NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Driving
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909430530965504 |
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| author | Wang, Chengyue Liao, Haicheng Wang, Bonan Guan, Yanchen Rao, Bin Pu, Ziyuan Cui, Zhiyong Xu, Chengzhong Li, Zhenning |
| author_facet | Wang, Chengyue Liao, Haicheng Wang, Bonan Guan, Yanchen Rao, Bin Pu, Ziyuan Cui, Zhiyong Xu, Chengzhong Li, Zhenning |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11682 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Driving Wang, Chengyue Liao, Haicheng Wang, Bonan Guan, Yanchen Rao, Bin Pu, Ziyuan Cui, Zhiyong Xu, Chengzhong Li, Zhenning Robotics Artificial Intelligence Machine Learning 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. |
| title | NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Driving |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.11682 |