The proposed learning-task formulations improve prediction performance across the selected benchmark datasets compared with the standard supervised-learning formulation and demonstrate that reformulating the decision process and training objective can improve an advanced pedestrian trajectory prediction architecture.
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.
The proposed framework improves the safety and crossing efficiency of autonomous vehicle decision-making at unsignalized intersections and introduces a composite prioritized replay mechanism into the Twin Delayed Deep Deterministic Policy Gradient algorithm.
Shufeng Wang, Yuhang Wang, Yongxin Lei et al.· Machines· 0 citations
Abstract. Understanding pedestrian behaviors is the foundation of simulation for space planning. However, conventional behavior modeling methods are insufficient for learning detailed interactions, and deep learning methods often lack interpretability. This study aims to develop a pedestrian trajectory modeling approach based on discovering causal relationships among pedestrians. The proposed method consists of two parts: analyzing causal relationships among pedestrians using statistical causal discovery methods and predicting trajectories using attention-based deep learning methods. The first part employs a semi-parametric method to identify the causal relationships underlying observed pedestrian behavior and construct a spatial-temporal graph based on these causal relationships. The second part primarily uses the graph attention network to learn interactions among pedestrians. The experimental results demonstrate that the proposed method achieves a good balance between prediction accuracy and interpretability, while also identifying limitations, including at low-density scenes and due to causal model assumptions.
Wen-Xin Qiu, T. Fuse· ISPRS Annals of the Photogra...· 0 citations
Efficient and reliable path planning remains a core challenge for autonomous
vehicles operating in dynamic and crowded environments. Although Deep
Reinforcement Learning (DRL) has shown considerable potential in autonomous
decision-making, it still faces challenges such as insufficient feature
extraction, sparse rewards, and low obstacle avoidance efficiency in complex
scenarios. To address these issues, this paper proposes an end-to-end path
planning framework, PPO-ICM-Attn. Built upon the Proximal Policy Optimization
(PPO) algorithm, the framework incorporates a dual-channel attention
convolutional neural network module (Attention-CNN) to enhance spatial and
semantic understanding of dynamic obstacles, and introduces an Intrinsic
Curiosity Module (ICM) to promote active exploration in sparse-reward settings.
Furthermore, a reactive avoidance reward function based on velocity-obstacle
theory is designed and embedded to achieve real-time proactive collision
avoidance in highly dynamic environments. Experiments are conducted in a
semi-structured dynamic crowd scenario constructed on the GAZEBO simulation
platform. The results demonstrate that PPO-ICM-Attn achieves significant
improvements in key metrics such as path success rate, travel time, and path
efficiency compared to baseline methods like A*+DWA and standard DRL. Although
the gap remains in path efficiency compared to A*+DWA, the proposed method
exhibits superior robustness and navigation performance overall, validating its
effectiveness in complex dynamic environments.
Shiquan Shen, Jiahao Liu, Zheng Chen et al.· SAE technical paper series· 0 citations
Autonomous driving in complex urban environments requires trajectory planning that balances safety, efficiency, and human-like behavior. Although imitation learning (IL) can capture expert driving patterns from large-scale demonstrations, existing IL-based planners still face challenges in safety-critical scenarios and long-tail traffic distributions. Meanwhile, optimization-based planners provide explicit constraint handling but are often separated from upstream learning modules, limiting their ability to jointly improve trajectory generation and planning feasibility. To address these issues, we propose a hybrid trajectory planning framework that integrates IL-based multimodal trajectory proposal with differentiable optimization. In the proposed framework, an IL backbone generates candidate ego trajectories and surrounding-agent predictions, while a differentiable optimizer refines the selected trajectory using multi-objective cost functions with learnable weights related to safety, efficiency, and comfort. This design enables optimization objectives and constraints to provide gradient feedback to the upstream planning network, improving the consistency between candidate generation and downstream planning objectives. In addition, we introduce a surrounding agent centric data augmentation strategy that reuses real-world trajectories of surrounding vehicles as additional expert demonstrations, thereby enriching complex interaction and long-tail scenarios without extra data collection. Closed-loop experiments on the nuPlan benchmark show that the proposed method achieves a composite score of 94.04, outperforming PLUTO’s 93.14 while using only 30% of the training data. The results demonstrate that the proposed framework improves closed-loop planning performance, trajectory feasibility, and data efficiency under complex urban driving scenarios.
Shihao Zhang, Ziyu Song, Zhaochen Xia et al.· Proceedings of the Instituti...· 0 citations
A Stochastic Gating Decoder for multimodal latent variable sampling, adaptively fusing kinematics and data-driven paths to capture driver intention uncertainty while maintaining kinematic consistency is introduced.
Accurate pedestrian trajectory prediction is fundamental for safe navigation of autonomous robots and vehicles in crowded environments. Although graph-based methods such as Social-STGCNN efficiently model social interactions among pedestrians, they largely ignore static environmental constraints such as walls and obstacles, which can lead to physically infeasible predictions. In this paper, we propose an extended trajectory prediction method that integrates local and global environmental information into Social-STGCNN via Multi-Head Cross-Attention, and incorporates environmental constraint learning through contrastive MapNCE and collision avoidance EnvCol losses. To generate diverse prediction while maintaining scene consistency, we further introduce a low-dimensional trajectory representation based on Singular Value Decomposition and Adaptive Anchors derived from K-means clustering. We evaluate the proposed method on the ETH/UCY benchmark across five scenes using Average Displacement Error (ADE), Final Displacement Error (FDE), obstacle collision rate, and inference time. The results show that the proposed method consistently improves the FDE over Social-STGCNN, while ADE increases. The obstacle collision rate also increases, revealing a trade-off between endpoint accuracy and full-trajectory environmental compliance.
Yuka Takahara, Yuka Kato· International Conference on...· 0 citations
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