mlsanityData to 7 Oct 2026

Learning the Pedestrian-Vehicle Interaction for Pedestrian Trajectory Prediction

arXiv 2202.05334 ↗First posted 10 Feb 2022cs.CV cs.AI cs.ROcited by 7

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Abstract (arXiv)

In this paper, we study the interaction between pedestrians and vehicles and propose a novel neural network structure called the Pedestrian-Vehicle Interaction (PVI) extractor for learning the pedestrian-vehicle interaction. We implement the proposed PVI extractor on both sequential approaches (long short-term memory (LSTM) models) and non-sequential approaches (convolutional models). We use the Waymo Open Dataset that contains real-world urban traffic scenes with both pedestrian and vehicle annotations. For the LSTM-based models, our proposed model is compared with Social-LSTM and Social-GAN, and using our proposed PVI extractor reduces the average displacement error (ADE) and the final displacement error (FDE) by 7.46% and 5.24%, respectively. For the convolutional-based models, our proposed model is compared with Social-STGCNN and Social-IWSTCNN, and using our proposed PVI extractor reduces the ADE and FDE by 2.10% and 1.27%, respectively. The results show that the pedestrian-vehicle interaction influences pedestrian behavior, and the models using the proposed PVI extractor can capture the interaction between pedestrians and vehicles, and thereby outperform the compared methods.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 10 Feb 2022.

Builds on

16 of 24 references.

References · 16

  1. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  2. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  3. 2018Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networkscited by 687
  4. 2020Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Predictioncited by 324
  5. 2018SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraintscited by 296
  6. 2019Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networkscited by 228
  7. 2018TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactionscited by 82
  8. 2019Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANscited by 78
  9. 2019Conditional Generative Neural System for Probabilistic Trajectory Predictioncited by 76
  10. 2017Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detectioncited by 42
  11. 2019Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMscited by 34
  12. 2018Scene-LSTM: A Model for Human Trajectory Predictioncited by 24
4 more
  1. 2019RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputscited by 16
  2. 2021SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUscited by 16
  3. 2020Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction using a Graph Vehicle-Pedestrian Attention Networkcited by 12
  4. 2021Social-IWSTCNN: A Social Interaction-Weighted Spatio-Temporal Convolutional Neural Network for Pedestrian Trajectory Prediction in Urban Traffic Scenarioscited by 7

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 2; 2025: 2.

7 papers.

  1. 2023Social-Transmotion: Promptable Human Trajectory Predictioncited by 33
  2. 2023Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Reviewcited by 18
  3. 2025Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happencited by 5
  4. 2025Social-Pose: Enhancing Trajectory Prediction with Human Body Posecited by 5
  5. 2023Polar Collision Grids: Effective Interaction Modelling for Pedestrian Trajectory Prediction in Shared Space Using Collision Checkscited by 2
  6. 2024Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized Crossingscited by 1
  7. 2024Predicting Pedestrian Crossing Behavior in Germany and Japan: Insights into Model Transferabilitycited by 0