TrajEvo: Trajectory Prediction Heuristics Design via LLM-driven Evolution

Trajectory prediction is a crucial task in modeling human behavior, especially in safety-critical fields such 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, slow inference speed, lack of explainability, and generalization issues that limit their practical adoption in such environments. 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 various real-world datasets, and remarkably outperforms both heuristics and deep learning methods when generalizing to an unseen real-world 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.

Authors

• Zhikai Zhao Korea Advanced Institute of Science and Technology
• Chuanbo Hua KAIST
• Federico Berto
• Kanghoon Lee Korea Advanced Institute of Science and Technology
• Zihan Ma School of Computer Science and Technology, Xi’an Jiaotong University, Ministry of Education Key Laboratory of Intelligent Networks and Network Security, Xi’an Jiaotong University. Shaanxi Province Key Laboratory of Big Data Knowledge Engineering, Xi’an Jiaotong University.
• Jiachen Li
• Jinkyoo Park Korea Advanced Institute of Science and Technology

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