用神经调制小世界超图模型，提升自动驾驶轨迹预测的准确与实时性。
NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Driving
- 构建小世界超图捕捉车辆局部与远距离交互关系。
- 神经调制器动态适应交通变化，提升复杂场景泛化能力。
- 在nuScenes、MoCAD等数据集上优于现有方法，适合高密度交通场景。
精准轨迹预测对自动驾驶的安全与效率至关重要。传统模型在实时处理、非线性与不确定性建模、密集交通下的效率及交互时序动态捕捉方面存在局限。本文提出NEST（Neuromodulated Small-world Hypergraph Trajectory Prediction）框架，融合小世界网络与超图结构，实现更优的交互建模与预测精度。该架构可同时捕获车辆间的局部与长程交互，神经调制器组件则能动态适应交通环境变化。我们在nuScenes、MoCAD和HighD等多个真实世界数据集上验证了NEST性能。结果表明，无论在何种交通场景下，NEST均显著优于现有方法，展现出卓越的泛化能力、计算效率与时间前瞻性。综合评估显示，NEST极大提升了自动驾驶系统的可靠性与运行效率，是复杂交通环境中轨迹预测的稳健解决方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。