To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict the lane change intentions of surrounding vehicles in advance and forecast their future trajectories. Existing motion prediction approaches have ample room for improvement, particularly in terms of long-term prediction accuracy and interpretability. In this paper, we address these challenges by proposing LC-LLM, an explainable lane change prediction model that leverages the strong reasoning capabilities and self-explanation abilities of Large Language Models (LLMs). Essentially, we reformulate the lane change prediction task as a language modeling problem, processing heterogeneous driving scenario information in natural language as prompts for input into the LLM and employing a supervised fine-tuning technique to tailor the LLM specifically for our lane change prediction task. This allows us to utilize the LLM's powerful common sense reasoning abilities to understand complex interactive information, thereby improving the accuracy of long-term predictions. Furthermore, we incorporate explanatory requirements into the prompts in the inference stage. Therefore, our LC-LLM model not only can predict lane change intentions and trajectories but also provides explanations for its predictions, enhancing the interpretability. Extensive experiments on the large-scale highD dataset demonstrate the superior performance and interpretability of our LC-LLM in lane change prediction task. To the best of our knowledge, this is the first attempt to utilize LLMs for predicting lane change behavior. Our study shows that LLMs can encode comprehensive interaction information for driving behavior understanding.

翻译：为确保自动驾驶车辆在动态环境中的安全行驶，其需具备提前准确预测周围车辆换道意图并预判未来轨迹的能力。现有运动预测方法在长期预测精度和可解释性方面仍有较大提升空间。针对这些挑战，本文提出LC-LLM——一种利用大语言模型（LLMs）强大推理能力与自解释能力的可解释换道预测模型。本质上，我们将换道预测任务重构为语言建模问题：将异构驾驶场景信息以自然语言提示形式输入LLM，并通过监督微调技术将LLM适配至换道预测任务。由此，我们借助LLM强大的常识推理能力理解复杂交互信息，从而提升长期预测精度。此外，在推理阶段我们向提示中融入解释性要求，使LC-LLM不仅能预测换道意图与轨迹，还能为其预测结果提供解释，增强了模型可解释性。在大规模highD数据集上的大量实验表明，LC-LLM在换道预测任务中展现出优越的性能与可解释性。据我们所知，这是首次尝试利用LLM进行换道行为预测，本研究表明LLM能够编码用于驾驶行为理解的全面交互信息。