![]() Methods: The required size of test suites for scenario-based approval of a so-called Autobahn-Chauffeur (SAE Level 3) is analyzed for an exemplary set of scenarios. In this study, the required size of test suites for scenario-based testing and the potential to reduce it by functional decomposition are quantified for the first time. ![]() Objective: Particular testing by functional decomposition of the automated driving function can potentially contribute to reducing the effort of validating highly automated driving functions. The motivation of this framework is to build a validation dataset to generate many critical concrete scenarios. The generated scenarios are represented in OpenX format to reuse them in the SUT evaluation easily. This method enables fully automatic scenario extraction compared to similar approaches in this area. This paper proposes a novel scenario extraction method to capture the lane change scenarios using point-cloud data and object tracking information. It is essential to capture the scenarios from the real world to encode the behaviour of actual traffic participants. The scenario-based testing in simulation requires the realistic behaviour of the traffic participants to assess the System Under Test (SUT). ![]() As it is focused directly on the relevant critical road situations, it can reduce the effort required in testing. The research community and industry have widely accepted scenario-based testing in the last few years. Exploring numerous, diverse and crucial scenarios is a time-consuming and expensive approach. Road testing is essential before the deployment, but scenarios are repeatable, and it's hard to collect challenging events. The modern autonomous vehicles will undoubtedly include machine learning and probabilistic techniques that add significant complexity to the traditional verification and validation methods. Autonomous Vehicles (AV)'s wide-scale deployment appears imminent despite many safety challenges yet to be resolved.
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