Due to the large number of electronic components and software systems they contain, automated vehicles are required to cover an almost infinite (and in reality barely achievable) number of test kilometres in order to obtain vehicle approval in the conventional way. One way of reducing the amount of test effort is to employ virtual testing. However, it is then essential that the results obtained are robust and equivalent to those that would be produced under realistic conditions. For this reason, several research projects have been initiated that are concerned with creating various test environments that enable reproducible vehicle testing to be performed in a mixed reality (partly virtual and partly real-life) environment – for instance the hazard-free evaluation of vehicle-pedestrian interaction. This approach aims not only at successfully virtualising parts of the vehicle tests hitherto performed in real life but also in identifying problems in the human-machine interface by integrating future consumers at an early stage (in the form of test participants) to suggest solutions for promoting the acceptance of automated vehicles by society as well as by individuals.
The subproject is dedicated to the identification of critical driving scenarios, taking into account the probability of occurrence and the hazard potential. The goal of this subproject is, among other things, to define and validate an evaluable method for the identification of critical driving maneuvers within a predefined scenario. For this purpose it is necessary to achieve complete test area coverage. To reduce the complexity of the simulation, both database-based (maneuver catalogue Euro NCAP, etc.) and stochastic methods (Monte Carlo, genetic algorithms, etc.) are used to optimize the simulation effort. Within the project, the two scenarios, car park and inner-city intersection, will be investigated in detail.
The subproject considers the interaction of HAF/VAF functions in urban traffic, especially a parking garage with mixed traffic as well as an intersection scenario with oncoming traffic, pedestrians, cyclists, weather conditions and possibly existing ITS infrastructure. In this setting, various test methods are to be developed and validated on the basis of the "mixed reality test environment" and, in coordination with the other subprojects, the implications of highly automated driving for various road users are to be investigated. For this purpose, an architecture for networking real (sensors, control units) and simulated components (simulation SW, HiL test bench) will be developed and, step by step, extended from pure simulation-based investigation (networking of hexapod, HiL, AR/VR system, weather models, human models) to real application (road test with test vehicle in the hall or parking garage).
In the SAVe research project, the applicant THI professors Huber (Test Methods/Effectiveness Analysis), Botsch (Autonomous Driving/AI Methods) and Facchi (Car2X Systems) have set the first stone in the creation of a prospective impact analysis for urban traffic space. The particularly accident-prone scenario of turning left without right of way was modelled for manual traffic (reference) taking into account stochastic variations in the behavior of all road users (including pedestrians, cyclists, e-scooters) and implemented in the specially developed Monte Carlo simulation environment "THIREKS". An automated vehicle was modelled and integrated as a treatment measure. However, the conservative environment detection purely with vehicle-internal sensor technology poses a risk for the driving function in inner-city traffic due to potential visibility obstructions. Therefore, the simulation of environment detection was extended by an overhead sensor model to estimate the performance gain of infrastructure measures (Car2X). In this sense, the overhead sensor is also a measure whose effectiveness must be evaluated. Findings have already been presented and published at international conferences.