# Agreeing to Cross: How Drivers and Pedestrians Communicate

arXiv: https://arxiv.org/abs/1702.03555 · HTML: https://mlsanity.com/paper/1702.03555/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2017.

## Built on by (most cited first, 23 of 23)

- [Explanations in Autonomous Driving: A Survey](https://mlsanity.com/paper/2103.05154/) (2021)
- [PedFormer: Pedestrian Behavior Prediction via Cross-Modal Attention Modulation and Gated Multitask Learning](https://mlsanity.com/paper/2210.07886/) (2022)
- [TrouSPI-Net: Spatio-temporal attention on parallel atrous convolutions and U-GRUs for skeletal pedestrian crossing prediction](https://mlsanity.com/paper/2109.00953/) (2021)
- [A Novel Benchmarking Paradigm and a Scale- and Motion-Aware Model for Egocentric Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2310.10424/) (2023)
- [Pedestrian Simulation: A Review](https://mlsanity.com/paper/2102.03289/) (2021)
- [Do Pedestrians Pay Attention? Eye Contact Detection in the Wild](https://mlsanity.com/paper/2112.04212/) (2021)
- [Ordinal-ResLogit: Interpretable Deep Residual Neural Networks for Ordered Choices](https://mlsanity.com/paper/2204.09187/) (2022)
- [Pedestrian Intention Prediction via Vision-Language Foundation Models](https://mlsanity.com/paper/2507.04141/) (2025)
- [CARLA-BSP: a simulated dataset with pedestrians](https://mlsanity.com/paper/2305.00204/) (2023)
- [ACIT: Attention-Guided Cross-Modal Interaction Transformer for Pedestrian Crossing Intention Prediction](https://mlsanity.com/paper/2511.20020/) (2025)
- [Modeling 3D Pedestrian-Vehicle Interactions for Vehicle-Conditioned Pose Forecasting](https://mlsanity.com/paper/2602.08962/) (2026)
- [Context-empowered Visual Attention Prediction in Pedestrian Scenarios](https://mlsanity.com/paper/2210.16933/) (2022)
- [Pedestrian and Passenger Interaction with Autonomous Vehicles: Field Study in a Crosswalk Scenario](https://mlsanity.com/paper/2312.07606/) (2023)
- [Pedestrian intention prediction in Adverse Weather Conditions with Spiking Neural Networks and Dynamic Vision Sensors](https://mlsanity.com/paper/2406.00473/) (2024)
- [MMHU: A Massive-Scale Multimodal Benchmark for Human Behavior Understanding](https://mlsanity.com/paper/2507.12463/) (2025)
- [Towards Everyday Virtual Reality through Eye Tracking](https://mlsanity.com/paper/2203.15703/) (2022)
- [Pedestrian Crossing Intent Classification From Event-Based Vision Using Convolutional Spiking Neural Networks With Temporal Augmentation](https://mlsanity.com/paper/2609.13328/) (2026)
- [A taxonomy of strategic human interactions in traffic conflicts](https://mlsanity.com/paper/2109.13367/) (2021)
- [Intend-Wait-Perceive-Cross: Exploring the Effects of Perceptual Limitations on Pedestrian Decision-Making](https://mlsanity.com/paper/2302.03816/) (2023)
- [Comparison of Pedestrian Prediction Models from Trajectory and Appearance Data for Autonomous Driving](https://mlsanity.com/paper/2305.15942/) (2023)
- [ARCANE-PedSynth: Synthetic Multi-Pedestrian Datasets with Behavioural Crossing Annotations](https://mlsanity.com/paper/2605.24950/) (2026)
- [Adaptive Cross-Modal Fusion with Sparse Attention for Pedestrian Crossing Intention Prediction](https://mlsanity.com/paper/2607.12293/) (2026)
- [LLM-Driven Autonomous Vehicles Inherit Human Driver Biases in Pedestrian Yielding: Results and Implications From A New Benchmark](https://mlsanity.com/paper/2609.00192/) (2026)

## Abstract (arXiv)

The contribution of this paper is twofold. The first is a novel dataset for studying behaviors of traffic participants while crossing. Our dataset contains more than 650 samples of pedestrian behaviors in various street configurations and weather conditions. These examples were selected from approx. 240 hours of driving in the city, suburban and urban roads. The second contribution is an analysis of our data from the point of view of joint attention. We identify what types of non-verbal communication cues road users use at the point of crossing, their responses, and under what circumstances the crossing event takes place. It was found that in more than 90% of the cases pedestrians gaze at the approaching cars prior to crossing in non-signalized crosswalks. The crossing action, however, depends on additional factors such as time to collision (TTC), explicit driver's reaction or structure of the crosswalk.