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.
翻译:通过十字路口导航是自主车辆面临的主要挑战任务之一。然而,对于交通灯监管的大多数十字路口而言,问题可以通过简单的基于规则的方法来解决,在这种方法中,自主车辆的行为与交通灯状态密切相关。在这项工作中,我们侧重于实施一个能够通过仅提供交通标志的交叉路口导航的系统。我们建议采用一个多试剂系统,使用一种连续的、无模式的深层强化学习算法,用于培训神经网络,以预测加速度和方向角的每一步。我们证明,代理人通过了解环境中其他学习者的优先事项,并安全地沿他们的道路前进,既学习了处理交叉点的基本规则。此外,我们系统与基于规则的方法之间的比较证明,我们的模型取得了更好的效果,特别是在交通条件密集的情况下。最后,我们用真实的交通数据对现实世界情景进行测试,证明我们的模块能够向看不见的环境和不同的交通条件进行普及。