ABSTRACT

Automated steering technology offers significant benefits to the safety of vehicles, but desire to keep the human driver in the loop requires a better understanding of the interaction between driver and vehicle. An existing noncooperative–game–theoretic framework for modelling such interaction is revisited, leading to the development of two driver steering control models. Both bear Nash–equilibrium properties, but involve different assumptions about driver steering behaviour. A simulation study is performed to demonstrate the difference between the two driver models. An experiment using a fixed–base driving simulator is conducted to measure six test subjects' steering angles in response to the lane–change manoeuvres generated by an automated steering controller. The two driver models' capabilities for representing driver steering behaviour are investigated through fitting them to measured driver steering angles. Key model parameters are identified using a system identification procedure. It is found that the two driver models have equivalent capability in capturing the trend of the six test subjects' measured steering angles, but less good at reproducing the overshoot and oscillation involved in two subjects' steering angles. It is found that the inclusion of an arm neuromuscular system model can improve the performance of the proposed driver models.

摘要

自动转向技术为车辆的安全提供了显着的好处，但要让人类驾驶员参与其中，需要更好地了解驾驶员和车辆之间的交互。重新审视了现有的非合作博弈理论框架，用于对此类交互进行建模，从而开发了两种驾驶员转向控制模型。两者都具有纳什均衡特性，但涉及关于驾驶员转向行为的不同假设。进行模拟研究以证明两种驾驶员模型之间的差异。进行了一项使用固定基座驾驶模拟器的实验，以测量六名测试对象的转向角，以响应自动转向控制器产生的车道变换操作。两种驱动程序模型 通过将它们拟合到测量的驾驶员转向角来研究表示驾驶员转向行为的能力。使用系统识别程序识别关键模型参数。发现两种驾驶员模型在捕捉六名测试对象测量的转向角趋势方面具有相当的能力，但在再现两名对象转向角所涉及的超调和振荡方面较差。发现包含手臂神经肌肉系统模型可以提高所提出的驾驶员模型的性能。测量的转向角，但不太擅长再现两个受试者转向角所涉及的过冲和振荡。发现包含手臂神经肌肉系统模型可以提高所提出的驾驶员模型的性能。测量的转向角，但不太擅长再现两个受试者转向角所涉及的过冲和振荡。发现包含手臂神经肌肉系统模型可以提高所提出的驾驶员模型的性能。