Abstract

Commercial Advanced Driver Assistance Systems (ADAS) can achieve a certain level of autonomous driving on a motorway using functions such as Smart Cruise Control (SCC) and Lane Keeping Assist System (LKAS). The next step of automation is to enable the vehicle to change lanes in traffic so it can maintain a desired speed. This paper demonstrates how to design such a system using parametric optimization of a simple algorithm that mimics the way a human would perform this function. The algorithm uses lidar sensors to determine when a lane change is beneficial to maintaining a high average travel speed. The orientation of the sensors along with algorithm parameters are explored using Design of Experiments (DOE) techniques and optimization. The study is performed within a Model Based System Engineering (MBSE) context to enable consideration of system level requirements set by the manufacturer throughout the design process. This approach delivered superior performance when compared to addressing the same design scenario using Artificial Intelligence (AI) methods. The results give rise to the question of when it is appropriate to use AI versus parametric optimization of human-developed algorithms. An argument is presented that whenever there is a problem which has already been addressed by humanity or nature, and it is well understood, then it makes sense to use the available answer. Being well understood, such a solution can be parametrized. Optimization can then help to automatically configure and adapt the solution for a particular application. When such a solution is not available or well understood, then Artificial Intelligence may provide a superior answer. Further highlighting the importance of keeping a human in the loop, the paper compares using an automated optimization algorithm with a computer-enhanced but human-led search for the best design. The relative effectiveness of each approach is examined along with the benefits of the MBSE integration.