# Communication Disturbance Observer Based Delay-Tolerant Control for Autonomous Driving Systems

## TL;DR

This paper introduces a new control method for autonomous vehicles that improves path tracking despite communication and computation delays.

## Contribution

A delay-tolerant communication disturbance observer (CDOB) is proposed to enhance path-tracking control in autonomous driving systems.

## Key findings

- The CDOB framework maintains accurate trajectory tracking under varying time delays.
- Simulation results show improved tracking accuracy and delay robustness compared to conventional methods.
- The method performs well in scenarios like lane changes and collision avoidance with delayed systems.

## 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 connected autonomous driving applications.