Functional Simulation of automotive Lidar and Camera Sensors

This paper presents a method to simulate various automotive sensors based on functional properties. An extraction of the road users in the surroundings is determined by Deep Learning based object detectors to generate a sufficient perception of the vehicle environment. Simultaneously, it is important that the data processing runs in real time and with a high detection accuracy to ensure a safe maneuvering of the vehicle. For this reason the object detectors considered in this paper are the single shot multibox detector with MobileNet as base network for image processing and the Sparsely Embedded Convolutional Detection detector for lidar point clouds. The dataset used for the evaluation is KITTI. The detection accuracies for different object distances are investigated and compared. To create a functional simulation for the different sensor systems, the evaluation of the object detection for various sensor types is used to model the respective sensor behavior and its properties. The modeled object detectors are evaluated with respect to the detection accuracies of the real detectors. These sensor models can then be used in simulations for different sensor topologies.