A Method for Evaluation and Optimization of Automotive Camera Systems based on Simulated Raw Sensor Data

Automated driving functions make mobility safer and more comfortable. The perception of these functions relies on sensors that capture the vehicle’s environment. A sound configuration of the sensor’s intrinsic and extrinsic parameters is crucial for a high perception performance. Simulation is a method of increasing importance for the evaluation and optimization of sensor sets since it enables fast and cost-effective procedures. This paper presents a method for evaluating and optimizing cameras during traffic scenarios in a simulation based on their raw sensor data quality. We present a framework using the CARLA simulator and evaluate the coverage and visibility of the surrounding objects during virtual traffic scenarios. For this purpose, we propose a general metric to measure the acquired information content of camera systems. We compare our metric to the performance of a state-of the-art object detector to investigate the ability to represent detection performance. The suitability of the metric for the optimization of intrinsic hardware parameters and extrinsic mounting parameters is explored. We show in our experiments that the metric correlates strongly with a benchmark perception function’s performance. Furthermore, we prove that sensor configurations optimized by our metric outperform a baseline sensor set in terms of object detection performance.