Trade-Off between Coverage and Robustness of Automotive Environment Sensor Systems

Car manufacturers, research institutions and governments are challenged by decreasing the number of road traffic fatalities in spite of rapidly increasing traffic volumes. One approach is to enhance the "intelligence" of modern cars in anticipating dangerous driving situations. Besides wireless communications with the traffic environment, context perception enabled by well-known technologies such as radar, laser or video is regarded as a promising instrument in driver assistance and collision avoidance. There is a rich variety of both hardware configurations of automotive environment sensor systems and strategies for sensor data fusion and processing. For sensor systems assembled of multiple devices, the trade-off between increased spatial coverage, robustness against interfering factors and accuracy of measured data is a crucial aspect of system design. We investigate up-to-date automotive sensor systems with respect to detection performance and detection quality. We built UML-based discrete-event simulation models to resemble sensors, context perception, signal processing and data fusion in realistic road traffic scenarios. Our results allow for a sound comparison on sensor systems and data acquisition strategies at an early stage of system development.