NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Driving

TL;DR

NEST is a novel trajectory prediction framework for autonomous driving that combines small-world networks, hypergraphs, and neuromodulation to improve accuracy, efficiency, and adaptability in complex traffic scenarios.

Contribution

The paper introduces NEST, a new model integrating small-world hypergraph structures and neuromodulation for enhanced trajectory prediction in autonomous driving.

Findings

Outperforms existing methods on real-world datasets

Demonstrates superior generalization and efficiency

Effectively captures local and extended vehicle interactions

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.