V2I-Based Platooning Design with Delay Awareness (2012.03243v1)

Abstract: This paper studies the vehicle platooning system based on vehicle-to-infrastructure (V2I) communication, where all the vehicles in the platoon upload their driving state information to the roadside unit (RSU), and RSU makes the platoon control decisions with the assistance of edge computing. By addressing the delay concern, a platoon control approach is proposed to achieve plant stability and string stability. The effects of the time headway, communication and edge computing delays on the stability are quantified. The velocity and size of the stable platoon are calculated, which show the impacts of the radio parameters such as massive MIMO antennas and frequency band on the platoon configuration. The handover performance between RSUs in the V2I-based platooning system is quantified by considering the effects of the RSU's coverage and platoon size, which demonstrates that the velocity of a stable platoon should be appropriately chosen, in order to meet the V2I's Quality-of-Service and handover constraints.

• The paper introduces a novel V2I-based platooning design that addresses communication delays to enhance both plant and string stability.
• It employs massive MIMO antennas and edge computing, using the D-subdivision method to calculate control gains for robust stability under delay conditions.
• Performance simulations validate the design's scalability, rapid error correction, and effective handling of handover constraints in platoon vehicles.

V2I-Based Platooning Design with Delay Awareness

Introduction

The paper explores a Vehicle-to-Infrastructure (V2I) based platooning system that leverages edge computing to enhance autonomous vehicle control. Traditional Adaptive Cruise Control (ACC) systems are prone to string instability, which can lead to traffic disruptions. By utilizing V2I communications, the proposed system addresses communication delays and improves both plant and string stability. This design aims to optimize the platoon's velocity and size while considering radio parameters such as massive MIMO antennas and frequency bands.

System Design and Model

In the V2I-based platooning system, as depicted in the model (Figure 1), vehicles relay their driving state information to a roadside unit (RSU). This RSU, equipped with massive MIMO antennas and edge computing capabilities, processes the information to generate control commands broadcasted back to the vehicles. The control law employed by the RSU ensures homogeneous control inputs, overcoming the limitations associated with vehicle-to-vehicle (V2V) communication, such as limited range and interference.

Figure 1: An illustration of V2I-based platooning system with edge computing.

String stability is achieved through the careful selection of control gains, which are determined using the D-subdivision method. This method helps in calculating the feasible regions for control gains to ensure stability under delay conditions.

Stability Analysis

The paper conducts a comprehensive frequency-domain stability analysis, leveraging the Laplace transform to derive the conditions for both plant and string stability.

• Plant Stability: The plant stability condition is represented by $\Theta\left(s_0\right) = 0$, ensuring that characteristic roots have negative real parts. The D-subdivision method provides a feasible domain for the control gains $(\lambda, \eta)$.
• String Stability: Ensures that disturbances are not amplified along the vehicle string. The derived condition $\left(\lambda, \eta\right) \in \mathcal{S}(\tau)$ ensures that the transfer function's magnitude remains under unity, preventing amplification of vehicle spacing errors.

  Figure 2: The plant stability region $\mathcal{G}\left(\tau\right)$ for different levels of delay with different corner points.

Performance Evaluation

The performance of the proposed V2I-based platooning system is assessed via simulation, highlighting its advantages in terms of scalability and delay management:

• Efficiency: The V2I framework quickly enables error correction and establishes stability, as demonstrated in various scenarios with differing delay levels and platoon sizes.

Figure 3: The platooning performance of the proposed design.

• Impact of Control Gains and Delays: Different control gains and delay levels have marked effects on the system, influencing the speed of achieving stability and the magnitude of spacing errors.
• External Factors and Disturbance Robustness: Various scenarios tested show that the platoon is robust to typical disturbances despite variations in size and delays, demonstrating the scalability and applicability of the control design.

Handover and Dual Connectivity

The paper further discusses the implications of choosing the platoon's optimal velocity and its relationship with RSU coverage and handover constraints. Important considerations include:

• Velocity and Spacing: The optimal velocity must be chosen to avoid frequent handovers and to maintain communication quality.
• Dual Connectivity: Implementing dual connectivity during handovers maintains seamless control and communication between RSUs, leveraging massive MIMO to support this transition effectively.

  Figure 4: A platoon can be seamlessly served by RSUs as the platoon is in the dual-connectivity range during the handover.

Conclusion

The research provides a novel framework for V2I-based platooning utilizing edge computing, addressing the challenges of communication delay and stability in autonomous vehicle systems. The design demonstrates significant potential for enhancing traffic flow efficiency and vehicular control in practical scenarios. Future research could explore the integration with more sophisticated vehicular dynamics models and distributed control algorithms to further optimize platooning systems in dense traffic environments.