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

TL;DR

This paper presents a hybrid autonomous driving framework combining deep reinforcement learning for high-level decision making with hybrid A* path planning for local trajectory generation, ensuring traffic rule compliance and real-world feasibility.

Contribution

It introduces a novel integration of DRL with hybrid A* planning using LTL-based rewards to obey traffic rules, validated from simulation to real hardware.

Findings

Effective lane change decision making via DRL

Collision-free trajectories generated by hybrid A*

Successful real-world implementation

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.