Beyond Patterns: Harnessing Causal Logic for Autonomous Driving Trajectory Prediction
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912369993580544 |
|---|---|
| author | Wang, Bonan Liao, Haicheng Wang, Chengyue Rao, Bin Guan, Yanchen Yu, Guyang Zhang, Jiaxun Lai, Songning Xu, Chengzhong Li, Zhenning |
| author_facet | Wang, Bonan Liao, Haicheng Wang, Chengyue Rao, Bin Guan, Yanchen Yu, Guyang Zhang, Jiaxun Lai, Songning Xu, Chengzhong Li, Zhenning |
| contents | Accurate trajectory prediction has long been a major challenge for autonomous driving (AD). Traditional data-driven models predominantly rely on statistical correlations, often overlooking the causal relationships that govern traffic behavior. In this paper, we introduce a novel trajectory prediction framework that leverages causal inference to enhance predictive robustness, generalization, and accuracy. By decomposing the environment into spatial and temporal components, our approach identifies and mitigates spurious correlations, uncovering genuine causal relationships. We also employ a progressive fusion strategy to integrate multimodal information, simulating human-like reasoning processes and enabling real-time inference. Evaluations on five real-world datasets--ApolloScape, nuScenes, NGSIM, HighD, and MoCAD--demonstrate our model's superiority over existing state-of-the-art (SOTA) methods, with improvements in key metrics such as RMSE and FDE. Our findings highlight the potential of causal reasoning to transform trajectory prediction, paving the way for robust AD systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06856 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond Patterns: Harnessing Causal Logic for Autonomous Driving Trajectory Prediction Wang, Bonan Liao, Haicheng Wang, Chengyue Rao, Bin Guan, Yanchen Yu, Guyang Zhang, Jiaxun Lai, Songning Xu, Chengzhong Li, Zhenning Artificial Intelligence Robotics Accurate trajectory prediction has long been a major challenge for autonomous driving (AD). Traditional data-driven models predominantly rely on statistical correlations, often overlooking the causal relationships that govern traffic behavior. In this paper, we introduce a novel trajectory prediction framework that leverages causal inference to enhance predictive robustness, generalization, and accuracy. By decomposing the environment into spatial and temporal components, our approach identifies and mitigates spurious correlations, uncovering genuine causal relationships. We also employ a progressive fusion strategy to integrate multimodal information, simulating human-like reasoning processes and enabling real-time inference. Evaluations on five real-world datasets--ApolloScape, nuScenes, NGSIM, HighD, and MoCAD--demonstrate our model's superiority over existing state-of-the-art (SOTA) methods, with improvements in key metrics such as RMSE and FDE. Our findings highlight the potential of causal reasoning to transform trajectory prediction, paving the way for robust AD systems. |
| title | Beyond Patterns: Harnessing Causal Logic for Autonomous Driving Trajectory Prediction |
| topic | Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2505.06856 |