论文标题
在未信号的十字路口驱动自动驾驶汽车的冲突解决：一种差异游戏方法
Driving Conflict Resolution of Autonomous Vehicles at Unsignalized Intersections: A Differential Game Approach
论文作者
论文摘要
考虑到个性化的驾驶偏好，使用差异游戏方法开发了一个新的决策框架，以解决未信号交叉点的自动驾驶汽车（AV）的驾驶冲突。为了实现类似人类的驾驶和个性化的决策，首先定义了AVS的驾驶侵略性。为了提高驾驶安全性，为碰撞风险评估而建立了高斯潜在的现场模型。此外，在拟议的决策框架中，进一步使用碰撞风险评估模型来基于事件触发的机制降低计算复杂性。在构建回报功能时，全面考虑了驾驶安全性和通过效率，并且还反映了驾驶侵略性。讨论和解决了差异游戏的两种平衡解决方案，即NASH平衡和Stackelberg平衡。最后，提出的决策制定算法通过硬件式测试平台进行了测试，其可行性，有效性和实时实现性能得到了验证。
Considering personalized driving preferences, a new decision-making framework is developed using a differential game approach to resolve the driving conflicts of autonomous vehicles (AVs) at unsignalized intersections. To realize human-like driving and personalized decision-making, driving aggressiveness is first defined for AVs. To improve driving safety, a Gaussian potential field model is built for collision risk assessment. Besides, in the proposed decision making framework, the collision risk assessment model is further used to reduce the computational complexity based on an event-triggered mechanism. In the construction of payoff function, both driving safety and passing efficiency are comprehensively considered, and the driving aggressiveness is also reflected. Two kinds of equilibrium solution to the differential game, i.e., the Nash equilibrium and Stackelberg equilibrium, are discussed and solved. Finally, the proposed decision making algorithm is tested through a hardware-in-the-loop testing platform, and its feasibility, effectiveness and real-time implementation performance are validated.
