Highly automated and fully autonomous vehicles are much more likely to be accepted if they react in the same way as human drivers do, especially in a hybrid traffic situation, which allows autonomous vehicles and human-driven vehicles to share the same road. This paper proposes a human-like motion planning model to represent how human drivers assess environments and operate vehicles in signalized intersections. The developed model consists of a pedestrian intention detection model, gap detection model, and vehicle control model. These three submodels are individually responsible for situation assessment, decision making, and action, and also depend on each other in the process of motion planning. In addition, these submodels are constructed and learned on the basis of human drivers' data collected from real traffic environments. To verify the effectiveness of the proposed motion planning model, we compared the proposed model with actual human driver and pedestrian data. The experimental results showed that our proposed model and actual human driver behaviors are highly similar with respect to gap acceptance in intersections.


    Access

    Download


    Export, share and cite



    Title :

    Human-like motion planning model for driving in signalized intersections


    Contributors:


    Publication date :

    2017




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Signalized Intersections—Planning Methodology

    Prassas, Elena S. / P. Roess, Roger | Springer Verlag | 2020


    Signalized intersections

    Miller, A.J. | Engineering Index Backfile | 1968



    Distracted Driving Effects on Headways at Signalized Intersections

    Sherif, Bassel / Abou-Senna, Hatem / Radwan, Essam | Transportation Research Record | 2022


    Eco-driving at signalized intersections using V2I communication

    Rakha, H. / Kamalanathsharma, R. K. | IEEE | 2011