NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Driving
This addresses the problem of accurate and efficient trajectory prediction for autonomous driving systems, though it appears incremental as it builds on existing interaction modeling techniques.
The paper tackles trajectory prediction for autonomous driving by introducing NEST, a framework that integrates Small-world Networks and hypergraphs to model vehicle interactions, and it outperforms existing methods on real-world datasets like nuScenes, MoCAD, and HighD.
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.