Driver-Centric Lane-Keeping Assistance System Design: A Noncertainty-Equivalent Neuro-Adaptive Control Approach

• Published in: IEEE/ASME transactions on mechatronics 2023-12, Vol.28 (6), p.1-12
• Main Authors: Zhou, Xingyu, Shen, Heran, Wang, Zejiang, Ahn, Hyunjin, Wang, Junmin
• Format: Article
• Language: English
• Subjects: Adaptation models; Adaptive control; Algorithms; Behavioral sciences; Equivalence; Gudermannian-function-based smooth parameter projection; Lane keeping; lane-keeping assistance; Mechatronics; Neural networks; neuro network; noncertainty equivalence; Parameter estimation; Radial basis function; Reference signals; Simulator fidelity; Smoothness; Steering; Systems design; Torque; Uncertainty; Vehicle dynamics; Vehicles
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2023.3236245

Vehicle roadway departure accidents are a major traffic safety concern as they oftentimes result in severe injuries and fatalities. To address such an issue, this article originates a novel driver-centric and neuro-adaptive-control-based lane-keeping assistance system (LKAS). The proposed control strategy synergizes a noncertainty-equivalent adaptive control design scheme, an adaptive radial-basis-function-based neural network (RBFNN) that captures the human driver's lane-keeping steering behavior, and a Gudermannian-function-based smooth parameter projection operator. The benefit and uniqueness of the proposed solution are threefold. First and foremost, the noncertainty-equivalent adaptive control design, which leverages the immersion-and-invariance-like methodology, ensures the asymptotical convergence of the parameter-estimation-error-induced perturbation despite the reference signal's persistency of excitation condition. Second, the LKAS is devised to be driver-centric, i.e., an adaptive RBFNN-based human driver steering model is embedded inside the LKAS's algorithm such that a human driver is assisted in a personalized and adaptive manner. Third, the Gudermannian-function-based smooth parameter projection operator ascertains the prescribed boundedness of the control parameters while maintaining the control action's smoothness. A pilot human-subject study using a high-fidelity moving-base driving simulator is conducted to validate the proposed LKAS. Further, its performance is compared with a baseline certainty-equivalent neuro-adaptive controller.