Design and Implementation of Path Trackers for Ackermann Drive based Vehicles

TL;DR

This paper reviews and compares various path tracking methods for Ackermann drive vehicles, evaluating their performance in simulation and real-world tests, and highlights the effectiveness of model predictive control at medium speeds.

Contribution

It provides a comprehensive analysis, implementation, and tuning of multiple path tracking methods, including nonlinear approaches, with real-world validation on a passenger vehicle.

Findings

Model predictive control outperforms others at medium speeds

Performance varies significantly between methods at different velocities

Tuning methodologies are developed for practical implementation

Abstract

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.