Advancing ADAS testing with machine learning and optimization techniques

ADAS (Advanced Driving Assistance Systems) and AD (Autonomous Driving) systems are the next big frontier for automotive companies. The challenge lays in finding the right balance between minimizing the number of accidents and casualties while maximizing the comfort of traveling in complex conditions. ADAS/AD functions combine a number of components, including sensors (hardware and software processing), the algorithm fusing the data coming from multiple sensors, the algorithm deciding to act upon those inputs (braking, steering, accelerating), and finally, the actuators that will be implementing the decision. ADAS/AD functions are also divided into a number of "levels", each dictating who is responsible for the action, the car or the driver. From level-0 to level-2, the systems are the "eyes-on and hands-on" type, meaning that the function is there to support the driver in supplying more information or automating some parts of the driving. From level-3 to level-5, the vehicle is in charge and can eventually (in the case of level-3 and level-4) give the controls back to the driver if the driving condition is too complex.