Abstract

New features are introduced in vehicle to assist the driver and in the future proposed a full autonomous driving. The vehicle equipped with such systems must be safe by design in very large range of situations. Validation by testing at proving ground or open road are necessary and made as usual but can’t cover an extended range of situations because it becomes too long and too expensive. The use of simulation the only affordable solution to explore the huge number of situations which can be encountered by the vehicle within the system’s ODD. Classically, simulation is used during design phase to support definition and optimization of ADAS in link with other systems. Many methods and tools integrated to the engineering processes have been developed with success. But the characteristics of them are not fully adapted for direct application to certification activities. The main reason is that high fidelity of models is mandatory to ensure credibility of the validation provided by a massive simulation. Methods need to be reinforced. A simulation plan will reproduce many driving situations based on a model of the ego vehicle immerged in a scenario. The collection of scenarios with their parameters related to infrastructure, other vehicles, obstacle and environment conditions must be managed in a way of covering all the ODD attributes, all accident scenarios and all other relevant scenarios within the ODD, in particular for extreme and rare situation which are usually named “corner or edge cases”. The vehicle model is an assembly of subsystems. The vehicle becomes a cybersystem including multiphysics, control and communication. New methodologies are proposed for assembling such complex systems with a high level of reliability. At the end, a high level of fidelity can be obtained by filling the gap between numerical results and testing signals with technics of “hybrid twin” based on AI.