One of the expectations from fully or partially automated vehicles is to never cause an accident and actively avoid dangerous situations. However, an automated vehicle may not be able to avoid all collisions, e.g., collisions caused by other vehicles. Hence, it is important for the system developers to understand the boundary case scenarios where an autonomous vehicle can no longer avoid a collision. In this paper, an automated test generation approach that utilizes Rapidly-exploring Random Trees is presented to explore these boundary scenarios. An important advantage of the approach is the openness of the test scenarios: one can set the road geometry and the number of adversarial objects and let the system search for interesting trajectories and environment parameters. A cost function is proposed which guides the test generation toward almost-avoidable collisions or near-misses.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Rapidly-exploring Random Trees for Testing Automated Vehicles


    Contributors:


    Publication date :

    2019-10-01


    Size :

    931688 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Rapidly-exploring Random Trees-based Test Generation for Autonomous Vehicles

    Tuncali, Cumhur Erkan / Fainekos, Georgios | ArXiv | 2019

    Free access

    Curvature Aware Motion Planning with Closed-Loop Rapidly-exploring Random Trees

    van den Berg, Berend / Brito, Bruno / Alirezaei, Mohsen et al. | IEEE | 2021


    Information-Rich Path Planning with General Constraints Using Rapidly-Exploring Random Trees

    Levine, Daniel / Luders, Brandon / How, Jonathan | AIAA | 2010


    Information-Rich Path Planning with General Constraints using Rapidly-Exploring Random Trees

    Levine, D. / Luders, B. / How, J. et al. | British Library Conference Proceedings | 2010


    Research on Automatic Lane Change Path Planning Based on Improved Rapidly-Exploring Random Trees

    Hua, Hang / chen, huan’ming | British Library Conference Proceedings | 2021