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Driver brake response to sudden unintended acceleration while parking
Transportation Research Interdisciplinary Perspectives 2 (2019) 100039
Contents lists available at ScienceDirect
Transportation Research Interdisciplinary Perspectives journal homepage: https://www.journals.elsevier.com/transportation-researchinterdisciplinary-perspectives
Driver brake response to sudden unintended acceleration while parking ⁎
John G. Gaspar , Daniel V. McGehee National Advanced Driving Simulator, University of Iowa, United States of America
A R T I C L E
I N F O
Article history: Received 15 May 2019 Received in revised form 26 July 2019 Accepted 6 August 2019 Available online xxxx Keywords: Driver response Unexpected events Unintended acceleration Pedal misapplication
A B S T R A C T
In the past decade, there has been a rapid increase in reports of sudden unintended acceleration (SUA). While the precipitating conditions for SUA are well known, we know little about how drivers respond in such emergencies and how particular responses might lead to crashes. The goal of this study was to examine how drivers respond to a SUA in a controlled high-fidelity driving simulator experiment that closely replicated the motion cues of real driving. Younger and older drivers encountered a SUA event that mimicked a vehicle malfunction at the end of a simulated drive while executing a parking maneuver. A hierarchical cluster analysis revealed three distinct brake response patterns: hard braking, gradual braking combined with brake pumping, and light or no braking. The critical point in these brake responses, that is, the point at which response types diverged, was about 1 s after the onset of the SUA. Furthermore, older female drivers responded with less brake force than did younger or male drivers. These results indicate that over half of drivers react to SUA with indecisive responses that could lead to crashes. These results have important implications understanding how SUA may lead to crashes. The results also highlight the potential need for advanced driver assistance systems to aid drivers in hazard situations.
1. Introduction Sudden unintended acceleration (SUA) is the unintentional and unexpected rapid acceleration of the vehicle. SUA events are rare but very dangerous, often leading to prolonged periods of high, uncontrolled acceleration lasting several seconds. The National Highway Traffic Safety Administration (NHTSA, 2015) estimates 16,000 crashes occur annual due to unintended acceleration events. In many of the cases where SUA leads to a crash, driver often express confusion and insist they pressed the brake instead of the accelerator, despite evidence to suggest that driver error (i.e., pedal misapplication) is responsible for many of these events (Lococo et al., 2012). SUA events typically share some common features. A majority of reported SUA events and crashes occur in low-speed settings, such as parking lots, where the driver is transitioning between pedals (Lococo et al., 2012; Compton, 2010). Compton (2010) found that 64% of media-reported pedal error crashes occurred in commercial parking lots, with 49% occurring when entering a parking. Particular driver demographics are also associated with the frequency of SUA involvement. Older female drivers (65+), in particular, are most likely to be involved in pedal misapplication and SUA events (Lococo et al., 2012). Older drivers in general are overrepresented in SUA crashes. In his survey of media reports, Compton (2010) ⁎ Corresponding author at: National Advanced Driving Simulator, University of Iowa, 2401 Oakdale Blvd., Iowa City, IA 52242, United States of America. E-mail address: [email protected]. (J.G. Gaspar).
http://dx.doi.org/10.1016/j.trip.2019.100039 2590-1982/© 2019 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
reported that over 50% of pedal misapplication crashes involved a driver age fifty-five or older. Increased older adult involvement in SUA events is potentially linked to a number of factors, both physical and cognitive. Simulator studies of foot movement show greater variability in pedal position among older drivers, particularly older females, compared to younger drivers (Cantin et al., 2004; Wu et al., 2015). Differences in height, strength, and foot size could contribute to the amount of brake force a driver is able to apply (Lococo et al., 2012). Age-related perceptual and cognitive declines could also prolong the time needed to respond to the SUA. In order to promote sustained mobility throughout the lifespan, it is necessary to understand how drivers respond in emergency situations. These data are important for a number of reasons, including the design of vehicles and roadway infrastructure to mitigate crashes and injuries. This information is also important to policy makers, who might initiate standards to reduce crashes or provide additional support to drivers. While much is known about the factors associated with SUA events, little is known about how drivers respond in such situations and how response behaviors correspond to crash risk. The goal of this study was to use a high-fidelity motion driving simulator to examine how drivers respond to a severe SUA event caused by vehicle malfunction during a parking maneuver. Specifically, how do drivers respond to SUA during low-speed maneuvers and how do particular response behaviors manifest in crash outcomes? This is one of the first experimental studies of
J.G. Gaspar, D.V. McGehee /
Transportation Research Interdisciplinary Perspectives 2 (2019) 100039
2.3. Driving task
how drivers respond to SUA. We compared responses of younger and older male and female drivers in the same event, allowing us to identify different patterns of response behavior and associate them with crash outcomes.
The study consisted of an approximately fifteen-minute drive along a predetermined route in the Springfield virtual database (https://www.nads-sc.uiowa.edu/springfield/). The drive started in a rural section and ended in a commercial parking lot in an urban environment. The route mimicked a typical commute, and light ambient traffic was interspersed throughout the drive. Participants received auditory navigation cues throughout the drive. Previous research indicates that drivers adapt to control of the simulator vehicle within as little as 2 min (McGehee et al., 2004). Therefore, drivers likely felt comfortable with the simulation following this practice drive. At the end of the drive, participants entered a large commercial parking lot and received an audio instruction to park in a marked parking space (Fig. 2). The parking lot was populated with sparse parked vehicles, however there was a gap in front of the driver with a line of vehicles 350 ft. away and several cars parked to the right and concrete curbs on the driver's left (Fig. 3). As the driver entered the parking space, the simulation triggered a SUA. This acceleration was equivalent to completely depressing the accelerator pedal, and lasted for 4 s. The other vehicle functionality remained normal throughout the event. For example, full depression of the brake could “override” the SUA and bring the vehicle under control. The push-button start also functioned normally in the simulator vehicle, and drivers could have turned off the vehicle during the event (although no drivers did so during the drive).
2. Method 2.1. Participants Thirty-two licensed adult drivers (16 male, 16 female) provided written informed consent and participated in the study. The sample consisted of sixteen younger drivers (age 21–45) and sixteen older drivers (age 60–80). 2.2. Apparatus Data were collected on the NADS-1 driving simulator at the National Advanced Driving Simulator at the University of Iowa (Fig. 1). The NADS1 consists of a 24-foot-diameter dome, enclosing a full size 2014 Toyota Camry equipped with active steering, brake, and accelerator pedal feedback and a fully operational dashboard. The 13 degree-of-freedom motion system provided drivers motion cues. Realistic motion was critical for this study because the initial acceleration cues are important for detection of SUA and motion feedback throughout the event ensured realistic response behavior. The simulator has sixteen high definition (1920 × 1200) LED (light emitting diode) projectors to display seamless imagery on the interior walls of the dome with a 360-degree horizontal, 40-degree vertical field-ofview. The data sampling rate was 240 Hz.
Fig. 1. Exterior and interior views of the NADS-1 simulator and pedal configuration in the cab. 2
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Transportation Research Interdisciplinary Perspectives 2 (2019) 100039
2.4. Procedure Prior to the study drive, participants completed a five-minute practice drive, during which they made several right and left turns and practiced braking and parking to become familiar with the simulator and vehicle. This practice drive also screened for simulator sickness. We instructed participants that the goal of the study was to study everyday driving behavior. During the practice drive, participants parked in the marked parking space, such that during the study drive they thought they were parking to complete the experiment. Following the study drive, we debriefed participants about the true purpose of the study. 3. Results The analysis focused on two goals. First, to describe and categorize how drivers responded to the SUA. Second, understand whether driver demographics, age and sex, led to different response patterns and crash outcomes. The analysis window consisted of the 4 s following the onset of the SUA, or until the driver collided with ambient vehicles or the simulation was aborted due to motion concerns (e.g., the driver performed a rapidly steering wheel reversal that might have rolled the vehicle). 3.1. Driver response to SUA The first objective was to describe and classify driver responses to the SUA event. Initial video review indicated that all drivers responded by initially depressing the brake pedal. To categorize driver brake responses, we
Fig. 2. Images of the commercial parking lot and marked parking space.
Fig. 3. Diagram of the SUA event. 3
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Transportation Research Interdisciplinary Perspectives 2 (2019) 100039
To understand how response behavior led to conflicts and crashes, we plotted travel paths for individual drivers, shown in Fig. 6. Drivers with light brake responses traveled considerably farther into the parking lot, where they were more likely to come into conflict with ambient vehicles and other obstacles. Some of these drivers interestingly attempted to steer out of the parking lot, and post-experiment surveys indicated that these drivers were attempting to steer towards locations with fewer objects. The hard-braking drivers, on the other hand, and the drivers with gradual brake responses, to a lesser extent, traveled considerably shorter distances into the parking lot and were able to maintain control of the vehicle. That said, drivers with gradual brake responses still crossed several rows of parking spaces, which could have led to conflicts in situations of higher traffic density. It is also important to consider the initial response processes that led to these different response patterns. Fig. 7 shows mean brake response curves (brake force averaged across individuals within each cluster). Two interesting patterns appear to emerge (although it is worth noting that hard brakers were a small proportion of the sample). First, hardbraking drivers responded with over twice the brake force as drivers with gradual or light braking responses. Secondly, and perhaps most importantly, response behaviors began to diverge between one and one-anda-half seconds into the event. Hard-braking drivers, in fact, responded later on average than either gradual-braking or light-braking drivers (p = 0.07). This result suggests that drivers take roughly 1 s to perceive the SUA and that most drivers responded with an increase in brake force. The difference appears in the subsequent decision making stage, where some drivers selected a definitive response (hard braking), other more gradually increased their brake pressure or began pumping the brakes, and the majority were indecisive or confused and did not significantly increase brake force.
performed a hierarchical cluster analysis on the time series brake response data. Hierarchical cluster analysis was selected over other cluster methods (e.g., k-means) due to the ease of interpreting the resulting clustering and cluster dendrograms. This approach Brake pedal force was sampled at 240 Hz over the course of the event. We entered the brake force time-series data into a hierarchical cluster analysis to objectively categorize brake response patterns. Hierarchical cluster analysis is an unsupervised learning technique whereby a collection of data is organized into homogeneous clusters, such that objects in the same cluster are more similar than objects in different clusters. The cluster analysis was performed in R with the tsclust package (Montero and Vilar, 2014). The analysis used dynamic time warping to compute the distance between time series data and allow for the identification of unique shapes in the time series. The cluster analysis resulted in three distinct response clusters, seen in the cluster dendrogram in Fig. 4. By visualizing individual brake response curves by cluster affiliation, as in Fig. 5, these three clusters were defined based on the following characteristics: • Hard Braking: braking with greater than 125lbs of force within the first 1.5 s following the SUA event. • Brake Pumping/Gradual Braking: braking to a maximum of approximately 125lbs brake force occurring over the 5 s following the SUA event. • Light Brake Press: brake force less than 75lbs over the span of 5 s following the SUA event. Half the participants responded with light braking, less than 75lbs brake force, whereas just three drivers responded with hard braking. Crashes were identified via manual video coding by two independent raters and comprised collisions with ambient vehicles and static objects in the environment (curbs, light poles). Seven drivers crashed during the SUA event. Importantly, all seven crashes occurred for drivers who made light braking responses, with no crashes for either the hard or gradual braking response groups.
3.2. Age and sex effects The second goal was to understand age and sex effects with respect to response to SUA and crash outcomes. We examined two components of response behavior, initial response time, measured as the time from the start of the event until an increase in brake force, and maximum brake force applied. Analyses were conducted as ANOVAs with age group (younger, older) and sex (male, female) as between-subjects measures. Fig. 8 shows initial brake response time. Neither the main effect of age (F(1,30) = 0.47, p = 0.50) nor the main effect of sex (F(1,30) = 0.59, p = 0.45) were significant. However, there was a marginally significant age by sex interaction such that younger male drivers had slower initial brake response then the three other age and sex combinations (F(1,30) = 3.14, p = 0.08). For maximum brake forces, shown in Fig. 9, there was a significant main effect of sex (F(1,30) = 4.26, p = 0.04) but not of age (F(1,30) = 0.77, p = 0.39). The interaction between age and sex was not significant (F(1,30) = 0.10, p = 0.75). Younger drivers responded with significantly harder braking than older drivers. Male drivers similarly responded with harder braking than female drivers. In combination, these results show that younger drivers, particularly young males, were slower than other drivers to respond initially (by approximately 250 ms on average), but ultimately responded with much greater brake force. Older females, on the other hand, responded with significantly less brake force. These results build on the preceding cluster analysis to show that specific groups of drivers, specifically older females, were more likely to respond with light braking, even though their initial response times were just as fast as other drivers (see Table 1). While younger male drivers had slower initial response times, their more severe subsequent braking allowed them to avoid crashes. 4. Discussion
Fig. 4. Cluster dendrogram of brake responses. Numbers indicate participant identification numbers. Height is proportional to the value of intergroup dissimilarity between nodes.
The goal of this study was to examine driver response to sudden unintended acceleration caused by vehicle malfunction using a high-fidelity 4
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Transportation Research Interdisciplinary Perspectives 2 (2019) 100039
Fig. 5. Individual brake response curves by cluster affiliation. Numbers indicate participant identification numbers. Red = hard braking, Black = gradual braking, Green = light braking. YM = younger male, YF = younger female, OM = older male, OF = older female. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
respond to these emergency events and how particular responses might lead to crashes. Using a cluster analysis, the present study categorized brake response patterns and compared the response patterns of younger and older male and female drivers to a surprise SUA event in a commercial parking lot. The results demonstrate three distinct patterns of response behavior: hard braking, gradual braking, and light/minimal braking. There was a link between the response type and likelihood of crash involvement. All of the observed crashes (7/32 drivers) occurred when drivers responded with light or minimal braking. Conversely, drivers who responded with hard or gradual braking were able to overcome the acceleration and avoid crashes. Only three of thirty-two drivers in the sample responded with hard braking. The results showing that late responses led to larger steering responses is also consistent with previous analyses of naturalistic response data (Muttart, 2015). Initial decision making in response to the SUA therefore appears critical in mitigating crash risk. These data indicate that the critical response window was between 0.5 and 1.5 s. At this point, responses diverged such that drivers either continued increasing brake pressure or were confused and released the brake. From a theoretical perspective, this is consistent with a break in the perception-decision-action response chain at the decision making stage (Adam et al., 1996). Our results suggest that almost all drivers responded to the initial SUA, increasing brake pedal pressure. The critical decision point is whether to continue increasing brake pressure or to look around for a different solution. This finding meshes with other studies of driver response to hazard events, suggesting response times range from 1 to 3 s, depending on the nature of the task and driving situation (Otto et al., 1980; Dinakar and Muttart, 2019; Muttart et al., 2005; Attalla et al., 2018; Hancock et al., 1990; Muttart, 2003; Fitch et al., 2010). Factors such as the probability of an event play an important role in resulting response times, with probable events yielding faster responses (e.g., 0.6–1.5 s; Muttart, 2003). It is also important to consider the phases of this brake response process. The present study is consistent with the preceding literature, showing that deceleration occurs in two phases, initial braking followed by harder braking (Prynne and Martin, 1995). It is important to note that both hard braking and gradual braking drivers were able to bring the vehicle under control. That is, in this situation, each of these response patterns was equally successful. However, it
Fig. 6. Individual travel paths by brake response cluster affiliation. Red = hard braking, Black = gradual braking, Green = light braking. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
motion driving simulator. While much is understood regarding the factors that contributing to SUA occurrence, little is known about how drivers 5
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Transportation Research Interdisciplinary Perspectives 2 (2019) 100039
Fig. 7. Brake response curves with standard error bars. Brake response time is the time from the onset of SUA (time 0) until >10lbs brake force.
Fig. 8. Boxplots of initial brake response time, with points representing individual drivers.
Fig. 9. Boxplots of maximum brake force, with points representing individual drivers.
Table 1 Number of responses and crashes by response type, age group, and sex.
is possible these response patterns would have been differentially advantageous in other driving situations. The pattern of later responses followed by hard braking, for instance, could reflect some initial confusion. The gradual braking drivers, on the other hand, executed fast and moderate control of the vehicle. An avenue for future research is to explore how response patterns translate from one driving situation to another, and whether an ideal “general” pattern of responses in such situations is most optimal. These results have important practical implications for vehicle design and road traffic safety. Over half of the sample of drivers responded with confusion and minimal braking responses, traveling far into the parking lot where they were more likely to come into conflict with ambient vehicles and infrastructure. This points to a need for advanced driver assistance systems (ADAS), which could prevent crashes in the event of SUA. Conventional and high-definition GPS mapping data, for instance, could let the vehicle identify that it is in a parking lot and prevent sudden or prolonged
Young
Hard brake Gradual brake Light brake
Responses Crashes Responses Crashes Responses Crashes
Old
Male
Female
Male
Female
3 0 3 0 2 1
0 0 3 0 5 1
0 0 3 0 5 2
0 0 0 0 8 3
acceleration, or disable the brake pedal entirely. Indeed, in the context of driver response behavior, ADAS technology can augment responses or compensate for errors in situations like forward collisions. In the absences of 6
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Transportation Research Interdisciplinary Perspectives 2 (2019) 100039
to the importance of continued development of advanced driver assistance systems. This involves not just design of the vehicle technology itself, but an understanding of how such technology will interact with the surrounding environment and how policy decisions, such as vehicle technology design guidelines, will impact the likelihood of SUA events.
driver brake response, for example, the vehicle could begin applying the brake, perhaps also aiding as a decision making tool for the driver. It is important to note that this study represents just a single SUA situation. The severe nature of SUA raises the importance of the situational context—the surrounding traffic and pedestrians, infrastructure, and driver state (e.g., distraction). Our goal was to study driver response to SUA in a representative event, in which SUA most commonly occurs. Future research should further evaluate SUA during parking, as well as other maneuvers where it is common, such as low-speed turns. It is also important to note that the crash outcomes observed in this study were the product of the driving context. That is, it was possible for drivers to travel far into the parking lot and steer around other vehicles and objects. If the situation were more confined, such as a vehicle parked a short distance from a storefront, most drivers in the study would have crashed. The critical conclusion from this study is that, by failing to respond with hard braking, many drivers allowed the vehicle to continue traveling at high speed in a situation where longer travel distance brought them into conflict with other vehicles and objects. Several limitations are worth noting. First, our SUA event was triggered programmatically based on vehicle location. It was similar in that regard to SUA stemming from temporary vehicle malfunction or pedal entrapment. In addition to vehicle malfunction, however, SUA can result from a variety of factors including driver error (i.e., pedal misapplication), pedal location, vehicle carpeting, and other mechanical issues (Lococo et al., 2012). It is important to note that instances of pedal misapplication are rare, however, and therefore difficult to reproduce on-demand in an experimental setting (Wu et al., 2015). Additional research is needed to understand differences in driver response as a function of the precipitating cause of SUA. Most SUA events occur in a personal vehicles. Unfamiliarity with the simulator vehicle may therefore have altered response behavior. Drivers may have, for example, been less likely to press the start button ignition to turn off the vehicle or to shift the vehicle into neutral in the simulator compared to their personal vehicle. Given that participants in simulator research studies nearly never perform such functions, these results are best considered in terms of brake response times. Future research is needed to understand what, if any, effect vehicle familiarity has on response to SUA and whether drivers might execute a broader range of responses in different experimental settings. The study included just a single simulator vehicle and vehicle characteristics, such as pedal configuration and travel distance, are known to impact driver response (Lococo et al., 2012), research is also needed to understand how vehicle characteristics, such as pedal configuration, contribute to driver response in SUA situations.
Acknowledgements This research was funded by the Toyota Safety Research and Education Program Settlement. All research a is developed independently by the University of Iowa and conclusions are expressed by the University of Iowa and have not been sponsored, approved or endorsed by Toyota or the plaintiff's class counsel. The authors would like to thank Omar Ahmad, Timothy Brown, Chris Schwarz, Rose Schmitt, and David Heitbrink for their contributions to this research. References Adam, J.J., Paas, F.G.W.C., Buekers, M.J., Wuyts, I.J., Spijkers, W.A.C., Wallmeyer, P., 1996. Perception-action coupling in choice reaction time tasks. Hum. Mov. Sci. 15 (4), 511–519. Attalla, S., Toxopeus, R., Kodsi, S., Oliver, M., 2018. Driver response time to left-turning vehicle sat traffic signal controlled intersections (No. 2018-01-0521). Paper, SAE Technical. Cantin, V., Blouin, J., Simoneau, M., Teasdale, N., 2004. Driving in a simulator and lower limb movement in elderly persons: can we infer something about pedal errors. Advances in Transportation Studies, 39–46 Retrieved from. http://trid.trb.org/view.aspx?id= 749499. Compton, R.P., 2010. Human factors considerations: unintended ac-celeration and pedal errors. NHTSA Informational Briefing, June 30, 2010. Dinakar, S., Muttart, J., 2019. Driver behavior in left turn across path from opposite direction crash and near crash events from SHRP2 naturalistic driving (No. 2019-01-0414). Paper, SAE Technical. Fitch, G.M., Blanco, M., Morgan, J.F., Wharton, A.E., 2010. Driver braking performance to surprise and expected events. Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 54, No. 24, pp. 2075–2080). Sage CA. SAGE Publications, Los Angeles, CA. Hancock, P.A., Wulf, G., Thom, D., Fassnacht, P., 1990. Driver workload during differing driving maneuvers. Accid. Anal. Prev. 22 (3), 281–290. Lococo, K. H., Staplin, L., Martell, C. A., & Sifrit, K. J. (2012). Pedal Application Errors. (Report No. DOT HS 811 597). Washing-ton, DC: National Highway Traffic Safety Administration. McGehee, D.V., Lee, J.D., Rizzo, M., Dawson, J., Bateman, K., 2004. Quantitative analysis of steering adaptation on a high performance fixed-base driving simulator. Transport. Res. F: Traffic Psychol. Behav. 7 (3), 181–196. Montero, P., Vilar, J.A., 2014. TSclust: an R package for time series clustering. J. Stat. Softw. 62 (1), 1–43. Muttart, J.W., 2003. Development and evaluation of driver response time predictors based upon meta analysis. SAE Trans. 876–896. Muttart, J., 2015. Influence of Age, Secondary Tasks and Other Factors on Drivers' Swerving Responses before Crash or Near-Crash Events (No. 2015-01-1417). SAE Technical Paper. Muttart, J.W., Messerschmidt, W.F., Gillen, L.G., 2005. Relationship between relative velocity detection and driver response times in vehicle following situations (No. 2005-01-0427). Paper, SAE Technical. National Highway Traffic Safety Administration “Prevent Pedal Crashes” available at https:// www.cars.com/articles/nhtsa-prevent-pedalerror-crashes-1420680532557/ May 29 2015. Otto, W. M., Otto, C. L., & Overton, R. K. (1980). Response characteristics of motorcycle riders to a complex emergency situation (No. HS-031 135). Prynne, K., Martin, P., 1995. Braking Behaviour in Emergencies (No. 950969). SAE Technical Paper. Wu, Y., Boyle, L.N., McGehee, D., Roe, C.A., Ebe, K., Foley, J., 2015. Modeling types of pedal applications using a driving simulator. Hum. Factors 57 (7), 1276–1288.
4.1. Conclusions This is one of the first controlled experimental studies of sudden unintended acceleration. Many questions therefore remain about the nature of UA events and potential countermeasures. For instance, what cues do drivers use to initially detect SUA, and why do some drivers continue braking while others disengage? Our results demonstrate the range of driver responses to a single SUA event, demonstrate a method for capturing responses in an experimental setting, and suggest the need for driver assistance technology in rare emergencies. In addition to providing guidance to vehicle designers and data to the research community on driver response behavior, the present study speaks
7
Contents lists available at ScienceDirect
Transportation Research Interdisciplinary Perspectives journal homepage: https://www.journals.elsevier.com/transportation-researchinterdisciplinary-perspectives
Driver brake response to sudden unintended acceleration while parking ⁎
John G. Gaspar , Daniel V. McGehee National Advanced Driving Simulator, University of Iowa, United States of America
A R T I C L E
I N F O
Article history: Received 15 May 2019 Received in revised form 26 July 2019 Accepted 6 August 2019 Available online xxxx Keywords: Driver response Unexpected events Unintended acceleration Pedal misapplication
A B S T R A C T
In the past decade, there has been a rapid increase in reports of sudden unintended acceleration (SUA). While the precipitating conditions for SUA are well known, we know little about how drivers respond in such emergencies and how particular responses might lead to crashes. The goal of this study was to examine how drivers respond to a SUA in a controlled high-fidelity driving simulator experiment that closely replicated the motion cues of real driving. Younger and older drivers encountered a SUA event that mimicked a vehicle malfunction at the end of a simulated drive while executing a parking maneuver. A hierarchical cluster analysis revealed three distinct brake response patterns: hard braking, gradual braking combined with brake pumping, and light or no braking. The critical point in these brake responses, that is, the point at which response types diverged, was about 1 s after the onset of the SUA. Furthermore, older female drivers responded with less brake force than did younger or male drivers. These results indicate that over half of drivers react to SUA with indecisive responses that could lead to crashes. These results have important implications understanding how SUA may lead to crashes. The results also highlight the potential need for advanced driver assistance systems to aid drivers in hazard situations.
1. Introduction Sudden unintended acceleration (SUA) is the unintentional and unexpected rapid acceleration of the vehicle. SUA events are rare but very dangerous, often leading to prolonged periods of high, uncontrolled acceleration lasting several seconds. The National Highway Traffic Safety Administration (NHTSA, 2015) estimates 16,000 crashes occur annual due to unintended acceleration events. In many of the cases where SUA leads to a crash, driver often express confusion and insist they pressed the brake instead of the accelerator, despite evidence to suggest that driver error (i.e., pedal misapplication) is responsible for many of these events (Lococo et al., 2012). SUA events typically share some common features. A majority of reported SUA events and crashes occur in low-speed settings, such as parking lots, where the driver is transitioning between pedals (Lococo et al., 2012; Compton, 2010). Compton (2010) found that 64% of media-reported pedal error crashes occurred in commercial parking lots, with 49% occurring when entering a parking. Particular driver demographics are also associated with the frequency of SUA involvement. Older female drivers (65+), in particular, are most likely to be involved in pedal misapplication and SUA events (Lococo et al., 2012). Older drivers in general are overrepresented in SUA crashes. In his survey of media reports, Compton (2010) ⁎ Corresponding author at: National Advanced Driving Simulator, University of Iowa, 2401 Oakdale Blvd., Iowa City, IA 52242, United States of America. E-mail address: [email protected]. (J.G. Gaspar).
http://dx.doi.org/10.1016/j.trip.2019.100039 2590-1982/© 2019 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
reported that over 50% of pedal misapplication crashes involved a driver age fifty-five or older. Increased older adult involvement in SUA events is potentially linked to a number of factors, both physical and cognitive. Simulator studies of foot movement show greater variability in pedal position among older drivers, particularly older females, compared to younger drivers (Cantin et al., 2004; Wu et al., 2015). Differences in height, strength, and foot size could contribute to the amount of brake force a driver is able to apply (Lococo et al., 2012). Age-related perceptual and cognitive declines could also prolong the time needed to respond to the SUA. In order to promote sustained mobility throughout the lifespan, it is necessary to understand how drivers respond in emergency situations. These data are important for a number of reasons, including the design of vehicles and roadway infrastructure to mitigate crashes and injuries. This information is also important to policy makers, who might initiate standards to reduce crashes or provide additional support to drivers. While much is known about the factors associated with SUA events, little is known about how drivers respond in such situations and how response behaviors correspond to crash risk. The goal of this study was to use a high-fidelity motion driving simulator to examine how drivers respond to a severe SUA event caused by vehicle malfunction during a parking maneuver. Specifically, how do drivers respond to SUA during low-speed maneuvers and how do particular response behaviors manifest in crash outcomes? This is one of the first experimental studies of
J.G. Gaspar, D.V. McGehee /
Transportation Research Interdisciplinary Perspectives 2 (2019) 100039
2.3. Driving task
how drivers respond to SUA. We compared responses of younger and older male and female drivers in the same event, allowing us to identify different patterns of response behavior and associate them with crash outcomes.
The study consisted of an approximately fifteen-minute drive along a predetermined route in the Springfield virtual database (https://www.nads-sc.uiowa.edu/springfield/). The drive started in a rural section and ended in a commercial parking lot in an urban environment. The route mimicked a typical commute, and light ambient traffic was interspersed throughout the drive. Participants received auditory navigation cues throughout the drive. Previous research indicates that drivers adapt to control of the simulator vehicle within as little as 2 min (McGehee et al., 2004). Therefore, drivers likely felt comfortable with the simulation following this practice drive. At the end of the drive, participants entered a large commercial parking lot and received an audio instruction to park in a marked parking space (Fig. 2). The parking lot was populated with sparse parked vehicles, however there was a gap in front of the driver with a line of vehicles 350 ft. away and several cars parked to the right and concrete curbs on the driver's left (Fig. 3). As the driver entered the parking space, the simulation triggered a SUA. This acceleration was equivalent to completely depressing the accelerator pedal, and lasted for 4 s. The other vehicle functionality remained normal throughout the event. For example, full depression of the brake could “override” the SUA and bring the vehicle under control. The push-button start also functioned normally in the simulator vehicle, and drivers could have turned off the vehicle during the event (although no drivers did so during the drive).
2. Method 2.1. Participants Thirty-two licensed adult drivers (16 male, 16 female) provided written informed consent and participated in the study. The sample consisted of sixteen younger drivers (age 21–45) and sixteen older drivers (age 60–80). 2.2. Apparatus Data were collected on the NADS-1 driving simulator at the National Advanced Driving Simulator at the University of Iowa (Fig. 1). The NADS1 consists of a 24-foot-diameter dome, enclosing a full size 2014 Toyota Camry equipped with active steering, brake, and accelerator pedal feedback and a fully operational dashboard. The 13 degree-of-freedom motion system provided drivers motion cues. Realistic motion was critical for this study because the initial acceleration cues are important for detection of SUA and motion feedback throughout the event ensured realistic response behavior. The simulator has sixteen high definition (1920 × 1200) LED (light emitting diode) projectors to display seamless imagery on the interior walls of the dome with a 360-degree horizontal, 40-degree vertical field-ofview. The data sampling rate was 240 Hz.
Fig. 1. Exterior and interior views of the NADS-1 simulator and pedal configuration in the cab. 2
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2.4. Procedure Prior to the study drive, participants completed a five-minute practice drive, during which they made several right and left turns and practiced braking and parking to become familiar with the simulator and vehicle. This practice drive also screened for simulator sickness. We instructed participants that the goal of the study was to study everyday driving behavior. During the practice drive, participants parked in the marked parking space, such that during the study drive they thought they were parking to complete the experiment. Following the study drive, we debriefed participants about the true purpose of the study. 3. Results The analysis focused on two goals. First, to describe and categorize how drivers responded to the SUA. Second, understand whether driver demographics, age and sex, led to different response patterns and crash outcomes. The analysis window consisted of the 4 s following the onset of the SUA, or until the driver collided with ambient vehicles or the simulation was aborted due to motion concerns (e.g., the driver performed a rapidly steering wheel reversal that might have rolled the vehicle). 3.1. Driver response to SUA The first objective was to describe and classify driver responses to the SUA event. Initial video review indicated that all drivers responded by initially depressing the brake pedal. To categorize driver brake responses, we
Fig. 2. Images of the commercial parking lot and marked parking space.
Fig. 3. Diagram of the SUA event. 3
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To understand how response behavior led to conflicts and crashes, we plotted travel paths for individual drivers, shown in Fig. 6. Drivers with light brake responses traveled considerably farther into the parking lot, where they were more likely to come into conflict with ambient vehicles and other obstacles. Some of these drivers interestingly attempted to steer out of the parking lot, and post-experiment surveys indicated that these drivers were attempting to steer towards locations with fewer objects. The hard-braking drivers, on the other hand, and the drivers with gradual brake responses, to a lesser extent, traveled considerably shorter distances into the parking lot and were able to maintain control of the vehicle. That said, drivers with gradual brake responses still crossed several rows of parking spaces, which could have led to conflicts in situations of higher traffic density. It is also important to consider the initial response processes that led to these different response patterns. Fig. 7 shows mean brake response curves (brake force averaged across individuals within each cluster). Two interesting patterns appear to emerge (although it is worth noting that hard brakers were a small proportion of the sample). First, hardbraking drivers responded with over twice the brake force as drivers with gradual or light braking responses. Secondly, and perhaps most importantly, response behaviors began to diverge between one and one-anda-half seconds into the event. Hard-braking drivers, in fact, responded later on average than either gradual-braking or light-braking drivers (p = 0.07). This result suggests that drivers take roughly 1 s to perceive the SUA and that most drivers responded with an increase in brake force. The difference appears in the subsequent decision making stage, where some drivers selected a definitive response (hard braking), other more gradually increased their brake pressure or began pumping the brakes, and the majority were indecisive or confused and did not significantly increase brake force.
performed a hierarchical cluster analysis on the time series brake response data. Hierarchical cluster analysis was selected over other cluster methods (e.g., k-means) due to the ease of interpreting the resulting clustering and cluster dendrograms. This approach Brake pedal force was sampled at 240 Hz over the course of the event. We entered the brake force time-series data into a hierarchical cluster analysis to objectively categorize brake response patterns. Hierarchical cluster analysis is an unsupervised learning technique whereby a collection of data is organized into homogeneous clusters, such that objects in the same cluster are more similar than objects in different clusters. The cluster analysis was performed in R with the tsclust package (Montero and Vilar, 2014). The analysis used dynamic time warping to compute the distance between time series data and allow for the identification of unique shapes in the time series. The cluster analysis resulted in three distinct response clusters, seen in the cluster dendrogram in Fig. 4. By visualizing individual brake response curves by cluster affiliation, as in Fig. 5, these three clusters were defined based on the following characteristics: • Hard Braking: braking with greater than 125lbs of force within the first 1.5 s following the SUA event. • Brake Pumping/Gradual Braking: braking to a maximum of approximately 125lbs brake force occurring over the 5 s following the SUA event. • Light Brake Press: brake force less than 75lbs over the span of 5 s following the SUA event. Half the participants responded with light braking, less than 75lbs brake force, whereas just three drivers responded with hard braking. Crashes were identified via manual video coding by two independent raters and comprised collisions with ambient vehicles and static objects in the environment (curbs, light poles). Seven drivers crashed during the SUA event. Importantly, all seven crashes occurred for drivers who made light braking responses, with no crashes for either the hard or gradual braking response groups.
3.2. Age and sex effects The second goal was to understand age and sex effects with respect to response to SUA and crash outcomes. We examined two components of response behavior, initial response time, measured as the time from the start of the event until an increase in brake force, and maximum brake force applied. Analyses were conducted as ANOVAs with age group (younger, older) and sex (male, female) as between-subjects measures. Fig. 8 shows initial brake response time. Neither the main effect of age (F(1,30) = 0.47, p = 0.50) nor the main effect of sex (F(1,30) = 0.59, p = 0.45) were significant. However, there was a marginally significant age by sex interaction such that younger male drivers had slower initial brake response then the three other age and sex combinations (F(1,30) = 3.14, p = 0.08). For maximum brake forces, shown in Fig. 9, there was a significant main effect of sex (F(1,30) = 4.26, p = 0.04) but not of age (F(1,30) = 0.77, p = 0.39). The interaction between age and sex was not significant (F(1,30) = 0.10, p = 0.75). Younger drivers responded with significantly harder braking than older drivers. Male drivers similarly responded with harder braking than female drivers. In combination, these results show that younger drivers, particularly young males, were slower than other drivers to respond initially (by approximately 250 ms on average), but ultimately responded with much greater brake force. Older females, on the other hand, responded with significantly less brake force. These results build on the preceding cluster analysis to show that specific groups of drivers, specifically older females, were more likely to respond with light braking, even though their initial response times were just as fast as other drivers (see Table 1). While younger male drivers had slower initial response times, their more severe subsequent braking allowed them to avoid crashes. 4. Discussion
Fig. 4. Cluster dendrogram of brake responses. Numbers indicate participant identification numbers. Height is proportional to the value of intergroup dissimilarity between nodes.
The goal of this study was to examine driver response to sudden unintended acceleration caused by vehicle malfunction using a high-fidelity 4
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Fig. 5. Individual brake response curves by cluster affiliation. Numbers indicate participant identification numbers. Red = hard braking, Black = gradual braking, Green = light braking. YM = younger male, YF = younger female, OM = older male, OF = older female. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
respond to these emergency events and how particular responses might lead to crashes. Using a cluster analysis, the present study categorized brake response patterns and compared the response patterns of younger and older male and female drivers to a surprise SUA event in a commercial parking lot. The results demonstrate three distinct patterns of response behavior: hard braking, gradual braking, and light/minimal braking. There was a link between the response type and likelihood of crash involvement. All of the observed crashes (7/32 drivers) occurred when drivers responded with light or minimal braking. Conversely, drivers who responded with hard or gradual braking were able to overcome the acceleration and avoid crashes. Only three of thirty-two drivers in the sample responded with hard braking. The results showing that late responses led to larger steering responses is also consistent with previous analyses of naturalistic response data (Muttart, 2015). Initial decision making in response to the SUA therefore appears critical in mitigating crash risk. These data indicate that the critical response window was between 0.5 and 1.5 s. At this point, responses diverged such that drivers either continued increasing brake pressure or were confused and released the brake. From a theoretical perspective, this is consistent with a break in the perception-decision-action response chain at the decision making stage (Adam et al., 1996). Our results suggest that almost all drivers responded to the initial SUA, increasing brake pedal pressure. The critical decision point is whether to continue increasing brake pressure or to look around for a different solution. This finding meshes with other studies of driver response to hazard events, suggesting response times range from 1 to 3 s, depending on the nature of the task and driving situation (Otto et al., 1980; Dinakar and Muttart, 2019; Muttart et al., 2005; Attalla et al., 2018; Hancock et al., 1990; Muttart, 2003; Fitch et al., 2010). Factors such as the probability of an event play an important role in resulting response times, with probable events yielding faster responses (e.g., 0.6–1.5 s; Muttart, 2003). It is also important to consider the phases of this brake response process. The present study is consistent with the preceding literature, showing that deceleration occurs in two phases, initial braking followed by harder braking (Prynne and Martin, 1995). It is important to note that both hard braking and gradual braking drivers were able to bring the vehicle under control. That is, in this situation, each of these response patterns was equally successful. However, it
Fig. 6. Individual travel paths by brake response cluster affiliation. Red = hard braking, Black = gradual braking, Green = light braking. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
motion driving simulator. While much is understood regarding the factors that contributing to SUA occurrence, little is known about how drivers 5
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Fig. 7. Brake response curves with standard error bars. Brake response time is the time from the onset of SUA (time 0) until >10lbs brake force.
Fig. 8. Boxplots of initial brake response time, with points representing individual drivers.
Fig. 9. Boxplots of maximum brake force, with points representing individual drivers.
Table 1 Number of responses and crashes by response type, age group, and sex.
is possible these response patterns would have been differentially advantageous in other driving situations. The pattern of later responses followed by hard braking, for instance, could reflect some initial confusion. The gradual braking drivers, on the other hand, executed fast and moderate control of the vehicle. An avenue for future research is to explore how response patterns translate from one driving situation to another, and whether an ideal “general” pattern of responses in such situations is most optimal. These results have important practical implications for vehicle design and road traffic safety. Over half of the sample of drivers responded with confusion and minimal braking responses, traveling far into the parking lot where they were more likely to come into conflict with ambient vehicles and infrastructure. This points to a need for advanced driver assistance systems (ADAS), which could prevent crashes in the event of SUA. Conventional and high-definition GPS mapping data, for instance, could let the vehicle identify that it is in a parking lot and prevent sudden or prolonged
Young
Hard brake Gradual brake Light brake
Responses Crashes Responses Crashes Responses Crashes
Old
Male
Female
Male
Female
3 0 3 0 2 1
0 0 3 0 5 1
0 0 3 0 5 2
0 0 0 0 8 3
acceleration, or disable the brake pedal entirely. Indeed, in the context of driver response behavior, ADAS technology can augment responses or compensate for errors in situations like forward collisions. In the absences of 6
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to the importance of continued development of advanced driver assistance systems. This involves not just design of the vehicle technology itself, but an understanding of how such technology will interact with the surrounding environment and how policy decisions, such as vehicle technology design guidelines, will impact the likelihood of SUA events.
driver brake response, for example, the vehicle could begin applying the brake, perhaps also aiding as a decision making tool for the driver. It is important to note that this study represents just a single SUA situation. The severe nature of SUA raises the importance of the situational context—the surrounding traffic and pedestrians, infrastructure, and driver state (e.g., distraction). Our goal was to study driver response to SUA in a representative event, in which SUA most commonly occurs. Future research should further evaluate SUA during parking, as well as other maneuvers where it is common, such as low-speed turns. It is also important to note that the crash outcomes observed in this study were the product of the driving context. That is, it was possible for drivers to travel far into the parking lot and steer around other vehicles and objects. If the situation were more confined, such as a vehicle parked a short distance from a storefront, most drivers in the study would have crashed. The critical conclusion from this study is that, by failing to respond with hard braking, many drivers allowed the vehicle to continue traveling at high speed in a situation where longer travel distance brought them into conflict with other vehicles and objects. Several limitations are worth noting. First, our SUA event was triggered programmatically based on vehicle location. It was similar in that regard to SUA stemming from temporary vehicle malfunction or pedal entrapment. In addition to vehicle malfunction, however, SUA can result from a variety of factors including driver error (i.e., pedal misapplication), pedal location, vehicle carpeting, and other mechanical issues (Lococo et al., 2012). It is important to note that instances of pedal misapplication are rare, however, and therefore difficult to reproduce on-demand in an experimental setting (Wu et al., 2015). Additional research is needed to understand differences in driver response as a function of the precipitating cause of SUA. Most SUA events occur in a personal vehicles. Unfamiliarity with the simulator vehicle may therefore have altered response behavior. Drivers may have, for example, been less likely to press the start button ignition to turn off the vehicle or to shift the vehicle into neutral in the simulator compared to their personal vehicle. Given that participants in simulator research studies nearly never perform such functions, these results are best considered in terms of brake response times. Future research is needed to understand what, if any, effect vehicle familiarity has on response to SUA and whether drivers might execute a broader range of responses in different experimental settings. The study included just a single simulator vehicle and vehicle characteristics, such as pedal configuration and travel distance, are known to impact driver response (Lococo et al., 2012), research is also needed to understand how vehicle characteristics, such as pedal configuration, contribute to driver response in SUA situations.
Acknowledgements This research was funded by the Toyota Safety Research and Education Program Settlement. All research a is developed independently by the University of Iowa and conclusions are expressed by the University of Iowa and have not been sponsored, approved or endorsed by Toyota or the plaintiff's class counsel. The authors would like to thank Omar Ahmad, Timothy Brown, Chris Schwarz, Rose Schmitt, and David Heitbrink for their contributions to this research. References Adam, J.J., Paas, F.G.W.C., Buekers, M.J., Wuyts, I.J., Spijkers, W.A.C., Wallmeyer, P., 1996. Perception-action coupling in choice reaction time tasks. Hum. Mov. Sci. 15 (4), 511–519. Attalla, S., Toxopeus, R., Kodsi, S., Oliver, M., 2018. Driver response time to left-turning vehicle sat traffic signal controlled intersections (No. 2018-01-0521). Paper, SAE Technical. Cantin, V., Blouin, J., Simoneau, M., Teasdale, N., 2004. Driving in a simulator and lower limb movement in elderly persons: can we infer something about pedal errors. Advances in Transportation Studies, 39–46 Retrieved from. http://trid.trb.org/view.aspx?id= 749499. Compton, R.P., 2010. Human factors considerations: unintended ac-celeration and pedal errors. NHTSA Informational Briefing, June 30, 2010. Dinakar, S., Muttart, J., 2019. Driver behavior in left turn across path from opposite direction crash and near crash events from SHRP2 naturalistic driving (No. 2019-01-0414). Paper, SAE Technical. Fitch, G.M., Blanco, M., Morgan, J.F., Wharton, A.E., 2010. Driver braking performance to surprise and expected events. Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 54, No. 24, pp. 2075–2080). Sage CA. SAGE Publications, Los Angeles, CA. Hancock, P.A., Wulf, G., Thom, D., Fassnacht, P., 1990. Driver workload during differing driving maneuvers. Accid. Anal. Prev. 22 (3), 281–290. Lococo, K. H., Staplin, L., Martell, C. A., & Sifrit, K. J. (2012). Pedal Application Errors. (Report No. DOT HS 811 597). Washing-ton, DC: National Highway Traffic Safety Administration. McGehee, D.V., Lee, J.D., Rizzo, M., Dawson, J., Bateman, K., 2004. Quantitative analysis of steering adaptation on a high performance fixed-base driving simulator. Transport. Res. F: Traffic Psychol. Behav. 7 (3), 181–196. Montero, P., Vilar, J.A., 2014. TSclust: an R package for time series clustering. J. Stat. Softw. 62 (1), 1–43. Muttart, J.W., 2003. Development and evaluation of driver response time predictors based upon meta analysis. SAE Trans. 876–896. Muttart, J., 2015. Influence of Age, Secondary Tasks and Other Factors on Drivers' Swerving Responses before Crash or Near-Crash Events (No. 2015-01-1417). SAE Technical Paper. Muttart, J.W., Messerschmidt, W.F., Gillen, L.G., 2005. Relationship between relative velocity detection and driver response times in vehicle following situations (No. 2005-01-0427). Paper, SAE Technical. National Highway Traffic Safety Administration “Prevent Pedal Crashes” available at https:// www.cars.com/articles/nhtsa-prevent-pedalerror-crashes-1420680532557/ May 29 2015. Otto, W. M., Otto, C. L., & Overton, R. K. (1980). Response characteristics of motorcycle riders to a complex emergency situation (No. HS-031 135). Prynne, K., Martin, P., 1995. Braking Behaviour in Emergencies (No. 950969). SAE Technical Paper. Wu, Y., Boyle, L.N., McGehee, D., Roe, C.A., Ebe, K., Foley, J., 2015. Modeling types of pedal applications using a driving simulator. Hum. Factors 57 (7), 1276–1288.
4.1. Conclusions This is one of the first controlled experimental studies of sudden unintended acceleration. Many questions therefore remain about the nature of UA events and potential countermeasures. For instance, what cues do drivers use to initially detect SUA, and why do some drivers continue braking while others disengage? Our results demonstrate the range of driver responses to a single SUA event, demonstrate a method for capturing responses in an experimental setting, and suggest the need for driver assistance technology in rare emergencies. In addition to providing guidance to vehicle designers and data to the research community on driver response behavior, the present study speaks
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