Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework
<|reference_start|>Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement Learning-Based Planning Framework: 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.<|reference_end|>
@article{li2024let,
    title={Let Hybrid A* Path Planner Obey Traffic Rules: A Deep Reinforcement
  Learning-Based Planning Framework},
    author={Xibo Li, Shruti Patel and Christof B"uskens},
    journal={arXiv preprint arXiv:2407.01216},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01216},
    primaryClass={cs.RO cs.AI}
}