu, Zhao, and Ou (2011) even found 22–31% overall gas savings for a fuel-economy optimization system (FEOS) which displayed the optimal acceleration/deceleration profile to the driver. Other systems focused on the gear shift behavior of the drivers because measurements have proven that gear-shifting behavior can influence fuel consumption significantly. According to Dhaou (2011) changing gears from 3rd to 4th can reduce fuel-consumption by 19% and from 4th to 5th gear by 25%. van der Voort, Dougherty, and Maarseveen (2001) introduced a driver feedback system including an HMI with gear shift recommendations, which helped to save 16% of fuel compared to normal driving and 7% fuel compared to a fuel efficient driving style. Lange, Schmitt, Arcadi, Bengler, and Bubb (2010) analyzed the emission reduction potential of a haptic gear shift feedback on the accelerator pedal additionally to visual recommendations and found possible CO2 savings up to 15.8 g/km. All these reported fuel saving effects could even be larger if besides the current traffic situations also future developments were taken into consideration. Using cooperative systems this will become possible in the next few years. Hoyer (2012) described the huge fuel saving potential of cooperative traffic lights, which communicate green light onset and offset times to the drivers. Within the same field experiment Otto (2011) found a fuel saving potential up to 5%. Fujimaki, Kinoshita, and Inoue (2012) conducted a field trial of a green light speed advisory system (GLSAS) and found higher fuel consumption efficiency, fewer stops per run and less acceleration when the GLSAS-services were used and Barth and Boriboonsomsin (2009) found a 13% fuel reduction potential in a real world experiment with a dynamic eco-driving feedback with real-time recommendations about the changing traffic conditions. For this study the validation approach of the eCoMove project was used (Themann et al., 2012). According to this approach research questions were defined in order to research the performance of the eco-driving support systems in different evaluation categories. Those were environment, mobility, driver behavior, safety, and user acceptance of the participants. Within each category hypothesis were defined and appropriate performance indicators were identified (Table 1). The results of each category will be presented in this paper. 2. Material and methods 2.1. Participants Thirty participants (15 male, 15 female) aged between 20 and 69 years (M = 41.5; SD = 15.2) took part in the study. The participants were drawn from the DLR test driver data base that contains more than 850 participants of all ages and different driving characteristics according to their ages, their sex and their driving experience. These participants were recruited at organizational events like open days and via newspaper advertisements. Twelve participants reported a medium to low annual mileage (between 3000 and 9000 km/year), twelve a medium annual mileage (between 9000 and 20,000 km/year), and six participants reported a high annual mileage (more than 20,000 km/year). The participants did not have any special experiences with eco-driving. The participation in this study was voluntarily and was compensated with eight Euro per hour. Table 1 Evaluation categories and performance indicators. Evaluation category Environment Mobility Driving performance Safety
Acceptance
Performance indicator – – – – – – – – – – – – –
Fuel reduction (%) Travel time (s) Number of stops Speed standard deviation Gear shifting behavior (revolutions per minute, RPM) TTC: percentage of time with TTC < 2.6s Number of hard braking events (longitudinal acceleration > 3.5 m/s2 Speed violations Gaze analysis: duration of glances towards the HMI Perceived ease of use (questionnaire) Perceived usefulness (questionnaire) Behavioral intentions (questionnaire) Van der Laan Scale (1997)
M. Staubach et al. / Transportation Research Part F 27 (2014) 11–21
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2.2. Evaluation categories and performance indicators For each evaluation category performance indicators were defined and published in the eCoMove Validation and Evaluation plan (Isasi et al., 2012). They are listed and described in Table 1. For most of the performance indicators the changes between the drives with the eco-driving support system compared to the baseline where no system was applied have been measured. Only the gaze analysis and the acceptance questionnaires were just applied within the condition with the eco-driving support system. The goal of using an eco-driving support system is the reduction of fuel. Therefore the fuel reduction potential of the system was measured within this study. For determination of fuel consumption a generic model (Dobre, 2012) was used. This model calculates the fuel consumption dependent on the velocity and the acceleration behavior. It uses input parameters of the vehicle (like mass, drag coefficient) to determine the driving resistances. Therefore the parameters of a Ford car were considered in the driving dynamics of the simulation. It was also important to research the effects the eco-driving support system on mobility parameters. It was hypothesized that the travel time and the number of stops, e. g. before traffic lights, would not increase with the system. The third evaluation category referred to driving performance indicators. In order to find out whether the eco-driving support system helped to realize the recommendations of the eco-driving support system, speed standard deviation and the gear shifting behavior were measured. According to the golden rules of eco-driving which were defined within the ECOWILL-project (www.ecodrive.org) ‘‘driving with high or even medium engine RPM always consumes more fuel than driving at low RPM at whatever speed.’’ Hence, it is recommended to shift to higher gears at approximately 2000 RPM. Further it suggested to ‘‘drive smoothly at low RPM using the highest possible gear’’. Another very crucial point was to make sure that the researched eco-driving support system would not have any negative effects on safety. In order to answer this question several hypothesis have been defined in the eCoMove Validation and Evaluation plan and in a next step safety indicators and cut-offs have been proposed. This process has been influenced by former project-deliverables (e.g. Johansson et al., 2006) in which amongst others TTC, speed metrics, and glance duration were recommended as metrics for ADAS and IVIS assessment. Moreover, mean and maximum speed measurements have been proposed. However, within eCoMove it was hypothesized that the eco-driving support systems would not encourage the drivers to speeding. Therefore the distance the participants drove faster than the speed limit was compared between the drives with the system and the baseline. For the TTC measurements the authors suggested to discard situations where the TTC is above 15s because they cannot be regarded as car-following situations. As threshold for a safe TTC 2.6s was used (Hogema & Janssen, 1996). Using the number of hard braking maneuvers as a safety performance indicator was recommended within the FESTA Handbook (Kircher et al., 2008). The cut-off value for hard braking maneuvers has been adapted from van Driel (2007) who suggested 3.5 m/s2. The duration of glances towards the HMI was measured using a Dikabilis eye tracking system. Last but not least acceptance questionnaires were used in order to find out about the users attitudes and behavioral intentions to use the eco-driving support system. More information about the questionnaires can be found in chapter 2.3.2. 2.3. Materials 2.3.1. Driving simulator In order to examine the research questions a study using the motion based dynamic driving simulator of the German Aerospace Center (DLR) was conducted. The motion system of the simulator is characterized by a hexapod system with the cabin hanging in below the upper articulations. A high-quality projection system provides the visualization of the surrounding environment and traffic. Besides the forward view the driver can also observe the simulated traffic behind him by the rear-view mirror on a screen and by LC-displays in the lateral rear mirrors. For a realistic overall impression the direct environment of the driver is very important. Therefore a complete vehicle has been integrated into the cabin with which the driver is ‘‘driving’’ the simulator. For this purpose the actions of the driver are transmitted via CAN-Bus to the simulation computer, vice versa the simulation system controls the instruments inside the cockpit, which for example inform the driver about his current speed. All inputs and actions made by the driver, from braking over steering to operating the radio, can be recorded and analyzed. 2.3.2. Questionnaires In order to measure system acceptance questionnaires were developed following the Theory of the Technology Acceptance Model (Davis, 1986). According to this model the acceptance of technical systems is dependent on the perceived usefulness and the perceived ease of use of the system, as well as the behavioral intention and the attitude toward using the system. Suitable questions concerning the eco-driving support were developed considering user requirements which were analyzed in earlier studies (Eikelenberg et al., 2011; Höltl et al., 2011) and the inefficiencies which are addressed by the eco-system (Themann et al., 2012). Each item was evaluated by the participants on a five point Likert Scale (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree). A list of the used questions can be found in Table 5. In order to find out something about the participants’ attitudes towards the system the acceptance scale from van der Laan, Heino, and de Waard (1997) was used. The participants had to rate on a five point scale (from 2 to +2) how much they associated the adjectives (see Table 7) with the evaluated system. Moreover, open questions were asked about the amount of money the participants would like to spend on an eco-driving support system. Further open questions concerned the modalities they preferred, what
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M. Staubach et al. / Transportation Research Part F 27 (2014) 11–21
Fig. 1. FFA driver support system visual display for two different situations.
would motivate them to use the systems regularly, whether they would like to be able to turn off the system and for the reasons why they would like an eco-driving support system in their cars. They were also asked whether they followed all the systems recommendations, to give reasons in case they did not sometimes and to reflect also disadvantages of the system. All questionnaires were filled out at the end of the study and referred only to the trial with the eco driving support system. 2.4. Experimental design The participants drove an urban (speed limit 50 km/h) and a rural scenario (speed limit 70 km/h). Each of the scenarios contained three different traffic light situations, two curves (left and right) and one stop sign. For the traffic light approaches in both scenarios there were three different speed recommendations from the traffic light to the eco-driving support system about which speed was adequate to pass the green traffic light when approaching in the red phase. Hence, in the urban scenario the drivers had to decelerate their vehicle to a target speed of either 35 or 40 km/h or keep the speed of 50 km/h, in the rural scenario the participants had to decelerate to 49 or 56 km/h or keep their speed at 70 km/h in order to pass the traffic light within the green phase without stopping. Further, in the curve situations the participants had to slow down their cars in order to pass the curves safely, and at the stop signs they had to slow down their cars to standstill. For the stop sign situations it was obvious in pretests that the intervention with the target speed of zero km/h started too early and took too long until the stopping of the car. Therefore the target speed was changed from zero to 30 km/h under the assumption that the drivers would slow down manually shortly before they arrive at the stop sign. Each situation was experienced three times by the participants in order to improve the reliability of the measurements. The study design was a within subject design. Thus, each participant experienced the urban and the rural situations with and without the eco-driving support system (baseline) in balanced order. In the condition with the eco-driving support the gaze directions of the drivers were measured. The order of the scenarios and situations was randomized in order to prevent test effects. The results were analyzed using paired t-tests for the comparison of the results of the eco-driving support and the baseline. For the fuel consumption a single sample t-test was used. The system recommendations were given using a visual display and a haptic force-feedback signal on the acceleration pedal (counter pressure). The visual display is presented in Fig. 1 for two different situations. In both pictures a grey bar can be seen with a green range on the bottom which grows larger when the driver is supposed to accelerate. The grey needle shows the current amount of acceleration. When the grey needle is in the green range the acceleration respectively the driven speed is appropriate to the oncoming traffic conditions. When the needle is above the green range, the driver is supposed to slow down his car in order to meet the traffic conditions and thus, to save fuel. Moreover, the traffic conditions are displayed via symbols, which constitute the systems recommendations. The symbol on the bottom of the left picture in Fig. 1 shows how many seconds are left until the red light turns green. If the driver follows the systems recommendations, he does not have to slow down to standstill approaching the traffic light. Instead he starts slowing moderately but earlier before reaching the traffic light which leads to a reduction in fuel consumption. On the right part of the picture the traffic sign ‘‘Curve ahead’’ is displayed on the bottom as a reason for the recommendation to slow down. 3. Results 3.1. Environment In urban scenarios the mean fuel reduction was 15.9% when participants drove with the eco-driving support recommendations; in rural scenarios the reduction potential for all situations together was 18.4%. In both scenarios the highest potentials could be found in the curve situations and in the traffic light situations with a recommended speed reduction of 30%
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M. Staubach et al. / Transportation Research Part F 27 (2014) 11–21 Table 2 Fuel reduction (%) in different situations. Scenarios, situations
Mean
SD
Median
Shapiro–Wilk Test (p)
t29
p
Effect size d
Urban
15.9 26.3 15.0 1.3 19.6 36.8 18.4 34.0 16.2 3.5 29.9 24.5
11.0 20.8 19.4 13.6 19.8 17.5 12.5 21.5 21.3 19.1 22.5 19.9
14.7 28.1 12.2 1.4 21.0 37.1 20.7 36.2 17.5 5.5 38.1 20.4
.25 .03 .06 .50 .18 .89 .84 .49 .62 .36 .02 .65
7.90 – 4.24 0.54 5.44 11.53 8.07 8.65 4.17 0.99 – 6.76
.000 – .000 .591 .000 .000 .000 .000 .000 .329 – .000
1.45 – 0.77 0.10 0.99 2.10 1.47 1.58 0.76 0.18 – 1.23
Rural
Total distance Traffic light (35 km/h) Traffic light (40 km/h) Traffic light (50 km/h) Stop sign Curve (30 km/h) Total distance Traffic light (49 km/h) Traffic light (56 km/h) traffic light (70 km/h) Stop sign Curve (30 km/h)
(35 km/h urban, 49 km/h rural). The stop sign situation and the traffic light situations where the participants were told to slow down 20% (40 km/h urban, 56 km/h rural) also had a high potential to save fuel. In those traffic light situations where the participants were supposed to keep their current speed the fuel reduction potential was very low. The results are summarized in Table 2. Before the single sample t-tests have been calculated a Shapiro–Wilk-Test was performed in order to ensure the normal distribution of the test data. According to this test there were two statistics which are not distributed normal: ‘‘urban – traffic light (35 km/h)’’ and ‘‘rural – stop sign’’. Looking at the boxplots there was one outlier in the scenario ‘‘urban – traffic light (35 km/h), who needed more fuel in most of the situations using the eco-driving support system. Excluding this outlier would lead to a normal distribution. For the stop sign there is no special outlier but just an asymmetric distribution. Hence, a lot of participants did not save a lot of fuel while a large group of participants (n = 14) managed to save more than 40% of fuel. This observation fits well to the results of the open questions in the acceptance chapter where 28% of the participants stated that they did not follow the recommendations when approaching stop signs and curves. 3.2. Mobility Using the eco-driving support system the travel time of the test drives did not increase significantly in the urban scenario. Within the rural scenario it took the participants significantly more time to complete the scenario (+5.4%) using the eco-driving support. When only the traffic light approaches were considered the travel times did not differ significantly in both scenarios. The results are presented in Table 3. Further the number of stops decreased significantly in the urban (from 5 to 3.5 stops, z = 3.65, p = .000, d = .75) and in the rural scenario (from 3.9 to 3.3, z = 1.94, p = .050; d = .29). Therefore, using the ecodriving support system fuel consuming acceleration procedures at the traffic lights could be prevented by the drivers. 3.3. Driving performance The recommendations of the eco-driving support system lead to a smoother driving style with more constant velocities and less deceleration and acceleration manoeuvers especially in those situations where the participants had to reduce their velocities in order to pass the traffic light at the green phase or when they had to slow down to pass the curve safely or on stop signs. Therefore the speed standard deviations in the treatment condition were significantly smaller than those in the baseline phase. The results are integrated in Table 4. The smaller speed standard deviations with the eco-driving support systems were realized through applying coasting strategies earlier which led to steady deceleration manoeuvers with smaller slopes than in the baseline condition. As an outcome the speeds of the participants in the condition with the eco-driving support system when the traffic lights turned green were higher and thus they did not have to accelerate as much as they did in the without the system in order to reach their target velocities. Fig. 2a and b show examples of the mean speed profiles for two different situations. They follow similar patterns: 400 m before the traffic light the velocities in both conditions are very close to each other. Now with the system the participants received the advice depending on the speed they drove at. In the rural scenarios this was about 400–350 m
Table 3 Travel time of the test drives. Travel time
Urban Rural
All scenarios Traffic lights All scenarios Traffic lights
Baseline
Treatment
Mean
SD
Mean
SD
1253 s 972 s 1229 s 844 s
107 s 98 s 70 s 43 s
1269 s 955 s 1295 s 831 s
76 s 58 s 100 s 48 s
t29
p
d
t = .96 t = 1.12 t = 2.79 t = 1.05
.34 .27 .01 .32
d = .17 d = .21 d = .54 d = .29
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Table 4 Speed standard deviations of the test drives. Baseline
Urban
Rural
Traffic light Traffic light Traffic light Curve 30 Stop sign Traffic light Traffic light Traffic light Curve 30 Stop sign
50 40 35
70 56 49
Treatment
Mean
SD
Mean
SD
4.2 7.2 9.2 6.3 10.3 6.2 9.6 12.9 8.3 13.1
2.0 3.4 2.5 2.3 1.7 3.9 4.4 4.5 3.1 2.5
3.1 4.2 6.0 6.0 9.3 4.1 6.3 7.7 7.3 13.3
1.8 1.9 2.1 1.2 1.0 3.0 3.7 3.1 1.9 1.8
z
p
d
3.99 6.30 6.90 0.99 4.50 3.50 6.50 7.10 2.80 1.50
.000 .000 .000 .280 .000 .000 .000 .000 .004 .140
0.58 1.09 1.38 0.14 0.72 0.60 0.81 1.35 0.39 0.09
Table 5 Gear shifting behavior, mean RPMs at which the participants shifted. Baseline
Treatment
Gear shift
N
Mean
SD
n
Mean
SD
1st into 2nd 2nd into 3rd 3rd into 4th 4th into 5th
373 816 1246 672
3243 2850 2176 1860
1015 702 514 396
274 488 956 647
2974 2606 2047 1831
927 668 414 297
t
p
d
2.48 4.33 4.19 1.86
.019 .000 .000 .007
0.28 0.36 0.28 0.08
before reaching the traffic light, in the urban scenarios about 300–250 m. While approaching the traffic light further and following the advice of the eco-driving support system the velocities decreased steadily with a small slope. When the traffic light turned green about three seconds before reaching the participants started to accelerate again. In all cases they passed the traffic lights with higher velocities than in the baseline condition. In the latter the drivers started decelerating much closer to the traffic lights and the slope was much sharper. Also in curves and before stop signs the deceleration slopes were smaller when the participants were driving with the system. Considering the gear shifting behavior when following the systems recommendations the participants shifted at lower RPMs than in the baseline phase. For the first four gears the differences were statistically significant with small effect sizes. The shift from the 4th into the 5th gear the difference was smaller. This is probably due to the lower and slower rising in RPM-levels at higher gears. The results are summarized in Table 5. 3.4. Safety In a few situations the participants had to follow a lead car and the percentage of time when the time to collision (TTC) was less than 2.6 s was measured. Therefore only situations were considered in which the TTC was less than 15 s. As a result the amount of critical TTC-situations decreased to 1.6% driving with the eco-driving support system from 2.8% in the baseline drive (z = 3.3, p = .0008, d = 0.75). Further the number of hard braking events (longitudinal acceleration < 3.5 m/s2) decreased significantly from 10.3 in the baseline condition to 3.3 in drives with the system (z = 4.19, p = .000, d = 0.9). Also the distance driven exceeding the speed limit decreased in the treatment phase. Hence, the participants exceeded the speed limit up to 10 km/h in 17.0% of the distance using the system compared to 28.8% in the drives without the system (z = 4.38, p = .000, d = 0.8). The amount of speed deviations above 10 km/h decreased from 3.7% to 0.4% (z = 3.48, p = .000, d = 0.5). The results of the gaze analysis suggest that the distraction initially was indeed too high since most of the drivers looked away from the street and towards the HMI more than two seconds. However, over the period of testing the glances away from the street towards the HMI became shorter. The results are shown in Fig. 3. There was a significant negative correlation between the mean (r = .914, p < .001) and the maximum (r = 0.688, p < .001) HMI glance duration with the duration of the trial. Therefore, the initial distraction through the systems might be reduced soon by the easy learning of the systems functioning. 3.5. Acceptance In Table 6 the results for perceived ease of use, perceived usefulness and behavioral intention to use are presented. For the perceived ease of use scale Cronbach’s Alpha was sufficiently high (a = 0.781) for interpreting the items as a common scale (Nunnally & Bernstein, 1994). Overall most of the participants found the system easy to use (item 11). Especially the learnability (item 9) and the comprehensibility (item 5) were rated very well by the participants. However, some of the
M. Staubach et al. / Transportation Research Part F 27 (2014) 11–21
(a) Scenario: urban, traffic light approach with 50 km/h
(b) Scenario: rural, traffic light approach with 56 km/h
Fig. 2. Mean speed profiles of the different situations.
Fig. 3. Maximum and mean duration of glances towards the HMI.
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M. Staubach et al. / Transportation Research Part F 27 (2014) 11–21
Table 6 Means and Standard deviations of means of answers to the acceptance questions. Item
Description
Mean score
SD
Perceived 1 2 3 4 5 6 7 8 9 10 11
Ease of Use I found the system intuitive to use Using the system distracted me from driving For me the feedback of the system was precise enough My interaction with the system was clear and understandable It was easy for me to follow the information provided by the system Interacting with the system was frustrating Interacting with the system was comfortable I perceived the feedback of the system as encouraging Learning to use this system was easy for me The system provides adequate information that is in control of my driving Overall, I find the system easy to use
3.6 3.0 3.8 3.6 4.0 2.5 3.5 3.3 4.3 3.6 4.2
1.1 0.9 0.9 0.9 0.9 1.1 1.0 0.9 0.8 0.9 0.8
Perceived 12 13 14 15 16 17 18 19 20 21
Usefulness Using the system increased my awareness of economical driving Using the system restricts my freedom while driving Using the system helped me to accelerate in a more fuel efficient way Using the system helped me to decelerate in a more fuel efficient way Using the system helped me to avoid economically inefficient gear shifting Using the system helped me to choose more fuel efficient velocities Using the system supported me avoiding unnecessary stops (e.g. at traffic lights) Using the system helps me to improve my driving Using the system helps me to save fuel Overall, I find the system useful
3.8 3.9 3.6 3.9 3.7 3.9 4.6 4.0 4.1 3.9
1.2 1.0 1.1 1.1 0.9 0.6 0.9 0.8 0.9 1.0
4.1 4.0 4.4 3.8 2.4
0.8 0.7 0.6 1.2 1.0
Behavioural intention to use 22 For me it is important to drive fuel efficiently 23 For me it is important to reduce the CO2-emissions 24 I believe that the system can help to reduce the fuel consumption and thus the CO2-emissions 25 If I had such a system, I would use it frequently during my trips 26 I would be willing to pay more for using the system than what I would save in fuel costs and reduced emissions
participants (n = 7) felt that the system was distracting them from driving. The items for perceived usefulness also have a very high internal reliability (a = 0.783) and can thus be treated as one scale. Overall the participants found the eco-driving support system useful (item 21). They confirmed that the system helped them to avoid unnecessary stops (item 18) and to avoid economically inefficient gear shifting (item 16). Moreover, the participants stated that the system helped them to accelerate (item 14) and decelerate (item 15) in a more fuel efficient way. Yet, the participants also reported that they felt restricted by the system (item 13). The reliability for the behavioral intention to use scale was not sufficiently high in order to treat the answers of the participants as one scale (a = 0.542). Nevertheless, for the participants it was important to drive fuel efficiently (item 22) and to reduce CO2-emissions (item 23). They also believed that the system could help them to reach this goal (item 24) and stated that they would use this system frequently (item 25). However, most of the participants would not like to pay more money on the system than they could save using the system. Thus the economic considerations were stronger than the ecological awareness. When asked how much money they would like to spend on the system 10% of the participants answered that they would not spend any money, 15% would spend up to 100€, 30% would spend between 101€ and 200€, 10% between 201€ and 300€ and another 10% would spend more than 300€. The attitude towards using the eco driving support system was measured using the van der Laan Acceptance Scale (1997). In Table 7 the results are shown. For this table the results of the items 3, 6 and 8 were mirrored. Thus all numbers have a positive value and represent the positive part of the adjective pairs. As a result of the questionnaire analyzes participants perceived the eco driving support system as very useful (item 1), good (item 3), effective (item 5) and assisting (item 7). All those items belong to the usefulness scale. Therefore the attitudes of the participants towards the usefulness of the system seem to be very positive. The items from the satisfying scale were rated also positively but not as good as the usefulness-items. The results of the open questions show that 40% of the participants preferred the haptic feedback, 30% the visual and 23% participants the combination of both modalities. Furthermore 47% of the participants would like to turn on the advice themselves; 53% would like to have it turned on from which 25% would like to be able to turn it off again. Asked for the reasons they would like to have the eco driving support system in their car, 67% of the participants answered that they would like to have the system for reducing their fuel consumption, 37% for comfort and traffic flow reasons, and 27% for environmental reasons. 10% of the participants referred to safety reasons. However, there were also disadvantages pointed out by the participants: Some participants felt patronized by the system (27%) because the haptic feedback was too strong and/or they found the system intervened too early (20%) especially in the stop sign and curve situations. Therefore 28% stated that they
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M. Staubach et al. / Transportation Research Part F 27 (2014) 11–21 Table 7 Results from van der Laan Acceptance Scale. Item
Description
Scale
Mean scores
SD
1 2 3 4 5 6 7 8 9
Useful–useless Pleasant–unpleasant Bad–good Nice–annoying Effective–superfluous Irritating–likeable Assisting–worthless Undesirable–desirable Raising alertness–sleep inducing
Usefulness Satisfying Usefulness Satisfying Usefulness Satisfying Usefulness Satisfying Usefulness
1.00 0.73 1.03 0.27 1.14 0.57 1.17 0.83 0.07
1.15 1.02 0.89 1.23 0.83 1.17 0.93 1.15 1.14
did not follow the recommendations of the eco driving support system when approaching the stop signs and curves. Furthermore 37% of the participants addressed that they felt distracted from their driving task through the systems recommendations. These results fit well to the moderate satisfying scores on the van der Laan-scale. 4. Discussion The eco-driving support system helped the participants of the present driving simulator study to save fuel of about 16% in urban and 18% in rural scenarios. Following the recommendations of the system the participants applied coasting strategies earlier before traffic lights, curves and stop signs. This led to steady deceleration manoeuvers with small slopes and less stops at traffic lights. Furthermore the participants shifted at lower RPMs. The outcomes of the study also show that using the system did reduce safety critical behaviors like speeding, hard braking events or critical time to collision distances. These results confirm the findings of the simTD (2013) project simulator studies where cooperative traffic light assistance helped to decrease the likelihood of stopping at traffic lights and the mean percentage of speeding events in urban environments. The eco-driving support system was further accepted very well by the participants, especially considering the perceived ease of use and the perceived usefulness. However, whether drivers will purchase the system in the future depends on the price of the system. Most of the participants would not like to spend more money on the system than they could save using the system regularly and only a few people would spend more than 300€. Another question is about the amount of time drivers are ready to invest. Even though only small increases in travel time could be found in the present study, it would be interesting to know under which circumstances drivers are ready to invest this time. Further the future usage of the system will depend on the situations which are supported by the system. For the traffic light approaches almost all participants were content with the recommendations of the system. Yet, concerning the stop sign situation and the curve situations some of the participants mentioned that the intervention was too early. In contrast to the traffic light situations they felt restricted because they would have liked to drive faster and they had to invest additional time in order to save fuel. This fact shows that the deceleration recommendation should not start too early in situations where the car has to slow down to standstill, because this could reduce the acceptance of the recommendations. This assumption can be supported by the findings of van Driel, Hoedemaeker, and van Arem (2007) congestion assistant study. Thus, congestion warnings which were too early had low impact on the change of driving behavior and that an active gas pedal was overruled by most of the participants when the traffic jam was still far ahead. Some participants also complained that the haptic feedback was too strong and could not be overridden easily which they felt was annoying. However, others found the strength of the haptic feedback adequate. In multiple previous studies participants have already expressed that they preferred other warning modalities over the haptic feedback, because they felt restricted, e. g. Meschtscherjakov, Wilfinger, Scherndl, and Tscheligi (2009), Staubach et al. (2014), Adell, Varhelyi, and Hjälmdahl (2008). On the other hand is the haptic feedback a very good supplementation of exclusive visual information. The results of the gaze analysis suggest that the participants learned really fast to use the haptic information from the gas pedal instead of relying only to the visual information. These results fit very well to the findings of Azzi, Reymond, Mérienne, and Kemeny (2011) that drivers ‘‘apparently relied more on haptic modality to achieve the eco-driving driving task, when they used both visual and haptic assistance’’. If future users do not feel comfortable with the systems feedback they might like to turn it off. This runs the risk that the systems potential to save fuel and emissions will not be utilized fully. In order to solve this dilemma personalized feedback mechanisms might be useful. Thus, the drivers should not only be able to choose whether they liked their feedback visually, haptically or combined but they should also be able to choose the strength of the haptic feedback or the situations in which they like to have feedback. Further the users could also choose beforehand in which situations they would like to be supported. So it would be better if some drivers will only be supported in traffic light situations than turning off the whole system. In the future development phase it is also important to adjust the feedback of this system to the feedback of other driver assistance systems. Therefore the system might be a part of an advanced active cruise control system especially for urban
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environments. Also for the implementation of the system in real vehicles, it is important to define at which point in time the information about the traffic light status should be displayed, so that the drivers will not be able to misuse the information about the traffic light phases for speeding up in order to pass the traffic light at a green phase. Moreover future research questions should also target in which situations or under which circumstances recommendations will be useful and safe and which recommended velocities will be accepted by the equipped drivers. According to a field study by Otto (2011) for example the participants did not follow recommendations below 20 km/h. Last but not least should future studies focus on the acceptance of non-equipped drivers who are driving behind vehicles with an eco-driving support system. They should consider both perspectives: Does the driver of the equipped vehicle feel comfortable if there are vehicles pushing from the back? Will the drivers of the vehicles in the back feel comfortable if they do not know why the car in front is going slower than the speed limit? How could the information about the eco-driving support system be displayed in order to inform other road users? 5. Conclusion Within the presented driving simulator study a cooperative system which recommends fuel saving driving strategies to the drivers was evaluated considering aspects of environment, mobility and driving behavior. Results showed mean fuel reduction potentials between 15.9 and 18.4 percent, less stops, and smoother driving styles but a small increase in travel time. The system had no negative effects on traffic safety even though the distraction of the HMI should be reduced if possible before the system will be sold on the market. Last but not least, the overall acceptance of the eco-driving support system was rather good. Acknowledgement This work is part of the eCoMove (Cooperative Mobility Systems and Services for Energy Efficiency) Integrated Project cofunded under the 7th RTD Framework Programme, Information Society and Media Directorate-General (FP7-ICT-2009-4) – Grant agreement no 247978. http://www.ecomove-project.eu References Adell, E., Várhelyi, A., & Hjälmdahl, M. (2008). Auditory and haptic systems for in-car speed management – A comparative real life study. Transportation Research Part F, 445–458. Azzi, S., Reymond, G., Mérienne, F., & Kemeny, A. (2011). Eco-driving performance assessment with in-car visual and haptic feedback assistance. Journal of Computing and Information Science in Engineering, 11(041005), 1–5. Barth, M., & Boriboonsomsin, K. (2009). Energy and emissions impacts of a freewa-based dynamic eco-driving system. Transportation Research Part D, 14, 400–410. Beusen, B., Broekx, S., Denys, T., Beckx, C., Degraeuwe, B., Gijsbers, M., et al (2009). Using on-board logging devices to study the longer-term impact of an eco-driving course. Transportation Research Part D, 14, 514–520. Birrel, S. A., Young, M. S., & Weldon, A. M. (2013). Vibrotactile pedals: provision of haptic feedback to support economical driving. Ergonomics, 56(2), 282–292. Castermans, J., Brusselmans, A. & Pandazis, J.C. (2010). D1.2 Project Presentation and fact sheet. Cooperative Mobility Systems and Services for Energy Efficiency. Available from: http://www.ecomove-project.eu/assets/Documents/Deliverables/100714-DEL-D1.2-ProjectPresentationfactsheetweb.pdf [2014-0122]. Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: theory and results. Doctoral Dissertation, Sloan School of Management, Massachusetts Institute of technology. Dhaou, B. I. (2011). Fuel estimation model for ECO-driving and ECO-routing. IEEE Intelligent Vehicles Symposium (IV), 37–42. Dobre, C. (2012). Using intelligent traffic lights to reduce vehicle emissions. International Journal of Innovative Computing, Information, and Control, 8(9), 6283–6302. Eikelenberg, N., Subbian, J., Blanco, R., Sanchez, D., Andreone, L., Damiani, S. et al. (2011). EcoSmartDriving use cases & system requirements. Available from: http://ecomove-project.eu/assets/Documents/Deliverables/New/To-be-updated/111031-DEL-SP3-WP3.2-D3.1-ecoSmartDriving-Use-Cases-SystemReq.pdf [2014-01-22]. European Commission (2011). White paper: roadmap to a single European transport Area – towards a competitive and resource efficient transport system. Available from: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=COM:2011:0144:FIN:EN:PDF [2014-01-22]. Fujimaki, T., Kinoshita, Y., & Inoue, S. (2012). A field trial of the green light optimal speed advisory. In Proceedings 19th World Congress on ITS, Vienna. ERTICO. Gonder, J., Earleywine, M., & Sparks, W. (2011). Final Report on the Fuel Saving Effectiveness of Various Driver Feedback Approaches. National Renewable Energy Laboratory. Available from: http://www.nrel.gov/vehiclesandfuels/vsa/pdfs/50836.pdf [2014-01-22]. Hogema, J. H., & Janssen, W. H. (1996). Effects of intelligent cruise control on driving behaviour: a simulator study. TNO report TM-96-C012, TNO Human Factors research Institute, Soesterberg, The Netherlands. Höltl, A., Trommer, S., Schießl, C., Fricke, N., Staubach, M., Hoeck, R. et al. (2011). Requirements and motivators for private and commercial drivers http:// www.ecomove-project.eu/publications/deliverables [2014-01-22]. Hoyer, R. (2012). Verkehrliche Potenziale des vorausschauenden Fahrens an kooperativen Lichtsignalanlagen. 5. Tagung Fahrerassistenz, TU München. International Transport Forum (2010). Drop in global travel volume leads to cut in transport emissions – but drivers for increased Greenhouse gas emissions from mobility remain. Available from: http://www.internationaltransportforum.org/Press/PDFs/2010-05-19GHG.pdf [2014-01-22]. Isasi, L., Rodriguez, T., Larburu, M., Urquiza, A., Blanco, R. & Mejuto, P. (2012). D6.2 Validation and evaluation plan (version 3.0). Cooperative mobility systems and services for energy efficiency. Available from: http://www.ecomove-project.eu/assets/Documents/Deliverables/New/To-be-updated/120622-DELSP6-WP6-D6.2-V3.pdf [2014-01-22]. Jellinek, R. (2010). The ECOWILL project. Ecodriving – Widespread Implementation for Learner Drivers and Licensed Drivers. Available from: http:// www.ecodrive.org/download/downloads/ecowill_project_slides_nov2011.pdf [2014-01-22]. Johansson, E., Engström, J., Cherri, C., Nodari, E. Tofetti, A., Schindhelm, R. & Gelau, C. (2006). D2.2.1. Review of existing techniques and metrics for IVIS and ADAS assessment. Adaptive integrated driver-verhicle interface. Available from: http://www.aide-eu.org/pdf/sp2_deliv_new/aide_d2_2_1.pdf [2014-01-22].
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Kircher, K., Gelau, C., Vierkötter, M., Rakic, M., Bärgman, J., Kotiranta, R. et al. (2008). D2.1 – A Comprehensive framework of performance indicators and their interaction. fiel operational test setup support action. Available from: http://www.its.leeds.ac.uk/festa/downloads/FESTA_D2_1_FinalVersion.pdf (201401-22). Lange, C., Schmitt, G., Arcati, A., Bengler, K., & Bubb, H. (2010). Haptic gear shifting indication: Naturalistic driving study for parametrization, selection of variants and to determine the potential for fuel consumption reduction, In: Proceedings of the 3rd international conference on applied human factors and ergonomics, Miami. Larsson, H., & Ericsson, E. (2009). The effects of an acceleration advisory tool in vehicles for reduced fuel consumption and emissions. Transportation Research Part D, 14, 141–146. Meschtscherjakov, A., Wilfinger, D., Scherndl, T., & Tscheligi (2009). Acceptance of future persuasive in-car interfaces towards a more economic driving behaviour. Proceedings of the first international conference on automotive user interfaces and interactive vehicular applications, Essen, Germany. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill. Otto, T. (2011). Kooperative Verkehrsbeeinflussung und Verkehrssteuerung an signalisierten Knotenpunkten. PHD Thesis, University of Kassel. SimTD (2013). Factsheets. Available from: http://www.simtd.de/dyn/mediaout.dhtml/-/server/backup_publications/Informationsmaterial/infomaterial/ 201306_Informationsblaetter_2013/Informationsblaetter_2013/@teaser_long/@s4/simTD_factsheets_2013_de_web.pdf [2014-01-22]. Staubach, M., Fricke, N., Schießl, C., Brockmann, M., & Kuck, D. (2014). Information modalities and timing of ecological driving support recommendations. IET Intelligent Transport Systems, 8, 534–542. Themann, P., Isasi, L., Larburu, M., Trommer, S., Vreeswijk, J. & Wilmink, I. (2012). Validation methodology focussing on fuel efficiency as applied in the eCoMove project. In Proceedings 19th world congress on ITS, Vienna. ERTICO. van der Laan, J. D., Heino, A., & de Waard, D. (1997). A simple procedure for the assessment of acceptance of advanced transport telematics. Transportation Research Part C, 5(1), pp. 1–1. van der Voort, M., Dougherty, M. S., & van Maarseveen, M. (2001). A prototype fuel-efficiency support tool. Transportation Research Part C, 9, 279–296. van Driel, C. J. G. (2007). Driver Support in Congestion – An Assessment of user needs and impacts on driver and traffic flow. TRAIL Thesis Series T2007/10, the Netherlands TRAIL Research School. van Driel, C. J. G., Hoedemaeker, M., & van Arem, B. (2007). Impacts of a congestion assistant on driving behaviour and acceptance using a driving simulator. Transportation Research Part F, 10, 139–152. Várhelyi, A., Hjälmdahl, M., Hydén, C., & Draskócszy, M. (2004). Effects of an active accelerator pedal on driver behavior and traffic safety after long-term use in urban areas. Accident Analysis and Prevention, 36, 729–737. Wu, C., Zhao, G., & Ou, B. (2011). A fuel economy optimization system with applications in vehicles with human drivers and autonomous vehicles. Transportation Research Part D, 16, 515–524.
Contents lists available at ScienceDirect
Transportation Research Part F journal homepage: www.elsevier.com/locate/trf
Evaluation of an eco-driving support system Maria Staubach a,⇑, Norbert Schebitz a, Frank Köster a, Detlef Kuck b a b
Institute of Transportation Systems, German Aerospace Center (DLR), Germany Ford Forschungszentrum Aachen GmbH, Süsterfeldstr. 200, 52072 Aachen, Germany
a r t i c l e
i n f o
Article history: Received 3 December 2013 Received in revised form 6 September 2014 Accepted 6 September 2014
Keywords: Eco driving support User acceptance Driving simulator Evaluation Fuel consumption
a b s t r a c t In this study an eco-driving support system was evaluated within a driving simulator study. The presented system was able to communicate with traffic lights and to detect traffic signs. On the basis of this information the system gave recommendations to the participants concerning fuel efficient gear shifting and acceleration/deceleration behavior. Thirty participants experienced three different driving situations (traffic light approach, curves, and stop signs) with and without the eco-driving support system in an urban and a rural scenario. As a result with the systems mean fuel savings between 15.9 and 18.4 percent could be realized because the participants shifted up earlier and used more coasting strategies than in the condition without the system. Thus the number of stops decreased. At the same time travel time increased significantly only in the rural scenario (+5.4%) and there were no negative effects on safety. Further, the eco-driving support system was highly accepted by the participants. The high potential of the system for fuel saving should be researched further with a focus on interactions between vehicle drivers. Ó 2014 Elsevier Ltd. All rights reserved.
1. Introduction According to a press release of the International Transport Forum (2010) global CO2-emissions from transport grew by 45% from 1990 to 2007 and will rise further by another 40% until 2030. Since CO2 emissions seem to be closely connected to the global greenhouse effect it is necessary to develop measures in order to reduce CO2 emissions. Therefore the European Union strives for fuel savings and the reduction of CO2 emissions in transport by at least 60% compared to the year 1990. Until 2020 it is the goal to limit the increase of CO2 emissions to 8% (European Commission, 2011). One possibility to pursue this goal is to promote eco-driving among car drivers. Thus, for example within the ECOWILL project (Jellinek, 2010) ecodriving training programs in Europe are researched and promoted. Beusen et al. (2009) used on board devices to study longer-term impacts of an ecodriving course via GPS-tracking and found reductions of 5.8% after four months. Other research programs focus on the change of driving behaviors using advanced driver assistance and cooperative systems. For example within the eCoMove project (Cooperative Mobility Systems and services for energy efficiency, Castermans, Brusselmans, & Pandazis, 2010) a lot of technologies and applications were developed which altogether aim to reduce fuel consumption and CO2 emissions by up to 20%. The Ford eco-driving support system is one of those systems. Since it is able to detect road signs and to communicate with infrastructural components such as traffic lights it can recommend the drivers how and when to accelerate and decelerate fuel-efficiently and which gear to choose. Therefore an HMI was realized with a visual display and a haptic force feedback pedal which signalizes the drivers when to decelerate and when to shift gears.
⇑ Corresponding author at: Lilienthalplatz 7, 38108 Braunschweig, Germany. Tel.: +49 531 2953478. E-mail address: [email protected] (M. Staubach). http://dx.doi.org/10.1016/j.trf.2014.09.006 1369-8478/Ó 2014 Elsevier Ltd. All rights reserved.
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Both modalities were implemented because in an earlier study three different advice modalities (visual, haptic, visual-haptic) were researched regarding their fuel saving potential and acceptance issues and it was found that the combination of visual and haptic recommendations lead to the best gear shifting and acceleration behavior for fuel-efficient driving (Staubach, Fricke, Schießl, Brockmann, & Kuck, 2014). Consequently in the present study a combination of visual and haptic recommendations was used. In the past some authors have already researched the impact of eco-driving support systems on the reduction of fuel consumption and emissions. Várhelyi, Hjälmdahl, Hydén, and Draskócszy (2004), for example, reported significant effects of an active acceleration pedal on the reduction of speed and emissions especially in urban surroundings. Birrel, Young, and Weldon (2013) as well as Larsson and Ericsson (2009) found that a haptic force feedback on the acceleration pedal led to reduced maxima of acceleration, which is one important precondition for the realization of fuel consumption reductions. Further, Gonder, Earleywine, & Sparks, 2011 compared the effectiveness of different advice categories towards their fuel reduction potential and found that acceleration/deceleration recommendations do have a potential to save 6% of fuel. Wu, Zhao, and Ou (2011) even found 22–31% overall gas savings for a fuel-economy optimization system (FEOS) which displayed the optimal acceleration/deceleration profile to the driver. Other systems focused on the gear shift behavior of the drivers because measurements have proven that gear-shifting behavior can influence fuel consumption significantly. According to Dhaou (2011) changing gears from 3rd to 4th can reduce fuel-consumption by 19% and from 4th to 5th gear by 25%. van der Voort, Dougherty, and Maarseveen (2001) introduced a driver feedback system including an HMI with gear shift recommendations, which helped to save 16% of fuel compared to normal driving and 7% fuel compared to a fuel efficient driving style. Lange, Schmitt, Arcadi, Bengler, and Bubb (2010) analyzed the emission reduction potential of a haptic gear shift feedback on the accelerator pedal additionally to visual recommendations and found possible CO2 savings up to 15.8 g/km. All these reported fuel saving effects could even be larger if besides the current traffic situations also future developments were taken into consideration. Using cooperative systems this will become possible in the next few years. Hoyer (2012) described the huge fuel saving potential of cooperative traffic lights, which communicate green light onset and offset times to the drivers. Within the same field experiment Otto (2011) found a fuel saving potential up to 5%. Fujimaki, Kinoshita, and Inoue (2012) conducted a field trial of a green light speed advisory system (GLSAS) and found higher fuel consumption efficiency, fewer stops per run and less acceleration when the GLSAS-services were used and Barth and Boriboonsomsin (2009) found a 13% fuel reduction potential in a real world experiment with a dynamic eco-driving feedback with real-time recommendations about the changing traffic conditions. For this study the validation approach of the eCoMove project was used (Themann et al., 2012). According to this approach research questions were defined in order to research the performance of the eco-driving support systems in different evaluation categories. Those were environment, mobility, driver behavior, safety, and user acceptance of the participants. Within each category hypothesis were defined and appropriate performance indicators were identified (Table 1). The results of each category will be presented in this paper. 2. Material and methods 2.1. Participants Thirty participants (15 male, 15 female) aged between 20 and 69 years (M = 41.5; SD = 15.2) took part in the study. The participants were drawn from the DLR test driver data base that contains more than 850 participants of all ages and different driving characteristics according to their ages, their sex and their driving experience. These participants were recruited at organizational events like open days and via newspaper advertisements. Twelve participants reported a medium to low annual mileage (between 3000 and 9000 km/year), twelve a medium annual mileage (between 9000 and 20,000 km/year), and six participants reported a high annual mileage (more than 20,000 km/year). The participants did not have any special experiences with eco-driving. The participation in this study was voluntarily and was compensated with eight Euro per hour. Table 1 Evaluation categories and performance indicators. Evaluation category Environment Mobility Driving performance Safety
Acceptance
Performance indicator – – – – – – – – – – – – –
Fuel reduction (%) Travel time (s) Number of stops Speed standard deviation Gear shifting behavior (revolutions per minute, RPM) TTC: percentage of time with TTC < 2.6s Number of hard braking events (longitudinal acceleration > 3.5 m/s2 Speed violations Gaze analysis: duration of glances towards the HMI Perceived ease of use (questionnaire) Perceived usefulness (questionnaire) Behavioral intentions (questionnaire) Van der Laan Scale (1997)
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2.2. Evaluation categories and performance indicators For each evaluation category performance indicators were defined and published in the eCoMove Validation and Evaluation plan (Isasi et al., 2012). They are listed and described in Table 1. For most of the performance indicators the changes between the drives with the eco-driving support system compared to the baseline where no system was applied have been measured. Only the gaze analysis and the acceptance questionnaires were just applied within the condition with the eco-driving support system. The goal of using an eco-driving support system is the reduction of fuel. Therefore the fuel reduction potential of the system was measured within this study. For determination of fuel consumption a generic model (Dobre, 2012) was used. This model calculates the fuel consumption dependent on the velocity and the acceleration behavior. It uses input parameters of the vehicle (like mass, drag coefficient) to determine the driving resistances. Therefore the parameters of a Ford car were considered in the driving dynamics of the simulation. It was also important to research the effects the eco-driving support system on mobility parameters. It was hypothesized that the travel time and the number of stops, e. g. before traffic lights, would not increase with the system. The third evaluation category referred to driving performance indicators. In order to find out whether the eco-driving support system helped to realize the recommendations of the eco-driving support system, speed standard deviation and the gear shifting behavior were measured. According to the golden rules of eco-driving which were defined within the ECOWILL-project (www.ecodrive.org) ‘‘driving with high or even medium engine RPM always consumes more fuel than driving at low RPM at whatever speed.’’ Hence, it is recommended to shift to higher gears at approximately 2000 RPM. Further it suggested to ‘‘drive smoothly at low RPM using the highest possible gear’’. Another very crucial point was to make sure that the researched eco-driving support system would not have any negative effects on safety. In order to answer this question several hypothesis have been defined in the eCoMove Validation and Evaluation plan and in a next step safety indicators and cut-offs have been proposed. This process has been influenced by former project-deliverables (e.g. Johansson et al., 2006) in which amongst others TTC, speed metrics, and glance duration were recommended as metrics for ADAS and IVIS assessment. Moreover, mean and maximum speed measurements have been proposed. However, within eCoMove it was hypothesized that the eco-driving support systems would not encourage the drivers to speeding. Therefore the distance the participants drove faster than the speed limit was compared between the drives with the system and the baseline. For the TTC measurements the authors suggested to discard situations where the TTC is above 15s because they cannot be regarded as car-following situations. As threshold for a safe TTC 2.6s was used (Hogema & Janssen, 1996). Using the number of hard braking maneuvers as a safety performance indicator was recommended within the FESTA Handbook (Kircher et al., 2008). The cut-off value for hard braking maneuvers has been adapted from van Driel (2007) who suggested 3.5 m/s2. The duration of glances towards the HMI was measured using a Dikabilis eye tracking system. Last but not least acceptance questionnaires were used in order to find out about the users attitudes and behavioral intentions to use the eco-driving support system. More information about the questionnaires can be found in chapter 2.3.2. 2.3. Materials 2.3.1. Driving simulator In order to examine the research questions a study using the motion based dynamic driving simulator of the German Aerospace Center (DLR) was conducted. The motion system of the simulator is characterized by a hexapod system with the cabin hanging in below the upper articulations. A high-quality projection system provides the visualization of the surrounding environment and traffic. Besides the forward view the driver can also observe the simulated traffic behind him by the rear-view mirror on a screen and by LC-displays in the lateral rear mirrors. For a realistic overall impression the direct environment of the driver is very important. Therefore a complete vehicle has been integrated into the cabin with which the driver is ‘‘driving’’ the simulator. For this purpose the actions of the driver are transmitted via CAN-Bus to the simulation computer, vice versa the simulation system controls the instruments inside the cockpit, which for example inform the driver about his current speed. All inputs and actions made by the driver, from braking over steering to operating the radio, can be recorded and analyzed. 2.3.2. Questionnaires In order to measure system acceptance questionnaires were developed following the Theory of the Technology Acceptance Model (Davis, 1986). According to this model the acceptance of technical systems is dependent on the perceived usefulness and the perceived ease of use of the system, as well as the behavioral intention and the attitude toward using the system. Suitable questions concerning the eco-driving support were developed considering user requirements which were analyzed in earlier studies (Eikelenberg et al., 2011; Höltl et al., 2011) and the inefficiencies which are addressed by the eco-system (Themann et al., 2012). Each item was evaluated by the participants on a five point Likert Scale (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, 5 = strongly agree). A list of the used questions can be found in Table 5. In order to find out something about the participants’ attitudes towards the system the acceptance scale from van der Laan, Heino, and de Waard (1997) was used. The participants had to rate on a five point scale (from 2 to +2) how much they associated the adjectives (see Table 7) with the evaluated system. Moreover, open questions were asked about the amount of money the participants would like to spend on an eco-driving support system. Further open questions concerned the modalities they preferred, what
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Fig. 1. FFA driver support system visual display for two different situations.
would motivate them to use the systems regularly, whether they would like to be able to turn off the system and for the reasons why they would like an eco-driving support system in their cars. They were also asked whether they followed all the systems recommendations, to give reasons in case they did not sometimes and to reflect also disadvantages of the system. All questionnaires were filled out at the end of the study and referred only to the trial with the eco driving support system. 2.4. Experimental design The participants drove an urban (speed limit 50 km/h) and a rural scenario (speed limit 70 km/h). Each of the scenarios contained three different traffic light situations, two curves (left and right) and one stop sign. For the traffic light approaches in both scenarios there were three different speed recommendations from the traffic light to the eco-driving support system about which speed was adequate to pass the green traffic light when approaching in the red phase. Hence, in the urban scenario the drivers had to decelerate their vehicle to a target speed of either 35 or 40 km/h or keep the speed of 50 km/h, in the rural scenario the participants had to decelerate to 49 or 56 km/h or keep their speed at 70 km/h in order to pass the traffic light within the green phase without stopping. Further, in the curve situations the participants had to slow down their cars in order to pass the curves safely, and at the stop signs they had to slow down their cars to standstill. For the stop sign situations it was obvious in pretests that the intervention with the target speed of zero km/h started too early and took too long until the stopping of the car. Therefore the target speed was changed from zero to 30 km/h under the assumption that the drivers would slow down manually shortly before they arrive at the stop sign. Each situation was experienced three times by the participants in order to improve the reliability of the measurements. The study design was a within subject design. Thus, each participant experienced the urban and the rural situations with and without the eco-driving support system (baseline) in balanced order. In the condition with the eco-driving support the gaze directions of the drivers were measured. The order of the scenarios and situations was randomized in order to prevent test effects. The results were analyzed using paired t-tests for the comparison of the results of the eco-driving support and the baseline. For the fuel consumption a single sample t-test was used. The system recommendations were given using a visual display and a haptic force-feedback signal on the acceleration pedal (counter pressure). The visual display is presented in Fig. 1 for two different situations. In both pictures a grey bar can be seen with a green range on the bottom which grows larger when the driver is supposed to accelerate. The grey needle shows the current amount of acceleration. When the grey needle is in the green range the acceleration respectively the driven speed is appropriate to the oncoming traffic conditions. When the needle is above the green range, the driver is supposed to slow down his car in order to meet the traffic conditions and thus, to save fuel. Moreover, the traffic conditions are displayed via symbols, which constitute the systems recommendations. The symbol on the bottom of the left picture in Fig. 1 shows how many seconds are left until the red light turns green. If the driver follows the systems recommendations, he does not have to slow down to standstill approaching the traffic light. Instead he starts slowing moderately but earlier before reaching the traffic light which leads to a reduction in fuel consumption. On the right part of the picture the traffic sign ‘‘Curve ahead’’ is displayed on the bottom as a reason for the recommendation to slow down. 3. Results 3.1. Environment In urban scenarios the mean fuel reduction was 15.9% when participants drove with the eco-driving support recommendations; in rural scenarios the reduction potential for all situations together was 18.4%. In both scenarios the highest potentials could be found in the curve situations and in the traffic light situations with a recommended speed reduction of 30%
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M. Staubach et al. / Transportation Research Part F 27 (2014) 11–21 Table 2 Fuel reduction (%) in different situations. Scenarios, situations
Mean
SD
Median
Shapiro–Wilk Test (p)
t29
p
Effect size d
Urban
15.9 26.3 15.0 1.3 19.6 36.8 18.4 34.0 16.2 3.5 29.9 24.5
11.0 20.8 19.4 13.6 19.8 17.5 12.5 21.5 21.3 19.1 22.5 19.9
14.7 28.1 12.2 1.4 21.0 37.1 20.7 36.2 17.5 5.5 38.1 20.4
.25 .03 .06 .50 .18 .89 .84 .49 .62 .36 .02 .65
7.90 – 4.24 0.54 5.44 11.53 8.07 8.65 4.17 0.99 – 6.76
.000 – .000 .591 .000 .000 .000 .000 .000 .329 – .000
1.45 – 0.77 0.10 0.99 2.10 1.47 1.58 0.76 0.18 – 1.23
Rural
Total distance Traffic light (35 km/h) Traffic light (40 km/h) Traffic light (50 km/h) Stop sign Curve (30 km/h) Total distance Traffic light (49 km/h) Traffic light (56 km/h) traffic light (70 km/h) Stop sign Curve (30 km/h)
(35 km/h urban, 49 km/h rural). The stop sign situation and the traffic light situations where the participants were told to slow down 20% (40 km/h urban, 56 km/h rural) also had a high potential to save fuel. In those traffic light situations where the participants were supposed to keep their current speed the fuel reduction potential was very low. The results are summarized in Table 2. Before the single sample t-tests have been calculated a Shapiro–Wilk-Test was performed in order to ensure the normal distribution of the test data. According to this test there were two statistics which are not distributed normal: ‘‘urban – traffic light (35 km/h)’’ and ‘‘rural – stop sign’’. Looking at the boxplots there was one outlier in the scenario ‘‘urban – traffic light (35 km/h), who needed more fuel in most of the situations using the eco-driving support system. Excluding this outlier would lead to a normal distribution. For the stop sign there is no special outlier but just an asymmetric distribution. Hence, a lot of participants did not save a lot of fuel while a large group of participants (n = 14) managed to save more than 40% of fuel. This observation fits well to the results of the open questions in the acceptance chapter where 28% of the participants stated that they did not follow the recommendations when approaching stop signs and curves. 3.2. Mobility Using the eco-driving support system the travel time of the test drives did not increase significantly in the urban scenario. Within the rural scenario it took the participants significantly more time to complete the scenario (+5.4%) using the eco-driving support. When only the traffic light approaches were considered the travel times did not differ significantly in both scenarios. The results are presented in Table 3. Further the number of stops decreased significantly in the urban (from 5 to 3.5 stops, z = 3.65, p = .000, d = .75) and in the rural scenario (from 3.9 to 3.3, z = 1.94, p = .050; d = .29). Therefore, using the ecodriving support system fuel consuming acceleration procedures at the traffic lights could be prevented by the drivers. 3.3. Driving performance The recommendations of the eco-driving support system lead to a smoother driving style with more constant velocities and less deceleration and acceleration manoeuvers especially in those situations where the participants had to reduce their velocities in order to pass the traffic light at the green phase or when they had to slow down to pass the curve safely or on stop signs. Therefore the speed standard deviations in the treatment condition were significantly smaller than those in the baseline phase. The results are integrated in Table 4. The smaller speed standard deviations with the eco-driving support systems were realized through applying coasting strategies earlier which led to steady deceleration manoeuvers with smaller slopes than in the baseline condition. As an outcome the speeds of the participants in the condition with the eco-driving support system when the traffic lights turned green were higher and thus they did not have to accelerate as much as they did in the without the system in order to reach their target velocities. Fig. 2a and b show examples of the mean speed profiles for two different situations. They follow similar patterns: 400 m before the traffic light the velocities in both conditions are very close to each other. Now with the system the participants received the advice depending on the speed they drove at. In the rural scenarios this was about 400–350 m
Table 3 Travel time of the test drives. Travel time
Urban Rural
All scenarios Traffic lights All scenarios Traffic lights
Baseline
Treatment
Mean
SD
Mean
SD
1253 s 972 s 1229 s 844 s
107 s 98 s 70 s 43 s
1269 s 955 s 1295 s 831 s
76 s 58 s 100 s 48 s
t29
p
d
t = .96 t = 1.12 t = 2.79 t = 1.05
.34 .27 .01 .32
d = .17 d = .21 d = .54 d = .29
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Table 4 Speed standard deviations of the test drives. Baseline
Urban
Rural
Traffic light Traffic light Traffic light Curve 30 Stop sign Traffic light Traffic light Traffic light Curve 30 Stop sign
50 40 35
70 56 49
Treatment
Mean
SD
Mean
SD
4.2 7.2 9.2 6.3 10.3 6.2 9.6 12.9 8.3 13.1
2.0 3.4 2.5 2.3 1.7 3.9 4.4 4.5 3.1 2.5
3.1 4.2 6.0 6.0 9.3 4.1 6.3 7.7 7.3 13.3
1.8 1.9 2.1 1.2 1.0 3.0 3.7 3.1 1.9 1.8
z
p
d
3.99 6.30 6.90 0.99 4.50 3.50 6.50 7.10 2.80 1.50
.000 .000 .000 .280 .000 .000 .000 .000 .004 .140
0.58 1.09 1.38 0.14 0.72 0.60 0.81 1.35 0.39 0.09
Table 5 Gear shifting behavior, mean RPMs at which the participants shifted. Baseline
Treatment
Gear shift
N
Mean
SD
n
Mean
SD
1st into 2nd 2nd into 3rd 3rd into 4th 4th into 5th
373 816 1246 672
3243 2850 2176 1860
1015 702 514 396
274 488 956 647
2974 2606 2047 1831
927 668 414 297
t
p
d
2.48 4.33 4.19 1.86
.019 .000 .000 .007
0.28 0.36 0.28 0.08
before reaching the traffic light, in the urban scenarios about 300–250 m. While approaching the traffic light further and following the advice of the eco-driving support system the velocities decreased steadily with a small slope. When the traffic light turned green about three seconds before reaching the participants started to accelerate again. In all cases they passed the traffic lights with higher velocities than in the baseline condition. In the latter the drivers started decelerating much closer to the traffic lights and the slope was much sharper. Also in curves and before stop signs the deceleration slopes were smaller when the participants were driving with the system. Considering the gear shifting behavior when following the systems recommendations the participants shifted at lower RPMs than in the baseline phase. For the first four gears the differences were statistically significant with small effect sizes. The shift from the 4th into the 5th gear the difference was smaller. This is probably due to the lower and slower rising in RPM-levels at higher gears. The results are summarized in Table 5. 3.4. Safety In a few situations the participants had to follow a lead car and the percentage of time when the time to collision (TTC) was less than 2.6 s was measured. Therefore only situations were considered in which the TTC was less than 15 s. As a result the amount of critical TTC-situations decreased to 1.6% driving with the eco-driving support system from 2.8% in the baseline drive (z = 3.3, p = .0008, d = 0.75). Further the number of hard braking events (longitudinal acceleration < 3.5 m/s2) decreased significantly from 10.3 in the baseline condition to 3.3 in drives with the system (z = 4.19, p = .000, d = 0.9). Also the distance driven exceeding the speed limit decreased in the treatment phase. Hence, the participants exceeded the speed limit up to 10 km/h in 17.0% of the distance using the system compared to 28.8% in the drives without the system (z = 4.38, p = .000, d = 0.8). The amount of speed deviations above 10 km/h decreased from 3.7% to 0.4% (z = 3.48, p = .000, d = 0.5). The results of the gaze analysis suggest that the distraction initially was indeed too high since most of the drivers looked away from the street and towards the HMI more than two seconds. However, over the period of testing the glances away from the street towards the HMI became shorter. The results are shown in Fig. 3. There was a significant negative correlation between the mean (r = .914, p < .001) and the maximum (r = 0.688, p < .001) HMI glance duration with the duration of the trial. Therefore, the initial distraction through the systems might be reduced soon by the easy learning of the systems functioning. 3.5. Acceptance In Table 6 the results for perceived ease of use, perceived usefulness and behavioral intention to use are presented. For the perceived ease of use scale Cronbach’s Alpha was sufficiently high (a = 0.781) for interpreting the items as a common scale (Nunnally & Bernstein, 1994). Overall most of the participants found the system easy to use (item 11). Especially the learnability (item 9) and the comprehensibility (item 5) were rated very well by the participants. However, some of the
M. Staubach et al. / Transportation Research Part F 27 (2014) 11–21
(a) Scenario: urban, traffic light approach with 50 km/h
(b) Scenario: rural, traffic light approach with 56 km/h
Fig. 2. Mean speed profiles of the different situations.
Fig. 3. Maximum and mean duration of glances towards the HMI.
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Table 6 Means and Standard deviations of means of answers to the acceptance questions. Item
Description
Mean score
SD
Perceived 1 2 3 4 5 6 7 8 9 10 11
Ease of Use I found the system intuitive to use Using the system distracted me from driving For me the feedback of the system was precise enough My interaction with the system was clear and understandable It was easy for me to follow the information provided by the system Interacting with the system was frustrating Interacting with the system was comfortable I perceived the feedback of the system as encouraging Learning to use this system was easy for me The system provides adequate information that is in control of my driving Overall, I find the system easy to use
3.6 3.0 3.8 3.6 4.0 2.5 3.5 3.3 4.3 3.6 4.2
1.1 0.9 0.9 0.9 0.9 1.1 1.0 0.9 0.8 0.9 0.8
Perceived 12 13 14 15 16 17 18 19 20 21
Usefulness Using the system increased my awareness of economical driving Using the system restricts my freedom while driving Using the system helped me to accelerate in a more fuel efficient way Using the system helped me to decelerate in a more fuel efficient way Using the system helped me to avoid economically inefficient gear shifting Using the system helped me to choose more fuel efficient velocities Using the system supported me avoiding unnecessary stops (e.g. at traffic lights) Using the system helps me to improve my driving Using the system helps me to save fuel Overall, I find the system useful
3.8 3.9 3.6 3.9 3.7 3.9 4.6 4.0 4.1 3.9
1.2 1.0 1.1 1.1 0.9 0.6 0.9 0.8 0.9 1.0
4.1 4.0 4.4 3.8 2.4
0.8 0.7 0.6 1.2 1.0
Behavioural intention to use 22 For me it is important to drive fuel efficiently 23 For me it is important to reduce the CO2-emissions 24 I believe that the system can help to reduce the fuel consumption and thus the CO2-emissions 25 If I had such a system, I would use it frequently during my trips 26 I would be willing to pay more for using the system than what I would save in fuel costs and reduced emissions
participants (n = 7) felt that the system was distracting them from driving. The items for perceived usefulness also have a very high internal reliability (a = 0.783) and can thus be treated as one scale. Overall the participants found the eco-driving support system useful (item 21). They confirmed that the system helped them to avoid unnecessary stops (item 18) and to avoid economically inefficient gear shifting (item 16). Moreover, the participants stated that the system helped them to accelerate (item 14) and decelerate (item 15) in a more fuel efficient way. Yet, the participants also reported that they felt restricted by the system (item 13). The reliability for the behavioral intention to use scale was not sufficiently high in order to treat the answers of the participants as one scale (a = 0.542). Nevertheless, for the participants it was important to drive fuel efficiently (item 22) and to reduce CO2-emissions (item 23). They also believed that the system could help them to reach this goal (item 24) and stated that they would use this system frequently (item 25). However, most of the participants would not like to pay more money on the system than they could save using the system. Thus the economic considerations were stronger than the ecological awareness. When asked how much money they would like to spend on the system 10% of the participants answered that they would not spend any money, 15% would spend up to 100€, 30% would spend between 101€ and 200€, 10% between 201€ and 300€ and another 10% would spend more than 300€. The attitude towards using the eco driving support system was measured using the van der Laan Acceptance Scale (1997). In Table 7 the results are shown. For this table the results of the items 3, 6 and 8 were mirrored. Thus all numbers have a positive value and represent the positive part of the adjective pairs. As a result of the questionnaire analyzes participants perceived the eco driving support system as very useful (item 1), good (item 3), effective (item 5) and assisting (item 7). All those items belong to the usefulness scale. Therefore the attitudes of the participants towards the usefulness of the system seem to be very positive. The items from the satisfying scale were rated also positively but not as good as the usefulness-items. The results of the open questions show that 40% of the participants preferred the haptic feedback, 30% the visual and 23% participants the combination of both modalities. Furthermore 47% of the participants would like to turn on the advice themselves; 53% would like to have it turned on from which 25% would like to be able to turn it off again. Asked for the reasons they would like to have the eco driving support system in their car, 67% of the participants answered that they would like to have the system for reducing their fuel consumption, 37% for comfort and traffic flow reasons, and 27% for environmental reasons. 10% of the participants referred to safety reasons. However, there were also disadvantages pointed out by the participants: Some participants felt patronized by the system (27%) because the haptic feedback was too strong and/or they found the system intervened too early (20%) especially in the stop sign and curve situations. Therefore 28% stated that they
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M. Staubach et al. / Transportation Research Part F 27 (2014) 11–21 Table 7 Results from van der Laan Acceptance Scale. Item
Description
Scale
Mean scores
SD
1 2 3 4 5 6 7 8 9
Useful–useless Pleasant–unpleasant Bad–good Nice–annoying Effective–superfluous Irritating–likeable Assisting–worthless Undesirable–desirable Raising alertness–sleep inducing
Usefulness Satisfying Usefulness Satisfying Usefulness Satisfying Usefulness Satisfying Usefulness
1.00 0.73 1.03 0.27 1.14 0.57 1.17 0.83 0.07
1.15 1.02 0.89 1.23 0.83 1.17 0.93 1.15 1.14
did not follow the recommendations of the eco driving support system when approaching the stop signs and curves. Furthermore 37% of the participants addressed that they felt distracted from their driving task through the systems recommendations. These results fit well to the moderate satisfying scores on the van der Laan-scale. 4. Discussion The eco-driving support system helped the participants of the present driving simulator study to save fuel of about 16% in urban and 18% in rural scenarios. Following the recommendations of the system the participants applied coasting strategies earlier before traffic lights, curves and stop signs. This led to steady deceleration manoeuvers with small slopes and less stops at traffic lights. Furthermore the participants shifted at lower RPMs. The outcomes of the study also show that using the system did reduce safety critical behaviors like speeding, hard braking events or critical time to collision distances. These results confirm the findings of the simTD (2013) project simulator studies where cooperative traffic light assistance helped to decrease the likelihood of stopping at traffic lights and the mean percentage of speeding events in urban environments. The eco-driving support system was further accepted very well by the participants, especially considering the perceived ease of use and the perceived usefulness. However, whether drivers will purchase the system in the future depends on the price of the system. Most of the participants would not like to spend more money on the system than they could save using the system regularly and only a few people would spend more than 300€. Another question is about the amount of time drivers are ready to invest. Even though only small increases in travel time could be found in the present study, it would be interesting to know under which circumstances drivers are ready to invest this time. Further the future usage of the system will depend on the situations which are supported by the system. For the traffic light approaches almost all participants were content with the recommendations of the system. Yet, concerning the stop sign situation and the curve situations some of the participants mentioned that the intervention was too early. In contrast to the traffic light situations they felt restricted because they would have liked to drive faster and they had to invest additional time in order to save fuel. This fact shows that the deceleration recommendation should not start too early in situations where the car has to slow down to standstill, because this could reduce the acceptance of the recommendations. This assumption can be supported by the findings of van Driel, Hoedemaeker, and van Arem (2007) congestion assistant study. Thus, congestion warnings which were too early had low impact on the change of driving behavior and that an active gas pedal was overruled by most of the participants when the traffic jam was still far ahead. Some participants also complained that the haptic feedback was too strong and could not be overridden easily which they felt was annoying. However, others found the strength of the haptic feedback adequate. In multiple previous studies participants have already expressed that they preferred other warning modalities over the haptic feedback, because they felt restricted, e. g. Meschtscherjakov, Wilfinger, Scherndl, and Tscheligi (2009), Staubach et al. (2014), Adell, Varhelyi, and Hjälmdahl (2008). On the other hand is the haptic feedback a very good supplementation of exclusive visual information. The results of the gaze analysis suggest that the participants learned really fast to use the haptic information from the gas pedal instead of relying only to the visual information. These results fit very well to the findings of Azzi, Reymond, Mérienne, and Kemeny (2011) that drivers ‘‘apparently relied more on haptic modality to achieve the eco-driving driving task, when they used both visual and haptic assistance’’. If future users do not feel comfortable with the systems feedback they might like to turn it off. This runs the risk that the systems potential to save fuel and emissions will not be utilized fully. In order to solve this dilemma personalized feedback mechanisms might be useful. Thus, the drivers should not only be able to choose whether they liked their feedback visually, haptically or combined but they should also be able to choose the strength of the haptic feedback or the situations in which they like to have feedback. Further the users could also choose beforehand in which situations they would like to be supported. So it would be better if some drivers will only be supported in traffic light situations than turning off the whole system. In the future development phase it is also important to adjust the feedback of this system to the feedback of other driver assistance systems. Therefore the system might be a part of an advanced active cruise control system especially for urban
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environments. Also for the implementation of the system in real vehicles, it is important to define at which point in time the information about the traffic light status should be displayed, so that the drivers will not be able to misuse the information about the traffic light phases for speeding up in order to pass the traffic light at a green phase. Moreover future research questions should also target in which situations or under which circumstances recommendations will be useful and safe and which recommended velocities will be accepted by the equipped drivers. According to a field study by Otto (2011) for example the participants did not follow recommendations below 20 km/h. Last but not least should future studies focus on the acceptance of non-equipped drivers who are driving behind vehicles with an eco-driving support system. They should consider both perspectives: Does the driver of the equipped vehicle feel comfortable if there are vehicles pushing from the back? Will the drivers of the vehicles in the back feel comfortable if they do not know why the car in front is going slower than the speed limit? How could the information about the eco-driving support system be displayed in order to inform other road users? 5. Conclusion Within the presented driving simulator study a cooperative system which recommends fuel saving driving strategies to the drivers was evaluated considering aspects of environment, mobility and driving behavior. Results showed mean fuel reduction potentials between 15.9 and 18.4 percent, less stops, and smoother driving styles but a small increase in travel time. The system had no negative effects on traffic safety even though the distraction of the HMI should be reduced if possible before the system will be sold on the market. Last but not least, the overall acceptance of the eco-driving support system was rather good. 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