Parameter Tuning Under Uncertain Road Perception in Driver Assistance Systems

Abstract

Advanced driver assistance systems have improved comfort, safety, and efficiency of modern vehicles. However, sensor limitations lead to noisy lane estimates that pose a significant challenge in developing performant control architectures. Lateral trajectory planning often employs an optimal control formulation to maintain lane position and minimize steering effort. The parameters are often tuned manually, which is a time-intensive procedure. This paper presents an automatic parameter tuning method for lateral planning in lane-keeping scenarios based on recorded data, while taking into account noisy road estimates. By simulating the lateral vehicle behavior along a reference curve, our approach efficiently optimizes planner parameters for automated driving and demonstrates improved performance on previously unseen test data.