Planning safe trajectories in Autonomous Driving Systems (ADS) is a complex problem to solve in real-time. The main challenge to solve this problem arises from the various conditions and constraints imposed by road geometry, semantics and traffic rules, as well as the presence of dynamic agents. Recently, Model Predictive Path Integral (MPPI) has shown to be an effective framework for optimal motion planning and control in robot navigation in unstructured and highly uncertain environments. In this paper, we formulate the motion planning problem in ADS as a nonlinear stochastic dynamic optimization problem that can be solved using an MPPI strategy. The main technical contribution of this work is a method to handle obstacles within the MPPI formulation safely. In this method, obstacles are approximated by circles that can be easily integrated into the MPPI cost formulation while considering safety margins. The proposed MPPI framework has been efficiently implemented in our autonomous vehicle and experimentally validated using three different primitive scenarios. Experimental results show that generated trajectories are safe, feasible and perfectly achieve the planning objective. The video results as well as the open-source implementation are available at: https://gitlab.uni.lu/360lab-public/mppi

翻译：在自动驾驶系统中实时规划安全轨迹是一个复杂问题。解决该问题的主要挑战源于道路几何、语义和交通规则所施加的各种条件和约束，以及动态代理的存在。最近，模型预测路径积分（MPPI）已被证明是在非结构化和高度不确定环境中进行机器人导航的最优运动规划与控制的有效框架。本文中，我们将自动驾驶系统中的运动规划问题建模为一个非线性随机动态优化问题，并采用MPPI策略求解。本研究的主要技术贡献在于提出了一种在MPPI框架内安全处理障碍物的方法。该方法通过将障碍物近似为圆形，在考虑安全裕度的同时，将其便捷地集成到MPPI代价函数中。所提出的MPPI框架已在我们自主研发的自动驾驶车辆上高效实现，并通过三种不同原始场景进行了实验验证。实验结果表明，生成的轨迹安全、可行，并完美实现了规划目标。视频结果及开源实现可访问：https://gitlab.uni.lu/360lab-public/mppi