Comparative study of trajectory tracking control for autonomous vehicles via geometric and model based method

• Main Authors: Shet, Raghavendra M., Iyer, Nalini C.
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Automobiles; Autonomous cars; Autonomous vehicles; Comparative studies; Control algorithms; Dynamic models; Motion control; Predictive control; Tracking control; Trajectory control
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0178664

With rapid development in automotive field, it is becoming increasingly important to have a comprehensive understanding of the various technologies used in modern hi-tech vehicles, among which Autonomous Vehicle/Self driving cars are gaining large popularity. A self-driving vehicle has a capability of sensing its surrounding environment and perform functioning without any human involvement. Automated or autonomous control of vehicles is very much a challenging task due to the varying dynamics of system, nonlinearity, component noise, disturbances, and unknown parameters. Motion control in self-driving cars is one of the core issues that is capable of tracking a reference trajectory and is researched intensively other than environment perception and planning area. The paper compares the performance behavior of various trajectory tracking control algorithms. The control algorithms compared to track the trajectory are basic geometric methods like Pure-pursuit, Stanley methods and model-based techniques like MPC (Model Predictive Control). The simulation is performed over a lateral dynamic model for trajectory tracking of autonomous vehicle.