Centralized Model-Predictive Control with Human-Driver Interaction for Platooning (2205.09259v5)

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 the infinite horizon design. To the best of our knowledge, this is the first platoon controller that integrates human-driven vehicles. We illustrate the performance of our proposed design with a numerical study, demonstrating that the safety distance, velocity, and actuation constraints are obeyed. Additionally, in simulation we illustrate a key property of string stability where the impact of a disturbance is reduced through the platoon.

• The paper introduces a centralized MPC algorithm that integrates human-driver interaction to manage both autonomous and manual control in vehicle platooning.
• It leverages real-time inter-vehicle data exchange to optimize velocity and spacing, ensuring safety even when drivers intermittently take control.
• Simulation results confirm robust platoon performance with maintained string stability and adherence to safety constraints under dynamic conditions.

Centralized Model-Predictive Control with Human-Driver Interaction for Platooning

Introduction

The paper "Centralized Model-Predictive Control with Human-Driver Interaction for Platooning" (2205.09259) presents a novel approach in the domain of vehicle platooning, addressing the interaction between autonomous systems and human drivers. Cooperative Adaptive Cruise Control (CACC) technologies offer significant improvements in road transport efficiency, energy reduction, and safety. Yet, achieving these benefits in heterogeneous vehicle platoons requires sophisticated control and communication strategies.

The research introduces a centralized Model Predictive Control (MPC) algorithm capable of managing a platoon with both autonomous and human-driven vehicles. This is a significant departure from current decentralized architectures that typically overlook human interaction components. By permitting human drivers to temporarily assume control while maintaining safety, the paper aims to provide a pragmatic solution for real-world vehicle platooning deployment.

Control Algorithm Design

The core contribution is the centralized MPC design which integrates human-driver control within the platoon. The algorithm targets optimal velocity and inter-vehicle distances, accommodating variations in driver preferences and vehicle types. Vehicles share their position, velocity, and acceleration via inter-vehicle communications, and the platoon controller processes these data points to compute the control actions.

This centralized approach contrasts with decentralized systems that might not use the full spectrum of data available or handle human-driver concerns effectively. The MPC is designed with constraints to ensure robust performance, avoiding collisions and maintaining legal speed limits even when a human driver controls a vehicle temporarily. It assumes drivers adhere to road speed and vehicle performance limits, allowing the platoon to dynamically adjust.

Human-Driver Interaction

Crucially, the paper addresses the integration of human-driven vehicles into autonomous platoons, a topic that is not widely covered in existing platooning research. The approach provides flexibility for drivers to switch between manual and automated control, highlighting the importance of considering comfort and safety from a human perspective.

The control system adapts by predicting probable driver actions based on limited assumptions, ensuring the platoon behaves safely despite potential disturbances from manual driving. This capacity to alternate seamlessly between human and machine control offers promising implications for transition periods where legacy vehicles are common on roads.

Results and Performance

Simulation results within the paper demonstrate the efficacy of the proposed approach, showing smooth transitions and adherence to safety constraints even when encountering human-driver interventions. The paper illustrates string stability properties, essential for maintaining the integrity of the platoon during dynamic driving situations. The simulations confirm that disturbances are efficiently minimized across the platoon, reflecting robust performance in real-world scenarios.

The constraints embedded within the control algorithm maintain operational boundaries, such as acceleration limits and safe stopping distances, that are essential for ensuring practical road safety.

Implications and Future Directions

The implications of this research are multifaceted, impacting theoretical aspects of control systems design as well as practical deployment strategies for autonomous vehicle platooning. By addressing both machine automation and human interaction, this paper lays the groundwork for implementing adaptive platooning systems that can work harmoniously across varied vehicle types.

Future research could explore distributed control architectures that incorporate the centralized approach while handling communication delays and imperfect data transmission. As infrastructure evolves to support more interconnected vehicle networks, the integration of human driver models will be critical for realizing the full potential of intelligent transportation systems.

Conclusion

The paper provides significant insights into centralized control of vehicle platoons, emphasizing safety, efficiency, and human factors. By integrating human-driver interaction in a centralized MPC framework, the paper offers a promising direction for future platooning systems adapted to real-world conditions with legacy vehicle presence. As vehicular networks innovate, such approaches may lead to more inclusive and practical autonomous driving solutions on roads worldwide.