Centralized Model-Predictive Control with Human-Driver Interaction for Platooning
Justin M. Kennedy, Julian Heinovski, Daniel E. Quevedo, Falko Dressler

TL;DR
This paper introduces a centralized model predictive control framework for vehicle platoons that integrates human-driven vehicles, allowing for human intervention while maintaining safety, stability, and efficiency in heterogeneous platoons.
Contribution
It presents the first platoon control method that incorporates human-driven vehicles with interruption capability, ensuring safety and stability in mixed-traffic platoons.
Findings
The controller maintains safety distances and velocity constraints.
The platoon demonstrates string stability, reducing disturbance effects.
Simulation results confirm effective human-vehicle integration.
Abstract
Cooperative adaptive cruise control presents an opportunity to improve road transportation through increase in road capacity and reduction in energy use and accidents. Clever design of control algorithms and communication systems is required to ensure that the vehicle platoon is stable and meets desired safety requirements. In this paper, we propose a centralized model predictive controller for a heterogeneous platoon of vehicles to reach a desired platoon velocity and individual inter-vehicle distances with driver-selected headway time. As a novel concept, we allow for interruption from a human driver in the platoon that temporarily takes control of their vehicle with the assumption that the driver will, at minimum, obey legal velocity limits and the physical performance constraints of their vehicle. The finite horizon cost function of our proposed platoon controller is inspired from…
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Taxonomy
TopicsTraffic control and management · Vehicular Ad Hoc Networks (VANETs) · Transportation Planning and Optimization
