Complex-Track Following in Real-Time Using Model-Based Predictive Control Wael Farag 1,2 Received: 28 February 2019 /Revised: 13 April 2020 /Accepted: 21 May 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract In this paper, a comprehensive Model-Predictive-Control (MPC) controller that enables effective complex track maneuvering for Self-Driving Cars (SDC) is proposed. The paper presents the full design details and the implementation stages of the proposed SDC-MPC. The controller receives several input signals such as an accurate car position measurement from the localization module of the SDC measured in global map coordinates, the instantaneous vehicle speed, as well as, the reference trajectory from the path planner of the SDC. Then, the SDC-MPC generates a steering (angle) command to the SDC in addition to a throttle (speed/brake) command. The proposed cost function of the SDC-MPC (which is one of the main contributions of this paper) is very comprehensive and is composed of several terms. Each term has its own sub-objective that contributes to the overall optimization problem. The main goal is to find a solution that can satisfy the purposes of these terms according to their weights (contribution) in the combined objective (cost) function. Extensive simulation studies in complex tracks with many sharp turns have been carried out to evaluate the performance of the proposed controller at different speeds. The analysis shows that the proposed controller with its tuning technique outperforms the other classical ones like PID. The usefulness and the shortcomings of the proposed controller are also discussed in details. Keywords MPC control . Self-driving Car . Autonomous driving . MPC tuning 1 Introduction Increasing safety, reducing road accidents, improving energy efficiency and enhancing comfort and driving experience are the major motivations behind equipping modern cars with Advanced Driving Assistance Systems (ADAS) [1–8]. In the past couple of decades, major car manufacturers introduce many sophisticated ADAS functions like Lane Departure Warning (LDW), Lane Keep Assist (LKA), Electronic Stability Control (ESC), Anti-lock Brake System (ABS), etc. Both LKA and ESC functions are examples of how important for the car to follow and track the lane or road boundaries accurately and on time. Future ADAS functions like Collision Avoidance, Automated Highway Driving (Autopilot), Automated Urban Driving [9], Automated Parking and Cooperative Maneuvering requires more and more fast and reliable road boundaries follower functionali- ties, which should be designed to maintain the control over the vehicle in extreme circumstances, and being very effective at high speeds [10, 11]. The road boundaries follower functionality requires the localization of the road, the determination of the relative position between vehicle and road/lane centerline, and the analysis of the vehicle’s heading direction. The mentioned systems represent incremental steps toward a hypothetical future of safe fully self-driven vehicles [12–14]. There are two categories of vehicle control [15]. The first category is the longitudinal control which deals with the movement of the vehicle in the forward and backward direc- tions (responsible for regulating the vehicle cruise velocity) [16]. The second one is the lateral control which deals with sideways movements perpendicular on the vehicle’s heading, in other words, the steering of the vehicle to follow a given trajectory [17]. The focus of this paper is on both categories with the objective of minimizing a sophisticated objective function that prioritizes both safety and comfort. In previous literature, various studies have been carried-out to explore different hypotheses and techniques. These studies incorporate the PID control strategies [18, 19], the predictive * Wael Farag wael.farag@aum.edu.kw; wael.farag@cu.edu.eg 1 College of Engineering & Technology, American University of the Middle East, Egaila, Kuwait 2 Electrical Engineering Department, Cairo University, Giza, Egypt International Journal of Intelligent Transportation Systems Research https://doi.org/10.1007/s13177-020-00226-1