Agreeing to cross: How drivers and pedestrians communicate

By Amir Rasouli, Iuliia Kotseruba, John K. Tsotsos. 2017

223 citations recorded by the source corpus.

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 rural 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, explicit driver's reaction or structure of the crosswalk.

Subfields: Computer Vision and Pattern Recognition, Global and Planetary Change, Safety, Risk, Reliability and Quality

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Citations in the corpus

Multi-Agent Common Knowledge Reinforcement Learning (2018, background): “, observing each other’s actions or their effects (Heider & Simmel, 1944; Rasouli et al., 2017).”
Communicating Awareness and Intent in Autonomous Vehicle-Pedestrian Interaction (2018, background): “While much of the research emphasis thus far is on the technological challenges associated with autonomous driving (such as motion planning, localization, perception, and control), a relatively less explored domain is autonomous vehicle-pedestrian interaction [22].”
PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction (2019, background): “This is particularly important when dealing with pedestrians at the point of crossing as they exhibit highly variable behavior patterns [26].”
RT-GENE: Real-Time Eye Gaze Estimation in Natural Environments (2018, methodology): “Gaze estimation has also been applied in domains other than computer vision, such as navigation for eye gaze controlled wheelchairs [12,46], detection of non-verbal behaviors of drivers [16, 47], and inferring the object of interest in human-robot interactions [14].”
Autonomous Vehicles That Interact With Pedestrians: A Survey of Theory and Practice (2018, methodology): “[39], and we believe that there is a need for more in the future.”
Explanations in Autonomous Driving: A Survey (2021, background): “Physical context includes the traffic signs [166], signals [167], road structures [164], and weather conditions [159].”
Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction (2017, methodology): “We note that owing to the interactive nature of this scenario we elected to collect our own dataset using the simulator rather than use existing real-world data (e.g., [38], [39], [40]).”