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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.16377 |
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| _version_ | 1866909589029519360 |
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| author | Weiming, Qu Jia, Wang Jiawei, Du Yuanhao, Zhu Jianfeng, Yu Rui, Xia Song, Cao Xihong, Wu Dingsheng, Luo |
| author_facet | Weiming, Qu Jia, Wang Jiawei, Du Yuanhao, Zhu Jianfeng, Yu Rui, Xia Song, Cao Xihong, Wu Dingsheng, Luo |
| contents | Trajectory prediction is a fundamental technology for advanced autonomous driving systems and represents one of the most challenging problems in the field of cognitive intelligence. Accurately predicting the future trajectories of each traffic participant is a prerequisite for building high safety and high reliability decision-making, planning, and control capabilities in autonomous driving. However, existing methods often focus solely on the motion of other traffic participants without considering the underlying intent behind that motion, which increases the uncertainty in trajectory prediction. Autonomous vehicles operate in real-time environments, meaning that trajectory prediction algorithms must be able to process data and generate predictions in real-time. While many existing methods achieve high accuracy, they often struggle to effectively handle heterogeneous traffic scenarios. In this paper, we propose a Subjective Intent-based Low-latency framework for Multiple traffic participants joint trajectory prediction. Our method explicitly incorporates the subjective intent of traffic participants based on their key points, and predicts the future trajectories jointly without map, which ensures promising performance while significantly reducing the prediction latency. Additionally, we introduce a novel dataset designed specifically for trajectory prediction. Related code and dataset will be available soon. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16377 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SILM: A Subjective Intent Based Low-Latency Framework for Multiple Traffic Participants Joint Trajectory Prediction Weiming, Qu Jia, Wang Jiawei, Du Yuanhao, Zhu Jianfeng, Yu Rui, Xia Song, Cao Xihong, Wu Dingsheng, Luo Robotics Trajectory prediction is a fundamental technology for advanced autonomous driving systems and represents one of the most challenging problems in the field of cognitive intelligence. Accurately predicting the future trajectories of each traffic participant is a prerequisite for building high safety and high reliability decision-making, planning, and control capabilities in autonomous driving. However, existing methods often focus solely on the motion of other traffic participants without considering the underlying intent behind that motion, which increases the uncertainty in trajectory prediction. Autonomous vehicles operate in real-time environments, meaning that trajectory prediction algorithms must be able to process data and generate predictions in real-time. While many existing methods achieve high accuracy, they often struggle to effectively handle heterogeneous traffic scenarios. In this paper, we propose a Subjective Intent-based Low-latency framework for Multiple traffic participants joint trajectory prediction. Our method explicitly incorporates the subjective intent of traffic participants based on their key points, and predicts the future trajectories jointly without map, which ensures promising performance while significantly reducing the prediction latency. Additionally, we introduce a novel dataset designed specifically for trajectory prediction. Related code and dataset will be available soon. |
| title | SILM: A Subjective Intent Based Low-Latency Framework for Multiple Traffic Participants Joint Trajectory Prediction |
| topic | Robotics |
| url | https://arxiv.org/abs/2504.16377 |