Advanced driver-assistance systems (ADAS) have matured over the past few decades with the dedication to enhance user experience and gain a wider market penetration. However, personalization features, as an approach to make the current technologies more acceptable and trustworthy for users, have been gaining momentum only very recently. In this work, we aim to learn personalized longitudinal driving behaviors via a Gaussian Process (GP) model. The proposed method learns from individual driver’s naturalistic car-following behavior, and outputs a desired acceleration profile that suits the driver’s preference. The learned model, together with a predictive safety filter that prevents rear-end collision, is used as a personalized adaptive cruise control (PACC) system. Numerical experiments show that GP-based PACC (GP-PACC) can almost exactly reproduce the driving styles of an intelligent driver model. Additionally, GP-PACC is further validated by human-in-the-loop experiments on the Unity game engine-based driving simulator. Trips driven by GP-PACC and two other baseline ACC algorithms with driver override rates are recorded and compared. Results show that on average, GP-PACC reduces the human override duration by 60% and 85% as compared to two widely-used ACC models, respectively, which shows the great potential of GP-PACC in improving driving comfort and overall user experience.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Gaussian Process-Based Personalized Adaptive Cruise Control


    Beteiligte:
    Wang, Yanbing (Autor:in) / Wang, Ziran (Autor:in) / Han, Kyungtae (Autor:in) / Tiwari, Prashant (Autor:in) / Work, Daniel B. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.11.2022


    Format / Umfang :

    2032176 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Personalized Adaptive Cruise Control via Gaussian Process Regression

    Wang, Yanbing / Wang, Ziran / Han, Kyungtae et al. | IEEE | 2021


    Student-T process personalized adaptive cruise control

    GUPTA ROHIT / WANG YANBING / WANG ZIRAN et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    STUDENT-T PROCESS PERSONALIZED ADAPTIVE CRUISE CONTROL

    GUPTA ROHIT / WANG YANBING / WANG ZIRAN et al. | Europäisches Patentamt | 2023

    Freier Zugriff


    Personalized self-adaptive cruise control system

    HE RUI / YANG NINGNING / ZHANG SUMIN | Europäisches Patentamt | 2020

    Freier Zugriff