Abstract
Traditional trajectory planning methods for autonomous vehicles have several limitations. Heuristic and explicit simple rules make trajectory lack generality and complex motion. One of the approaches to resolve the above limitations of traditional trajectory planning methods is trajectory planning using reinforcement learning. However, reinforcement learning suffers from instability of learning and prior works of trajectory planning using reinforcement learning didn't consider the uncertainties. In this paper, we propose a trajectory planning method for autonomous vehicles using reinforcement learning. The proposed method includes iterative reward prediction method that stabilizes the learning process, and uncertainty propagation method that makes the reinforcement learning agent to be aware of the uncertainties. The proposed method is experimented in the CARLA simulator. Compared to the baseline method, we have reduced the collision rate by 60.17%, and increased the average reward to 30.82 times.
Abstract (translated)
传统的轨迹规划方法对于自动驾驶车辆具有多个局限性。基于策略和显式简单的规则使轨迹缺乏普适性和复杂运动。解决传统轨迹规划方法上述局限性的一个方法是使用强化学习进行轨迹规划。然而，强化学习的学习不稳定，而使用强化学习进行轨迹规划的前人工作没有考虑到不确定性。在本文中，我们提出了一种使用强化学习进行自动驾驶车辆轨迹规划的方法。所提出的方法包括迭代奖励预测方法，该方法稳定了学习过程，以及不确定性传播方法，该方法使强化学习智能体意识到不确定性。所提出的方法在CARLA仿真器上进行了实验。与基线方法相比，我们降低了碰撞率60.17%，并将平均奖励提高了30.82倍。
URL
https://arxiv.org/abs/2404.12079
