基于自然驾驶数据的城市交叉口纵向驾驶特征分析

doi:10.19562/j.chinasae.qcgc.2022.06.003

Abstract

Abstract:

In order to meet the higher requirements of driver assistance systems for human-like driving ability in urban intersection， the longitudinal driving characteristics of drivers in real traffic in that area are investigated in this paper. 778 sample data of drivers approaching urban intersections are extracted from natural driving data， and YOLOv4 is applied to identifying various types of road users in traffic scene. ANOVA is used to investigate the differences in reaction characteristics across motion types and traffic densities， and hierarchical regression models are established to analyze the relationships between braking characteristics and motion state， motion type and road users. The results show that high-density traffic significantly reduces approaching speed. Compared with right-turning drivers， stopping drivers have longer reaction distance and may approach intersection with higher acceleration and braking intensity in a shorter time with braking started 4.46s earlier when approach speed is higher or reaction distance is shorter. Different road users have different effects on braking characteristics： stopping drivers primarily pay attentions to the vehicles traveling in the same direction， and right-turning drivers are mainly concerned with pedestrians and cyclists.

Key words: urban intersections, driving behavior, ANOVA, hierarchical regression, natural driving data, human-like driving

Tian Yuan,Xuan Zhao,Rui Liu,Qiang Yu,Xichan Zhu,Shu Wang. An Analysis on Longitudinal Driving Characteristics in Urban Intersection Based on Natural Driving Data[J].Automotive Engineering, 2022, 44(6): 821-830.

Figures/Tables 3

References 28

1	HARTWICH F， BEGGIATO M， KREMS J F. Driving comfort， enjoyment and acceptance of automated driving-effects of drivers' age and driving style familiarity［J］. Ergonomics， 2018，61：1017-1032.
2	NORDHOFF S， AREM B V， HAPPEE R. A conceptual model to explain， predict， and improve user acceptance of driverless vehicles［J］. Transportation Research Record： Journal of the Transportation Research Board， 2016， 2602（1）：60-67.
3	DELIGIANNI S P， QUDDUS M， MORRIS A， et al. Analyzing and modeling drivers' deceleration behavior from normal driving［J］. Transportation Research Record： Journal of the Transportation Research Board， 2017， 2663（1）：134-141.
4	LI S， LI P， YAO Y， et al. Analysis of drivers' deceleration behavior based on naturalistic driving data［J］. Traffic Injury Prevention， 2020， 21（12）：1-6.
5	MERAT N， LEE J D. Preface to the special section on human factors and automation in vehicles： designing highly automated vehicles with the driver in mind［J］. Human Factors the Journal of the Human Factors and Ergonomics Society， 2012， 54（5）：681-686.
6	ANJANA S， ANJANEYULU M. Safety analysis of urban signalized intersections under mixed traffic［J］. J Safety Res， 2015， 52：9-14.
7	唐克双，谈超鹏，周楠. 基于轨迹数据的交叉口相位切换期间危险驾驶行为实证分析［J］. 中国公路学报， 2018， 31（4）：10.
	TANG K S， TAN C P， ZHOU N. Empirical analysis of risky driving behaviors during the phase transition intervals at signalized intersection based on vehicle trajectory data［J］. China Journal of Highway and Transport， 2018， 31（4）： 10.
8	龙岩松，徐进，潘存书，等.基于实车驾驶数据的信号交叉口车辆运行特性［J］.科学技术与工程，2021，21（31）：13567-13573.
	LONG Y S， XU J， PAN C S， et al. Vehicle operating characteristics signalized intersections based on field driving data［J］. Science Technology and Engineering， 2021， 21（31）： 13567-13573.
9	SATO T， AKAMATSU M. Influence of traffic conditions on driver behavior before making a right turn at an intersection： analysis of driver behavior based on measured data on an actual road［J］. Transportation Research Part F：Traffic Psychology & Behavior， 2007， 10（5）：397-413.
10	THAPA R， HALLMARK S， ONEYEAR N. Braking behavior of major approach turning vehicles at rural two-way stop controlled intersections： a naturalistic driving study［J］. Traffic Injury Prevention，2020.
11	ONEYEAR N L， HALLMARK S L， WANG B. Evaluating the relationship between the driver and roadway to address rural intersection safety using the SHRP 2 naturalistic driving study data［J］. DOI： 10.15787/VTT1MW2P.2015.
12	WU J， XU H. Driver behavior analysis for right-turn drivers at signalized intersections using SHRP 2 naturalistic driving study data［J］. J Safety Res， 2017， 63：177-185.
13	BERNDT H， WENDER S， DIETMAYER K. Driver braking behavior during intersection approaches and implications for warning strategies for driver assistant systems［C］. Intelligent Vehicles Symposium. IEEE， 2007.
14	EL-SHAWARBY I， RAKHA H， AMER A， et al. Impact of driver and surrounding traffic on vehicle deceleration behavior at onset of yellow indication［J］. Transportation Research Record Journal of the Transportation Research Board， 2011， 2248（2248）：10-20.
15	GATES T J， NOYCE D A， LARACUENTE L， et al. Analysis of driver behavior in dilemma zones at signalized intersections［J］. Transportation Research Record， 2007， 2030（1）： 29-39.
16	HAAS R， INMAN V， DIXSON A， et al. Use of intelligent transportation system data to determine driver deceleration and acceleration behavior［J］. Transportation Research Record Journal of the Transportation Research Board， 2004， 1899（1899）：3-10.
17	WANG J， DIXON K K， LI H， et al. Normal deceleration behavior of passenger vehicles at stop sign-controlled intersections evaluated with in-vehicle global positioning system data［J］. Transportation Research Record Journal of the Transportation Research Board， 2005， 1937（1937）：120-127.
18	BAO S， BOYLE L. Driver performance at two-way stop-controlled intersections on divided highways［J］. Transportation Research Record Journal of the Transportation Research Board， 2008， 2069：26-32.
19	王雪松，杨敏明.基于自然驾驶数据的变道切入行为分析［J］.同济大学学报（自然科学版），2018，46（8）：1057-1063.
	WANG X S， YANG M M. Cut-in behavior analyses based on naturalistic driving data［J］. Journal of Tongji University （Natural Science）， 2018， 46（8）： 1057-1063.
20	GIRSHICK R. Fast r-cnn ［C］.Proceedings of the IEEE International Conference on Computer Vision. 2015： 1440-1448.
21	REN S， HE K， GIRSHICK R， et al. Faster R-CNN： towards real-time object detection with region proposal networks［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2016， 39（6）： 1137-1149.
22	LIU W， ANGUELOV D， ERHAN D， et al. Ssd： single shot multibox detector［C］.European Conference on Computer Vision. Springer， Cham， 2016： 21-37.
23	REDMON J， FARHADI A. YOLO9000： better， faster， stronger［C］.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017： 7263-7271.
24	BOCHKOVSKIY A， WANG C Y， LIAO H. YOLOv4： optimal speed and accuracy of object detection［J］. arXiv：2004.10934，2020.
25	LUO H， YANG Y， TONG B， et al. Traffic sign recognition using a multi-task convolutional neural network［J］. IEEE Transactions on Intelligent Transportation Systems， 2018：1100-1111.
26	ZHU Y， ZHANG C， ZHOU D， et al. Traffic sign detection and recognition using fully convolutional network guided proposals［J］. Neurocomputing， 2016， 214：758-766.
27	OUYANG W， ZHOU H， LI H， et al. Jointly learning deep features， deformable parts， occlusion and classification for pedestrian detection［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence， 2017：1-1.
28	SANG J， WU Z， GUO P， et al. An improved YOLOv2 for vehicle detection［J］. Sensors， 2018， 18（12）.

[1]	ZHOU Wei, ZHANG Cheng-Ning, LI Jun-Qiu. [J]. , 2016, 38(12): 1407 -1414 .
[2]	ZONG Yi-Qi, GU Zheng-Qi, LUO Ze-Min, JIANG Cai-Mao, ZHANG Qi-Dong, YANG Zhen-Dong. [J]. , 2016, 38(12): 1427 -1433 .
[3]	ZHANG Ying-Chao, ZHAN Da-Peng, ZHAO Jing, ZHANG Zhe, LI Jie. [J]. , 2016, 38(12): 1434 -1439 .
[4]	TANG You-Ming, YAN Ling-Bo, LUO Qian, CAO Li-Bo. [J]. , 2016, 38(12): 1452 -1458 .
[5]	ZHANG Lei, LU Jian-Wei, JIANG Jun-Zhao, YAN Pei-Lei, LI Lei. [J]. , 2016, 38(12): 1477 -1482 .
[6]	ZHAO Liang, ZHANG Qiang, GUO Kong-Hui. [J]. , 2017, 39(1): 86 -91 .
[7]	SHEN Deng-Feng, WANG Chen, YU Hai-Sheng, ZHANG Tong, YI Xian-Ke. [J]. , 2017, 39(1): 15 -22 .
[8]	ZOU Tie-Fang, HU Lin, LI Ping-Fan, YI Liang. [J]. , 2017, 39(1): 41 -46 .
[9]	ZHOU Kai, ZANG Jing-Lun. [J]. , 2017, 39(2): 127 -132 .
[10]	ZUO Wen-Jie, CHEN Ji-Shun, LI Yi-Wen, LIU Nian. [J]. , 2017, 39(2): 145 -149 .

An Analysis on Longitudinal Driving Characteristics in Urban Intersection Based on Natural Driving Data

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 3

References 28

Related Articles 3

Metrics

Comments

Recommended 10

[1]	Dongjian Song,Bing Zhu,Jian Zhao,Jiayi Han,Yanchen Liu. Human-Like Behavior Decision-Making of Intelligent Vehicles Based on Driving Behavior Generation Mechanism [J]. Automotive Engineering, 2022, 44(12): 1797-1808.
[2]	Wei Yuan,Guosong Yang,Rui Fu,Zhi Zhang,Kangkang Zhang. Research on the Influence of Driver’s Inhibitory Control on Risky Driving Behavior [J]. Automotive Engineering, 2021, 43(4): 601-609.
[3]	Xiong Ying, Mao Xuesong. Research on the Method of Navigating Autonomous Driving Vehicle Through Expressway Toll Region [J]. Automotive Engineering, 2020, 42(9): 1263-1269.