This paper characterizes safe following distances for on-road driving when vehicles can avoid collisions by either braking or by swerving into an adjacent lane. In particular, we focus on safety as defined in the Responsibility-Sensitive Safety (RSS) framework. We extend RSS by introducing swerve maneuvers as a valid response in addition to the already present brake maneuver. These swerve maneuvers use the more realistic kinematic bicycle model rather than the double integrator model of RSS. We show that these swerve maneuvers allow a vehicle to safely follow a lead vehicle more closely than the RSS braking maneuvers do. The use of the kinematic bicycle model is then validated by comparing these swerve maneuvers to swerves of a dynamic single-track model. The analysis in this paper can be used to inform both offline safety validation as well as safe control and planning.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Safe Swerve Maneuvers for Autonomous Driving


    Contributors:


    Publication date :

    2020-10-19


    Size :

    703602 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    SAFE SWERVE MANEUVERS FOR AUTONOMOUS DRIVING

    De Iaco, Ryan / Smith, Stephen L. / Czarnecki, Krzysztof | British Library Conference Proceedings | 2020


    Universally Safe Swerve Manoeuvres for Autonomous Driving

    De Iaco, Ryan / Smith, Stephen L. / Czarnecki, Krzysztof | ArXiv | 2020

    Free access

    HUMAN-LIKE PLANNING OF SWERVE MANEUVERS FOR AUTONOMOUS VEHICLES

    Gu, Tianyu / Dolan, John M. / Lee, Jin-Woo | British Library Conference Proceedings | 2016


    SWERVE-DRIVE ROBOT

    HALLER KEVIN NICOLAS / MEYERS WILLIAM MICHAEL / BIERSACK JOSEPH MICHAEL et al. | European Patent Office | 2024

    Free access

    Planning for Safe Abortable Overtaking Maneuvers in Autonomous Driving

    Palatti, Jiyo / Aksjonov, Andrei / Alcan, Gokhan et al. | IEEE | 2021