Pedestrian Intention Prediction

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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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TrajFusionNet+: Transformer-Based Prediction of Pedestrian Crossing Intention via Fusion of Trajectory Representations and Scene Graphs
Sep 11, 2026François G. Landry, Moulay A. AkhloufiPedestrian Intention Prediction Multimodal Fusion
Perception, Robotics and Intelligent Machines Research Group (PRIME) Department of Computer Science, Universit´e de Moncton, Canada
Unified Vision-Centric Pedestrian Crossing Action Prediction via Adaptive Patch Projection and Proactive Spatial Rectification
Sep 7, 2026Yao Tian, Le Yang, Binglu WangPedestrian Intention Prediction VLMs for Autonomous Driving
School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China · School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China
UniTraffic-Agent: Unified Traffic Video Reasoning for AI City Challenge 2026 Track 3 with Two Out-of-Domain Evaluations
Aug 13, 2026Peng Li, Qianqian Xu, Shilong Bao +2Pedestrian Intention Prediction Intelligent Transportation Systems
School of Computer Science and Technology, University of Chinese Academy of Sciences (UCAS), Beijing, China · State Key Laboratory of AI Safety, Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS), Beijing, China · Beijing Academy of Artificial Intelligence (BAAI), Beijing, China +1
Adaptive Cross-Modal Fusion with Sparse Attention for Pedestrian Crossing Intention Prediction
Jul 14, 2026Md Mahfuzur Rahman, Pengzhan Zhou, A F M Abdun Noor +5Pedestrian Intention Prediction Autonomous Driving
College of Computer Science, Chongqing University, Chongqing, China · Department of Software Engineering, Daffodil International University, Bangladesh · Faculty of Information Science & Technology, Multimedia University, Malaysia
Learning Robot Visual Navigation in Crowds via Intention-Aware Scene Representations
Jun 24, 2026Han Bao, Bingyi Xia, Hanjing Ye +5Pedestrian Intention Prediction Robot Navigation
Shenzhen Key Laboratory of Robotics Perception and Intelligence, Department of Electronic and Electrical Engineering, SUSTech, Shenzhen, China · Xiamen Key Laboratory of Visual Perception Technology and Application, and the Algorithm Research Center at Reconova Information Technology Co., Ltd. in Xiamen, China · Research Institute of MA&EI, Peng Cheng Laboratory, Shenzhen, China +1
Where Will They Go? Modelling Multimodal Pedestrian Manoeuvres from Ego-centric Videos
Jun 17, 2026Yuxuan Xie, Nicolas Pugeault, Chongfeng Wei +2Pedestrian Intention Prediction Pedestrian Behavior Modeling
School of Computing Science, University of Glasgow, Glasgow, United Kingdom · James Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom · Department of Computer Science, Durham University, Durham, United Kingdom
Decoding Pedestrian Crossing Intention from Egocentric Vision via Vision Language Models
Jun 8, 2026Danya Li, Xiang Su, Yan Feng +1Pedestrian Intention Prediction VLM Adaptation
Department of Technology, Management, and Economics, Technical University of Denmark, Denmark · Department of Computer Science and Department of Agricultural Sciences, University of Helsinki, Finland · Department of Transport and Planning, Delft University of Technology, Netherlands
PEDESTRIANQA: A Benchmark for Vision-Language Models on Pedestrian Intention and Trajectory Prediction
May 23, 2026Naman Mishra, Shankar Gangisetty, C. V. JawaharVLM Evaluation Pedestrian Intention Prediction
CVIT, IIIT-Hyderabad, India.
Pedestrian-Aware LLM-Driven Behavioral Planning for Autonomous Vehicles
May 16, 2026Aidana Baimbetova, Haruki Yonekura, Hamada Rizk +1Pedestrian Intention Prediction Autonomous Driving Planning
1The University of Osaka, Japan · 2RIKEN Center for Computational Science, Japan · 3Tanta University, Egypt
ESIA: An Energy-Based Spatiotemporal Interaction-Aware Framework for Pedestrian Intention Prediction
Apr 26, 2026Yanping Wu, Meiting Dang, Lin Wu +3Pedestrian Intention Prediction Pedestrian Behavior Modeling
James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, United Kingdom · School of Computing Science, University of Glasgow, Glasgow, G12 8QQ, United Kingdom

Sep 11, 2026, cs.CVJ/K move · Enter open · S save

TrajFusionNet+: Transformer-Based Prediction of Pedestrian Crossing Intention via Fusion of Trajectory Representations and Scene Graphs

François G. Landry, Moulay A. Akhloufi

Perception, Robotics and Intelligent Machines Research Group (PRIME) Department of Computer Science, Universit´e de Moncton, Canada

Pedestrian Intention Prediction Multimodal Fusion

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The pedestrian crossing intention task involves predicting whether pedestrians are likely to cross the road from the point of view of an autonomous vehicle. We introduce TrajFusionNet+, a novel transformer-based model for pedestrian crossing intention prediction. TrajFusionNet+ combines sequential and visual representations of pedestrian trajectory with a graph-based representation of the scene context in order to predict pedestrian crossing intention. The proposed architecture builds upon our previous model, TrajFusionNet, and comprises three branches: a Sequence Attention Module (SAM), which processes a sequential representation of past and predicted pedestrian trajectories; a Visual Attention Module (VAM), which utilizes a visual representation of the pedestrian trajectories by overlaying observed and predicted bounding boxes onto scene images; and a Graph Attention Module (GAM), which extracts pedestrian-centric graphs from segmented scene images and captures the relational dependencies between pedestrians and traffic elements. TrajFusionNet+ achieves improved state-of-the-art performance on the two most widely used pedestrian crossing intention datasets, PIE and JAAD. Furthermore, we introduce a new evaluation protocol in which models are trained jointly on the PIE and JAAD datasets but evaluated separately on each. Under this setting, TrajFusionNet+ demonstrates superior generalization compared to existing approaches.

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