Unified Pedestrian Path Prediction Using Inverse Reinforcement Learning
Pedestrian path prediction is crucial for enhancing the safety of autonomous vehicles and advanced driver-assistance systems. Previous studies explored different learning-task formulations for pedestrian path prediction and compared these formulations using shallow neural networks, but did not extend this analysis to more complex deep-learning models. This paper adapts the Spatial-Temporal Graph Attention Network (STGAT) to a unified pedestrian path prediction framework and introduces state and action definitions specific to STGAT. The resulting formulations support deterministic and stochastic policies, one-time and sequential decision-making, and reinforcement-learning algorithms includ...
| Authors | Šimon Sukup, Ariyan Bighashdel, Pavol Jancura |
|---|---|
| Primary category | cs.AI |
| Published | 16 August 2026 |
| Last updated at source | 16 August 2026 |
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