基于危险边界搜索的自动驾驶系统加速测试方法

doi:10.13229/j.cnki.jdxbgxb20211177

摘要/Abstract

摘要：

针对现有基于危险场景强化生成的自动驾驶系统加速测试方法探索初期算力浪费、测试过程存在重复等问题，提出了一种基于危险边界搜索的自动驾驶系统加速测试方法。首先，定义场景危害度、场景暴露频次、场景敏感度三类场景固有属性，采用场景测试优先度作为参数空间划分和场景搜索的基础。其次，根据场景测试优先度进行具体场景提取；根据场景实际危害度进行场景测试优先度更新实现实验流程迭代。然后，利用支持向量回归对实验结果进行危险边界拟合。最后，搭建了Matlab/PreScan/CarSim联合仿真平台，选取前车切入典型场景对某黑盒自动驾驶系统进行仿真测试以验证加速测试方法的有效性。结果表明，本文方法可有效搜索被测算法的危险场景边界并提升测试效率。

关键词: 车辆工程, 自动驾驶系统, 加速测试, 危险边界, 场景测试优先度

Abstract:

Aiming at the problems of computing power waste and repetitive testing in the current accelerate test methods which enhanced generating of hazardous scenarios， an accelerate test method based on hazardous boundary search for the automated driving system is proposed. Firstly， three types of inherent attributes of the scenarios are defined： scenario hazardous degree， exposure frequency and sensitivity， and the scenario test priority is proposed with the three inherent attributes which is treated as the basis of the division of parameter space and the search sequence of scenarios. Next， specific scenarios are extracted according to the scenario test priority； and the scenario test priority is updated according to the scenario hazard degree in test results by iterating the update process. Then， the support vector regression is used to fit the hazardous boundary of the experimental results. Finally， the Matlab/PreScan/CarSim co-simulation platform is built and a black-box automated driving algorithm is tested with the front vehicle cut-in scenario in virtual environment to verify the effectiveness of the proposed method. The results show that the proposed method can effectively search the hazardous boundary of the tested automated driving algorithm and improve the test efficiency.

Key words: vehicle engineering, automated driving system, accelerate test method, hazardous boundary, scenario test priority

中图分类号:

U461.91

朱冰,范天昕,赵健,张培兴,孙宇航. 基于危险边界搜索的自动驾驶系统加速测试方法[J]. 吉林大学学报(工学版), 2023, 53(3): 704-712.

Bing ZHU,Tian-xin FAN,Jian ZHAO,Pei-xing ZHANG,Yu-hang SUN. Accelerate test method of automated driving system based on hazardous boundary search[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 704-712.

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参考文献 24

1	余荣杰, 田野, 孙剑. 高等级自动驾驶汽车虚拟测试:研究进展与前沿[J]. 中国公路学报, 2020, 33(11): 125-138.
	Yu Rong-jie, Tian Ye, Sun Jian. Highly automated vehicle virtual testing: a review of recent developmentsand research frontiers[J]. China Journal of Highway and Transport, 2020, 33(11): 125-138.
2	朱冰, 张培兴, 赵健. 面向多维度逻辑场景的自动驾驶安全性聚类评价方法[J]. 汽车工程, 2020, 316(11): 13-18.
	Zhu Bing, Zhang Pei-xing, Zhao Jian. Clustering evaluation method of autonomous driving safety for multi-dimensional logical scenario[J]. Automotive Engineering, 2020, 316(11): 13-18.
3	Wang C, Storms K, Winner H. Online safety assessment of automated vehicles using silent testing[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(8): 13069-13083.
4	朱冰, 张培兴, 赵健, 等.基于场景的自动驾驶汽车虚拟测试研究进展[J]. 中国公路学报, 2019, 32(6): 1-19.
	Zhu Bing, Zhang Pei-xing, Zhao Jian,et al. Research progress on scene-based virtual test of autonomous driving vehicles[J]. China Journal of Highway and Transport, 2019, 32(6): 1-19.
5	Riedmaier S, Ponn T, D Ludwig et al. Survey on scenario-based safety assessment of automated vehicles[J]. IEEE Access, 2020, 8: 87456-87477.
6	Chen W, Kloul L. An advanced driver assistance test cases generation methodology based on highway traffic situation description ontologies[C]∥Knowledge Discovery, Knowledge Engineering and Knowledge Management, Berlin, Germany, 2020:93-113.
7	白雪松, 邓伟文, 任秉韬, 等. 一种自动驾驶仿真场景要素的提取方法[J]. 汽车工程, 2021, 43(7): 1030-1036.
	Bai Xue-song, Deng Wei-wen, Ren Bing-tao, et al. Anextraction method of scenario elements for autonomous driving simulation[J]. Automotive Engineering, 2021, 43(7): 1030-1036.
8	Krajewski R, Moers T, Nerger D, et al. Data-driven maneuver modeling using generative adversarial networks and variational autoencoders for safety validation of highly automated vehicles[C]∥The 21st IEEE International Conference on Intelligent Transportation Systems, New York, America, 2018:2383-2390.
9	Khastgir S, Dhadyalla G, Birrell S, et al. Test scenariogeneration for driving simulators using constrained randomization technique[C]∥SAE World Congress Experience, New York, America, 2017-01-1672.
10	Huang Z, Lam H, Zhao D. Towards affordable on track testing for autonomous vehicle—a Kriging-based statistical approach[C]∥2017 IEEE 20th International Conference on Intelligent Transportation Systems, NewYork, America, 2017: 1-6.
11	Schwalb E. Accelerated evaluation of autonomous drivers using neural network quantile generators[C]∥2020 IEEE International Conference on Big Data, New York, America, 2020: 4751-4758.
12	Zhu B, Zhang P, Zhao J, et al. Hazardous scenario enhanced generation for automated vehicle testing based on optimization searching method[J]. IEEE Transactions on Intelligent Transportation Systems,2021, 99: 1-11.
13	Zhao D, Lam H, Peng H, et al. Accelerated evaluation of automated vehicles safety in lane-change scenarios based on importance sampling techniques[J]. Transactions on Intelligent Transportation Systems, 2017, 18(3): 595-607.
14	Jonathan M, Hankey, Miguel A, et al.Dscription of the SHRP 2 naturalistic database and the crash, near-crash, and baseline data sets[R]. New York: Virginia Tech Transportation Institute, 2016.
15	Minderhoud M M, Bovy P H L. Extended time-to-collision measures for road traffic safety assessment[J]. Accident Analysis and Prevention, 2001, 33(1): 89-97.
16	Balas V E, Balas M M. Driver assisting by inverse time to collision[C]∥2006 World Automation Congress, Budapest, Hungary, 2006: No.376059.
17	Butakov V A, Ioannou P A. Driver/vehicle response diagnostic system for the vehicle-following case[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(5): 1947-1957.
18	李霖,朱西产,董小飞. 自主紧急制动系统避撞策略的研究[J]. 汽车工程, 2015, 37(2): 168-174.
	Li Lin, Zhu Xi-chan, Dong Xiao-fei. A research on the collision avoidance strategy for autonomous emergency braking system[J]. Automotive Engineering, 2015, 37(2):168-174.
19	Quante L, Zhang M, Preuk K, et al. Human performance in critical scenarios as a benchmark for highly automated vehicles[J]. Automotive Innovation, 2021, 4(3): 274-283.
20	吴坚,赵阳,何睿.基于支持向量机回归算法的电子机械制动传感器系统故障诊断[J].吉林大学学报: 工学版, 2013, 43(5):1178-1183.
	Wu Jian, Zhao Yang, He Rui. Fault detection and diagnosis of EMB sensor system based on SVR[J]. Journalof Jilin University(Engineering and Technology Edition), 2013, 43(5): 1178-1183.
21	Feng S, Feng Y, Yu C, et al. Testing scenario librarygeneration for connected and automated vehicles, part I: methodology[J]. IEEE Transactions on Intelligent Transportation Systems, 2021,22(3): 1573-1582.
22	Doecke S D, Baldock M R J, Kloeden C N,et al. Impact speed and the risk of serious injury in vehicle crashes[J]. Accident Analysis and Prevention, 2020,144:1-7.
23	Doecke S D, Dutschke J K, Baldock M R J,et al. Travel speed and the risk of serious injury in vehicle crashes[J]. Accident Analysis and Prevention, 2021,161:1-8.
24	Yong P, Wang X, Peng S, et al. Investigation on the injuries of drivers and copilots in rear-end crashes between trucks based on real world accident data in china[J]. Future Generation Computer Systems, 2017, 86(9):1251-1258.

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