Bounding the number of relevant objects in automotive environment perception systems

Multi-sensor data fusion systems for environment perception in the automotive domain are regarded as a promising instrument for obtaining dependable vehicular context information. Sensor data from remote sensing devices like radar or laserscanners is transmitted via intra-car networks to electronic control units (ECU) that enable an intelligent, context sensitive vehicle behavior depending on the current traffic situation. Although new bus systems, such as Flexray, offer increased data rates, the communication resources need to be utilized efficiently. In order to do so, two aspects have to be considered: 1) The size of a single object description and 2) the overall number of perceivable objects. In this paper we focus on the latter of the two aspects. We created a flexible discrete event simulation framework that allows for an in-depth analysis of various aspects of environment perception systems. Our simulation covers scenarios consisting of different sensor-sets, traffic scenarios, fusion benefits, and algorithms for context perception. Using this framework we were able to limit the number of objects a single sensor is allowed to perceive and analyze the impact of this limitation on the overall system performance without such restrictions. Our findings include: 1) Bounding the number of relevant objects to a number between 4–8 does not affect the false negative ratio of the system, 2) the overall error and false positive error ratio does not increase by bounding the number of relevant objects, 3) in safety-relevant environment perception systems the number of relevant objects can be reduced even further without compromising the system integrity, and 4) bounding the number of objects at an early stage of signal processing is superior to a reduction at a late stage of signal processing.