How can I improve multi-agent trajectory prediction at intersections?
Knowledge-Informed Multi-Agent Trajectory Prediction at Signalized Intersections for Infrastructure-to-Everything
This paper introduces I2XTraj, a new model for predicting the movements of multiple vehicles (multi-agent trajectory prediction) at intersections controlled by traffic lights. It leverages information from traffic signals, map data, and learned driving behaviors to improve prediction accuracy.
Key points for LLM-based multi-agent systems: I2XTraj utilizes infrastructure (like traffic lights and roadside units) as a centralized source of knowledge for improved prediction, addressing limitations in individual vehicle perception. It employs a novel continuous encoding of traffic signal information and a driving strategy awareness mechanism that learns from the topological structure of the intersection and typical driving patterns. This knowledge-driven approach, combined with dynamic graph attention across spatial, temporal, and modality dimensions, allows the model to reason about complex interactions between vehicles in a more informed manner. The focus on infrastructure-based prediction also suggests potential application in collaborative multi-agent scenarios where LLMs could leverage shared knowledge from a central source for enhanced coordination.