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Statistical Model Checking Applied on Perception and Decision-making Systems for Autonomous Driving

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Abstract

Automotive systems must undergo a strict process of validation before their release on commercial vehicles. The currently-used methods are not adapted to latest autonomous systems, which increasingly use probabilistic approaches. Furthermore , real life validation, when even possible, often imply costs which can be obstructive. New methods for validation and testing are necessary. In this paper, we propose a generic method to evaluate complex automotive-oriented systems for automation (perception, decision-making, etc.). 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 feeding the values of these metrics during a large number of simulations, and the properties representing the KPIs to our statistical model checker, we evaluate the probability to meet the KPIs. We applied this method to two different subsystems of an autonomous vehicles: a perception system (CMCDOT framework) and a decision-making system. An overview of the two system is given to understand related validation challenges. We show that the methodology is suited to efficiently evaluate some critical properties of automotive systems, but also their limitations.
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Dates and versions

hal-01888556 , version 1 (05-10-2018)

Identifiers

  • HAL Id : hal-01888556 , version 1

Cite

Jean Quilbeuf, Mathieu Barbier, Lukas Rummelhard, Christian Laugier, Axel Legay, et al.. Statistical Model Checking Applied on Perception and Decision-making Systems for Autonomous Driving. PPNIV 2018 - 10th Workshop on Planning, Perception and Navigation for Intelligent Vehicles, Oct 2018, Madrid, Spain. pp.1-8. ⟨hal-01888556⟩
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