用参数化方法让大模型精准预测变道轨迹，结果更可解释、更省资源。
sam-llm: interpretable lane change trajectoryprediction via parametric finetuning
- 让大模型输出变道物理参数，而非直接生成坐标点。
- 变道轨迹输出量减少80%，意图识别准确率达98.73%。
- 适合需要可解释性与低算力的自动驾驶系统部署。
本文提出SAM-LLM，一种融合大语言模型上下文推理能力与运动学模型物理精度的混合架构，用于可解释的变道轨迹预测。该模型通过微调大模型，输出变道轨迹的核心物理参数，而非原始坐标。在保持变道行为预测能力的同时，显著提升可解释性与效率：相比坐标输出法，轨迹数据量减少80%；整体意图预测准确率达到98.73%，达到当前最优水平。对于保持车道场景，模型仍预测离散坐标点；对于变道动作，则生成增强型正弦加速度模型（SAM）的参数，包括横向位移、变道时长、初始横向速度及纵向速度变化，从而生成完整、连续且符合物理规律的轨迹。
原文摘要 · Abstract (English)
This work introduces SAM-LLM, a novel hybrid architecture that bridges the gap between the contextual reasoning of Large Language Models (LLMs) and the physical precision of kinematic lane change models for autonomous driving. The system is designed for interpretable lane change trajectory prediction by finetuning an LLM to output the core physical parameters of a trajectory model instead of raw coordinates. For lane-keeping scenarios, the model predicts discrete coordinates, but for lane change maneuvers, it generates the parameters for an enhanced Sinusoidal Acceleration Model (SAM), including lateral displacement, maneuver duration, initial lateral velocity, and longitudinal velocity change. This parametric approach yields a complete, continuous, and physically plausible trajectory model that is inherently interpretable and computationally efficient, achieving an 80% reduction in output size compared to coordinate-based methods. The SAM-LLM achieves a state-of-the-art overall intention prediction accuracy of 98.73%, demonstrating performance equivalent to traditional LLM predictors while offering significant advantages in explainability and resource efficiency.
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