Cooperative interaction in traffic is vital for resolving a wide range of ambiguities arising from road users' actions. Autonomous vehicles are no exception and require the ability to understand the intention of road users and communicate with them in order to ensure their safety and maintain traffic flow. In this paper, we address the problem of traffic interaction by analyzing a large sample of pedestrians communicating with drivers. We highlight the ways pedestrians communicate and use a logistic regression model to identify what factors influence communication patterns of pedestrians and how. We also discuss practical challenges regarding the recognizing and understanding of pedestrians' intention and how our theoretical findings can help to solve them. Our analysis suggests that pedestrians predominantly rely on implicit communication cues such as stepping onto the road to transmit their intention of crossing. In addition, we found that the presence of traffic signal, street width, and pedestrian group size can influence the frequency and type of pedestrian communication, while factors such as pedestrians' age and gender did not show any significant impact.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Towards Social Autonomous Vehicles: Understanding Pedestrian-Driver Interactions


    Contributors:


    Publication date :

    2018-11-01


    Size :

    2773140 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Towards Collision-Free Probabilistic Pedestrian Motion Prediction for Autonomous Vehicles

    Li, Kunming / Shan, Mao / Eiffert, Stuart et al. | IEEE | 2022


    PEDESTRIAN BEHAVIOR PREDICTIONS FOR AUTONOMOUS VEHICLES

    RUSSELL JARED STEPHEN / DING KAI | European Patent Office | 2023

    Free access

    Pedestrian behavior predictions for autonomous vehicles

    RUSSELL JARED STEPHEN / DING KAI | European Patent Office | 2021

    Free access

    PEDESTRIAN BEHAVIOR PREDICTIONS FOR AUTONOMOUS VEHICLES

    RUSSELL JARED STEPHEN / DING KAI | European Patent Office | 2019

    Free access

    PEDESTRIAN BEHAVIOR PREDICTIONS FOR AUTONOMOUS VEHICLES

    RUSSELL JARED / DING KAI | European Patent Office | 2019

    Free access