When a vehicle drives on the road, its behaviors will be affected by surrounding vehicles. Prediction and decision should not be considered as two separate stages because all vehicles make decisions interactively. This paper constructs the multi-vehicle driving scenario as a non-zero-sum game and proposes a novel game control framework, which consider prediction, decision and control as a whole. The mutual influence of interactions between vehicles is considered in this framework because decisions are made by Nash equilibrium strategy. To efficiently obtain the strategy, ADP, a model-based reinforcement learning method, is used to solve coupled Hamilton-Jacobi-Bellman equations. Driving performance is evaluated by tracking, efficiency, safety and comfort indices. Experiments show that our algorithm could drive perfectly by directly controlling acceleration and steering angle. Vehicles could learn interactive behaviors such as overtaking and pass. In summary, we propose a non-zero-sum game framework for modeling multi-vehicle driving, provide an effective way to solve the Nash equilibrium driving strategy, and validate at non-signalized intersections.

翻译：当车辆在道路上行驶时，其行为会受到周围车辆的影响。预测与决策不应被视为两个独立阶段，因为所有车辆都在交互中做出决策。本文将多车驾驶场景构建为非零和博弈，提出了一种新颖的博弈控制框架，将预测、决策与控制视为一个整体。该框架考虑了车辆间相互作用的相互影响，因为决策是通过纳什均衡策略制定的。为高效获取该策略，采用基于模型的强化学习方法ADP求解耦合的哈密顿-雅可比-贝尔曼方程。驾驶性能通过跟踪、效率、安全性和舒适性指标进行评估。实验表明，我们的算法能够通过直接控制加速度和转向角实现完美驾驶，车辆可学习超车、通行等交互行为。综上，我们提出了一个用于建模多车驾驶的非零和博弈框架，提供了求解纳什均衡驾驶策略的有效方法，并在无信号交叉口进行了验证。