The ultimate goal of self-driving technologies is to offer a safe and human-like driving experience. As one of the most important enabling functionalities, trajectory planning has been extensively studied from the perspective of safety. However, human-like trajectory planning on curved roads has rarely been studied. In this paper, we characterize and model human driving using extensive experimental driving collected on an urban curved road with 30 participants (10 experienced and 20 novice drivers) and five vehicles of different types. Differential global positioning system (GPS) is used to measure vehicle positions in high precision. We study factors that affect the driving trajectory, including vehicle speed, road curvature, and sight distance. We find that the human drivers typically do not follow lane centerline and the human-driven trajectories are very different from planners like rapidly exploring random tree (RRT). To generate human-like driving trajectory, we develop a data-driven trajectory model using general regression neural network (GRNN). The model was validated in various cases with promising performance.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Human-Like Trajectory Planning on Curved Road: Learning From Human Drivers


    Contributors:
    Li, Aoxue (author) / Jiang, Haobin (author) / Li, Zhaojian (author) / Zhou, Jie (author) / Zhou, Xinchen (author)


    Publication date :

    2020-08-01


    Size :

    4114596 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Modeling Driving Behavior of Human Drivers for Trajectory Planning

    Ziegler, Christoph / Willert, Volker / Adamy, Jürgen | DataCite | 2022


    Modeling Driving Behavior of Human Drivers for Trajectory Planning

    Ziegler, Christoph / Willert, Volker / Adamy, Jurgen | IEEE | 2022


    A Human-like Trajectory Planning Method by Learning from Naturalistic Driving Data

    He, Xu / Xu, Donghao / Zhao, Huijing et al. | IEEE | 2018


    Parking Like a Human: A Direct Trajectory Planning Solution

    Liu, Wei / Li, Zhiheng / Li, Li et al. | IEEE | 2017


    Trajectory Planning for Automated Lane-Change on a Curved Road for Collision Avoidance

    Zhong, Hong / Zhuang, Weichao / Qian, Yahui et al. | SAE Technical Papers | 2019