HighwayLLM: Decision-Making and Navigation in Highway Driving with RL-Informed Language Model
This addresses the problem of safe and explainable autonomous driving for highway scenarios, representing an incremental improvement through hybrid method integration.
The study tackled autonomous highway driving by integrating a large language model (LLM) with reinforcement learning (RL) and PID control to predict waypoints and generate safe, explainable trajectories, resulting in enhanced decision-making and interpretability.
Autonomous driving is a complex task which requires advanced decision making and control algorithms. Understanding the rationale behind the autonomous vehicles' decision is crucial to ensure their safe and effective operation on highway driving. This study presents a novel approach, HighwayLLM, which harnesses the reasoning capabilities of large language models (LLMs) to predict the future waypoints for ego-vehicle's navigation. Our approach also utilizes a pre-trained Reinforcement Learning (RL) model to serve as a high-level planner, making decisions on appropriate meta-level actions. The HighwayLLM combines the output from the RL model and the current state information to make safe, collision-free, and explainable predictions for the next states, thereby constructing a trajectory for the ego-vehicle. Subsequently, a PID-based controller guides the vehicle to the waypoints predicted by the LLM agent. This integration of LLM with RL and PID enhances the decision-making process and provides interpretability for highway autonomous driving.