Design and Implementation of Path Trackers for Ackermann Drive based Vehicles
This work provides a practical comparison and tuning methodology for path tracking methods for engineers developing autonomous vehicle systems, with an incremental gain in performance for medium velocity ranges.
This paper analyzes, implements, tunes, and compares several common path tracking methods for autonomous vehicles in both simulation and a real car, a Mahindra e2o. Their model predictive control-based approach demonstrated better performance in medium velocity ranges compared to other methods.
This article is an overview of the various literature on path tracking methods and their implementation in simulation and realistic operating environments.The scope of this study includes analysis, implementation,tuning, and comparison of some selected path tracking methods commonly used in practice for trajectory tracking in autonomous vehicles. Many of these methods are applicable at low speed due to the linear assumption for the system model, and hence, some methods are also included that consider nonlinearities present in lateral vehicle dynamics during high-speed navigation. The performance evaluation and comparison of tracking methods are carried out on realistic simulations and a dedicated instrumented passenger car, Mahindra e2o, to get a performance idea of all the methods in realistic operating conditions and develop tuning methodologies for each of the methods. It has been observed that our model predictive control-based approach is able to perform better compared to the others in medium velocity ranges.