  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.
