吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (7): 1935-1943.doi: 10.13229/j.cnki.jdxbgxb.20221144

• 交通运输工程·土木工程 • 上一篇    

基于避险脱离的自动驾驶路测安全影响因素

涂辉招(),鹿畅,陆淼嘉(),李浩   

  1. 同济大学 道路与交通工程教育部重点实验室,上海 201804
  • 收稿日期:2022-09-05 出版日期:2024-07-01 发布日期:2024-08-05
  • 通讯作者: 陆淼嘉 E-mail:huizhaotu@tongji.edu.cn;miaojialu@tongji.edu.cn
  • 作者简介:涂辉招(1977-),男,教授,博士. 研究方向:交通风险管理,智能网联汽车与智慧交通,交通行为分析和交通规划. E-mail:huizhaotu@tongji.edu.cn
  • 基金资助:
    国家重点研发计划项目(2019YFE0108300)

Risk factors for autonomous vehicle road testing based on risk-avoiding disengagement

Hui-zhao TU(),Chang LU,Miao-jia LU(),Hao LI   

  1. Key Laboratory of Road and Traffic Engineering of the Ministry of Education,Tongji University,Shanghai 201804,China
  • Received:2022-09-05 Online:2024-07-01 Published:2024-08-05
  • Contact: Miao-jia LU E-mail:huizhaotu@tongji.edu.cn;miaojialu@tongji.edu.cn

摘要:

本文采用自动驾驶路测避险脱离率来表征自动驾驶路测风险。首先基于大量自动驾驶路测实测数据,采用LSTM方法辨识自动驾驶路测避险脱离,并基于Tobit模型分析车道宽度、天气等动静态因素对路测避险脱离率的影响,由此建立基于避险脱离的自动驾驶路测安全模型。本文安全模型从更为积极主动的角度来规避自动驾驶路测事故,保障自动驾驶路测安全有序地进行。

关键词: 交通运输工程, 自动驾驶路测, 避险脱离, 安全影响因素, Tobit模型

Abstract:

This study uses the risk-avoiding disengagement to represent the risk. This study first uses the LSTM method to identify the risk-avoiding disengagement of autonomous vehicles in the road-testing phase based on a large scale of realistic road-testing data, then use the Tobit model to analyze the impacts of factors on the risk-avoiding disengagement rate, and the risk analysis model of autonomous vehicle road testing was established. This method can avoid the accidents of autonomous vehicle road testing in advance, and promote the autonomous vehicle road testing in a safe and regulated way.

Key words: transportation engineering, autonomous vehicle road testing, risk-avoiding disengagement, risk factors, Tobit model

中图分类号: 

  • U491

图1

避险脱离数量分布"

图2

避险脱离率分布"

表1

自变量说明"

编号变量名变量表示变量解释
1车道宽度1>3.25 m
2(2.75,3.25] m
3[0,2.75] m
2道路线形0直线
1曲线
3道路平整度0平整
1一般
4坡度0无坡路段
1有坡路段
5限速140 km/h
260 km/h
380 km/h
6交叉口0非交叉口路段
1交叉口路段
7接入点数量0无接入点
1有接入点
8机非分隔带0有机非分隔带
1无机非分隔带
9道路等级1二级公路
2主干路
3次干路
4支路
10流量等级1[0,200] veh/(h·lane)
2(200,350] veh/(h·lane)
3(350,500] veh/(h·lane)
4(500,+∞) veh/(h·lane)
11大车比例1[0,10%]
2(10%,100%]
12天气0好天气
1雨雪天气
13车道数1
2
3
4

图3

LSTM模型训练结果"

表2

不同场景下避险脱离与非避险脱离占比"

项目城市道路高快速路
避险脱离占比/%3813
非避险脱离占比/%6287

图4

自动驾驶车辆避险脱离率随时间变化图"

表3

安全影响因素分析结果"

变量Coef.Std. Err.tP>|t|SigVIF
年份-205.902922.289 15-9.240.000***1.084
车道宽度67.638725.854 072.620.009**1.260
道路线形-46.587229.958 71-1.560.1201.070
道路平整度77.310 7432.798 592.360.019*1.478
坡度23.264 99153.31460.150.8791.048
限速69.840 5814.426 884.840.000***1.890
交叉口107.947522.93034.710.000***1.149
接入点数量14.854 4423.880 860.620.5341.273
车道数155.623735.821 784.340.000***2.878
机非隔离带202.634833.043 636.130.000***2.169
道路等级-4.915 70910.145 39-0.480.6281.822
流量等级-19.710 5912.2432-1.610.1081.142
大车比例-43.256 9229.406 83-1.470.1421.702
天气55.610 7921.388 542.600.009**1.021
间距41 5496.845 034.139.230.000***N/A
1 Saifuzzaman M, Zheng Z. Incorporating human-factors in car-following models: a review of recent developments and research needs[J]. Transportation Research Part C: Emerging Technologies, 2014, 48: 379-403.
2 Hoogendoorn R, Van Arerm B, Hoogendoom S. Automated driving, traffic flow efficiency, and human factors: literature review[J]. Transportation Research Record, 2014, 2422(1): 113-120.
3 Favarò F M, Nader N, Eurich S O, et al. Examining accident reports involving autonomous vehicles in California[J]. PLoS one, 2017, 12(9):No. e0184952.
4 Randazzo R. A slashed tire, a pointed gun, bullies on the road: why do Waymo self-driving vans get so much hate?[N]. Arizona Republic, 2018-07-12(5).
5 王雪松, 宋洋, 黄合来, 等. 基于分层负二项模型的城郊公路安全影响因素研究[J]. 中国公路学报,2014(1): 100-106.
Wang Xue-song, Song Yang, Huang He-lai, et al. Analysis of risk factors for suburan highways using hierarchical negative binomial model[J]. China Journal of Highway and Transport, 2014(1): 100-106.
6 殷炬元, 李铁男, 孙剑. 基于贝叶斯空间相关模型的城市快速路安全影响因素研究[J]. 交通信息与安全,2016,34(3): 27-33, 40.
Yin Ju-yuan, Li Tie-nan, Sun Jian. An analysis of effective factors of safety for urban expressways based on Bayesian spatial models[J]. Journal of Transport Information and Safety. 2016(3): 27-33, 40.
7 Lord D, Mannering F. The statistical analysis of crash-frequency data: a review and assessment of methodological alternatives[J]. Transp Resarch Part A: Policy and Practice, 2010, 44(5): 291-305.
8 Miaou S P, Lum H. Modeling vehicle accidents and highway geometric design relationships[J]. Accident Analysis & Prevention, 1993, 25(6): 689-709.
9 Miaou S P. The relationship between truck accidents and geometric design of road sections - poisson versus negative binomial regressions[J]. Accident Analysis & Prevention, 1994, 26(4): 471-482.
10 Malyshkina N V, Mannering F. Empirical assessment of the impact of highway design exceptions on the frequency and severity of vehicle accidents[J]. Accident Analysis & Prevention, 2010, 42(1): 131-139.
11 Geedipally S R, Lord D, Dhavala S S. The negative binomial-Lindley generalized linear model: characteristics and application using crash data[J]. Accident Analysis & Prevention, 2012, 45: 258-265.
12 Lord D, Miranda-Moreno L F. Effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter of Poisson-Gamma models for modeling motor vehicle crashes: a Bayesian perspective[J]. Safety Science, 2008, 46(5): 751-770.
13 Heydari S, Fu L P, Thakali L, et al. Benchmarking regions using a heteroskedastic grouped random parameters model with heterogeneity in mean and variance: applications to grade crossing safety analysis [J]. Analytic Methods in Accident Research, 2018, 19: 33-48.
14 Yu R, Wang Y, Quddus M, et al. A marginalized random effects hurdle negative binomial model for analyzing refined-scale crash frequency data[J]. Analytic Methods in Accident Research, 2019, 22:No. 100092.
15 Lord D, Washington S, Ivan J N. Further notes on the application of zero-inflated models in highway safety[J]. Accident Analysis & Prevention, 2007, 39(1): 53-57.
16 Xie Y C, Zhang Y L. Crash frequency analysis with generalized additive models[J]. Transportation Research Record, 2008, 2061(1): 39-45.
17 Zhang Y L, Xie Y C, Li L H. Crash frequency analysis of different types of urban roadway segments using generalized additive model[J]. Journal of Safety Research, 2012, 43(2): 107-114.
18 Quddus M A. Time series count data models: an empirical application to traffic accidents[J]. Accident Analysis & Prevention, 2008, 40(5): 1732-1741.
19 Guo F, Wang X S, Abdel-Aty M A. Modeling signalized intersection safety with corridor-level spatial correlations[J]. Accident Analysis & Prevention, 2010, 42(1): 84-92.
20 Zou Y J, Lin B, Yang X X, et al. Application of the Bayesian model averaging in analyzing freeway traffic incident clearance time for emergency management [J]. Journal of Advanced Transportation, 2021, 2021: No.6671983.
21 Lao Y T, Wu Y J, Corey J, et al. Modeling animal-vehicle collisions using diagonal inflated bivariate Poisson regression[J]. Accident Analysis & Prevention, 2011, 43(1): 220-227.
22 Huang H L, Chang F R, Zhou H C, et al. Modeling unobserved heterogeneity for zonal crash frequencies: a Bayesian multivariate random-parameters model with mixture components for spatially correlated data [J]. Analytic Methods in Accident Research, 2019, 24: No.100105.
23 Wang K, Ivan J N, Ravishanker N, et al. Multivariate Poisson lognormal modeling of crashes by type and severity on rural two lane highways[J]. Accident Analysis & Prevention, 2017, 99: 6-19.
24 Lao Y T, Zhang G H, Wang Y H, et al. Generalized nonlinear models for rear-end crash risk analysis[J]. Accident Analysis & Prevention, 2014, 62: 9-16.
25 Wu L T, Zou Y J, Lord D. Comparison of sichel and negative binomial models in hot spot identification [J]. Transportation Research Record, 2014, 2460(1): 107-116.
26 Saha D, Alluri P, Dumbaugh E, et al. Application of the Poisson-Tweedie distribution in analyzing crash frequency data[J]. Accident Analysis & Prevention, 2020, 137: No.100105. .
27 Wu L T, Meng Y, Kong X Q, et al. Incorporating survival analysis into the safety effectiveness evaluation of treatments: jointly modeling crash counts and time intervals between crashes[J]. Journal of Transportation Safety & Security, 2022, 14(2): 338-358.
28 Chang L Y, Chen W C. Data mining of tree-based models to analyze freeway accident frequency[J]. Journal of Safety Research, 2005, 36(4): 365-375.
29 Xie Y C, Lord D, Zhang Y L. Predicting motor vehicle collisions using Bayesian neural network models: an empirical analysis[J]. Accident Analysis & Prevention, 2007, 39(5): 922-933.
30 Li X G, Lord D, Zhang Y L, et al. Predicting motor vehicle crashes using support vector machine models [J]. Accident Analysis & Prevention, 2008, 40(4): 1611-1618.
31 Abdel-Aty M, Haleem K. Analyzing angle crashes at unsignalized intersections using machine learning techniques[J]. Accident Analysis & Prevention, 2011, 43: 461-470.
32 Haleem K, Gan A, Lu J Y. Using multivariate adaptive regression splines (MARS) to develop crash modification factors for urban freeway interchange influence areas[J]. Accident Analysis & Prevention, 2013, 55: 12-21.
33 Zeng Q, Huang H L, Pei X, et al. Rule extraction from an optimized neural network for traffic crash frequency modeling[J]. Accident Analysis & Prevention, 2016, 97: 87-95.
34 Zhang X, Waller S T, Jiang P. An ensemble machine learning-based modeling framework for analysis of traffic crash frequency[J]. Computer-Aided Civil and Infrastructure Engineering, 2020, 35(3): 258-76.
35 Pu Z Y, Li Z B, Ke R M, et al. Evaluating the nonlinear correlation between vertical curve features and crash frequency on highways using random forests [J]. Journal of Transportation Engineering, Part A: Systems, 2020, 146(10): No.4020115.
36 Wen X, Xie Y, Wu L, et al. Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP[J]. Accident Analysis & Prevention, 2021, 159: No.106261.
37 Anastasopoulos P C, Tarko A P, Mannering F L. Tobit analysis of vehicle accident rates on interstate highways[J]. Accident Analysis & Prevention, 2008, 40: 768-775.
38 Guo Y, Li Z, Liu P, et al. Modeling correlation and heterogeneity in crash rates by collision types using full Bayesian random parameters multivariate Tobit model[J]. Accident Analysis & Prevention, 2019, 128: 164-174.
39 Zeng Q, Wen H, Huang H, et al. A Bayesian spatial random parameters Tobit model for analyzing crash rates on roadway segments[J]. Accident Analysis & Prevention, 2017, 100: 37-43.
40 Xu C, Ding Z, Wang C, et al. Statistical analysis of the patterns and characteristics of connected and autonomous vehicle involved crashes[J]. Journal of Safety Research, 2019, 71: 41-47.
41 Chen H, Chen H, Liu Z, et al. Analysis of factors affecting the severity of automated vehicle crashes using XGBoost model combining POI data[J]. Journal of Advanced Transportation, 2020, 2020:No.8881545.
42 Boggs A M, Wali B, Khattak A J. Exploratory analysis of automated vehicle crashes in California: a text analytics & hierarchical Bayesian heterogeneity-based approach[J]. Accident Analysis & Prevention, 2020, 135:No. 105354.
43 涂辉招, 崔航, 鹿畅, 等. 面向自动驾驶路测驾驶能力评估的避险脱离率模型 [J]. 同济大学学报:自然科学版,2020,48(11): 1562-1569.
Tu Hui-zhao, Cui Hang, Lu Chang, et al. A risk-avoiding disengagement frequency model for assessing driving ability of autonomous vehicles in road testing[J]. Journal of Tongji University (Natural Science),2020,48(11):1562-1569.
44 Kingma D P, Ba J. Adam: a method for stochastic optimization[J]. arXiv preprint arXiv:, 2014.
45 Ordóñez F J, Roggen D. Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition[J]. Sensors, 2016, 16(1): No.e115.
46 Hou Q, Meng X, Leng J, et al. Application of a random effects negative binomial model to examine crash frequency for freeways in China[J]. Physica A: Statistical Mechanics and its Applications, 2018, 509: 937-944.
47 Hair J F, Black W C, Babin B J, et al. Multivariate Data Analysis[M].London: Pearson Education Limited, 2013.
[1] 胡钊政,孙勋培,张佳楠,黄戈,柳雨婷. 基于时空图模型的车-路-图协同定位方法[J]. 吉林大学学报(工学版), 2024, 54(5): 1246-1257.
[2] 涂辉招,王万锦,乔鹏,郭静秋,鹿畅,吴海飞. 自动驾驶卡车路测安全员接管干预行为解析[J]. 吉林大学学报(工学版), 2024, 54(3): 727-740.
[3] 李津,孙雨彤,魏小忠,焦玉玲. 考虑柔性车道设置的公交优先信号设计[J]. 吉林大学学报(工学版), 2023, 53(2): 448-456.
[4] 张惠臻,高正凯,李建强,王晨曦,潘玉彪,王成,王靖. 基于循环神经网络的城市轨道交通短时客流预测[J]. 吉林大学学报(工学版), 2023, 53(2): 430-438.
[5] 李文勇,马从若,胡清玮,刘承堃,廉冠,顾国斌,周旦. 基于站台容量限制和路段绿波控制的公交速度引导模型[J]. 吉林大学学报(工学版), 2023, 53(11): 3088-3103.
[6] 徐洪峰, 高霜霜, 郑启明, 章琨. 信号控制交叉口的复合动态车道管理方法[J]. 吉林大学学报(工学版), 2018, 48(2): 430-439.
[7] 王海玮, 温惠英, 刘敏. 夜间环境驾驶员精神负荷的生理特性评估与实验[J]. 吉林大学学报(工学版), 2017, 47(2): 420-428.
[8] 姜桂艳, 刘彬, 隋晓艳, 马明芳. 基于IC卡收费系统的公交客流信息实时采集方法[J]. 吉林大学学报(工学版), 2016, 46(4): 1076-1082.
[9] 宗芳, 王占中, 贾洪飞, 焦玉玲, 吴杨. 基于支持向量机的通勤日活动-出行持续时间预测[J]. 吉林大学学报(工学版), 2016, 46(2): 406-411.
[10] 李世武, 徐艺, 孙文财, 王琳虹, 郭梦竹, 柴萌. 基于瞳孔直径的撞固定物冲突自反馈识别方法[J]. 吉林大学学报(工学版), 2016, 46(2): 418-425.
[11] 潘义勇, 马健霄, 孙璐. 基于可靠度的动态随机交通网络耗时最优路径[J]. 吉林大学学报(工学版), 2016, 46(2): 412-417.
[12] 赵淑芝, 梁士栋, 马明辉, 刘华胜, 朱永刚. 信号交叉口实时排队长度估计[J]. 吉林大学学报(工学版), 2016, 46(1): 85-91.
[13] 刘华胜,赵淑芝,朱永刚,李晓玉. 基于有效路径的轨道交通接运线路设计模型[J]. 吉林大学学报(工学版), 2015, 45(2): 371-378.
[14] 祝进城,肖峰,帅斌,刘晓波. 城市出租车拥挤收费[J]. 吉林大学学报(工学版), 2015, 45(1): 89-96.
[15] 游峰, 张荣辉, 王海玮, 徐建闽, 温惠英. 欠驱动半挂汽车列车的运动建模与跟踪控制[J]. 吉林大学学报(工学版), 2014, 44(5): 1296-1302.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!