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：自动驾驶车辆具备在开放道路上自主行驶的智能。然而，车辆管理者（如政府或工业企业）仍需向这些自动驾驶车辆传达鼓励或禁止的行为。与人类驾驶员不同，当前的自动驾驶车辆无法理解交通法规，因此依赖程序员手动将相应规则编写到驾驶系统中。当车辆采用数据驱动的决策算法时，这种方式效率低下且难以适应临时交通法规。此外，现有自动驾驶车辆系统很少考虑交通法规的变更。本研究旨在设计一种道路交通法规自适应决策方法。该决策算法基于强化学习设计，其中交通规则通常以隐式编码的方式存在于深度神经网络中。核心思路是通过一种法规自适应备用策略，赋予自动驾驶车辆对交通法规的适应能力。本研究首先利用线性时序逻辑方法，将基于自然语言的交通法规转化为逻辑表达式。随后，系统通过设计长期强化学习动作空间，预先监测自动驾驶车辆可能违反交通法规的行为。最后，当车辆可能违反交通规则时，采用基于采样的规划方法重新规划轨迹。该方法在CARLA仿真器搭建的北京冬奥会专用车道场景和超车案例中得到验证。结果表明，采用该方法后，自动驾驶车辆能够有效遵守新发布或更新的交通法规。该方法有助于自动驾驶车辆受数字交通法规的约束，这对于自动驾驶的广泛普及至关重要。