This study introduces a novel approach to autonomous motion planning, informing an analytical algorithm with a reinforcement learning (RL) agent within a Frenet coordinate system. The combination directly addresses the challenges of adaptability and safety in autonomous driving. Motion planning algorithms are essential for navigating dynamic and complex scenarios. Traditional methods, however, lack the flexibility required for unpredictable environments, whereas machine learning techniques, particularly reinforcement learning (RL), offer adaptability but suffer from instability and a lack of explainability. Our unique solution synergizes the predictability and stability of traditional motion planning algorithms with the dynamic adaptability of RL, resulting in a system that efficiently manages complex situations and adapts to changing environmental conditions. Evaluation of our integrated approach shows a significant reduction in collisions, improved risk management, and improved goal success rates across multiple scenarios. The code used in this research is publicly available as open-source software and can be accessed at the following link: https://github.com/TUM-AVS/Frenetix-RL.

翻译：本研究提出了一种新颖的自动驾驶运动规划方法，通过在弗勒内坐标系中引入强化学习智能体来增强解析算法。该组合直接应对了自动驾驶中适应性和安全性的挑战。运动规划算法对于在动态且复杂的场景中进行导航至关重要。然而，传统方法缺乏应对不可预测环境所需的灵活性，而机器学习技术，特别是强化学习，虽然提供了适应性，但存在不稳定性和缺乏可解释性的问题。我们的独特解决方案将传统运动规划算法的可预测性和稳定性与强化学习的动态适应性相结合，形成了一种能够高效管理复杂情况并适应环境条件变化的系统。对我们集成方法的评估显示，在多种场景下碰撞显著减少，风险管理得到改善，目标达成率也有所提升。本研究中使用的代码作为开源软件公开发布，可通过以下链接访问：https://github.com/TUM-AVS/Frenetix-RL。