基于驾驶风险和驾驶能力的人机控制权分配

doi:10.13229/j.cnki.jdxbgxb.20240842

摘要/Abstract

摘要：

为减少人机协作模式下车辆和驾驶人之间的操控冲突，实现车辆控制权的柔性切换，首先，开展了驾驶模拟器跟车实验，基于碰撞时间、越过车道线时间和制动时间建立驾驶风险评估模型；其次，基于纵向加速度和方向盘转角，使用随机森林回归建立驾驶人跟车能力损失预测模型；最后，基于驾驶风险和驾驶员能力损失边界阈值建立了车辆控制权分配策略。结果表明：在纵向碰撞和濒临碰撞事件中，所提模型建议车辆应干预的时刻分别比驾驶员实际采取制动措施提前了0.62 s和1.03 s，有效识别率为87%。本研究可以为人机协作驾驶模式下车辆与驾驶人的控制权分配和过渡设计提供初步的参考。

关键词: 交通运输系统工程, 人机协作, 驾驶风险, 驾驶能力, 随机森林

Abstract:

In order to reduce the conflict between vehicle and driver in the human-machine collaboration mode and to realize the flexible transition of control authority. Firstly， a simulated car-following experiment was carried out， and a driving risk assessment model was established based on time to collision （TTC）， time to line crossing （TLC）， and time to brake （TTB）. Secondly， a predictive model of driver ability loss was developed using random forest regression （RFR） based on longitudinal acceleration and steering wheel angle. Finally， a control authority allocation strategy was formulated based on the boundary thresholds of driving risk and driver ability loss. The results show that in longitudinal collision events and near-collision events， the model suggests that the moment when the vehicle should intervene is 0.62 seconds and 1.03 seconds earlier than the driver steps on the brake pedal， respectively， with an effective recognition rate of 87%. The study can provide some theoretical assistance for the design of control allocation and transition between the vehicle and the driver in the human-machine collaborative driving.

Key words: transportation systems engineering, human-machine collaboration, driving risk, driver's ability, random forest

中图分类号:

U491.2

李坤宸,袁伟,王畅,张会明,穆雨薇. 基于驾驶风险和驾驶能力的人机控制权分配[J]. 吉林大学学报(工学版), 2026, 56(2): 575-584.

Kun-chen LI,Wei YUAN,Chang WANG,Hui-ming ZHANG,Yu-wei MU. Allocating control authority based on driving risk and driver's ability[J]. Journal of Jilin University(Engineering and Technology Edition), 2026, 56(2): 575-584.

图/表 11

图1

图2

图3

表1

RFR的预测输入变量和预测变量"

预测变量

输入变量

加速度

a i d e a l

纵向速度/（m·s^-1）

前车速度/（m·s^-1）

相对速度/（m·s^-1）

跟车距离/m

TTC/s

TTB/s

前车加速度/（m·s^-2）

转向角

s i d e a l

横向距离/m

距离车道中心线距离/m

跟车距离/m

表1

图4

表2

RFR模型的最优训练参数"

参数	加速度 $a i d e a l$	转向角 $s i d e a l$
n_estimates	100	120
max_depth	40	30
criterion	MSE	MSE
R²	0.93	0.86

表2

图5

图6

表3

模型离线验证结果"

描述	纵向碰撞	临近纵向碰撞	车道偏离
事件次数/次	47	35	27
干预次数/次	41	31	16
遗漏次数/次	6	4	11
平均 $t a d v a n c e$ /s	0.59^**	1.03^**	0.66
有效率/%	87.2	88.6	59.3

表3

图7

图8

参考文献 24

[1]	Bassani M, Catani L, Hazoor A, et al. Do driver monitoring technologies improve the driving behaviour of distracted drivers? A simulation study to assess the impact of an auditory driver distraction warning device on driving performance[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2023, 95: 239-250.
[2]	You F, Zhang J, Zhang J, et al. A novel cooperation-guided warning of invisible danger from AR-HUD to enhance driver's perception[J]. International Journal of Human-Computer Interaction, 2024, 40(8): 1873-1891.
[3]	Yu Z, Xu G, Jiang K, et al. Constructing the behavioral sequence of the takeover process—TOR, behavior characteristics and phases division: a real vehicle experiment[J]. Accident Analysis & Prevention, 2023, 186: No.107040.
[4]	Huang C, Naghdy F, Du H, et al. Review on human-machine shared control system of automated vehicles[C]∥2019 3rd International Symposium on Autonomous Systems (ISAS), Shanghai, China, 2019: 47-51.
[5]	Sanders D A, Gegov A, Tewkesbury G E, et al. Sharing driving between a vehicle driver and a sensor system using trust-factors to set control gains[C]∥ Intelligent Systems and Applications: Proceedings of the 2018 Intelligent Systems Conference, London, UK, 2019: 1182-1195.
[6]	Noh S, An K, Han W. Situation assessment and behavior decision for vehicle/driver cooperative driving in highway environments[C]∥ 2015 IEEE International Conference on Automation Science and Engineering, Gothenburg, Sweden, 2015: 626-633.
[7]	Yue M, Fang C, Zhang H, et al. Adaptive authority allocation-based driver-automation shared control for autonomous vehicles[J]. Accident Analysis & Prevention, 2021, 160: No.106301.
[8]	Wang Z, Suga S, Nacpil E J C, et al. Adaptive driver-automation shared steering control via forearm surface electromyography measurement[J]. IEEE Sensors Journal, 2020, 21(4): 5444-5453.
[9]	Benloucif M A, Sentouh C, Floris J, et al. Online adaptation of the level of haptic authority in a lane keeping system considering the driver's state[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2019, 61: 107-119.
[10]	Goodarzi A, Ghajar M. Integrating lane-keeping system with direct yaw moment control tasks in a novel driver assistance system[J]. Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, 2014, 229(1): 16-38.
[11]	Ko S, Langari R. Shared control between human driver and machine based on game theoretical model predictive control framework[C]∥ 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Boston, USA, 2020: 649-654.
[12]	Zhang W, Zhao Y, Zhang X, et al. Shared control for lane keeping assistance system based on multiple-phase handling inverse dynamics[J]. Control Engineering Practice, 2019, 93: No.104182.
[13]	Yang L, Yue M, Zhang H, et al. Toward hazard reduction of road vehicle after tire blowout: a driver steering assist control strategy[C]∥ 2019 Chinese Control Conference, Guangzhou, China, 2019: 6600-6605.
[14]	Liang J, Lu Y, Feng J, et al. Robust shared control system for aggressive driving based on cooperative modes identification[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53(11): 6672-6684.
[15]	Fang Z, Wang J, Liang J, et al. Authority allocation strategy for shared steering control considering human-machine mutual trust level[J]. IEEE Transactions on Intelligent Vehicles, 2024, 9(1): 2002-2015.
[16]	刘芳, 朱天贺, 苏卫星, 等. 基于高斯隐马尔可夫模型的人机共享控制区域化决策算法[J]. 电子学报, 2022, 50(11): 2659-2667.
	Liu Fang, Zhu Tian-he, Su Wei-xing, et al. Regionalized decision algorithm for human-machine shared control based on gaussian hidden Markov model[J]. Acta Electronica Sinica, 2022, 50(11): 2659-2667.
[17]	Tang F, Gao F, Wang Z. Driving capability-based transition strategy for cooperative driving: from manual to automatic[J]. IEEE Access, 2020, 8: 139013-139022.
[18]	孙博华. 考虑驾驶能力及驾驶习性的个性化人机共驾策略研究[D]. 长春: 吉林大学汽车工程学院, 2020.
	Sun Bo-hua. Research on personalized shared control considering driver's driving capability and style[D]. Changchun: College of Automotive Engineering, Jilin University, 2020.
[19]	Li X, Wang Y. Shared steering control for human-machine co-driving system with multiple factors[J]. Applied Mathematical Modelling, 2021,100: 471-490.
[20]	徐江. 基于驾驶能力的人机共驾接管策略研究[D]. 重庆: 重庆大学机械与运载工程学院, 2019.
	Xu Jiang. Study on driver capability based take-over strategy of cooperative driving system[D]. Chongqing: School of Mechanical and Transportation Engineering, Chongqing University, 2019.
[21]	Liu F, Zhu T, Su W. Servo-level human–machine shared control flexible strategy based on driving ability, status and regionalized environmental risk[J]. IEEE Transactions on Transportation Electrification, 2023, 9(3): 4418-4436.
[22]	Bausell R B, Li Y. Power Analysis for Experimental Research: a Practical Guide for the Biological, Medical and Social Sciences[M]. London: Cambridge University Press, 2002.
[23]	Wang H, Gu M, Wu S, et al. A driver's car-following behavior prediction model based on multi-sensors data[J]. EURASIP Journal on Wireless Communications and Networking, 2020, 2020(1): 1-12.
[24]	Kudarauskas N. Analysis of emergency braking of a vehicle[J]. Transport, 2007, 22(3): 154-159.

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