Model in the Loop Testing and Validation of Embedded Autonomous Driving Algorithms

This paper presents a Model in the Loop (MiL) framework to validate embedded autonomous driving (AD) and advanced driver assistant systems (ADAS) algorithms development. Recently, it has been recognized in the autonomous driving industry that simulation based testing is an efficient method to validate ADAS/AD functionalities complementing with physical testing. Coupled with AD algorithms such as perception, planning and control, an MiL toolchain can be executed and analyzed with various traffic scenarios and different algorithm parameters. This saves time and cost before actual vehicle test. Our contribution is twofold. First, we demonstrate our developed MiL toolchain combining high fidelity simulation models of vehicle, traffic and physics-based sensors with ADAS/AD functionalities closed loop algorithms. The solutions are implemented with both realistic traffic scenarios and standard scenario for certification. Second, our framework focuses on embedded development for the complete stack i.e. both algorithms and communication between different AD components. Thus, high performance, low latency, service, and type safety are the key parameters under consideration. The advantage is an efficient development process when transforming from MiL to hardware-vehicle in the loop testing.