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Volume: 34 | Article ID: AVM-110
Paving the way for certified performance: Quality assessment and rating of simulation solutions for ADAS and autonomous driving
  DOI :  10.2352/EI.2022.34.16.AVM-110  Published OnlineJanuary 2022

Simulation plays a key role in the development of Advanced Driver Assist Systems (ADAS) and Autonomous Driving (AD) stacks. A growing number of simulation solutions addresses development, test, and validation of these systems at unprecedented scale and with a large variety of features. Transparency with respect to the fitness of features for a given task is often hard to come by, and sorting marketing claims from product performance facts is a challenge. New players – on users’ and vendors’ side – will lead to further diversification. Evolving standards, regulatory requirements, verification and validation practices etc. will add to the list of criteria that might be relevant for identifying the best-fit solution for a given task. There is a need to evaluate and measure a solution’s compliance with these criteria on the basis of objective test scenarios in order to quantitatively compare different simulation solutions. The goal shall be a standardized catalog of tests which simulation solutions have to undergo before they can be considered fit (or certified) for a certain use case. Here, we propose a novel evaluation framework and detailed testing procedure as a first step towards quantifying simulation quality. We will illustrate the use of this method with results from an initial implementation, thereby highlighting the top-level properties Determinism, Real-time Capability, and Standards Compliance. We hope to raise awareness that simulation quality is not a nice-to-have feature but rather a central aspect for the whole spectrum of stakeholders, and that it needs to be quantified for the development of safe autonomous driving.

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Marius Dupuis, "Paving the way for certified performance: Quality assessment and rating of simulation solutions for ADAS and autonomous drivingin Proc. IS&T Int’l. Symp. on Electronic Imaging: Autonomous Vehicles and Machines,  2022,  pp 110-1 - 110-6,

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