Sensor resource management with cooperative sensors for preventive vehicle safety applications

Preventive vehicle safety applications require a reliable detection, classification, and localization of objects in the vehicle's surroundings. This is typically achieved by combining object detections of multiple local perception sensors, such as camera, radar, or lidar. However, to enable the detection of occluded objects as well as to improve the reliability of object classification, the principle of cooperative sensors has been recently proposed. The sensor principle uses a communication signal for object classification and localization. Therefore, in contrast to typical perception sensors, a fusion system including a cooperative sensor system requires a strategy to schedule the measurements of different objetcs considering a limited communication capacity. In this paper, we propose an information based approach for sensor resource management suited for cooperative sensor systems in vehicular applications. We will apply the strategy to a pedestrian perception system and analyze the behavior in different critical traffic situations in comparison to other possible approaches. Finally, we will use real world measurement data gathered with a prototype sensor at 5.9 GHz to justify the theoretical results.