How many crashes can connected vehicle and automated vehicle technologies prevent: A meta-analysis

Accident Analysis and Prevention 136 (2020) 105299

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How many crashes can connected vehicle and automated vehicle technologies prevent: A meta-analysis☆

T

Ling Wanga,*, Hao Zhonga, Wanjing Maa, Mohamed Abdel-Atyb, Juneyoung Parkc a

Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai, PR China Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL, 32816, USA c Department of Transportation and Logistics Engineering, Hanyang University, South Korea b

A R T I C LE I N FO

A B S T R A C T

Keywords: Meta-analysis Connected vehicle and automated vehicle technologies Safety effectiveness Crash data

The connected and automated vehicle (CAV) technologies have made great progresses. It has been commonly accepted that CV or AV technologies would reduce human errors in driving and benefit traffic safety. However, the answer of how many crashes can be prevented because of CV or AV technologies has not reached a consistent conclusion. In order to quantitatively answer this question, this study used meta-analysis to evaluate the safety effectiveness of nine common and important CV or AV technologies, and tested the safety effectiveness of these technologies for six countries. First, 73 studies about the safety impact of CV or AV technologies were filtered out from 826 CAV-related papers or reports. Second, the safety impacts of these technologies with regard to assistant types and triggering times have been compared. It shows AV technologies can play a more significant role than CV technologies, and the technologies with closer triggering time to collision time have greater safety effectiveness. Third, in the meta-analysis, the random effect model was used to evaluate the safety effectiveness, and the funnel plots and trim-and-fill method were used to evaluate and adjust publication bias, so as to objectively evaluate the safety effectiveness of each technology. Then, according to the crash data of six countries, the comprehensive safety effectiveness and compilation of safety effectiveness of the above technologies were calculated. The results show that if all of technologies were implemented in the six countries, the average number of crashes could be reduced by 3.40 million, among which the India would reduce the most (54.24%). Additionally, different countries should develop different development strategies, e.g., USA should prioritize the development of the lane change warning and intersection warning, the UK should prioritize applications related to intersection warning and rear-end warning. Overall, this study provides comprehensive and quantitative understating of the safety effectiveness of CA or AV technologies and would contribute to government, vehicle companies, and agencies in deciding the development priority of CA or AV technologies.

1. Introduction With the development of emerging technologies, the connected and automated vehicles (CAV) have made great progress. At present, the effectiveness of CV or AV technologies are in the following aspects, i.e., safety, environmental impact, and mobility. Among them, safety is the main purpose, and the focus of most studies (Tian et al., 2018). It has been commonly accepted that CV or AV technologies would reduce human errors in driving and benefit traffic safety. However, how many crashes can be reduced because of CV or AV technologies cannot reach a common conclusion. Objectively answering this question would help government and vehicle companies in understanding the safety effectiveness, which is the ability that a technology can reduce crashes, of

each technology and formulate development policies. There are many factors that would contribute to the variance of safety effectiveness even for the same CV or AV technologies, e.g., assessment methods, experimental conditions, and driver conditions. According to the research by Yue et al. (2018), there are four methods on evaluating the safety effectiveness, field operation test (FOT), simulator study, statistical analysis methodology (SAM), and safety impact methodology (SIM). The assessment methods would result in differences. For example, the forward collision warning system (FCW) can reduce about 39% and 27% rear-end crash by adopting the FOT and SIM, respectively (Son et al., 2015; Bärgman et al., 2017). What’s more, the lane departure warning system (LDW) can reduce 17% and 33% crash by adopting the FOT and SIM, respectively (Kusano et al., 2014;

☆ ⁎

This paper has been handled by associate editor N.N. Sze. Corresponding author. E-mail addresses: [email protected] (L. Wang), [email protected] (W. Ma).

https://doi.org/10.1016/j.aap.2019.105299 Received 25 June 2019; Received in revised form 2 September 2019; Accepted 13 September 2019 0001-4575/ © 2019 Elsevier Ltd. All rights reserved.

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Holmes et al., 2018). The difference in evaluation methods is not the only reason for the difference in evaluation results. Additionally, experimental conditions and driver conditions are also important. For example, 32 drivers drove on urban road, urban expressway, and highway to test the effectiveness of FCW and found 13% crash reduction (Lyu et al., 2018). On the other hand, 108 drivers took experiments on highways and concluded 16.5% crash reduction (Nodine et al., 2011). Therefore, it can be found that the existing studies on the CA or AV safety effectiveness are limited to experimental conditions or evaluation methods, and this would lead to the limited applicability of conclusions. In some studies (Li et al., 2016; Yue et al., 2018), in order to test the comprehensive effectiveness of CV or AV technologies, the relationships between CV or AV technologies and crash types were linked, and the safety effectiveness of a CV or AV technology was obtained from previous research. Thus, the number of crashes would be reduced by a CV or AV technology was calculated. In these studies, the effectiveness of one CV or AV technology was the average of a few studies, which might be biased. Meanwhile, these studies only concentrated on one country, but it might not be representative since there is a big difference between the proportion of various crash types in different countries. It is hard to make a more universal conclusion for other countries. Therefore, there is a need take more studies from the literature and more countries into consideration. On the other hand, in order to objectively and effectively analyze the effectiveness, some research used meta-analysis. Meta-analysis is a comprehensive and objective research method to quantitatively summarize research results. It has been widely used in the transportation field (Ziakopoulos et al., 2019; Elvik, 2001). However, the number of CV or AV technologies’ studies based on meta-analysis is rare. Only autonomous emergency braking (AEB) (Chauvel et al., 2013; Fildes et al., 2015) and electronic stability control (ESC) (Erke, 2008) were explored. In recent year, due to some new studies have come out, and the meta-analysis need to be update. Additionally, Yue et al. (2019) used meta-analysis to analyze the effect of drivers’ age on ADAS adoption rate and further adopt Monte-Carlo procedure to simulate the practical crash avoidance effectiveness of ADAS. On the other hand, the number of important and common CV or AV technologies (United States Department of Transportation, 2018; China Academy of Information and Communications Technology, 2017) is far more than that, e.g., forward collision warning system (FCW), pedestrian collision and mitigate (PCAM), blind spot warning (BSW), and lane change warning (LCW). To sum up, there is a need to conduct meta-analysis to include more new research and more CV or AV technologies. This study intended to combine the results of meta-analysis and crash data from different countries to answer the following questions. Which CV or AV technologies has the greatest comprehensive safety effectiveness? How many crashes could be reduced if all vehicles were equipped with CV or AV technologies? In different countries, what is the development strategy of CV or AV technologies? In order to answer these questions, the following framework, in Fig. 1, has been used. First, the relevant studies on CV or AV technologies were searched, and the studies related to the safety effectiveness of CV or AV technologies have been selected. Second, in order to gain an understanding of safety impacts of CV or AV technologies, preliminary analysis is conducted to know whether different assistant types and different triggering times would result in different safety effectiveness. Third, this study uses the meta-analysis to objectively and comprehensively evaluate the safety effectiveness of each CV or AV technology. It includes heterogeneity test, effect size combination, and publication bias test. Finally, by using the crash data from six representative countries, proportion of crash types could be used as weights to measure the safety impact of a CV or AV technology to a country.

Fig. 1. Research Framework.

2. Data preparation and descriptive analysis 2.1. Literature retrieval method Meta-analysis is the secondary processing of existing literature, by which, researchers can get high-quality scientific conclusions without experimentations. Thus, in order to make sure the result of meta-analysis can give a comprehensive and representative conclusion, it is especially important to fully search for articles. The first part is to obtain the CV or AV technologies which are common and important. The technologies were partly from the CAV plans of United States Department of Transportation, 2018 and CAICT (2017). Furthermore, the related reports and literature were also included. In total, there are 12 technologies, i.e., intersection movement assist (IMA), curve speed warning (CSW), forward collision warning (FCW), adaptive cruise control system (ACC), automated emergency braking (AEB), lane departure warning (LDW), electronic stability control (ESC), blind spot warning (BSW), lane change warning (LCW), pedestrian collision and mitigate (PCAM), left turn assist (LTA), and cooperation adaptive cruise control (CACC). Additionally, in order to not miss the other important technologies, the study also used three comprehensive terms, i.e., connected and automated vehicle (CAV), connected vehicle (CV), automated vehicle (AV) and advanced driver assist system (ADAS). Thus, in total, there were 15 technology names selected. Some of the technologies is a combination of several technologies, for example, ADAS might contain FCW, LDW, etc. For this type of the technology, the sub- technology was explored. Then, this study used “Google Scholar”, “ScienceDirect” and “Scopus” to retrieve the literature, and the keyword styles were as following, “Technology abbreviation” OR “Technology name” AND “Safe”; “Technology abbreviation” OR “Technology name” AND “Accident” OR “Crash”. Additionally, the cited papers in all of the searched literature were checked to make sure the important references were not missing. In this paper, 826 relevant studies from the literature were retrieved and sorted out with ENDNOTE. Since one study might include several techniques and the focus in this study is the technique, the literature was organized to form a dataset, in which, each record was the result of a technology. In total, the initial number of records was 912. 2.2. Exclusion criteria Because the research goal is to accurately and objectively evaluate the safety effectiveness of each CV or AV technology. Five literature exclusion criteria were designed, i.e., (1) not related to safety effectiveness; (2) comprehensive safety effectiveness of multiple technologies; (3) related to crash severity level but not crash occurrence; (4) 2

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Fig. 2. Literature filtering.

per kilometer, which was more convenient to get as evaluation index, to define the safety effectiveness (Birrell et al., 2014). Formula (5) used the ratio of Time Exposed Time-to-collision (the total time whose TTC smaller than 1.5 s) and total time to express the safety effectiveness (Li et al., 2017). What’s more, if the evaluation results were given by the means of a certain range, the mean value, which means the average of minimum and maximum value, will be used for the safety evaluation. As for the standard error, many studies do not illustrate it. This studyused the formula (6) to calculate it (Wang et al., 2012).

sample size is unclear; (5) secondary utilization based on existing research. The filtering steps is shown in Fig. 2. Finally, 73 papers and 89 results were selected. 2.3. Coding After the literature filtering, for each included study, the following information were extracted. (1) Authors; (2) Year; (3) Country where the research was conducted; (4) Evaluation method: FOT, simulator, SIM; (5) Sample size; (6) Technology: FCW, LDW, ACC, IMA, BSW, CSW, ESC, PCAM, LCW, AEB, and LTA; (7) Safety effectiveness; (8) Standard error of safety effectiveness. Not all studies can directly provide safety effectiveness and standard error. If a study contains the safety effectiveness and standard error, it can be directly used; but if not, to compare the differences between studies and generate the summarized effect, other forms of safety effectiveness were converted by formula (1) (Yue et al., 2018). Crash is one of the most important safety indicator (Yu et al., 2018). The safety effectiveness is derived by comparing crash rates P for vehicles with and without a certain technology proposed above. Among them, P is selected according to the research content in formula (2), (3), (4), (5). In addition to the crash, conflict is also a useful safety indicator. Formula (2) used the idea of conflict to define the safety, and it is based on the assumption that conflict number is a reliable surrogate measurement of crashes (Yue et al., 2018). Formula (3) used the length of lateral deviation per kilometer to represent the safety effectiveness (Sayer et al., 2011). Formula (4) used the number of lane departures warning

ES= 1 −

Pwith Pwithout

(1)

P=

No. of TTC < Threshold TTC Total No. of TTC

(2)

P=

Meters of Lane Departures × 100 Driving Mileage

(3)

P=

No. of Warning of Lane Departures × 100 Driving Mileage

(4)

P=

TET Total Time

(5)

se=

ES × (1 − ES ) n−1

(6)

Where ES refers to the safety effectiveness of each technology in each 3

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Table 1 Included Papers. Authors Li et al. Shinar et al. JoonwooSon et al. Forkenbrock et al. Chang et al. Nodine et al. Nodine et al. Kusano et al. Lehmer et al. Hickman et al. Battelle et al. Lyu et al. Birrell et al. Cicchino et al. Kusano et al. Woodrooffe et al. Hesham et al. JonasBargman et al. Pomerleau et al. Georgi et al. Guglielmi et al. Harding et al. Chen et al. Guglielmi et al. Cicchino et al. Guglielmi et al. Schaudt et al. Spicer et al. Yi Qi et al. Woodrooffe et al. JoonwooSon et al. JoonwooSon et al. Wilson et al. Kusano et al. Hickman et al. Birrell et al. Yi Qi et al. Lyu et al. Cicchino et al. Hickman et al. Kusano et al. Sternlund et al. Hellman et al. Aksan et al. Hellman et al. Spicer et al. Gordon et al. Scanlon et al. Gorman et al. Scanlon et al. Kusano et al. Guglielmi et al. Holmes et al. Wilson et al. Alkim et al. Wassim et al. Lehmer et al. Cafiso et al. Li et al. MakotoItoh et al. Auken et al. Georgi et al. Bärgman et al. Anderson et al. Doyle et al. Chauvel et al. Yanagisawa et al. Roséna et al. Fredriksson et al. Rosén et al. Yanagisawa et al. Páez et al. Scully et al. Dang et al. Anderslie et al.

Year 2016 2002 2015 2011 2016 2011 2011 2014 2007 2013 2007 2018 2014 2017 2015 2013 2010 2017 1999 2009 2017 2014 2012 2017 2018 2017 2014 2018 2009 2013 2015 2015 2007 2014 2013 2014 2009 2018 2017 2013 2015 2016 2018 2016 2018 2018 2010 2015 2013 2016 2012 2017 2018 2007 2007 1999 2007 2012 2017 2013 2011 2009 2017 2013 2015 2013 2017 2010 2014 2013 2014 2016 2008 2004 2004

Country China Israel Korea USA USA USA USA USA USA USA USA China UK USA Japan USA USA Sweden USA Germany USA USA Australia USA USA USA USA USA USA USA Korea Korea USA USA Japan UK USA China USA USA Japan Sweden Sweden USA Sweden USA USA USA USA USA USA USA USA USA Dutch USA USA Italy China Japan USA Germany Sweden Australia UK France USA Sweden USA USA USA France Australia USA Sweden

Method FOT FOT FOT FOT FOT FOT FOT FOT FOT FOT FOT FOT FOT FOT FOT FOT SIM SIM SIM SIM Simulator SIM SIM SIM FOT SIM FOT FOT FOT SIM FOT FOT FOT FOT FOT FOT FOT FOT FOT FOT FOT FOT FOT FOT FOT FOT SIM SIM SIM SIM SIM SIM SIM FOT FOT FOT FOT SIM SIM Simulator Simulator SIM SIM SIM SIM SIM SIM SIM SIM SIM SIM SIM FOT FOT FOT

Sample size 20 43 52 21 33 18 108 16 100 10 50 32 40 23649 54 282 6274 34 195 1103 18 144 32 48 4620 16 20 15507 30 16 52 52 76 10 14 40 30 32 5433 6705 54 1853 562 76 454 15507 76 478 890 478 47 16 42 76 19 106 100 5000 1067 20 12 1103 34 104 26715 3959 790 243 68 543 843 43 7699 35716 2409

Technology FCW FCW FCW FCW FCW FCW FCW FCW FCW FCW FCW FCW FCW FCW FCW FCW FCW FCW FCW FCW FCW IMA IMA IMA BSW BSW BSW BSW LCW LCW LDW LDW LDW LDW LDW LDW LDW LDW LDW LDW LDW LDW LDW LDW LDW LDW LDW LDW LDW LDW LDW LDW LDW CSW ACC ACC ACC ACC ACC AEB AEB AEB AEB AEB AEB PCAM PCAM PCAM PCAM PCAM PCAM PCAM ESC ESC ESC

Effectiveness 0.230 0.250 0.390 0.260 0.410 0.270 0.170 0.310 0.210 0.210 0.210 0.130 0.120 0.270 0.310 0.200 0.210 0.270 0.200 0.230 0.410 0.480 0.450 0.530 0.140 0.220 0.189 0.140 0.210 0.220 0.190 0.300 0.350 0.310 0.478 0.150 0.210 0.180 0.180 0.350 0.170 0.320 0.300 0.300 0.270 0.310 0.320 0.261 0.258 0.300 0.270 0.300 0.330 0.310 0.129 0.170 0.120 0.140 0.086 0.390 0.281 0.250 0.220 0.300 0.210 0.382 0.420 0.400 0.370 0.450 0.400 0.410 0.324 0.350 0.220

S.e. 0.094 0.066 0.068 0.096 0.086 0.105 0.036 0.116 0.041 0.129 0.058 0.059 0.051 0.038 0.063 0.021 0.005 0.076 0.029 0.013 0.116 0.042 0.088 0.072 0.056 0.104 0.088 0.064 0.074 0.104 0.054 0.064 0.055 0.146 0.134 0.056 0.074 0.068 0.033 0.039 0.051 0.107 0.038 0.053 0.021 0.059 0.054 0.02 0.015 0.021 0.065 0.043 0.023 0.053 0.077 0.036 0.032 0.056 0.009 0.109 0.13 0.013 0.071 0.026 0.061 0.008 0.018 0.031 0.059 0.021 0.017 0.075 0.071 0.094 0.107

(continued on next page) 4

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Table 1 (continued) Authors Chouinard et al. Farmer et al. Farmer et al. Thomas et al. Kreiss et al. Bahouth et al. Dang et al. Lie et al. Green et al. Farmer et al. Bahouth et al. Papelis et al. Riexinger et al. Bareiss et al.

Year 2011 2004 2006 2006 2005 2006 2007 2006 2006 2006 2005 2010 2019 2019

Country Canada USA USA UK Germany USA USA Sweden USA USA USA USA USA USA

Method FOT FOT FOT FOT FOT FOT FOT FOT FOT FOT FOT Simulator FOT SIM

Sample size 238632 4784 4394 8951 690000 43346 380 8242 1446 4394 19258 120 97 501

study; PWith refers to the probability of crashes when equipped with a technology; PWithout refers to the probability of crashes when not equipped; Time-to-collision (TTC) is the collision time, and the threshold is 1.5 s. The smaller the TTC, the greater the risk of collision (Yue et al., 2018). Additionally, what the mean lane position of vehicle is out of the lane line, the situation is considered as lane departure (Birrell et al., 2014). Included Papers are given in Table 1.

Technology ESC ESC ESC ESC ESC ESC ESC ESC ESC ESC ESC ESC ESC LTA

Effectiveness 0.493 0.410 0.410 0.470 0.324 0.310 0.480 0.310 0.305 0.410 0.526 0.524 0.506 0.510

S.e. 0.078 0.038 0.033 0.107 0.102 0.071 0.082 0.102 0.089 0.038 0.052 0.046 0.054 0.022

Table 3 The Average Percentage of Each Crash Type and Total Crash Number. Percentage(%)

Australia

Canada

India

New Zealand

UK

USA

13.51 8.38

15.15 5.11

9.90 14.85

8.41 28.40

31.60 2.74

14.79 7.53

12.97 1.77 1.64 53.70 8.03 100.00 18226

9.03 0.59 0.20 49.50 20.44 100.00 311243

11.55 10.00 4.00 45.00 4.70 100.00 489667

2.46 8.83 0.82 25.80 25.27 100.00 9508

1.13 0.13 0.01 38.40 25.99 100.00 259532

14.63 1.16 4.56 45.00 12.33 100.00 5991600

Types Rear-end Off-road or object Lane change Pedestrian Animal Intersection Others Percentage Total Crash Number (year)

2.4. Crash data It is significant to know how many crashes can be eliminated because of CV or AV technologies. However, each CV or AV technology might not be able to decrease all types of crashes, it is only effective in some type(s) of crashes, for example, the FCW would mitigate rear-end crash hazard. Thus, there is a need to know which type of crashes each technology can reduce and the number of this type of crashes. The CV or AV technology and its corresponding crash types (Najm et al., 2007; Li et al., 2016; Yue et al., 2018) are shown in Table 2. Among this, the hit object and run-off road crashes were combined, because the run-off road would result in hit objects which are off-road or beside the road. Meanwhile, the hit object crashes would be mitigated by LDW, ESC, and AEB which are the same as off-road crashes. The selection of countries of this study is based on two aspects, representation and crash data availability. This study selected five developed countries (USA, Canada, Australia, New Zealand, and the UK) and one developing country (India), and these countries are located on different continents, i.e., Asia, Europe, Oceania, and North America. As for the different type of crashes, the crash data of these six countries from 2012 to 2016 were collected. The countries are USA (National Highway Traffic Safety Administration, 2012-2016), Canada (Transport Canada, 2012-2016), Australia (Department of Planning, Transport and Infrastructure, 2012–2016), New Zealand (New Zealand Transport Agency, 2012-2016), the UK (Department for Transport, 2012-2016), and India (Road Accident Sampling System, 2012–2016). Among the six countries, the USA provides Traffic Safety Facts each year. From 2012 to 2015, it provided the crash information for all crashes, but in 2016, it only provided fatal crash information. Thus, the researchers used the crash information, e.g., the percentage of crash

type and also crash number, in 2015 to represent that in 2016. Crash data from other countries were obtained successfully. The average proportion and crash number of each crash type for each country are shown in Table 3. As shown in the above table, different countries have a different proportion of crash types. The intersection crash is the most common type of accidents in all countries. The safety of intersection is one of the main concerns for the intersection control (Yu et al., 2018). In addition to this, the crashes of USA, Canada, Australia, and the UK are mainly rear-end, the crashes of India and New Zealand are mainly off-road or object. This confirms that there is a need to analyze the CV or AV technologies safety effectiveness in different countries. 3. Experiment design 3.1. Preliminary assessment This study conducted a preliminary assessment on the overall safety effectiveness for CV and AV. CV is related to human driver's responses and AV is related to automated vehicle control. Among the studied technologies, FCW, LDW, IMA, LTA, BSW, CSW, and LCW belong to CV technologies. On the other hand, vehicles with PCAM, ACC, ESC, and

Table 2 The mapping relationship between CV or AV technologies and crash types. Technologies

Crash types

Describe

FCW, ACC, AEB LDW, ESC, AEB BSW, LCW PCAM, AEB AEB, ESC IMA, AEB

Rear-end Off-road or object Lane change Pedestrian Animal Intersection

Crashes between two vehicles moving in the same direction. Vehicle run off the roadway or vehicle hit an object. Angle/sideswipe crash between two vehicles in adjacent lanes moving in the same direction. Single vehicle crashes with pedestrian(s). Single vehicle crashes with animal(s). Running red light/Running stop sign

5

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A 95% confidence interval for the weighted mean effectiveness was calculated from (13):

AEB belong to AV technologies. With regard to the triggering time, relevant studies (Xie and Zhang, 2016) show that intelligent active safety technologies mainly trigger in 2–8 s before the collision, while emergency action technologies mainly occur in 0.5–2 s. Among them, intelligent active safety technologies include FCW, LDW, ACC, BSW, LCW, IMA, LTA, PCAM, and CSW; emergency action technologies include ESC and AEB. All studies were divided into two groups according to the assistant type, i.e., CV and AV. The crash reduction percentages of the two groups were compared using two-sample t-test to find whether the CV technologies and AV technologies have significantly different safety effectiveness. Furthermore, all the studies were divided into two groups according to the triggering time and a two-sample t-test was also used to explore the safety effectiveness differences.

¯ ± 1.96 × 95%CI = ES

3.3. Comprehensive assessment NHTSA (Guglielmi et al., 2017) shows that the safety effectiveness of CV or AV technologies are usually expressed as the number of crashes reduced each year, and the formula (14) is used to calculate the effectiveness. This method is useful for a given country or district. However, when it comes to the comparison between countries, it is difficult to use the change in the number of crashes as an evaluation index because the number of vehicles and crashes varies greatly from country to country. Therefore, this study also used the formula (15) to calculate the safety effectiveness. It stands for the proportion of crashes that can be reduced. Moreover, a type of crash can be eliminated by several technologies, e.g., FCW and ACC can be used to prevent rear-end crashes. Therefore, in order to evaluate the comprehensive effectiveness of these technologies, this study adopted the formula (16).

In this study, the meta-analysis was performed to evaluate the safety effectiveness of each CV or AV technology based on the selected studies from the literature. However, due to the insufficient number of studies (such as CSW), meta-analysis about these CV or AV technologies cannot be performed. Finally, the meta-analysis was conducted on ACC, AEB, BSW, ESC, FCW, LDW, LCW, IMA, and PCAM. In the meta-analysis, the statistical weight assigned to each study was obtained by inverse variance approach:

1 sei2

(7)

The weighted mean effectiveness based on a set of estimates is: g ∑i=1 ESi Wi g ∑i = 1 Wi

¯ = ES

1 sei2 + σθ2

Q=

2 i i

∑WY i=1

−

g ∑i = 1 Wi Yi g ∑i = 1 Wi

⎧ Q − g + 1 × 100% , Q > g − 1 Q ⎨ 0 , Q
n

¯ i )) ∑ (PCm × ∏ (1 − ES j=1

(10)

(16)

i=1

4. Results 4.1. Results of preliminary assessment (11) With regard to the assistant type, the t-test founded that the safety effectiveness of CV technologies is 0.264, the safety effectiveness of AV technologies is 0.348, and the difference is significant (p < 0.05), as shown in Table 4. It indicates that AV technologies can play a more significant role than CV related technologies in crash avoidance. For CV

The heterogeneity between included studies was evaluated by I 2 test which has higher reliability than the Q test because I 2 test determines the proportion of dispersion among the studies (between-study variance) compared to total dispersion:

I2 =

(15)

Where NA refers to the number of crashes in the corresponding crash type reduced each year due to the application of a CV or AV technology; NC refers to the number of crashes that occur in the corresponding crash type each year; PA refers to the proportion of crashes in the corresponding crash type reduced each year due to the application of a CV or AV technology; pC refers to the proportion of crashes that occur in the corresponding crash type each year; Comprehensive _Effectiveness is the comprehensive safety effectiveness of selected technologies; m refers to the crash types, while n refers to the technologies related to the crash type.

Where Q is the homogeneity test statistic described in (11): g

¯ PA = PC × ES m

(9)

Q−g+1 ∑ W − (∑ W 2/ ∑ W )

(14)

ComprehensiveEffectiveness = 1 −

Where σθ2 is the between study variance described in (10):

σθ2 =

¯ NA = NC × ES

(8)

Where g is the number of estimates of effect that have been combined. There are two methods related to inverse variance approach, i.e., the fixed effects model and the random effects model. The fixed effects model only considers the within-study variance, the formula is shown in (7), while the random effects model considers the systematic between-study variance, as shown in the following expression:

Wi* =

(13)

Furthermore, this research used funnel plot to obtain the presence and degree of publication bias (Duval and Tweedie, 2000). By eliminating this bias, the assessment results would be closer to the true effectiveness, the trim-and-fill method was used to deal with it. The method can effectively reduce the publication bias and test the robustness of the estimated result.

3.2. Meta-analysis

Wi =

1 g

∑i = 1 Wi

Table 4 The Results of T-test.

(12)

Assistant Type CV Technologies 0.264 (normality test p = 0.070)

If I 2 > 50%, there is significant heterogeneity between studies; if I 2 ≤ 50%, it can be considered that heterogeneity is within an acceptable range. Usually, when the I 2 > 50% or there exist significant differences between studies, a random effects method is recommended (Castillo-Manzano et al., 2019). However, since there are many influential factors in this study and a random-effects model will result in wider confidence intervals for the summary estimate, the random effect model is adopted (Elvik, 2005).

Triggering Time Intelligent Active Safety Technologies 0.269 (normality test p = 0.207)

6

AV Technologies 0.348 (normality test p = 0.207)

t 3.450

p-value 0.001

Emergency Action Technologies 0.377 (normality test p = 0.163)

t

p-value

4.460

< 0.05

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technologies, human drivers have reaction time and driving errors, and both would lead to a lower safety effectiveness. With the help of AV technologies, safety effectiveness would be expected to improve. In addition, the t-test of triggering time showed that the safety effectiveness of intelligent active safety technologies is 0.269, the safety effectiveness of emergency action technologies is 0.377, and the difference is significant (p < 0.05), as shown in Table 4. The closer the triggering time to collision time, the AV technologies can exert greater safety effectiveness. Because emergency action technologies can be regarded as completely replacing the drivers’ operation by the system, safety effectiveness is greater.

Table 5 Estimated Effect Size of Random Effect Model and Trim-and-fill. Random Effect Model

4.2. Results of meta-analysis All studies included in the meta-analysis are different from some aspects, such as driving conditions and evaluation methods. This difference is called the heterogeneity. Eliminating or reducing heterogeneity is the key to conducting a meta-analysis. This study tested the heterogeneity and calculated the safety effectiveness of each CV or AV technology using DerSimonian & Laird random effect model, which is the most commonly used method for fitting the random effects for meta-analysis (Jackson et al., 2010). The funnel plot was used to estimate the publication bias, and the result is shown in Fig. 3. The horizontal axis measures the safety effectiveness of each technology. Values greater than 0 indicate a reduction in the number of crashes. The vertical axis measures the standard error of each study. The statistical weights were calculated according to the random effects model (Duval and Tweedie, 2000). The funnel plot shows the shape of a funnel turned upside down has a few missing points, indicating that the literatures about each technology have a certain publication bias, and most studies have a tendency to exaggerate its safety effectiveness (Høye and Elvik, 2010). In order to mitigate the bias, the trim-and-fill was used, and the results of the meta-analysis are shown in Table 5. With regard to the rear-end crash related technologies, this table

After Trim-and-fill

Technology

Estimate

95% CI

I2

Estimate

95% CI

I2

ACC AEB BSW ESC FCW LCW LDW PCAM IMA

11.00% 26.00% 16.00% 41.00% 23.00% 21.00% 27.00% 41.00% 49.00%

[0.07,0.16] [0.22,0.31] [0.10,0.21] [0.37,0.46] [0.20,0.27] [0.15,0.27] [0.25,0.30] [0.38,0.43] [0.41,0.57]

41.00% 7.00% 0.00% 41.00% 36.00% 0.00% 48.00% 48.90% 0.00%

9.30% 25.70% 15.00% 43.20% 21.10% 21.00% 24.00% 38.90% 49.00%

[0.05,0.14] [0.20,0.31] [0.10,0.20] [0.38,0.48] [0.17,0.25] [0.10,0.33] [0.21,0.30] [0.36,0.42] [0.40,0.57]

48.30% 12.80% 0.00% 46.40% 49.60% 0.00% 48.00% 67.10% 0.00%

shows that AEB can achieve 25.7% safety effectiveness, which is greater than ACC and FCW. The result is consistent with a previous conclusion (Chauvel et al., 2013). ESC has the highest safety effectiveness, which plays an important role in off-road, object crashes, and animal crashes. As for the lane change crashes, LCW has higher safety effectiveness than BSW. 4.3. Results of comprehensive assessment This study systematically compared the comprehensive effectiveness of the above CV or AV technologies for selected countries, as shown in Fig. 4. The darker the purple on the graph, the greater the safety effectiveness of CV or AV. According to the percentage of each crash type in the six countries, the bar of each country, which refers to the crash proportion before using the selected technologies is drawn The results of the meta-analysis for each CV or AV technology were in combination with the average proportion of crash types in selected countries. If all of the technologies were implemented, the average proportion of crashes could be reduced by 3.40 million. The

Fig. 3. Funnel Plot After Trim-and-fill Method. 7

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Fig. 4. Comprehensive Effectiveness in Selected Countries.

for which IMA, AEB, ESC, and LDW can play a significant part in. Therefore, New Zealand and India should prioritize these techniques.

comprehensive safety effectiveness of each country were ranked by size as India (54.24%), Australia (51.55%), USA (48.07%), New Zealand (45.36%), Canada (44.71%), and the UK (40.95%), and the number of reduced crashes were 265584, 9395, 2879916, 4313, 139142, and 106,290 per year, respectively. Among them, India has the highest comprehensive safety effectiveness because the traffic safety of India is relative severer than other countries, the off-road, lane change, and intersection crashes count for about 70%. The safety effectiveness of corresponding CV or AV technologies which can play a significant role in off-road crash is about 67.93% (LDW, ESC, and AEB), in lane change crash about 32.85% (BSW and LCW), and in intersection crash is about 62.11% (IMA and AEB). Therefore, the comprehensive safety effectiveness of India is the highest. While the UK has the lowest comprehensive safety effectiveness, because the driving condition is good among these countries and the proportion of crash types which belongs to the “other” criteria is 25.99% and these crashes happened under complicated conditions and is hard to be prevented. Therefore, the safety effectiveness of the UK is the lowest. For the selected countries, the percentage of crashes can be reduced by the nine CV or AV technologies is 57.97%, among which, reduced by ACC is about 102,447 per year (1.45% of total crashes), AEB is 1,369,099 per year (19.34%), BSW is 91,636 per year (1.29%), ESC is 398,813 per year (5.63%), FCW is 232,433 per year (3.28%), IMA is 1,488,238 per year (21.02%), LDW is 189,761 per year (2.68%), PCAM is 103,184 per year (1.46%), and LCW is 128,290 per year (1.81%). In addition, countries with a similar percentage of each crash type can follow the same development strategy. The study group countries according to the first four technologies which would decrease the highest percentage of crashes. It can be seen from Fig. 5a, that USA, Canada, and Australia are similar. These countries’ crash types mainly are intersection crash and lane change crash. Given the variety of scenarios in which technologies can work, IMA, AEB, ESC, and LCW can play an important role in these countries. Therefore, USA, Canada, and Australia should prioritize the development of these technologies. From Fig. 5b, the UK and Canada are similar. The crashes of these countries are mainly intersection crash and rear-end. Therefore, IMA, AEB, FCW, and ACC have good performance. The two countries should prioritize applications related to rear-end warning and intersection warning. From Fig. 5c, New Zealand and India have a similar distribution. These countries’ crash types are mainly off-road crash and intersection crash,

5. Conclusions In order to objectively and effectively analyze the safety effectiveness of CV or AV technologies, this research used meta-analysis to evaluate it and further use the crash databases of six countries to analyze the comprehensive safety effectiveness and compare the safety advantages. The study intends to answer three questions, which was proposed in the introduction. They are as follows, Which CV or AV technologies have the greatest comprehensive safety effectiveness? According to the results of meta-analysis, this paper shows the safety effectiveness of each CV or AV technologies, and it has been found that the safety effectiveness of AEB is greater than ACC and FCW in rear-end crashes, ESC has the highest safety effectiveness in off-road, object crashes and animal crashes, and LCW has higher safety effectiveness than BSW in lane change crashes. How many crashes would be reduced if all vehicles were equipped with CV or AV technologies? For the six selected countries, the average number of crashes could be reduced by 47.48%. The comprehensive safety effectiveness of each country were ranked by size as India (54.24%), Australia (51.55%), USA (48.07%), New Zealand (45.36%), Canada (44.71%), and the UK (40.95%), and the number of reduced crashes were 265584, 9395, 2879916, 4313, 139142, and 106,290 per year, respectively. This can help agencies objectively know about the comprehensive safety effectiveness and improve the public's acceptance of CV or AV. In different countries, what is the development strategy of CV or AV technologies? It can be concluded that USA, Canada, and Australia should prioritize the development of the lane change warning and intersection warning, the UK should prioritize applications related to intersection warning and rear-end warning, and New Zealand and India should give prioritize to the lane departure warning and intersection warning. These can help different countries to decide the development strategies of CV or AV technologies. There are some limitations to this study. Since there is a difference between the development priority and the actual difficulty of development, in the future, the degree of technology realization difficulty and its effectiveness should be taken into account to formulate priority 8

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Fig. 5. Comparison of Safety Advantages in Selected Countries.

development strategies. Vehicles equipping with several CAV technologies is a trend. However, the study only focused on a single technology’s effectiveness because the literatures on the overall safety effectiveness of several technologies are rare. In the future, with the development of combined technologies, the meta-analysis with regard to combined technologies can be conducted. Additionally, in the future, with the number of studies on the safety effectiveness of CAV technologies increase, e.g., LCW, reversing camera (Fildes et al., 2018), and potential traffic flow related crash warning (Wang et al., 2019), the number of meta-analysis for the safety of CAV technologies will be increased and the results would be more precise.

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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This study was supported by the National Natural Science Foundation of China (51722809 and 71804127) and was also sponsored by Shanghai Sailing Program (19YF1451300). References1 *Aksan, N., Sager, L., Hacker, S., Lester, B., Dawson, J., Rizzo, M., 2016. Benefits from Heads-up Lane Departure Warnings Predicts Safety in the Real-world (No. 2016-011443). SAE Technical Paper. *Anderson, R., Doecke, S., Mackenzie, J., Ponte, G., 2013. Potential benefits of autonomous emergency braking based on in-depth crash reconstruction and simulation. May In: In Proceedings of the 23rd International Conference on Enhanced Safety of Vehicles, US National Highway Traffic Safety Administration. Washington DC. *Alkim, T.P., Bootsma, G., Hoogendoorn, S.P., 2007. Field operational test" the assisted driver". June In: In 2007 IEEE Intelligent Vehicles Symposium. IEEE. pp. 1198–1203. *Bahouth, G., 2005. Real world crash evaluation of vehicle stability control (VSC) technology. Annual Proceedings/Association for the Advancement of Automotive Medicine, vol. 49. Association for the Advancement of Automotive Medicine, pp. 19. *Bahouth, G., 2006. Reductions in Crash Injury and Fatalities Due to Vehicle Stability Control Technology (No. 2006-01-0925). SAE Technical Paper.Reductions in Crash Injury and Fatalities Due to Vehicle Stability Control Technology (No. 2006-010925). SAE Technical Paper. *Bärgman, J., Boda, C.N., Dozza, M., 2017. Counterfactual simulations applied to SHRP2 crashes: the effect of driver behavior models on safety benefit estimations of intelligent safety systems. Accid. Anal. Prev. 102, 165–180. *Birrell, S.A., Fowkes, M., Jennings, P.A., 2014. Effect of using an in-vehicle smart driving aid on real-world driver performance. Ieee Trans. Intell. Transp. Syst. 15 (4), 1801–1810. Bareiss, M., Scanlon, J., Sherony, R., Gabler, H.C., 2019. Crash and injury prevention estimates for intersection driver assistance systems in left turn across path/opposite direction crashes in the United States. Traffic Inj. Prev. 20 (sup 1), S133–S138. *Cafiso, S., Di Graziano, A., 2012. Evaluation of the effectiveness of ADAS in reducing multi-vehicle collisions. Int. J. Heavy Veh. Syst. 19 (2), 188–206. *Chang, J., 2016. Summary of NHTSA Heavy-vehicle Vehicle-to-vehicle Safety Communications Research (No. DOT HS 812 300). *Chauvel, C., Page, Y., Fildes, B., Lahausse, J., 2013. Automatic emergency braking for pedestrians effective target population and expected safety benefits. May. In 23rd

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