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

Mathieu Barbier1, Alessandro Renzaglia2, Jean Quilbeuf3, Lukas Rummelhard3, Anshul Paigwar4, Christian Laugier2, Axel Legay5, Javier Ibañez-Guzmán6, Olivier Simonin7

  • 1Inria-CHROMA , Renault
  • 2INRIA
  • 3Inria
  • 4Institut national de recherche en informatique et en automatique
  • 5Université Catholique de Louvain
  • 6Renault
  • 7INSA de Lyon

Details

13:30 - 17:30 | Sun 9 Jun | Room L109 | SuFT10.5

Session: FRCA-IAV: Formal Methods vs. Machine Learning Approaches for Reliable Navigation

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.