  To ensure safe driving in dynamic environments, autonomous vehicles should
possess the capability to accurately predict 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 as natural language
prompts for LLMs and employing supervised fine-tuning to tailor LLMs
specifically for lane change prediction task. Additionally, we finetune the
Chain-of-Thought (CoT) reasoning to improve prediction transparency and
reliability, and include explanatory requirements in the prompts during
inference stage. Therefore, our LC-LLM model not only predicts lane change
intentions and trajectories but also provides CoT reasoning and explanations
for its predictions, enhancing its interpretability. Extensive experiments
based 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 effectively encode comprehensive
interaction information for driving behavior understanding.
