Performance Modelling and Simulation of Automotive Camera Sensors : An exploration of methods and techniques to simulate the behaviour of lane detection cameras

Abstract: Nowadays safety, along with efficiency, is one of the two strongest shaping forces of the automotive world, with advanced active safety applications being the major concentration of effort. Their development depends heavily on the quality of sensor data, a detailed measure of which is often up to the automotive manufacturers to derive, since the original equipment manufacturers (OEMs) may not disclose it on trade secrecy grounds. A model would not only provide a measure of the real-world performance of the sensor, but would also enable a higher degree of simulation accuracy which is vital to active safety function development. This is largely due to the high cost and risk involved in testing, a significant part of which is possible to be done in simulation alone. This thesis is an effort to derive a sensor model on behalf of Volvo Trucks of the performance of one of the most crucial sensors in current active safety - a lane detection camera.The work is focused on investigating approaches for modelling and simulation implementation of the lane estimation process within the black-box camera using reverse-engineering of the sensor's principles of operation. The main areas of analysis to define the factors that affect performance are the optics, image sensor, software and computer vision algorithms, and system interface. Each of them is considered separately and then methods for modelling are proposed, motivated, and derived accordingly. Finally, the finished model is evaluated to provide a measure of work success and a basis for further development.