用大模型进化生成可解释的轨迹预测规则，速度快且泛化强。
TrajEvo: Designing Trajectory Prediction Heuristics via LLM-driven Evolution
- 用大模型驱动的演化算法自动生成轨迹预测规则
- 在ETH-UCY上优于传统启发式方法，在SDD上超越深度学习模型
- 适合需要快速、可解释轨迹预测的机器人与自动驾驶场景
轨迹预测是建模人类行为的关键任务，广泛应用于社交机器人和自动驾驶领域。传统基于人工规则的启发式方法精度不足，而近期深度学习方法存在计算成本高、可解释性差和泛化能力弱等问题，限制了实际应用。本文提出TrajEvo框架，利用大语言模型（LLM）自动设计轨迹预测启发式规则。该框架采用演化算法从历史轨迹数据中生成并优化预测规则，引入跨代精英采样以提升种群多样性，并设计统计反馈环让LLM分析替代预测结果。实验表明，TrajEvo在ETH-UCY数据集上优于以往启发式方法，在未见过的SDD数据集上显著超越启发式与深度学习方法。TrajEvo为自动化设计快速、可解释且具备强泛化能力的轨迹预测规则迈出关键一步。代码已开源，欢迎后续研究：https://github.com/ai4co/trajevo。
原文摘要 · Abstract (English)
Trajectory prediction is a crucial task in modeling human behavior, especially in fields as social robotics and autonomous vehicle navigation. Traditional heuristics based on handcrafted rules often lack accuracy, while recently proposed deep learning approaches suffer from computational cost, lack of explainability, and generalization issues that limit their practical adoption. In this paper, we introduce TrajEvo, a framework that leverages Large Language Models (LLMs) to automatically design trajectory prediction heuristics. TrajEvo employs an evolutionary algorithm to generate and refine prediction heuristics from past trajectory data. We introduce a Cross-Generation Elite Sampling to promote population diversity and a Statistics Feedback Loop allowing the LLM to analyze alternative predictions. Our evaluations show TrajEvo outperforms previous heuristic methods on the ETH-UCY datasets, and remarkably outperforms both heuristics and deep learning methods when generalizing to the unseen SDD dataset. TrajEvo represents a first step toward automated design of fast, explainable, and generalizable trajectory prediction heuristics. We make our source code publicly available to foster future research at https://github.com/ai4co/trajevo.
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