Vehicle obstacle avoidance control system considering driving style

doi:10.3785/j.issn.1008-973X.2024.07.007

Journal of ZheJiang University (Engineering Science)

2024, Vol. 58

Issue (7): 1377-1386 DOI: 10.3785/j.issn.1008-973X.2024.07.007

Vehicle obstacle avoidance control system considering driving style

Pan LI1(

),Bing ZHOU1,Tian CHAI1,*(

),Yuan DENG2,Qianxi PAN1,Xiaojian WU3

1. State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082, China
2. Schaeffler Intelligent Driving Technology (Changsha) Limited Company, Changsha 410036, China
3. School of Advanced Manufacturing, Nanchang University, Nanchang 330031, China

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Abstract

An obstacle avoidance control system integrating the quantified driving style was proposed to resolve the problem that the traditional obstacle avoidance control system did not consider the preferences of different drivers, leading to low acceptance of the system by drivers. Firstly, the driving data from several skilled drivers were acquired for style clustering, and a classifier based on support vector machine was obtained to realize the online recognition of driving styles. Then, the obstacle avoidance control system considering driving style was designed, dividing the obstacle avoidance process into an obstacle avoidance by steering stage and a stability restoration stage. In the obstacle avoidance by steering stage, the controller avoided obstacles by tracking the maximum lateral acceleration of the corresponding driving style. In the stability restoration stage, the controller controlled the vehicle to track the center line of the adjacent lane. Moreover, in the whole obstacle avoidance process, a direct yaw-moment control subsystem was added considering the obstacle avoidance safety and yaw stability of the vehicle. Finally, a simulation model of the system was established to analyse the similarities and differences of the obstacle avoidance operation and the vehicle state response under different driving styles. A survey of drivers’ subjective feelings was conducted, and the results showed that drivers’ acceptance of the proposed system increased by 16.58% compared to the traditional obstacle avoidance system.

Key words： driving style machine learning obstacle avoidance by steering lateral control subjective evaluation

Received: 09 November 2023 Published: 01 July 2024

CLC:

U 463.1

Fund: 国家自然科学基金资助项目（52002163, 52262054, 52202466）；湖南省自然科学基金资助项目（2022JJ40059）；湖南大学整车先进设计制造技术全国重点实验室开放基金资助项目（32065008）.

Corresponding Authors: Tian CHAI E-mail: lipan921@hnu.edu.cn;chaitian@hnu.edu.cn

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Cite this article:

Pan LI,Bing ZHOU,Tian CHAI,Yuan DENG,Qianxi PAN,Xiaojian WU. Vehicle obstacle avoidance control system considering driving style. Journal of ZheJiang University (Engineering Science), 2024, 58(7): 1377-1386.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2024.07.007 OR https://www.zjujournals.com/eng/Y2024/V58/I7/1377

考虑驾驶风格的车辆避障控制系统

传统避障控制系统设计未考虑不同驾驶员的偏好，导致驾驶员对系统的接受度不高，为此提出融入驾驶风格量化的避障控制系统. 采集多名熟练驾驶员的驾驶数据进行风格聚类，训练出基于支持向量机的分类器，实现对驾驶风格的在线识别. 设计考虑驾驶风格的避障控制系统，将避障过程分为转向避障和恢复稳定2个阶段. 在转向避障阶段，控制器通过跟踪相应驾驶风格对应的最大侧向加速度进行避障；在恢复稳定阶段，控制器控制车辆进行相邻车道中心线跟踪. 综合考虑车辆的避障安全性和横摆稳定性，将直接横摆力矩控制子系统加入所提系统. 构建系统仿真模型，分析不同驾驶风格下控制器避障操作和车辆状态响应的异同. 开展驾驶员主观感受的问卷调查，结果表明，相较于传统避障控制系统，驾驶员对所提系统的接受度提升了16.58%.

关键词： 驾驶风格, 机器学习, 转向避障, 横向控制, 主观评价

Fig.1 Eight-degree-of-freedom vehicle model

Fig.2 Driving simulator component

Fig.3 Distortion degree of data under different number of clusters

Fig.4 Driving style clustering results

Fig.5 Comparison of major features of different driving styles

Tab.1 Results of driving style classification algorithms

Fig.6 Confusion matrix of support vector machine classifier test set

Fig.7 Process of obstacle avoidance control

Fig.8 Overall structure of collision avoidance control system

Fig.9 Schematic diagram of collision avoidance safety constraint

Fig.10 Comparison of trajectories for different obstacle avoidance algorithms (v_x=20 m/s)

Fig.11 Comparison of front wheel angle for different obstacle avoidance algorithms (v_x=20 m/s)

Fig.12 Comparison of lateral acceleration for different obstacle avoidance algorithms (v_x=20 m/s)

Fig.13 Comparison of trajectories for different obstacle avoidance algorithms (v_x=24 m/s)

Fig.14 Comparison of front wheel angle for different obstacle avoidance algorithms (v_x=24 m/s)

Fig.15 Comparison of lateral acceleration for different obstacle avoidance algorithms (v_x=24 m/s)

Fig.16 Tire utilization during obstacle avoidance in aggressive driving style

Fig.17 Comparison of subjective evaluation scores for different obstacle avoidance algorithms


[1]	CUI Q, DING R, WU X, et al A new strategy for rear-end collision avoidance via autonomous steering and differential braking in highway driving[J]. Vehicle System Dynamics, 2020, 58 (6): 955- 986 doi: 10.1080/00423114.2019.1602732

[2]	ZONG X, XU G, YU G, et al Obstacle avoidance for self-driving vehicle with reinforcement learning[J]. SAE International Journal of Passenger Cars-Electronic and Electrical Systems, 2018, 11 (1): 30- 39

[3]	房亮, 关志伟, 王涛, 等基于深度学习 LSTM 的智能车辆避撞模型及验证[J]. 汽车安全与节能学报, 2022, 13 (1): 104- 111 FANG Liang, GUAN Zhiwei, WANG Tao, et al Collision avoidance model and its validation for intelligent vehicles based on deep learning LSTM[J]. Journal of Automotive Safety and Energy, 2022, 13 (1): 104- 111

[4]	WANG H, LIU B Path planning and path tracking for collision avoidance of autonomous ground vehicles[J]. IEEE Systems Journal, 2021, 16 (3): 3658- 3667

[5]	GUO J, LUO Y, LI K. Adaptive coordinated collision avoidance control of autonomous ground vehicles [J]. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering , 2018, 232(9): 1120–1133.

[6]	LEE K, KUM D Collision avoidance/mitigation system: motion planning of autonomous vehicle via predictive occupancy map[J]. IEEE Access, 2019, 7: 52846- 52857 doi: 10.1109/ACCESS.2019.2912067

[7]	BRÜCK Y, NIERMANN D, TRENDE A, et al. Investigation of personality traits and driving styles for individualization of autonomous vehicles [C]// International Conference on Intelligent Human Systems Integration . Palermo: Springer, 2021: 78–83.

[8]	MA Z, ZHANG Y Drivers trust, acceptance, and takeover behaviors in fully automated vehicles: effects of automated driving styles and driver’s driving styles[J]. Accident Analysis and Prevention, 2021, 159: 106238 doi: 10.1016/j.aap.2021.106238

[9]	NAVON–EYAL M, TAUBMAN–BEN-ARI O Can emotion regulation explain the association between age and driving styles?[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2020, 74: 439- 445 doi: 10.1016/j.trf.2020.09.008

[10]	FAN X, PAN G, MAO Y, et al Investigating the effect of personality on left-turn behaviors in various scenarios to understand the dynamics of driving styles[J]. Traffic Injury Prevention, 2019, 20 (8): 801- 806 doi: 10.1080/15389588.2019.1673893

[11]	SUN B, DENG W, WU J, et al An intention-aware and online driving style estimation based personalized autonomous driving strategy[J]. International Journal of Automotive Technology, 2020, 21: 1431- 1446 doi: 10.1007/s12239-020-0135-3

[12]	LI H, WU C, CHU D, et al Combined trajectory planning and tracking for autonomous vehicle considering driving styles[J]. IEEE Access, 2021, 9: 9453- 9463 doi: 10.1109/ACCESS.2021.3050005

[13]	董俊一. 考虑驾驶风格的智能驾驶换道决策模型研究[D]. 长春: 吉林大学, 2022. DONG Junyi. Research on lane changing decision model for the intelligent vehicle considering driving style [D]. Changchun: Jilin University, 2022.

[14]	LI G, YANG Y, ZHANG T, et al Risk assessment based collision avoidance decision-making for autonomous vehicles in multi-scenarios[J]. Transportation Research Part C: Emerging Technologies, 2021, 122: 102820 doi: 10.1016/j.trc.2020.102820

[15]	崔庆佳. 面向高速紧急工况的车辆避撞多目标协调控制研究[D]. 长沙: 湖南大学, 2020. CUI Qingjia. Research on multi-objective coordinated control strategies for vehicle collision-avoidance in highway driving [D]. Changsha: Hunan University, 2020.

[16]	PACEJKA H B, BAKKER E The magic formula tyre model[J]. Vehicle System Dynamics, 1992, 21 (Suppl.1): 1- 18

[17]	EBOLI L, MAZZULLA G, PUNGILLO G The influence of physical and emotional factors on driving style of car drivers: a survey design[J]. Travel Behaviour and Society, 2017, 7: 43- 51 doi: 10.1016/j.tbs.2017.02.001

[18]	蓟仲勋. 考虑驾驶员风格的车辆换道轨迹规划及跟踪控制研究[D]. 长沙: 湖南大学, 2019. JI Zhongxun. Research of vehicle lane-changing trajectory planning and tracking control considering driving style [D]. Changsha: Hunan University, 2019.

[19]	中华人民共和国公安部交通管理局. 2021年全国机动车保有量达3.95亿新能源汽车同比增59.25%. (2022-01-11)[2023-03-23]. https://www.mps.gov.cn/n2254314/n6409334/c8322353/content.html.

[20]	ZHU B, YAN S, ZHAO J, et al Personalized lane-change assistance system with driver behavior identification[J]. IEEE Transactions on Vehicular Technology, 2018, 67 (11): 10293- 10306 doi: 10.1109/TVT.2018.2867541

[21]	DENG Z, CHU D, WU C, et al. A probabilistic model for driving-style-recognition-enabled driver steering behaviors [J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2022, 52(3): 1838–1851.

[22]	SAGBERG F, SELPI, BIANCHI PICCININI G F, et al A review of research on driving styles and road safety[J]. Human Factors, 2015, 57 (7): 1248- 1275 doi: 10.1177/0018720815591313

[23]	马依宁, 姜为, 吴靖宇, 等基于不同风格行驶模型的自动驾驶仿真测试自演绎场景研究[J]. 中国公路学报, 2023, 36 (2): 216- 228 MA Yining, JIANG Wei, WU Jingyu, et al Self-evolution scenarios for simulation tests of autonomous vehicles based on different models of driving styles[J]. China Journal of Highway and Transport, 2023, 36 (2): 216- 228

[24]	马跃, 郭烈, 秦增科, 等考虑驾驶风格的路径跟踪控制方法[J]. 重庆理工大学学报: 自然科学, 2022, 36 (11): 20- 30 MA Yue, GUO Lie, QIN Zengke, et al Path tracking control method considering driving styles[J]. Journal of Chongqing University of Technology: Natural Science, 2022, 36 (11): 20- 30

[25]	SILVA I, EUGENIO NARANJO J A systematic methodology to evaluate prediction models for driving style classification[J]. Sensors, 2020, 20 (6): 1692 doi: 10.3390/s20061692

[26]	李升波, 王建强, 李克强软约束线性模型预测控制系统的稳定性方法[J]. 清华大学学报: 自然科学版, 2010, 50 (11): 1848- 1852 LI Shengbo, WANG Jianqiang, LI Keqiang Stabilization of linear predictive control systems with softening constraints[J]. Journal of Tsinghua University: Science and Technology, 2010, 50 (11): 1848- 1852

[27]	NILSSON J, FALCONE P, ALI M, et al Receding horizon maneuver generation for automated highway driving[J]. Control Engineering Practice, 2015, 41: 124- 133 doi: 10.1016/j.conengprac.2015.04.006

[28]	CUI Q, DING R, WEI C, et al A hierarchical framework of emergency collision avoidance amid surrounding vehicles in highway driving[J]. Control Engineering Practice, 2021, 109: 104751 doi: 10.1016/j.conengprac.2021.104751

[29]	DING S, LIU L, ZHENG W X Sliding mode direct yaw-moment control design for in-wheel electric vehicles[J]. IEEE Transactions on Industrial Electronics, 2017, 64 (8): 6752- 6762 doi: 10.1109/TIE.2017.2682024

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