让自动驾驶车在无信号路口识别恶意车辆并安全决策
Autonomous Vehicle Decision-Making Framework for Considering Malicious Behavior at Unsignalized Intersections
- 用可变权重强化紧急情况下的安全奖励
- 引入一阶心理模型作为额外奖励信号
- 适合研究自动驾驶安全与博弈决策的读者
本文提出一种基于Q-learning的决策框架，提升自动驾驶车辆在通过无信号交叉口时应对其他恶意行为车辆的安全性与效率。传统奖励信号通常基于安全和效率反馈，本文通过可变加权参数调节安全收益，在紧急情况下更强调安全。该框架在常规奖励基础上引入一阶理论心智推理，以一阶信念作为额外奖励信号，使自动驾驶车辆能够在遭遇潜在恶意行为车辆时做出更明智的决策，从而整体提升自动驾驶交通系统的安全性与效率。为验证框架性能，采用Prescan/Simulink联合仿真，结果表明其性能满足预设要求。
原文摘要 · Abstract (English)
In this paper, we propose a Q-learning based decision-making framework to improve the safety and efficiency of Autonomous Vehicles when they encounter other maliciously behaving vehicles while passing through unsignalized intersections. In Autonomous Vehicles, conventional reward signals are set as regular rewards regarding feedback factors such as safety and efficiency. In this paper, safety gains are modulated by variable weighting parameters to ensure that safety can be emphasized more in emergency situations. The framework proposed in this paper introduces first-order theory of mind inferences on top of conventional rewards, using first-order beliefs as additional reward signals. The decision framework enables Autonomous Vehicles to make informed decisions when encountering vehicles with potentially malicious behaviors at unsignalized intersections, thereby improving the overall safety and efficiency of Autonomous Vehicle transportation systems. In order to verify the performance of the decision framework, this paper uses Prescan/Simulink co-simulations for simulation, and the results show that the performance of the decision framework can meet the set requirements.
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