通过十字路口是自动驾驶汽车的主要挑战之一。然而，对于大多数受交通灯控制的交叉口，可以通过一种简单的基于规则的方法来解决问题，该方法将自动驾驶车辆的行为与交通灯状态密切相关。在这项工作中，我们关注的是能够通过路口导航的系统的实现，只有交通标志提供。我们提出了一个多智能体系统，使用一个连续的，无模型的深度强化学习算法，用于训练神经网络预测加速度和方向盘角度在每个时间步。我们演示了代理通过理解环境中其他学习者的优先级来学习处理交叉所需的基本规则，并在他们的路径上安全驾驶。此外，我们的系统与基于规则的方法的比较表明，我们的模型取得了更好的结果，特别是在密集的交通条件下。最后，我们使用真实记录的交通数据在真实世界的场景中测试了我们的系统，证明了我们的模块能够适用于不可见的环境和不同的交通条件。
原文题目：End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning
原文：Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. However, for the majority of intersections regulated by traffic lights, the problem could be solved by a simple rule-based method in which the autonomous vehicle behavior is closely related to the traffic light states. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step. We demonstrate that agents learn both the basic rules needed to handle intersections by understanding the priorities of other learners inside the environment, and to drive safely along their paths. Moreover, a comparison between our system and a rule-based method proves that our model achieves better results especially with dense traffic conditions. Finally, we test our system on real world scenarios using real recorded traffic data, proving that our module is able to generalize both to unseen environments and to different traffic conditions.
基于多智能体深度强化学习的端到端交叉处理.pdf