Nest: A neuromodulated small-world hypergraph trajectory prediction model for autonomous driving

Ziyuan Pu

Abstract:

Accurate trajectory prediction is essential for the safety and efficiency of autonomous driving. Traditional models often
struggle with real-time processing, capturing non-linearity and uncertainty in traffic environments, efficiency in dense
traffic, and modeling temporal dynamics of interactions. We introduce NEST (Neuromodulated Small-world Hypergraph
Trajectory Prediction), a novel framework that integrates Small-world Networks and hypergraphs for superior interaction
modeling and prediction accuracy. This integration enables the capture of both local and extended vehicle interactions,
while the Neuromodulator component adapts dynamically to changing traffic conditions. We validate the NEST
model on several real-world datasets, including nuScenes, MoCAD, and HighD. The results consistently demonstrate
that NEST outperforms existing methods in various traffic scenarios, showcasing its exceptional generalization capability,
efficiency, and temporal foresight. Our comprehensive evaluation illustrates that NEST significantly improves the
reliability and operational efficiency of autonomous driving systems, making it a robust solution for trajectory prediction
in complex traffic environments.