汽车工程 ›› 2021, Vol. 43 ›› Issue (11): 1565-1576.doi: 10.19562/j.chinasae.qcgc.2021.11.001
• • 下一篇
收稿日期:
2021-06-29
修回日期:
2021-08-09
出版日期:
2021-11-25
发布日期:
2021-11-22
通讯作者:
兰凤崇
E-mail:fclan@scut.edu.cn
基金资助:
Jiqing Chen,Chubin Weng,Fengchong Lan()
Received:
2021-06-29
Revised:
2021-08-09
Online:
2021-11-25
Published:
2021-11-22
Contact:
Fengchong Lan
E-mail:fclan@scut.edu.cn
摘要:
在缺乏有效的真实换道事故数据的情况下,基于交通冲突理论,分析车辆在换道过程不同阶段与周围车辆的潜在冲突形式。通过微观冲突风险指标推导、宏观换道风险特征提取和系统性换道风险分析,建立同时考虑潜在碰撞可能性与严重程度的换道风险综合量化方法。利用自然驾驶轨迹数据集,对风险综合量化方法进行应用试验分析。结果表明,所提出的量化方法能客观合理地反映不同类型、不同位置的换道风险特征,为智能车辆安全换道决策与规划提供了一种新思路。
陈吉清,翁楚滨,兰凤崇. 智能车辆换道潜在冲突分析与风险量化方法[J]. 汽车工程, 2021, 43(11): 1565-1576.
Jiqing Chen,Chubin Weng,Fengchong Lan. Potential Conflict Analysis and Risk Quantification Method of Intelligent Vehicle Lane Change[J]. Automotive Engineering, 2021, 43(11): 1565-1576.
1 | HOU Y, EDARA P, SUN C. Situation assessment and decision making for lane change assistance using ensemble learning methods[J]. Expert Systems with Applications, 2015, 42(8):3875-3882. |
2 | FERREIRA S, COUTO A. A probabilistic approach towards a crash risk assessment of urban segments[J]. Transportation Research Part C: Emerging Technologies, 2015, 50:97-105. |
3 | GLAUZ W D, MIGLETZ D J. Application of traffic conflict analysis at intersections[M]. Transportation Research Board, National Research Council, 1980. |
4 | 孙立云. 城市道路交叉口事故预测模型及算法研究[D]. 北京:北京交通大学, 2011. |
SUN L Y. Research on urban road intersection-accident prediction model and algorithm[D]. Beijing: Beijing Jiaotong University, 2011. | |
5 | PARK H, OH C, MOON J, et al. Development of a lane change risk index using vehicle trajectory data [J]. Accident Analysis & Prevention, 2018, 110:1-8. |
6 | ZHENG L, SAYED T. From univariate to bivariate extreme value models: approaches to integrate traffic conflict indicators for crash estimation[J]. Transportation Research Part C: Emerging Technologies, 2019, 103:211-225. |
7 | 容颖, 温惠英, 赵胜. 高速公路单向双车道车辆群行车风险度量研究[J]. 重庆交通大学学报(自然科学版), 2019, 38(9): 95-100. |
RONG Y, WEN H, ZHAO S. Study on driving risk measurement for two⁃lane freeway vehicle group[J]. Journal of Chongqing Jiaotong University(Natural Science), 2019, 38(9): 95-100. | |
8 | OH C, KIM T. Estimation of rear-end crash potential using vehicle trajectory data[J]. Accident Analysis & Prevention, 2010, 42(6):1888-1893. |
9 | WENG J, DU G, LI D, et al. Time⁃varying mixed logit model for vehicle merging behavior in work zone merging areas[J]. Accident Analysis and Prevention, 2018, 117(AUG.):328-339. |
10 | 周斌宇. 基于车车协同的车辆安全换道预警机制研究[D]. 长春: 吉林大学, 2020. |
Zhou B Y. Research on vehicle safety lane change warning strategy based on vehicle⁃to⁃vehicle collaboration[D]. Changchun:Jilin University, 2020. | |
11 | 何爱生. 考虑后车减速度的换道预警阈值研究[D]. 西安: 长安大学, 2019. |
HE A S. Research on the lane change warning threshold considering the deceleration of the rear vehicles[D]. Xi’an: Chang’an University, 2019. | |
12 | 杨俊. 车辆频繁换道对道路行车效率与安全影响研究[D].西安: 长安大学, 2016. |
YANG J. Research on the efficiency and safety of frequent lane change in urban roads[D]. Xi’an: Chang’an University, 2016. | |
13 | 王畅, 付锐, 郭应时, 等. 换道预警系统中越线时间的预测方法[J]. 汽车工程, 2014, 36(4):509-514. |
WANG C, FU R, GUO Y, et al. Prediction method of time-to-line⁃crossing in lane change warning system[J]. Automotive Engineering, 2014, 36(4):509-514. | |
14 | 何仁, 赵晓聪, 杨奕彬, 等. 基于驾驶人风险响应机制的人机共驾模型[J].吉林大学学报(工学版), 2021, 51(3):799-809. |
HE R, ZHAO X C, YANG Y B, et al. Man⁃machine shared driving model using risk⁃response mechanism of human driver[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(3):799-809. | |
15 | SVENSSON A. A method for analysing the traffic process in a safety perspective [D]. Lund University, 1998. |
16 | PARK H, OH C, MOON J, et al. Development of a lane change risk index using vehicle trajectory data[J]. Accident Analysis and Prevention, 110(JAN.):1-8 |
17 | 张航, 张肖磊, 吕能超. 高速公路停车视距可靠性设计[J]. 公路交通科技, 2019, 36(4):44-49,87. |
ZHANG H, ZHANG X L, LV N C, et al. Reliability design for stopping sight distance of expressway[J]. Journal of Highway and Transportation Research and Development, 2019, 36(4):44-49,87. | |
18 | BAGDADI O. Estimation of the severity of safety critical events[J]. Accident Analysis and Prevention, 2013, 50:167-174. |
19 | 丁明, 肖遥, 张晶晶, 等. 基于事故链及动态故障树的电网连锁故障风险评估模型[J]. 中国电机工程学报, 2015, 35(4):821-829. |
DING M, XIAO Y, ZHANG J, et al. Risk assessment model of power grid cascading failures based on fault chain and dynamic fault tree[J]. Proceedings of the Csee, 2015, 35(4):821-829. | |
20 | KRAJEWSKI R, BOCK J, KLOEKER L, et al. The highD dataset: a drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems[C]. Maui, HI, USA:IEEE International Conference on Intelligent Transportation Systems (ITSC), 2018:2118-2125. |
21 | 杨智强. 从德国高速公路看事故预防先进做法[J]. 湖南交通科技, 2019, 45(3):208-214. |
YANG Z Q. Advanced methods of accident prevention from German expressway[J]. Hunan Communication Science and Technology, 2019, 45(3):208-214. | |
22 | 蔡英凤, 朱南楠, 邰康盛, 等. 基于注意力机制的车辆行为预测[J]. 江苏大学学报(自然科学版), 2020, 41(2):125-130. |
CAI Y F, ZHU N N, TAI K S, et al. Vehicle behavior prediction based on attention mechanism[J]. Journal of Jiangsu University (Natural Science), 2020, 41(2):125-130. | |
23 | 田洪清, 丁峰, 郑讯佳, 等. 基于势能场虚拟力的智能网联车辆运动规划[J]. 汽车工程, 2021, 43(4):518-526. |
TIAN H Q, DING F, ZHENG X J, et al. Motion planning based on virtual force of potential field for intelligent connected vehicles[J]. Automotive Engineering, 2021, 43(4):518-526. | |
24 | 季学武, 费聪, 何祥坤, 等. 基于 LSTM 网络的驾驶意图识别及车辆轨迹预测[J]. 中国公路学报, 2019, 32(6):34-42. |
JI X W, FEI C, HE X K,et al. Intention recognition and trajectory prediction for vehicle using LSTM network[J]. China Journal of Highway and Transport, 2019, 32(6):34-42. | |
25 | 工程师测评. 德国2019年全年新车销量[EB/OL]. https://chejiahao.autohome.com.cn/info/5502083. |
EvaluationEngineer. New car sales in Germany in 2019[EB/OL]. https://chejiahao.autohome.com.cn/info/5502083. | |
26 | 张驰. 基于驾驶员风险认知的自动驾驶车辆运动规划研究[D]. 长春吉林大学, 2020. |
ZHANG Chi. Research on motion planning of autonomous vehicle based on driver's hazard perception[D].Changchun:Jilin University, 2020. |
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