Polar Collision Grids: Effective Interaction Modelling for Pedestrian Trajectory Prediction in Shared Space Using Collision Checks
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Abstract (arXiv)
Predicting pedestrians' trajectories is a crucial capability for autonomous vehicles' safe navigation, especially in spaces shared with pedestrians. Pedestrian motion in shared spaces is influenced by both the presence of vehicles and other pedestrians. Therefore, effectively modelling both pedestrian-pedestrian and pedestrian-vehicle interactions can increase the accuracy of the pedestrian trajectory prediction models. Despite the huge literature on ways to encode the effect of interacting agents on a pedestrian's predicted trajectory using deep-learning models, limited effort has been put into the effective selection of interacting agents. In the majority of cases, the interaction features used are mainly based on relative distances while paying less attention to the effect of the velocity and approaching direction in the interaction formulation. In this paper, we propose a heuristic-based process of selecting the interacting agents based on collision risk calculation. Focusing on interactions of potentially colliding agents with a target pedestrian, we propose the use of time-to-collision and the approach direction angle of two agents for encoding the interaction effect. This is done by introducing a novel polar collision grid map. Our results have shown predicted trajectories closer to the ground truth compared to existing methods (used as a baseline) on the HBS dataset.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 31 references that are in the map. First posted on arXiv 13 Aug 2023.
Builds on
12 of 31 references.
References · 12
- 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 690
- 2017DESIRE: Distant Future Prediction in Dynamic Scenes with Interacting Agentscited by 310
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- 2021LOKI: Long Term and Key Intentions for Trajectory Predictioncited by 57
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- 2021RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecastingcited by 36
- 2021Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Trackingcited by 35
- 2023Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Reviewcited by 18
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- 2020Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction using a Graph Vehicle-Pedestrian Attention Networkcited by 12
- 2022Learning the Pedestrian-Vehicle Interaction for Pedestrian Trajectory Predictioncited by 7
- 2020MCENET: Multi-Context Encoder Network for Homogeneous Agent Trajectory Prediction in Mixed Trafficcited by 4
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.