Gaussian Process-based Model Predictive Controller for Connected Vehicles with Uncertain Wireless Channel

TL;DR

This paper introduces a Gaussian Process-based Model Predictive Controller for connected autonomous vehicles that accounts for wireless channel uncertainties, optimizing safety and performance by predicting delays and focusing computations.

Contribution

It presents a novel data-driven control algorithm that incorporates wireless channel modeling into predictive control for autonomous vehicles, improving safety and efficiency.

Findings

Successfully predicts wireless delays using Gaussian Processes.

Enhances vehicle safety and trajectory optimality under uncertain communication.

Reduces computational complexity through a focused reachable set approach.

Abstract

In this paper, we present a data-driven Model Predictive Controller that leverages a Gaussian Process to generate optimal motion policies for connected autonomous vehicles in regions with uncertainty in the wireless channel. The communication channel between the vehicles of a platoon can be easily influenced by numerous factors, e.g. the surrounding environment, and the relative states of the connected vehicles, etc. In addition, the trajectories of the vehicles depend significantly on the motion policies of the preceding vehicle shared via the wireless channel and any delay can impact the safety and optimality of its performance. In the presented algorithm, Gaussian Process learns the wireless channel model and is involved in the Model Predictive Controller to generate a control sequence that not only minimizes the conventional motion costs, but also minimizes the estimated delay of the wireless channel in the future. This results in a farsighted controller that maximizes the amount of transferred information beyond the controller's time horizon, which in turn guarantees the safety and optimality of the generated trajectories in the future. To decrease computational cost, the algorithm finds the reachable set from the current state and focuses on that region to minimize the size of the kernel matrix and related calculations. In addition, we present an efficient recursive approach to decrease the time complexity of developing the data-driven model and involving it in Model Predictive Control. We demonstrate the capability of the presented algorithm in a simulated scenario.