# Learning the Pedestrian-Vehicle Interaction for Pedestrian Trajectory Prediction

arXiv: https://arxiv.org/abs/2202.05334 · HTML: https://mlsanity.com/paper/2202.05334/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 16 of 16)

- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction](https://mlsanity.com/paper/2002.11927/) (2020)
- [SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints](https://mlsanity.com/paper/1806.01482/) (2018)
- [Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks](https://mlsanity.com/paper/1907.03395/) (2019)
- [TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions](https://mlsanity.com/paper/1812.04767/) (2018)
- [Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs](https://mlsanity.com/paper/1904.09507/) (2019)
- [Conditional Generative Neural System for Probabilistic Trajectory Prediction](https://mlsanity.com/paper/1905.01631/) (2019)
- [Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection](https://mlsanity.com/paper/1702.05552/) (2017)
- [Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs](https://mlsanity.com/paper/1912.01118/) (2019)
- [Scene-LSTM: A Model for Human Trajectory Prediction](https://mlsanity.com/paper/1808.04018/) (2018)
- [RobustTP: End-to-End Trajectory Prediction for Heterogeneous Road-Agents in Dense Traffic with Noisy Sensor Inputs](https://mlsanity.com/paper/1907.08752/) (2019)
- [SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs](https://mlsanity.com/paper/2102.06361/) (2021)
- [Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction using a Graph Vehicle-Pedestrian Attention Network](https://mlsanity.com/paper/2006.12906/) (2020)
- [Social-IWSTCNN: A Social Interaction-Weighted Spatio-Temporal Convolutional Neural Network for Pedestrian Trajectory Prediction in Urban Traffic Scenarios](https://mlsanity.com/paper/2105.12436/) (2021)

## Built on by (most cited first, 7 of 7)

- [Social-Transmotion: Promptable Human Trajectory Prediction](https://mlsanity.com/paper/2312.16168/) (2023)
- [Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review](https://mlsanity.com/paper/2308.06419/) (2023)
- [Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen](https://mlsanity.com/paper/2505.21743/) (2025)
- [Social-Pose: Enhancing Trajectory Prediction with Human Body Pose](https://mlsanity.com/paper/2507.22742/) (2025)
- [Polar Collision Grids: Effective Interaction Modelling for Pedestrian Trajectory Prediction in Shared Space Using Collision Checks](https://mlsanity.com/paper/2308.06654/) (2023)
- [Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized Crossings](https://mlsanity.com/paper/2404.09574/) (2024)
- [Predicting Pedestrian Crossing Behavior in Germany and Japan: Insights into Model Transferability](https://mlsanity.com/paper/2412.03689/) (2024)

## 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.