Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework

• URL: http://arxiv.org/abs/2407.01216v1
• Date: Mon, 1 Jul 2024 12:00:10 GMT
• Title: Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework
• Authors: Xibo Li, Shruti Patel, Christof Büskens,
• Abstract summary: In this work, we combine low-level algorithms such as the hybrid A* path planning with deep reinforcement learning (DRL) to make high-level decisions. The hybrid A* planner is able to generate a collision-free trajectory to be executed by a model predictive controller (MPC) In addition, the DRL algorithm is able to keep the lane change command consistent within a chosen time-period.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep reinforcement learning (DRL) allows a system to interact with its environment and take actions by training an efficient policy that maximizes self-defined rewards. In autonomous driving, it can be used as a strategy for high-level decision making, whereas low-level algorithms such as the hybrid A* path planning have proven their ability to solve the local trajectory planning problem. In this work, we combine these two methods where the DRL makes high-level decisions such as lane change commands. After obtaining the lane change command, the hybrid A* planner is able to generate a collision-free trajectory to be executed by a model predictive controller (MPC). In addition, the DRL algorithm is able to keep the lane change command consistent within a chosen time-period. Traffic rules are implemented using linear temporal logic (LTL), which is then utilized as a reward function in DRL. Furthermore, we validate the proposed method on a real system to demonstrate its feasibility from simulation to implementation on real hardware.

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