# Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings

arXiv: https://arxiv.org/abs/2304.08260 · HTML: https://mlsanity.com/paper/2304.08260/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [Autonomous Vehicles that Interact with Pedestrians: A Survey of Theory and Practice](https://mlsanity.com/paper/1805.11773/) (2018)
- [Is the Pedestrian going to Cross? Answering by 2D Pose Estimation](https://mlsanity.com/paper/1807.10580/) (2018)
- [Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction](https://mlsanity.com/paper/1910.09077/) (2019)
- [Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation](https://mlsanity.com/paper/2207.05853/) (2022)
- [Analysis and Prediction of Pedestrian Crosswalk Behavior during Automated Vehicle Interactions](https://mlsanity.com/paper/2003.09996/) (2020)

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

- [Pedestrian Intention Prediction via Vision-Language Foundation Models](https://mlsanity.com/paper/2507.04141/) (2025)
- [Diving Deeper Into Pedestrian Behavior Understanding: Intention Estimation, Action Prediction, and Event Risk Assessment](https://mlsanity.com/paper/2407.00446/) (2024)
- [Predicting and Analyzing Pedestrian Crossing Behavior at Unsignalized Crossings](https://mlsanity.com/paper/2404.09574/) (2024)
- [Feature Importance in Pedestrian Intention Prediction: A Context-Aware Review](https://mlsanity.com/paper/2409.07645/) (2024)
- [Eye Gaze-Informed and Context-Aware Pedestrian Trajectory Prediction in Shared Spaces with Automated Shuttles: A Virtual Reality Study](https://mlsanity.com/paper/2603.19812/) (2026)
- [Predicting Pedestrian Crossing Behavior in Germany and Japan: Insights into Model Transferability](https://mlsanity.com/paper/2412.03689/) (2024)

## Abstract (arXiv)

Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving. Pedestrian crossing behavior is influenced by various interaction factors, including time to arrival, pedestrian waiting time, the presence of zebra crossing, and the properties and personality traits of both pedestrians and drivers. However, these factors have not been fully explored for use in predicting interaction outcomes. In this paper, we use machine learning to predict pedestrian crossing behavior including pedestrian crossing decision, crossing initiation time (CIT), and crossing duration (CD) when interacting with vehicles at unsignalized crossings. Distributed simulator data are utilized for predicting and analyzing the interaction factors. Compared with the logistic regression baseline model, our proposed neural network model improves the prediction accuracy and F1 score by 4.46% and 3.23%, respectively. Our model also reduces the root mean squared error (RMSE) for CIT and CD by 21.56% and 30.14% compared with the linear regression model. Additionally, we have analyzed the importance of interaction factors, and present the results of models using fewer factors. This provides information for model selection in different scenarios with limited input features.