Pedestrian Trajectory Prediction

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5 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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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
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
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
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 Pedestrian Behavior Modeling
Applied AI Institute, Moscow, Russia.
Rethinking Gaussian Trajectory Predictors: Calibrated Uncertainty for Safe Planning
Mar 11, 2026Fatemeh Cheraghi Pouria, Mahsa Golchoubian, Katherine Driggs-CampbellPedestrian Trajectory Prediction Safe Motion Planning
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820 USA · Department of Mathematical and Computational Sciences, University of Toronto, Toronto, Canada

Oct 6, 2026, cs.CVJ/K move · Enter open · S save

Revisiting Numerical Forecasting Models for Language-Based Trajectory Prediction

JunGyu Lee, Inhwan Bae, Hae-Gon Jeon

Yonsei University · DGIST

Reward Modeling RL for Language Models Trajectory Generation Pedestrian Trajectory Prediction Trajectory Prediction

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Language-based trajectory predictors represent coordinates as discrete tokens and learn auxiliary tasks such as destination and group reasoning. This formulation enables the model to capture behavioral intent and social context beyond coordinate dynamics alone. However, token-level objectives provide only indirect guidance for continuous coordinate-space dynamics. To address this limitation, we introduce MoRE (Mixture of Reward Experts), a refinement framework that transfers numerical forecasting priors into a pretrained language-based predictor through reinforcement learning. Five frozen numerical predictors provide complementary coordinate-level knowledge of motion and interactions. Their predictions are converted into expert rewards and combined through an uncertainty-weighted consensus that penalizes disagreement. A ground-truth reward anchors the prediction to the target trajectory. To focus refinement on difficult cases, MoRE refines the policy using the top 1% of training samples ranked by predictive entropy. Expert predictions are computed once and cached before PPO training, so the experts are not run during policy updates or inference. In this way, MoRE combines the contextual modeling of the language-based predictor with coordinate-level feedback from numerical experts. On ETH-UCY, MoRE reduces ADE from 0.22 to 0.20 m and FDE from 0.32 to 0.29 m. Relative to the base policy, ADE decreases by 17.9% on SDD and 12.7% on NBA. On ETH-UCY, MoRE also reduces collision rates and better matches ground-truth pedestrian spacing, without increasing measured inference memory or latency. The project page is available at https://jungyu0413.github.io/MoRE/.

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