1 Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, QC H3G 1M8, Canada. Electronic address: hamid.taghavifar@concordia.ca.
2 Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang, China; Department of Electrical Engineering, University of Bonab, Bonab, Iran.
3 Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A2, Canada.
4 School of Marine Science and Engineering, South China University of Technology, Guangzhou 511442, China. Electronic address: zhangchunwei@scut.edu.cn.

Path-tracking and lane-keeping tasks are critical to guarantee safety and navigation performance considerations for deploying autonomous cars. This paper presents a novel control framework for the path-tracking control of high-speed autonomous cars with structured uncertainties. This study introduces a nonlinear adaptive control system based on a fractional-order terminal sliding mode system while incorporating a novel Gaussian Nonsingleton type-3 fuzzy system (FOTSM-NT3FS). Therefore, the proposed controller is independent of the information about the ego vehicle's dynamic information, and instead, the dynamics are approximated through a developed NT3FLS. The developed control system exhibits robustness to measurement errors and faulty sensors, because the inputs to the NT3FS are uncertain. In order to guarantee the boundedness of the adaptation parameters, the s-mod approach is employed. The Lyapunov stability theorem and Barbalat's lemma are used to ensure the uniform ultimate boundedness of the closed loop system and the convergence of tracking errors to the origin in finite time. High-fidelity co-simulations with CarSim and MATLAB are performed to verify the effectiveness of the proposed control scheme and are also compared to other reported methods in the literature. Based on the obtained results, the schemed controller exhibits competitive effectiveness in path-tracking tasks and strong efficiency under various road conditions, parametric uncertainties, and unknown disturbances.