Gradient Aware - Shrinking Domain based Control Design for Reactive Planning Frameworks used in Autonomous Vehicles

TL;DR

This paper introduces a gradient-aware control law for autonomous vehicle speed regulation that integrates road surface gradients within a shrinking domain framework, enhancing speed tracking accuracy and responsiveness in high-frequency motion planning.

Contribution

The work presents a novel control law that incorporates road surface gradients into a shrinking domain scheme, improving speed tracking for autonomous vehicles.

Findings

Enhanced speed tracking on varying terrains.

Robust performance under high-frequency planning constraints.

Effective implementation on a stock electric vehicle.

Abstract

In this paper, we present a novel control law for longitudinal speed control of autonomous vehicles. The key contributions of the proposed work include the design of a control law that reactively integrates the longitudinal surface gradient of road into its operation. In contrast to the existing works, we found that integrating the path gradient into the control framework improves the speed tracking efficacy. Since the control law is implemented over a shrinking domain scheme, it minimizes the integrated error by recomputing the control inputs at every discretized step and consequently provides less reaction time. This makes our control law suitable for motion planning frameworks that are operating at high frequencies. Furthermore, our work is implemented using a generalized vehicle model and can be easily extended to other classes of vehicles. The performance of gradient aware-shrinking domain based controller is implemented and tested on a stock electric vehicle on which a number of sensors are mounted. Results from the tests show the robustness of our control law for speed tracking on a terrain with varying gradient while also considering stringent time constraints imposed by the planning framework.