Unscented Transform-based Pure Pursuit Path-Tracking Algorithm under Uncertainty

TL;DR

This paper introduces a modified pure pursuit path-tracking algorithm that incorporates the unscented transform to handle uncertainties in autonomous vehicle localization and environment understanding, improving path accuracy.

Contribution

It presents a novel geometric pure pursuit algorithm that explicitly accounts for uncertainties using the unscented transform, enhancing autonomous path tracking robustness.

Findings

Effective in simulations for straight and circular roads

Improves path-tracking accuracy under uncertainty

Demonstrates potential for safer autonomous driving

Abstract

Automated driving has become more and more popular due to its potential to eliminate road accidents by taking over driving tasks from humans. One of the remaining challenges is to follow a planned path autonomously, especially when uncertainties in self-localizing or understanding the surroundings can influence the decisions made by autonomous vehicles, such as calculating how much they need to steer to minimize tracking errors. In this paper, a modified geometric pure pursuit path-tracking algorithm is proposed, taking into consideration such uncertainties using the unscented transform. The algorithm is tested through simulations for typical road geometries, such as straight and circular lines.