Car manufacturers are facing the challenge of defining suitable sensor setups that cover all requirements for the particular SAE level of automated driving. Besides the sensors' performance and surround-view coverage, other factors like vehicle integration, costs and design aspects need to be taken into account. Additionally, a redundant sensor arrangement and the sensors' sensitivity to environmental influences are of crucial importance for safety. By increasing the degree of automation, vehicles require more external sensors to observe their surrounding environment sufficiently, which raises the variety of setup configurations and the difficulty to identify the optimal one. Concerning the vehicle development process, concepts for sensor setups need to be defined at a very early stage. In this concept stage, it is not feasible to explore every possible sensor arrangement with test drives or to simulate the setup performance with tools used for vehicle validation. Thus, we propose a new simulation-based evaluation method, which allows the configuration of arbitrary sensor setups and enables virtual test drives within specific scenarios to evaluate the setup performance in this early development phase with metrics and key performance indicators. Two different setups are analyzed to demonstrate the results of this evaluation method.

Automotive sensor systems play an essential role for highly and fully automated driving by enabling environmental perception. Besides improving the sensors' performance, the data quality and information processing, the arrangement of the vehicular surround sensors is crucial. The conceptual design of robust and reliable sensor systems is a complex task since they comprise a high number of different sensors, each with diverse, adjustable parameters. At this early vehicle concept phase, it is not possible to examine the performance of all system variations in field tests. Thus, we established a simulation-based evaluation methodology to investigate the interaction of multiple sensors as a system and the effects of particular sensor arrangements on object detection and vehicle's surround-view coverage. This paper highlights the considerations while designing sensor systems and analyzes the impact of different sensor positions, especially by systematically altering the mounting height.

Autonomous driving has the potential to disruptively change the automotive industry as we know it today. For this, fail-operational behavior is essential in the sense, plan, and act stages of the automation chain in order to handle safety-critical situations by its own, which currently is not reached with state-of-the-art approaches.The European ECSEL research project PRYSTINE realizes Fail-operational Urban Surround perceptION (FUSION) based on robust Radar and LiDAR sensor fusion and control functions in order to enable safe automated driving in urban and rural environments. This paper showcases some of the key results (e.g., novel Radar sensors, innovative embedded control and E/E architectures, pioneering sensor fusion approaches, AI controlled vehicle demonstrators) achieved until year 2.