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.

翻译：自动驾驶是一项需要先进决策与控制算法的复杂任务。理解自动驾驶车辆决策背后的原理，对于确保其在高速公路驾驶中的安全有效运行至关重要。本研究提出了一种新颖的方法——HighwayLLM，该方法利用大语言模型的推理能力来预测自车导航的未来路径点。我们的方法还利用一个预训练的强化学习模型作为高层规划器，以决策适当的元级动作。HighwayLLM结合了RL模型的输出和当前状态信息，对下一状态做出安全、无碰撞且可解释的预测，从而为自车构建一条轨迹。随后，一个基于PID的控制器引导车辆到达LLM智能体预测的路径点。这种将LLM与RL及PID相结合的方式，增强了决策过程，并为高速公路自动驾驶提供了可解释性。