# Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles
###### Abstract  
Due to the rapid progress in the development of automated vehicles over the last decade, their market entry is getting closer. One of the remaining challenges is the safety assessment and type approval of automated vehicles, as conventional testing in the real world would involve an unmanageable mileage. Scenario-based testing using simulation is a promising candidate for overcoming this approval trap. Although the research community has recognized the importance of safeguarding in recent years, the quality of simulation models is rarely taken into account. Without investigating the errors and uncertainties of models, virtual statements about vehicle safety are meaningless. This paper describes a whole process combining model validation and safety assessment. It is demonstrated by means of an actual type-approval regulation that deals with the safety assessment of lane-keeping systems. Based on a thorough analysis of the current state-of-the-art, this paper introduces two approaches for selecting test scenarios. While the model validation scenarios are planned from scratch and focus on scenario coverage, the type-approval scenarios are extracted from measurement data based on a data-driven pipeline. The deviations between lane-keeping behavior in the real and virtual world are quantified using a statistical validation metric. They are then modeled using a regression technique and inferred from the validation experiments to the unseen virtual type-approval scenarios. Finally, this paper examines safety-critical lane crossings, taking into account the modeling errors. It demonstrates the potential of the virtual-based safeguarding process using exemplary simulations and real driving tests. automoons vehicles; data analysis; model validation; safety assessment; scenario extraction; simulation; traffic safety; type approval; virtual methods +
Footnote †: journal: Medical  
In recent years, the safety assessment of automated vehicles (AVs) has been very much addressed in the literature and large research projects [6; 7], as it is a huge challenge due to the complexity of the traffic environment. Conventional testing in the physical world struggles with higher automation levels, since it requires an enormous amount of mileage to prove that an AV is at least as safe as a human driver [8]. A promising solution is the scenario-based approach. It discards the major part of the mileage that lacks interesting actions and events and focuses instead on a selection of individual traffic situations [9]. There is a large number of publications that propose scenario generation approaches for safety assessment of AVs. Some extract the scenarios from driving data [10; 11], others generate the scenarios from scratch [12], for example using Design of Experiments (DoE) techniques [13]. While conventional tests are physically performed on the road, more recent publications use pure simulations for their proof of concept. They convince by cost and effort reduction, increased safety and possible parallelization through computing clusters. Nevertheless, the simulations have to be accompanied by model validation activities in order to achieve trustworthiness. The combination of model validation and virtual safeguarding leads to an overall virtual-based process. This process uses physical tests to assess the quality of the simulation during model validation in order to finally exploit the strength of the simulation in virtual safeguarding [14]. The current literature focuses on safeguarding and, within safeguarding, on the selection of scenarios. However, an overall virtual-based process including model validation and including the selection of additional validation scenarios is missing. We address these research gaps in this paper.