Testing of autonomous vehicles involves enormous challenges for the automotive industry. The number of real-world driving scenarios is extremely large, and choosing effective test scenarios is essential, as well as combining simulated and real world testing. We present an industrial workbench of tools and workflows to generate efficient and effective test scenarios for active safety and autonomous driving functions. The workbench is based on existing engineering tools, and helps smoothly integrate simulated testing, with real vehicle parameters and software. We aim to validate the workbench with real cases and further refine the input model parameters and distributions.

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Autonomous driving has become an important research area for road traffic, whereas testing of autonomous driving systems to ensure a safe and reliable operation remains an open challenge. Substantial real-world testing or massive driving data collection does not scale since the potential test scenarios in real-world traffic are infinite, and covering large shares of them in the test is impractical. Thus, critical ones have to be prioritized. We have developed an approach for critical test scenario identification and in this study, we implement the approach and validate it on two real autonomous driving systems from industry by integrating it into their tool-chain. Our main contribution in this work is the demonstration and validation of our approach for critical scenario identification for testing real autonomous driving systems.

Autonomous driving has become an important research area for road traffic, whereas testing of autonomous driving systems to ensure a safe and reliable operation, remains an open challenge. Substantial real-world testing or massive driving data collection does not scale, as the potential test scenarios in real-world traffic are infinite and covering large shares of them in test is impractical, thus critical ones have to be prioritized. In this study, we establish a systematic approach for critical test scenario identification with integrated tools and a workflow, to explore the most critical test scenarios and facilitate testing of the autonomous driving functions. We also demonstrate the effectiveness of our approach by using two real autonomous driving systems from the industry by collaborating with Volvo Cars. Our main contribution in this work is a feasible and complete tool-chain for critical test scenario identification that is general for testing different autonomous driving systems.

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