Unified Pedestrian Path Prediction Using Inverse Reinforcement Learning

• URL: http://arxiv.org/abs/2608.15929v1
• Date: Sun, 16 Aug 2026 20:51:48 GMT
• Title: Unified Pedestrian Path Prediction Using Inverse Reinforcement Learning
• Abstract summary: This paper adapts the Spatial-Temporal Graph Attention Network (STGAT) to a unified pedestrian path prediction framework.<n>The resulting formulations support deterministic and policies, one-time and sequential decision-making, and reinforcement-learning algorithms.
• Score: 2.0263791972068628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 including REINFORCE and proximal policy optimization. The proposed learning-task formulations improve prediction performance across the selected benchmark datasets compared with the standard supervised-learning formulation. These results demonstrate that reformulating the decision process and training objective can improve an advanced pedestrian trajectory prediction architecture and may provide a path toward improving other graph-based prediction models.

Related papers

• Think2Go: Generative Next POI Recommendation with LLM Reasoning [57.37706163990998]
  Next Point-of-Interest (POI) recommendation task focuses on mining user behavioral preference patterns from historical check-ins to provide personalized suggestions for the next destination.<n>Existing methods rely on shallow contextual information and handcrafted feature interactions to predict the next POI.<n>We propose Think2Go, a novel generative next POI recommendation framework, which enhances the model's comprehension of SID representations and explores diverse spatial-temporal patterns via test-time computational scaling.
  arXiv  Detail & Related papers  (2026-07-31T03:52:20Z)
• PAWS: Preference Learning with Advantage-Weighted Segments [61.590521943273]
  We propose a segment-based preference learning method that performs policy updates directly using segment-level advantage functions.<n>Experiments on simulated robotic manipulation and locomotion tasks demonstrate that PAWS consistently outperforms existing PbRL approaches.
  arXiv  Detail & Related papers  (2026-06-10T12:00:17Z)
• ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving [64.42138266293202]
  ResAD is a Normalized Residual Trajectory Modeling framework.<n>It reframes the learning task to predict the residual deviation from an inertial reference.<n>On the NAVSIM benchmark, ResAD achieves a state-of-the-art PDMS of 88.6 using a vanilla diffusion policy.
  arXiv  Detail & Related papers  (2025-10-09T17:59:36Z)
• Adaptive Conformal Prediction Intervals Over Trajectory Ensembles [50.31074512684758]
  Future trajectories play an important role across domains such as autonomous driving, hurricane forecasting, and epidemic modeling.<n>We propose a unified framework based on conformal prediction that transforms sampled trajectories into calibrated prediction intervals with theoretical coverage guarantees.
  arXiv  Detail & Related papers  (2025-08-18T21:14:07Z)
• Enhanced Route Planning with Calibrated Uncertainty Set [7.7856491832713415]
  We introduce the Conformalized Quantile Regression for Graph Autoencoders (CQR-GAE), which leverages the conformal prediction technique to offer a coverage guarantee.<n>We demonstrate the effectiveness of our approach by applying the CQR-GAE model to a real-world traffic scenario.
  arXiv  Detail & Related papers  (2025-03-13T06:31:42Z)
• Motion Forecasting via Model-Based Risk Minimization [13.524251552252489]
  We propose a novel sampling method applicable to trajectory prediction based on the predictions of multiple models.<n>We first show that conventional sampling based on predicted probabilities can degrade performance due to missing alignment between models.<n>By using state-of-the-art models as base learners, our approach constructs diverse and effective ensembles for optimal trajectory sampling.
  arXiv  Detail & Related papers  (2024-09-16T09:03:28Z)
• Certified Human Trajectory Prediction [66.1736456453465]
  We propose a certification approach tailored for trajectory prediction that provides guaranteed robustness.<n>To mitigate the inherent performance drop through certification, we propose a diffusion-based trajectory denoiser and integrate it into our method.<n>We demonstrate the accuracy and robustness of the certified predictors and highlight their advantages over the non-certified ones.
  arXiv  Detail & Related papers  (2024-03-20T17:41:35Z)
• Towards Safe and Reliable Autonomous Driving: Dynamic Occupancy Set Prediction [12.336412741837407]
  This study introduces a novel method for Dynamic Occupancy Set (DOS) prediction, it effectively combines advanced trajectory prediction networks with a DOS prediction module. The innovative contributions of this study include the development of a novel DOS prediction model specifically tailored for navigating complex scenarios.
  arXiv  Detail & Related papers  (2024-02-29T17:36:39Z)
• Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction [15.454206825258169]
  Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Recent deep learning-based prediction approaches mainly utilize information like trajectory history and interactions between pedestrians. This paper proposes a graph transformer structure to improve prediction performance.
  arXiv  Detail & Related papers  (2024-01-10T01:50:29Z)
• Acceleration in Policy Optimization [50.323182853069184]
  We work towards a unifying paradigm for accelerating policy optimization methods in reinforcement learning (RL) by integrating foresight in the policy improvement step via optimistic and adaptive updates. We define optimism as predictive modelling of the future behavior of a policy, and adaptivity as taking immediate and anticipatory corrective actions to mitigate errors from overshooting predictions or delayed responses to change. We design an optimistic policy gradient algorithm, adaptive via meta-gradient learning, and empirically highlight several design choices pertaining to acceleration, in an illustrative task.
  arXiv  Detail & Related papers  (2023-06-18T15:50:57Z)
• RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction [12.945951957551317]
  We propose RESET, which combines a new metric-driven algorithm for trajectory set generation with a graph-based encoder. For unconditional prediction, RESET achieves comparable performance to a regression-based approach. For conditional prediction, RESET achieves reasonable results with late fusion of the planned trajectory.
  arXiv  Detail & Related papers  (2023-04-12T13:39:09Z)
• The Importance of Prior Knowledge in Precise Multimodal Prediction [71.74884391209955]
  Roads have well defined geometries, topologies, and traffic rules. In this paper we propose to incorporate structured priors as a loss function. We demonstrate the effectiveness of our approach on real-world self-driving datasets.
  arXiv  Detail & Related papers  (2020-06-04T03:56:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.