通過十字路口是自動駕駛汽車的主要挑戰之一。然而，對于大多數受交通燈控制的交叉口，可以通過一種簡單的基于規則的方法來解決問題，該方法将自動駕駛車輛的行為與交通燈狀态密切相關。在這項工作中，我們關注的是能夠通過路口導航的系統的實作，隻有交通标志提供。我們提出了一個多智能體系統，使用一個連續的，無模型的深度強化學習算法，用于訓練神經網絡預測加速度和方向盤角度在每個時間步。我們示範了代理通過了解環境中其他學習者的優先級來學習處理交叉所需的基本規則，并在他們的路徑上安全駕駛。此外，我們的系統與基于規則的方法的比較表明，我們的模型取得了更好的結果，特别是在密集的交通條件下。最後，我們使用真實記錄的交通資料在真實世界的場景中測試了我們的系統，證明了我們的子產品能夠适用于不可見的環境和不同的交通條件。
原文題目：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.
基于多智能體深度強化學習的端到端交叉處理.pdf