Delay Tolerant Control for Autonomous Driving Using CDOB

TL;DR

This paper introduces a delay-tolerant control framework using a CDOB for autonomous vehicles, effectively maintaining accurate path tracking despite communication and computation delays, outperforming traditional controllers.

Contribution

The paper presents a novel CDOB-based control method that compensates for delays, enhancing trajectory tracking robustness in autonomous driving systems.

Findings

Maintains accurate trajectory tracking under various delays.

Outperforms conventional controllers in delay robustness.

Effective in complex traffic scenarios like lane changes and collision avoidance.

Abstract

With the rapid growth of autonomous vehicle technologies, effective path-tracking control has become a critical component in ensuring safety and efficiency in complex traffic scenarios. When a high level decision making agent generates a collision free path, a robust low level controller is required to precisely follow this trajectory. However, connected autonomous vehicles (CAV) are inherently affected by communication delays and computation delays, which significantly degrade the performance of conventional controllers such as PID or other more advanced controllers like disturbance observers (DOB). While DOB-based designs have shown effectiveness in rejecting disturbances under nominal conditions, their performance deteriorates considerably in the presence of unknown time delays. To address this challenge, this paper proposes a delay-tolerant communication disturbance observer (CDOB) framework for path-tracking control in delayed systems. The proposed CDOB compensates for the adverse effects of time delays, maintaining accurate trajectory tracking even under uncertain and varying delay conditions. It is shown through a simulation study that the proposed control architecture maintains close alignment with the reference trajectory across various scenarios, including single lane change, double-= lane change, and Elastic Band generated collision avoidance paths under various time delays. Simulation results further demonstrate that the proposed method outperforms conventional approaches in both tracking accuracy and delay robustness, making it well suited for autonomous driving applications.