A Computationally Efficient Model for Pedestrian Motion Prediction
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Abstract (arXiv)
We present a mathematical model to predict pedestrian motion over a finite horizon, intended for use in collision avoidance algorithms for autonomous driving. The model is based on a road map structure, and assumes a rational pedestrian behavior. We compare our model with the state-of-the art and discuss its accuracy, and limitations, both in simulations and in comparison to real data.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Mar 2018.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.
5 papers.
- 2023What Truly Matters in Trajectory Prediction for Autonomous Driving?cited by 17
- 2025Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directionscited by 14
- 2024Intention-aware Denoising Diffusion Model for Trajectory Predictioncited by 11
- 2023DRL-Based Trajectory Tracking for Motion-Related Modules in Autonomous Drivingcited by 6
- 2026Multi-Person Human Motion Forecasting in Complex Scenescited by 0
- 2026Multi-Person Human Motion Forecasting in Complex Scenes27 Aug 2026
- 2025Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions5 Mar 2025
- 2024Intention-aware Denoising Diffusion Model for Trajectory Prediction14 Mar 2024
- 2023DRL-Based Trajectory Tracking for Motion-Related Modules in Autonomous Driving30 Aug 2023
- 2023What Truly Matters in Trajectory Prediction for Autonomous Driving?27 Jun 2023