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

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Abstract

Automotive systems must undergo a strict process of validation before their release on commercial vehicles. With the increased use of probabilistic approaches in autonomous systems, standard validation methods are not applicable to this end. Furthermore, real life validation, when even possible, implies costs which can be obstructive. New methods for validation and testing are thus necessary. In this paper, we propose a generic method to evaluate complex probabilistic frameworks for autonomous driving. The method is based on Statistical Model Checking (SMC), using specifically defined Key Performance Indicators (KPIs), as temporal properties depending on a set of identified metrics. By studying the behavior of these metrics during a large number of simulations via our statistical model checker, we finally evaluate the probability for the system to meet the KPIs. We show how this method can be applied to two different subsystems of an autonomous vehicle: a perception system and a decision-making approach. An overview of these two systems is given to understand related validation challenges. Extensive validation results are then provided for the decision-making case.
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Dates and versions

hal-02127889 , version 1 (13-05-2019)

Identifiers

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Mathieu Barbier, Alessandro Renzaglia, Jean Quilbeuf, Lukas Rummelhard, Anshul Paigwar, et al.. Validation of Perception and Decision-Making Systems for Autonomous Driving via Statistical Model Checking. IV 2019 - 30th IEEE Intelligent Vehicles Symposium, Jun 2019, Paris, France. pp.252-259, ⟨10.1109/IVS.2019.8813793⟩. ⟨hal-02127889⟩
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