GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction
Predicting the future trajectories of pedestrians on the road is an important task for autonomous driving. The pedestrian trajectory prediction is affected by scene paths, pedestrian’s intentions and decision-making, which is a multi-modal problem. Relying solely on historical coordinates...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11079594/ |
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| author | Amar Fadillah Ching-Lin Lee Zhi-Xuan Wang Kuan-Ting Lai |
| author_facet | Amar Fadillah Ching-Lin Lee Zhi-Xuan Wang Kuan-Ting Lai |
| author_sort | Amar Fadillah |
| collection | DOAJ |
| description | Predicting the future trajectories of pedestrians on the road is an important task for autonomous driving. The pedestrian trajectory prediction is affected by scene paths, pedestrian’s intentions and decision-making, which is a multi-modal problem. Relying solely on historical coordinates pedestrian data represents the most straightforward method for pedestrian trajectory prediction. Nevertheless, the accuracy achieved by this method is limited, primarily because it fails to account for the crucial scene paths impacting the pedestrian. Instead of predicting the future trajectory directly, we propose to use scene context and observed trajectory to predict the goal points first, and then reuse the goal points to predict the future trajectories. By leveraging the information from scene context and observed trajectory, the uncertainty can be limited to a few target areas, which represent the “goals” of the pedestrians. In this paper, we propose GoalNet, a new trajectory prediction neural network based on the goal areas of a pedestrian. Our network can predict both pedestrian’s trajectories and bounding boxes. The overall model is efficient and modular, and its outputs can be changed according to the usage scenario. Experimental results show that GoalNet significantly improves the previous state-of-the-art performance by 48.7% on the JAAD and 40.8% on the PIE dataset. |
| format | Article |
| id | doaj-art-210f601daffa49d4bcfef670bbab4e57 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-210f601daffa49d4bcfef670bbab4e572025-08-20T03:16:12ZengIEEEIEEE Access2169-35362025-01-011313253713254610.1109/ACCESS.2025.358881211079594GoalNet: Goal Areas Oriented Pedestrian Trajectory PredictionAmar Fadillah0https://orcid.org/0009-0001-2979-9325Ching-Lin Lee1https://orcid.org/0009-0006-4613-6423Zhi-Xuan Wang2Kuan-Ting Lai3https://orcid.org/0000-0002-8497-1562Department of Electronic Engineering, National Taipei University of Technology, Taipei, TaiwanDepartment of Electronic Engineering, National Taipei University of Technology, Taipei, TaiwanDepartment of Artificial Intelligence Technology, National Taipei University of Technology, Taipei, TaiwanDepartment of Electronic Engineering, National Taipei University of Technology, Taipei, TaiwanPredicting the future trajectories of pedestrians on the road is an important task for autonomous driving. The pedestrian trajectory prediction is affected by scene paths, pedestrian’s intentions and decision-making, which is a multi-modal problem. Relying solely on historical coordinates pedestrian data represents the most straightforward method for pedestrian trajectory prediction. Nevertheless, the accuracy achieved by this method is limited, primarily because it fails to account for the crucial scene paths impacting the pedestrian. Instead of predicting the future trajectory directly, we propose to use scene context and observed trajectory to predict the goal points first, and then reuse the goal points to predict the future trajectories. By leveraging the information from scene context and observed trajectory, the uncertainty can be limited to a few target areas, which represent the “goals” of the pedestrians. In this paper, we propose GoalNet, a new trajectory prediction neural network based on the goal areas of a pedestrian. Our network can predict both pedestrian’s trajectories and bounding boxes. The overall model is efficient and modular, and its outputs can be changed according to the usage scenario. Experimental results show that GoalNet significantly improves the previous state-of-the-art performance by 48.7% on the JAAD and 40.8% on the PIE dataset.https://ieeexplore.ieee.org/document/11079594/Pedestrianstrajectory predictionfuture trajectories |
| spellingShingle | Amar Fadillah Ching-Lin Lee Zhi-Xuan Wang Kuan-Ting Lai GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction IEEE Access Pedestrians trajectory prediction future trajectories |
| title | GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction |
| title_full | GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction |
| title_fullStr | GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction |
| title_full_unstemmed | GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction |
| title_short | GoalNet: Goal Areas Oriented Pedestrian Trajectory Prediction |
| title_sort | goalnet goal areas oriented pedestrian trajectory prediction |
| topic | Pedestrians trajectory prediction future trajectories |
| url | https://ieeexplore.ieee.org/document/11079594/ |
| work_keys_str_mv | AT amarfadillah goalnetgoalareasorientedpedestriantrajectoryprediction AT chinglinlee goalnetgoalareasorientedpedestriantrajectoryprediction AT zhixuanwang goalnetgoalareasorientedpedestriantrajectoryprediction AT kuantinglai goalnetgoalareasorientedpedestriantrajectoryprediction |