Threat number design for steering collision avoidance at extreme conditions

doi:10.3785/j.issn.1008-973X.2023.12.005

Journal of ZheJiang University (Engineering Science)

2023, Vol. 57

Issue (12): 2381-2390 DOI: 10.3785/j.issn.1008-973X.2023.12.005

Threat number design for steering collision avoidance at extreme conditions

Zi-wen HUANG1(

),Li LI1,Bing ZHOU1,*(

),Xiao-jian WU2,Tian CHAI1,Yan XU1

1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China
2. School of Advanced Manufacturing, Nanchang University, Nanchang 330031, China

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Abstract

The steady lateral acceleration based on road adhesion is usually denoted as the steer threat number, neglecting the limitation of vehicle characteristics on vehicle steering capability as well as the nonlinear and transient characteristics of high-speed steering maneuvers. To solve the issue, it is proposed to simulate the steering collision avoidance process by the steering wheel step test to obtain the maximum steady achievable lateral displacement as the steering threat number. Firstly, the critical stable lateral acceleration of the vehicle was determined by analyzing the tire lateral deflection and the vehicle characteristics. Secondly, the MAP of “lateral acceleration-longitudinal velocity-front wheel angle” was established according to the feedforward control algorithm. Thirdly, a steering feedforward control method considering lateral load transfer was proposed to improve the accuracy of the feed-forward front wheel angle in the steering wheel step input test. Finally, the stepping steering conditions under critical stability of the vehicle were established according to the MAP, and the nonlinear two-degree-of-freedom vehicle model was used for stepping steering simulation to obtain the maximum steady achievable lateral displacement plots. The proposed threat number and the steer threat number (STN) were compared and validated by the Monte Carlo method. The simulation results show that the proposed threat number can be more accurate in determining whether the vehicle can avoid collision by steering under extreme conditions compared to STN.

Key words： extreme conditions steering collision avoidance steer threat number steering feedforward control Monte Carlo method

Received: 24 February 2023 Published: 27 December 2023

CLC:

U 463.1

Fund: 国家自然科学基金资助项目(52002163, 51875184, 52062036)

Corresponding Authors: Bing ZHOU E-mail: 2441520765@qq.com;zhou_bingo@163.com

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Cite this article:

Zi-wen HUANG,Li LI,Bing ZHOU,Xiao-jian WU,Tian CHAI,Yan XU. Threat number design for steering collision avoidance at extreme conditions. Journal of ZheJiang University (Engineering Science), 2023, 57(12): 2381-2390.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2023.12.005 OR https://www.zjujournals.com/eng/Y2023/V57/I12/2381

极限工况下的车辆转向避撞风险指数

转向避撞风险指数通常根据路面附着限制以稳态侧向加速度进行车辆转向避撞能力评估，忽略了车辆特性对车辆转向能力的影响以及高速转向过程的非线性和瞬态特性，对此，提出通过前轮转角阶跃实验模拟转向避撞过程直接获得车辆最大可达侧向位移作为转向风险指数. 分析轮胎侧偏特性曲线和车辆特性，确定车辆的临界稳定侧向加速度；根据前馈控制算法建立“侧向加速度?纵向速度?前轮转角”MAP；提出考虑横向载荷转移的转向前馈控制方法，以提高车辆进行阶跃转向实验时的前馈前轮转角精度；根据MAP建立车辆临界稳定的角阶跃转向工况，采用非线性二自由度车辆模型进行阶跃转向仿真，得到车辆的最大可达侧向位移图. 通过蒙特卡洛法对提出的风险指数和转向风险指数（STN）进行对比、验证. 仿真结果表明，相比STN，所提转向风险指数在车辆极限工况可以更准确地判断车辆能否通过转向完成避撞.

关键词： 极限工况, 转向避撞, 转向风险指数, 转向前馈控制, 蒙特卡洛法

Fig.1 Nonlinear two-degree-of-freedom vehicle model

Tab.1 Vehicle model parameters

Fig.2 Comparison of three feedforward control methods with different desired lateral accelerations

Fig.3 Steering collision avoidance diagram

Fig.4 Lateral acceleration during steering maneuver

Fig.5 Flow chart of maximum steady-state lateral distance

Fig.6 Tire lateral deflection characteristic curve

Fig.7 Front wheel lateral deflection angle under extreme input

Fig.8 Lateral deflection characteristics curve of tires on roads with different adhesion coefficients

Fig.9 MAP of “lateral acceleration-longitudinal velocity-front wheel angle”

Fig.10 Plots of maximum lateral displacement under different adhesion coefficient

Fig.11 Random distribution of parameters for high adhesion coefficient test scenario

Fig.12 Confusion matrix of different threat numbers for high adhesion coefficient

Fig.13 Random distribution of parameters for medium adhesion coefficient test scenario

Fig.14 Confusion matrix of different threat numbers for medium adhesion coefficient

Fig.15 Random distribution of parameters for low adhesion coefficient test scenario

Fig.16 Confusion matrix of different threat numbers for low adhesion coefficient

Fig.17 Confusion matrix of normal situation results under different adhesion coefficient

Fig.18 Confusion matrix of different lateral displacement results


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