mlsanityData to 14 Oct 2026

Pedestrian Intention and Trajectory Prediction in Unstructured Traffic Using IDD-PeD

arXiv 2506.22111 ↗First posted 27 Jun 2025cs.CV cs.HCcited by 1

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Abstract (arXiv)

With the rapid advancements in autonomous driving, accurately predicting pedestrian behavior has become essential for ensuring safety in complex and unpredictable traffic conditions. The growing interest in this challenge highlights the need for comprehensive datasets that capture unstructured environments, enabling the development of more robust prediction models to enhance pedestrian safety and vehicle navigation. In this paper, we introduce an Indian driving pedestrian dataset designed to address the complexities of modeling pedestrian behavior in unstructured environments, such as illumination changes, occlusion of pedestrians, unsignalized scene types and vehicle-pedestrian interactions. The dataset provides high-level and detailed low-level comprehensive annotations focused on pedestrians requiring the ego-vehicle's attention. Evaluation of the state-of-the-art intention prediction methods on our dataset shows a significant performance drop of up to 15%, while trajectory prediction methods underperform with an increase of up to 1208 MSE, defeating standard pedestrian datasets. Additionally, we present exhaustive quantitative and qualitative analysis of intention and trajectory baselines. We believe that our dataset will open new challenges for the pedestrian behavior research community to build robust models. Project Page: https://cvit.iiit.ac.in/research/projects/cvit-projects/iddped

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 31 of its 56 references that are in the map. First posted on arXiv 27 Jun 2025.

Builds on

31 of 56 references.

References · 31

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  9. 2020Spatiotemporal Relationship Reasoning for Pedestrian Intent Predictioncited by 71
  10. 2021Stepwise Goal-Driven Networks for Trajectory Predictioncited by 62
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  12. 2022Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graphcited by 53
19 more
  1. 2020Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNscited by 48
  2. 2021Predicting Pedestrian Crossing Intention with Feature Fusion and Spatio-Temporal Attentioncited by 48
  3. 2020BiTraP: Bi-directional Pedestrian Trajectory Prediction with Multi-modal Goal Estimationcited by 47
  4. 2019Real-time Intent Prediction of Pedestrians for Autonomous Ground Vehicles via Spatio-Temporal DenseNetcited by 28
  5. 2020Multi-Modal Hybrid Architecture for Pedestrian Action Predictioncited by 23
  6. 2020RNN-based Pedestrian Crossing Prediction using Activity and Pose-related Featurescited by 21
  7. 2021Is attention to bounding boxes all you need for pedestrian action prediction?cited by 21
  8. 2021PSI: A Benchmark for Human Interpretation and Response in Traffic Interactionscited by 17
  9. 2021Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centerscited by 16
  10. 2021Coupling Intent and Action for Pedestrian Crossing Behavior Predictioncited by 14
  11. 2024IDD-X: A Multi-View Dataset for Ego-relative Important Object Localization and Explanation in Dense and Unstructured Trafficcited by 13
  12. 2023A Novel Benchmarking Paradigm and a Scale- and Motion-Aware Model for Egocentric Pedestrian Trajectory Predictioncited by 12
  13. 2022IDD-3D: Indian Driving Dataset for 3D Unstructured Road Scenescited by 11
  14. 2023Evaluating Pedestrian Trajectory Prediction Methods with Respect to Autonomous Drivingcited by 11
  15. 2020PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3Dcited by 6
  16. 2023TBD Pedestrian Data Collection: Towards Rich, Portable, and Large-Scale Natural Pedestrian Datacited by 6
  17. 2023Pedestrian Trajectory Prediction Using Dynamics-based Deep Learningcited by 4
  18. 2019Stochastic Sampling Simulation for Pedestrian Trajectory Predictioncited by 3
  19. 2024JaywalkerVR: A VR System for Collecting Safety-Critical Pedestrian-Vehicle Interactionscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026PEDESTRIANQA: A Benchmark for Vision-Language Models on Pedestrian Intention and Trajectory Predictioncited by 0