TrajEvo: Trajectory Prediction Heuristics Design via LLM-driven Evolution
This addresses the problem of improving accuracy and generalizability in trajectory prediction for safety-critical domains like social robotics and autonomous vehicles, representing an incremental advancement by automating heuristic design.
The paper tackles trajectory prediction by introducing TrajEvo, a framework that uses Large Language Models (LLMs) to automatically design heuristics, which outperforms existing heuristic methods and generalizes better to out-of-distribution scenarios than both heuristic and deep learning approaches.
Trajectory prediction is a critical task in modeling human behavior, especially in safety-critical domains such as social robotics and autonomous vehicle navigation. Traditional heuristics based on handcrafted rules often lack accuracy and generalizability. Although deep learning approaches offer improved performance, they typically suffer from high computational cost, limited explainability, and, importantly, poor generalization to out-of-distribution (OOD) scenarios. 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 propose two key innovations: Cross-Generation Elite Sampling to encourage population diversity, and a Statistics Feedback Loop that enables the LLM to analyze and improve alternative predictions. Our evaluations demonstrate that TrajEvo outperforms existing heuristic methods across multiple real-world datasets, and notably surpasses both heuristic and deep learning methods in generalizing to an unseen OOD real-world dataset. TrajEvo marks a promising step toward the automated design of fast, explainable, and generalizable trajectory prediction heuristics. We release our source code to facilitate future research at https://github.com/ai4co/trajevo.