Pedestrian Trajectory Prediction

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The field predicts pedestrians’ future paths to help autonomous vehicles and robots navigate safely around people. Methods range from recurrent networks and Transformers to state-space models, often using social interaction graphs, scene maps, gaze, or intent cues; probabilistic approaches such as CVAEs and flow matching represent multiple possible futures. Evaluation commonly uses displacement errors on ETH/UCY and SDD, with newer work also testing driving datasets, dense crowds, and robot-collected data. A central challenge is balancing accurate forecasts with calibrated uncertainty, social and physical safety, robustness to occlusion and domain shift, and manageable computation.

Latest papers

36 papers6 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

Trajectory Prediction Method for person under WSN with Hierarchical Envelope Adversarial Domain Adaptation
Oct 15, 2026Wenqian Jiang, Qifeng Sun, Xiaosong Gao +4Pedestrian Trajectory Prediction Pedestrian Behavior Modeling
Guangxi Power Grid Co., Ltd., Nanning, Guangxi 530000, China · Chongqing Liangjiang Satellite Mobile Communication Co., Ltd., Chongqing 400004, China · School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
ReForesight: Occlusion-Robust Re-Identification and Future Occupancy Forecasting for Dynamic Semantic Mapping
Oct 12, 2026Didar Seyidov, Faith Ogunwoye, Hajira Amjad +3Query Re-Identification Trajectory Prediction
Skolkovo Institute of Science and Technology, Moscow, Russia · 168robotics, Moscow, Russia
Revisiting Numerical Forecasting Models for Language-Based Trajectory Prediction
Oct 6, 2026JunGyu Lee, Inhwan Bae, Hae-Gon JeonReward Modeling RL for Language Models
Yonsei University · DGIST
Probabilistic Pedestrian Forecasts from a Handheld Phone: World-Frame Heat Maps, Visual-Inertial Height Drift, and Evaluation without Ground Truth
Oct 3, 2026Danial SafaeiPedestrian Trajectory Prediction Probabilistic Forecasting
WMG, University of Warwick, Coventry, United Kingdom
Socialality Anchors: Towards Group-bounded Trajectory Prediction
Sep 29, 2026Ziqian Zou, Conghao Wong, Qinmu Peng +1Pedestrian Trajectory Prediction Multi-Agent Trajectory Prediction
Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
STRIDE: Automated Evaluation of Text-to-Trajectory Alignment across Diverse Contexts
Sep 28, 2026Wanchun Ni, Tao Qi, Leonel Aguilar +4Pedestrian Behavior Modeling Trajectory Generation
ETH Zurich · Beijing University of Posts and Telecommunications
Destination Support Restoration for Finite-Set Multimodal Trajectory Prediction
Sep 22, 2026Fengrui Liu, Jiajun Peng, Duo Peng +1Pedestrian Trajectory Prediction Multimodal Trajectory Prediction
School of Computer Science and Technology, East China Normal University, Shanghai, China · School of Data Science, University of Science and Technology of China, Hefei, China · School of Computer Science and Technology, Tongji University, Shanghai, China +1
GEAR: From Dynamic Encoding to Dynamic Activation in Social Trajectory Prediction
Sep 12, 2026Jiaheng Chen, Jiaxing Li, Leixia Wang +2Human Trajectory Prediction Pedestrian Trajectory Prediction
Software College Northeastern University Shenyang, China
MamMA: A Mamba-Based Pedestrian Trajectory Prediction Algorithm Considering Occupancy Map and Pedestrian Awareness States
Sep 7, 2026Juncen Long, Xiaofeng Jin, Gianluca Bardaro +2Pedestrian Behavior Modeling Pedestrian Trajectory Prediction
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
CrowdTraj: A Benchmark for Dense Crowd Trajectory Prediction in Realistic Crowded Environments
Sep 7, 2026Antonius Bima Murti Wijaya, Paul Henderson, Marwa MahmoudPedestrian Trajectory Prediction Multi-Object Tracking
School of Computing Science University of Glasgow University Avenue G12 8QQ, Glasgow, United Kingdom
EgoNeMo: Transferable Map of Pedestrian Dynamics via Egocentric LiDAR Scan
Sep 5, 2026Azusa Sawada, Allan Wang, Hideo Saito +1Pedestrian Behavior Modeling Domain Generalization
NEC Corporation · Miraikan - The National Museum of Emerging Science and Innovation · Keio University +1
Safety-oriented pedestrian trajectory prediction at urban intersections using time-to-collision and crossing-zone context
Aug 31, 2026Erel Avineri, Yftach Gil, Yehudit ApersteinPedestrian Trajectory Prediction Trajectory Prediction
Energy and Power Systems Engineering, Afeka Academic College of Engineering, Tel Aviv, Israel · Intelligent Systems, Afeka Academic College of Engineering, Tel Aviv, Israel
Unified Pedestrian Path Prediction Using Inverse Reinforcement Learning
Aug 16, 2026Šimon Sukup, Ariyan Bighashdel, Pavol JancuraPedestrian Trajectory Prediction Trajectory Prediction
Eindhoven University of Technology, Eindhoven, The Netherlands · Delft University of Technology, Delft, The Netherlands
Social Graph Mamba: Forecasting Pedestrian Movements Based on Social Context
Aug 11, 2026Hong-Son Nguyen, Yen-Chen LiuPedestrian Trajectory Prediction Pedestrian Behavior Modeling
Department of Mechanical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
A Unified Framework for Trajectory Prediction with Explicit Planning and Reaction Decomposition
Aug 6, 2026Jiaheng Chen, Jiaxing Li, Tinghe Zhang +1Pedestrian Trajectory Prediction Multi-Agent Trajectory Prediction
Software College, Northeastern University Shenyang, China
ACME: A Multi-Cultural, Multi-Embodiment Social-Navigation Dataset
Jul 24, 2026Shashank Rao Marpally, Allan Wang, Atharva Ghotavadekar +35Pedestrian Trajectory Prediction Robotic Data Collection
School of Computing, National University of Singapore · Miraikan - The National Museum of Emerging Science and Innovation · George Mason University +7
TSCA-Net: Temporal-Spatial Clique Attention for Interpretable Multimodal Pedestrian Trajectory Prediction
Jul 11, 2026Md Mustafizur Rahman, Guangchao Yang, A F M Abdun Noor +3Pedestrian Behavior Modeling Pedestrian Trajectory Prediction
College of Computer Science, Chongqing University, Chongqing, China · Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh
TrajRS: Towards Certified Robustness in Pedestrian Trajectory Prediction
Jun 27, 2026Liang Zhang, Gaojie Jin, Yao Shi +4Randomized Smoothing Pedestrian Trajectory Prediction
Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, China · University of Chinese Academy of Sciences, China · Hangzhou Institute for Advanced Study, UCAS, China +1
Three-Step Hierarchical Transformer for Multi-Pedestrian Trajectory Prediction
Jun 22, 2026Raphaël Delécluse, Hazem Wannous, Laurent Grisoni +1Pedestrian Behavior Modeling Pedestrian Trajectory Prediction
IMT Nord Europe, University of Lille, CNRS UMR 9189 - CRIStAL, F-59000 Lille, France · University of Lille, CNRS UMR 9189 - CRIStAL, F-59000 Lille, France · Explain, F-59000 Lille, France
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
Learn to Quantify Social Interaction with Constraints for Pedestrian Walking
Jun 16, 2026Xiaodan ShiPedestrian Behavior Modeling Pedestrian Trajectory Prediction
Department of Computer and Systems Sciences, Stockholm University
MUSCLE-NET: Predicted-Multiscale-Aware Network for Pedestrian Trajectory Forecasting
May 30, 2026Yu Liu, Ming Huang, Xiao Ren +3Pedestrian Behavior Modeling Pedestrian Trajectory Prediction
Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, School of Automation and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech), Shenzhen 518055, China · Department of Mechanical Engineering, City University of Hong Kong, Hong Kong SAR, China
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.
On Improving Multimodal Pedestrian Trajectory Prediction with CVAE: A Study on Benchmark and Robot Data
May 18, 2026Yuzhou Liu, Cristina Olaverri-MonrealPedestrian Behavior Modeling Pedestrian Trajectory Prediction
Dept. Intelligent Transport Systems, Johannes Kepler University Linz, Altenberger Straße 69, 4040 Linz, Austria.
Social-Mamba: Socially-Aware Trajectory Forecasting with State-Space Models
May 14, 2026Po-Chien Luan, Wuyang Li, Yang Gao +1Pedestrian Behavior Modeling Pedestrian Trajectory Prediction
EPFL, Switzerland
JACoP: Joint Alignment for Compliant Multi-Agent Prediction
May 12, 2026Qingze Liu, Alen Mrdovic, Danrui Li +3Pedestrian Trajectory Prediction Markov Random Fields
Rutgers University, New Brunswick · The College of New Jersey
DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation
May 4, 2026Danil Tokhchukov, Veronika Morozova, Gonzalo FerrerSimultaneous Localization and Mapping Dynamic SLAM
Applied AI Institute, Moscow, Russia.
RankFormer: A Propose-then-Select Transformer for Multi-Agent Multimodal Trajectory Prediction
Apr 8, 2026Diyi Liu, Zihan Niu, Tu Xu +2Multi-Agent Trajectory Prediction Multimodal Trajectory Prediction
College of Metropolitan Transportation, Beijing University of Technology, Beijing, China · Zhejiang Police College, Zhejiang, China
ART: Adaptive Relational Transformer for Pedestrian Trajectory Prediction with Temporal-Aware Relations
Apr 4, 2026Ruochen Li, Ziyi Chang, Junyan Hu +3Pedestrian Trajectory Prediction Pedestrian Behavior Modeling
Department of Computer Science, Durham University, UK · the School of Computing and Information Systems, Singapore Management University, Singapore
STDDN: A Physics-Guided Deep Learning Framework for Crowd Simulation
Apr 3, 2026Zijin Liu, Xu Geng, Wenshuai Xu +3Pedestrian Trajectory Prediction Physics-Based Simulation
School of Computer Science and Engineering, Beihang University, China · Beijing Digital Native Digital City Research Center, Beijing, China · School of Software, Beihang University, China

Oct 15, 2026J/K move · Enter open · S save

Trajectory Prediction Method for person under WSN with Hierarchical Envelope Adversarial Domain Adaptation

Wenqian Jiang, Qifeng Sun, Xiaosong Gao, Xin Zou+3

Guangxi Power Grid Co., Ltd., Nanning, Guangxi 530000, China · Chongqing Liangjiang Satellite Mobile Communication Co., Ltd., Chongqing 400004, China · School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China

Pedestrian Trajectory Prediction Pedestrian Behavior Modeling Multi-Agent Trajectory Prediction Domain-Adversarial Learning

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Pedestrian trajectory prediction holds significant applications across autonomous driving, intelligent surveillance, and smart cities. However, the complexity and unpredictability of pedestrian interactions make this task a persistent challenge. Current models face two common limitations: 1) Consider only a single type of social coupling. This introduces redundant interactions. More critically, it fails to account for the varying nature of trajectory coupled across different scenarios and between different pedestrians. Consequently, models cannot deeply adapt to diverse scenes. 2) Inadequate domain shift handling. Although very few methods address domain shift, they rely on statistical criterion-based domain distribution alignment. Such approaches exhibit strong dependency on predefined statistical metrics. This leads to significant limitations in complex and dynamic environments. To address these problems, a Hierarchical Envelope Adversarial Domain Adaptation with Loosely-Tightly coupled model (HEADA-LTCTPM) is proposed. First, an envelope sample construction mechanism was proposed. It constructs envelope samples and extends them into graph structures. Secondly, an adversarial domain adaptation module was proposed. This integrates both local and global domain adaptation strategies. It dynamically adapts to diverse coupled relationships across scenarios. These innovations collectively enhance prediction accuracy and robustness. In the experimental section, two representative public datasets were employed for validation and comprehensive comparisons with six relevant baseline algorithms were conducted. Results demonstrate that our model achieves significantly higher accuracy compared to existing methods, with the ADE and FDE metrics reduced by 17.6% and 19.1% respectively. The time overhead meets practical requirements, which verifies the effectiveness of our key innovations.