# Analysis and Prediction of Pedestrian Crosswalk Behavior during Automated Vehicle Interactions

arXiv: https://arxiv.org/abs/2003.09996 · HTML: https://mlsanity.com/paper/2003.09996/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2020.

## Built on by (most cited first, 6 of 6)

- [Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review](https://mlsanity.com/paper/2211.05120/) (2022)
- [Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings](https://mlsanity.com/paper/2304.08260/) (2023)
- [Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion](https://mlsanity.com/paper/2203.02489/) (2022)
- [Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized Crossings](https://mlsanity.com/paper/2404.09574/) (2024)
- [A Real-time Evaluation Framework for Pedestrian's Potential Risk at Non-Signalized Intersections Based on Predicted Post-Encroachment Time](https://mlsanity.com/paper/2404.15635/) (2024)
- [Predicting Pedestrian Crossing Behavior in Germany and Japan: Insights into Model Transferability](https://mlsanity.com/paper/2412.03689/) (2024)

## Abstract (arXiv)

For safe navigation around pedestrians, automated vehicles (AVs) need to plan their motion by accurately predicting pedestrians trajectories over long time horizons. Current approaches to AV motion planning around crosswalks predict only for short time horizons (1-2 s) and are based on data from pedestrian interactions with human-driven vehicles (HDVs). In this paper, we develop a hybrid systems model that uses pedestrians gap acceptance behavior and constant velocity dynamics for long-term pedestrian trajectory prediction when interacting with AVs. Results demonstrate the applicability of the model for long-term (> 5 s) pedestrian trajectory prediction at crosswalks. Further we compared measures of pedestrian crossing behaviors in the immersive virtual environment (when interacting with AVs) to that in the real world (results of published studies of pedestrians interacting with HDVs), and found similarities between the two. These similarities demonstrate the applicability of the hybrid model of AV interactions developed from an immersive virtual environment (IVE) for real-world scenarios for both AVs and HDVs.