Self-driving vehicles have their own intelligence to drive on open roads. However, vehicle managers, e.g., government or industrial companies, still need a way to tell these self-driving vehicles what behaviors are encouraged or forbidden. Unlike human drivers, current self-driving vehicles cannot understand the traffic laws, thus rely on the programmers manually writing the corresponding principles into the driving systems. It would be less efficient and hard to adapt some temporary traffic laws, especially when the vehicles use data-driven decision-making algorithms. Besides, current self-driving vehicle systems rarely take traffic law modification into consideration. This work aims to design a road traffic law adaptive decision-making method. The decision-making algorithm is designed based on reinforcement learning, in which the traffic rules are usually implicitly coded in deep neural networks. The main idea is to supply the adaptability to traffic laws of self-driving vehicles by a law-adaptive backup policy. In this work, the natural language-based traffic laws are first translated into a logical expression by the Linear Temporal Logic method. Then, the system will try to monitor in advance whether the self-driving vehicle may break the traffic laws by designing a long-term RL action space. Finally, a sample-based planning method will re-plan the trajectory when the vehicle may break the traffic rules. The method is validated in a Beijing Winter Olympic Lane scenario and an overtaking case, built in CARLA simulator. The results show that by adopting this method, the self-driving vehicles can comply with new issued or updated traffic laws effectively. This method helps self-driving vehicles governed by digital traffic laws, which is necessary for the wide adoption of autonomous driving.
翻译:自驾车辆有自己的智能,可以在露天公路上驾驶。然而,车辆管理人员,例如政府或工业公司,仍然需要一种方法来告诉这些自驾车辆哪些行为受到鼓励或禁止。与人的驾驶者不同,目前的自驾车辆无法理解交通法,因此主要的想法是通过法律调整的备份政策提供自行驾驶车辆交通法的适应性。在这项工作中,基于语言的自然交通法首先被翻译成Linear Temporal逻辑方法的逻辑表达方式。然后,该系统将努力预先监测道路交通法的调整决策方法。在加强学习的基础上设计决策算法,其中交通规则通常在深层的神经网络中暗含编码。主要想法是通过法律调整备份政策来提供自行驾驶车辆交通法的适应性。在进行这项工作中,自然语言交通法首先转化为交通法的逻辑表达方式。随后,系统将努力监测自行驾驶交通法的调整决策方法是否有利于车辆的自行驾驶规则的自我驾驶,最终在深层心电路网网络中,在设计越越轨规则时,使用一种机动法的方法可以使车辆的车辆的交通法重新制定。