Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (9): 2511-2519.doi: 10.13229/j.cnki.jdxbgxb.20221425

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Construction of virtual test scenario for intelligent vehicle and pedestrian interaction

Hong-yan GUO1,2(),Jia-ming ZHANG1,2,Jun LIU1,3(),Yun-feng HU1,2   

  1. 1.National Key Laboratory of Automotive Chassis Integration and Bionics，Jilin University，Changchun 130022，China
    2.College of Communication Engineering，Jilin University，Changchun 130022，China
    3.College of Automotive Engineering，Jilin University，Changchun 130022，China
  • Received:2022-11-10 Online:2024-09-01 Published:2024-10-28
  • Contact: Jun LIU E-mail:guohy11@jlu.edu.cn;liujun20@jlu.edu.cn

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Abstract:

To satisfy the testing requirements for intelligent vehicles and pedestrians interaction under urban conditions， a scenario generation method that comprehensively considers the appearance frequency of scenarios in the real world and their challenges to intelligent vehicles performance is proposed. First， the original scenarios are extracted from the natural driving dataset. Then a critical scenario extraction method based on importance sampling theory is designed to extract essential scenarios from the original scenarios according to the accelerated test requirement， and pedestrian crossing road scenarios based on natural driving data are constructed. Finally， comparing the distribution of essential scenarios and original scenarios，the results show that this method can effectively screen out scenarios that may pose challenges to intelligent vehicles safety and it also realizes accelerated testing while retaining the statistical characteristics of test scenarios.

Key words: control science and engineering, scenario generation, intelligent vehicle, natural driving data, importance sampling, pedestrian

CLC Number: 

  • TP13

Fig.1

Typical test process of automated vehicle"

Fig.2

Generation framework of intelligent vehicle and pedestrian interaction scenarios"

Fig.3

Types and schematic diagrams of pedestrian and vehicle interaction scenarios"

Fig.4

Occurrence frequency of the longitudinal distances from the pedestrians at different vehicle speeds"

Fig.5

Schematic diagram of decisive variables"

Fig.6

Exposure frequency of pedestrians crossing the road ahead"

Fig.7

Braking process of longitudinal speed model"

Fig.8

Model of vehicle-pedestrian collision"

Fig.9

Relative time relationship between vehicle and pedestrian entering potentially dangerous area"

Fig.10

General process of scenario generation"

Fig.11

Schematic diagram of the nearest distance between vehicle and pedestrian"

Fig.12

Comparison of indicators between essential scenarios and original scenarios"

Fig.13

Comparison of TTC frequency distribution of scenarios extracted by different methods"

1 Ridel D, Rehder E, Lauer M, et al. A literature review on the prediction of pedestrian behavior in urban scenarios[C]∥Proceeding of 2018 21st International Conference on Intelligent Transportation Systems(ITSC), Maui, USA, 2018: 3105-3112.
2 Yuan Q, Hu M, Li Y. Scenario design and driving simulation experiment of vehicle-to-pedestrain accidents based on real accident data[J]. J Automotive Safety and Energy, 2012,3(1): 19-25.
3 Daniel J, Kara K. Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations[J]. Transportation Research Part A: Policy and Practice, 2015(77): 167-181.
4 J3016.Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles [S].
5 Rasouli A, Tsotsos J. Autonomous vehicles that interact with pedestrians: a survey of theory and practice[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(3): 900-918.
6 Ray L, Joshué P, Martin D. A complete framework for developing and testing automated driving controllers[J]. IFAC-Papers On Line, 2017,50(1): 258-263.
7 刘颖,贺锦鹏,朱西产,等.自动紧急制动系统行人测试场景的研究[J].汽车技术,2014(3):35-39.
Liu Ying, He Jin Peng, Zhu Xi-chan, et al. Research on test scenarios for AEB pedestrian system[J]. Automobile Technology, 2014(3): 35-39.
8 马峻岩,田叶凡,赵祥模,等.基于自然驾驶数据挖掘的二阶车辆与行人交互测试场景[J].中国公路学报, 2022,35(3):139-152.
Ma Jun-yan, Tian Ye-fan, Zhao Xiang-mo, et al. Two-stages test scenarios for interaction of vehicles and pedestrians based on naturalistic driving data mining[J]. China Journal of Highway and Transport, 2022,35(3):139-152.
9 V9.0.4. Vulnerable road user (VRU) protection [S].
10 Swanson E, Yanagisawa M, Naim W, et al. Crash avoidance needs and countermeasure profiles for safety applications based on light-vehicle-to-pedestrian communications[R]. Washington DC: National Highway Traffic Safety Administration, 2016.
11 Lenard J, Welsh R, Danton R. Time-to-collision analysis of pedestrian and pedal-cycle accidents for the development of autonomous emergency braking systems[J]. Accident Analysis & Prevention,2018,115: 128-136.
12 谭正平, 车瑶栎, 肖凌云, 等.面向自动驾驶的典型汽车与行人事故冲突场景研究[J].安全与环境学报,2021,21(4): 1573-1582.
Tan Zheng-ping, Che Yao-yue, Xiao Ling-yun, et al. Trace analysis for the typical precrash scenario between car vehicle and pedestrian caused by the automatic driving[J]. Journal of Safety and Environment, 2021, 21(4): 1573-1582.
13 Huang X, Zhao D, Peng H. Empirical study of DSRC performance based on safety pilot model deployment data[J]. IEEE Transactions on Intelligent Transportation Systems, 2017,18(10): 2619-2628.
14 中国汽车技术研究中心有限公司. 中国新车评价规程(C-NCAP)2018年版管理规则[EB/OL]. [2022-11-02].
15 郭晓东. 轿车前方行人识别及碰撞预警系统研究[D].沈阳:东北大学机械工程与自动化学院,2012.
Guo Xiao-dong. Study on pedestrian recognition ahead of vehicle and collision warning system[D]. Shenyang:School of Mechanical Engineering and Automation,Northeastern University, 2012.
16 李霖,朱西产,陈海林.驾驶员制动和转向避撞极限[J].同济大学学报: 自然科学版, 2016, 44(11): 1743-1748.
Li Lin, Zhu Xi-chan, Chen Hai-lin. Drivers´ collision avoidance limit by braking and steering[J]. Journal of Tongji University (Natural Science), 2016,44(11): 1743-1748.
17 蒋拯民,党少博,李慧云,等.自动驾驶汽车场景测试研究进展综述[J].汽车技术, 2022(8):10-22.
Jiang Zheng-min, Dang Shao-bo, Li Hui-yun, et al. A survey on the research progress of scenario-based testing for autonomous vehicles[J]. Automobile Technology, 2022(8): 10-22.
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