A crucial aspect that automotive systems need to face before being used in everyday life is the validation of their components. To this end, standard exhaustive methods are inappropriate to validate the probabilistic algorithms widely used in this field and new solutions need to be adopted. In this paper, we present an approach based on Statistical Model Checking (SMC) to validate the collision risk assessment generated by a probabilistic perception system. SMC represents an intermediate between test and exhaustive verification by relying on statistics and evaluates the probability of meeting appropriate Key Performance Indicators (KPIs) based on a large number of simulations. As a case study, a state-of-the-art algorithm is adopted to obtain the collision risk estimations. This algorithm provides an environment representation through Bayesian probabilistic occupancy grids and estimates positions in the near future of every static and dynamic part of the grid. Based on these estimations, time-to-collision probabilities are then associated with the corresponding cells. Using CARLA simulator, a large number of execution traces are then generated, considering both collisions and almost-collisions in realistic urban scenarios. Real experiments complete the analysis and show the reliability of the simulation results.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Probabilistic Collision Risk Estimation for Autonomous Driving: Validation via Statistical Model Checking


    Contributors:


    Publication date :

    2020-10-19


    Size :

    4157442 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    PROBABILISTIC COLLISION RISK ESTIMATION FOR AUTONOMOUS DRIVING: VALIDATION VIA STATISTICAL MODEL CHECKING

    Paigwar, Anshul / Baranov, Eduard / Renzaglia, Alessandro et al. | British Library Conference Proceedings | 2020


    Model-based probabilistic collision detection in autonomous driving

    Althoff, M. / Stursberg, O. / Buss, M. | Tema Archive | 2009


    Model-Based Probabilistic Collision Detection in Autonomous Driving

    Althoff, M. / Stursberg, O. / Buss, M. | IEEE | 2009



    Validation of Perception and Decision-Making Systems for Autonomous Driving via Statistical Model Checking

    Barbier, Mathieu / Renzaglia, Alessandro / Quilbeuf, Jean et al. | IEEE | 2019