#GITHUB OPENDRIVE VERIFICATION#
The results can be used to generate motions of the other primary vehicles to accelerate the verification of AVs in simulations and controlled experiments. In this paper, we propose an accelerated evaluation approach for AVs.
Due to the low exposure to safety-critical scenarios, N-FOTs are time consuming and expensive to conduct. A widely used evaluation approach is the Naturalistic-Field Operational Test (N-FOT), which tests prototype vehicles directly on the public roads. Thus, the findings have to be validated in further studies.Īutomated vehicles (AVs) must be thoroughly evaluated before their release and deployment. However, this study is based on assumptions and only a small set of exemplary scenarios. This means that the size of the required test suite can be reduced by 95–99% by particular testing compared to scenario-based testing of the complete system.Ĭonclusions: The reduction potential is a valuable contribution to overcome the parameter space explosion during the validation of highly automated driving. Results: The combination of all effects leads to a reduction in the test suites’ size by a factor between 20 and 130, depending on the required test coverage. The effects that lead to a reduction in the parameter space for particular testing of the decomposed driving function are analyzed and the potential to reduce the validation effort is estimated by comparing the resulting test suite sizes for both methods.
Based on those assumptions, the size of the test suites for testing the complete system is quantified. Based on studies of data from failure analyses in other domains, the possible range for the required test coverage is narrowed down and suitable discretization steps, as well as ranges for the influence parameters, are assumed. 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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