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Path Planning Considering Artificial Potential Fields Based on Trajectory Prediction
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AI
DOI：10.1109/tvt.2026.3680518.png)
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Abstract
En 中文
With the development of deep learning technologies, the accuracy of trajectory prediction has significantly improved. Leveraging predicted trajectories for motion planning can reduce the risk of collisions between vehicles more effectively. However, prior studies have shown that directly using predicted trajectories in planning often leads to suboptimal performance and may even cause collisions. To address this, we propose a novel framework that integrates trajectory prediction with artificial potential fields (APFs), enabling the effective use of prediction results in planning. In this paper, we introduce a trajectory planning framework that constructs a spatiotemporal risk (STR) field based on deep learning-based trajectory prediction and inter-vehicle interactions. The planning module then employs an optimization-based approach that considers multiple objectives, including safety, comfort, rule compliance, efficiency, and human likeness. Evaluated on a real-world dataset, our method achieves an 8.72% improvement in minimum Anticipated Collision Time ($\text{ACT}_{\min }$) and a 36.0% gain in efficiency compared to the baseline Driving Safety Field (DSF) method. Compared with other competing approaches, our method improves $\text{ACT}_{\min }$ by 10.67% while maintaining competitive performance in terms of comfort, efficiency, and human likeness. The results from the NuPlan closed-loop experiments indicate that our method exhibits superior planning capabilities in complex scenarios, surpassing the performance of all baseline methods. Furthermore, closed-loop experiments demonstrate that our approach enables continuous and collision-free planning in highly interactive traffic scenarios.
Keywords:
Risk assessment
trajectory prediction
path planning
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W