Virtual Testing of Automated Driving Systems. A Survey on Validation Methods

Abstract

This paper surveys the state-of-the-art contributions supporting the validation of virtual testing toolchains for Automated Driving System (ADS) verification. The work builds upon the well-known limitations of physical testing while conceiving the virtual counterpart as a fundamental ingredient for the type-approval of high automation level ADS. The purpose of the research effort is to summarize computational tools, validation methodologies, and the corresponding fidelity levels delivered by state-of-the-art simulation toolchains. The ultimate goal is to establish how effectively simulation can play the role of a “virtual proving ground” for ADS certification independently from any specific ADS implementation/effectiveness. The contribution includes classic high-level validation approaches and modern specific computational tools that can be adopted depending on the type of data under analysis. Moreover, the investigation covers approaches embraced both within the scientific community and in technical regulations for the sake of completeness. Ultimately, we identified two high-level validation schema: integrated environment and subsystem-based solutions. In addition, we found that modeling and validating virtual sensors for ADS is the most lacking area from a subsystem-level approach. On the other side, the closed-loop interaction between the ADS and other virtual traffic participants makes it difficult to directly compare the experimental results with simulated generated evidence as the emergent behaviors of the ADS may amplify minor discrepancies between the environments.

Keywords

• Automated driving
• model validation
• simulation
• virtual testing