Improving Safety and Efficiency of Connected and Autonomous Vehicle Platoons through Robust Control Design under Communication Delays

Concepts de base

Deriving lower bounds on time headway for CACC and CACC+ vehicle platoons to ensure robust string stability in the presence of communication delays.

Résumé

This paper investigates the effect of signal delay in communicated information on the performance of connected and autonomous vehicle platoons. It focuses on the selection of the time headway in predecessor-follower type vehicle platooning with a constant time headway policy (CTHP).
The key highlights are:

The paper considers two cases: cooperative adaptive cruise control (CACC) strategy where information from only one predecessor vehicle is employed, and CACC+ where information from multiple predecessor vehicles is used.

It derives a lower bound on the time headway that ensures robust string stability of the platoon under the presence of signal delay due to wireless communication. This lower bound depends on the level of communication delay.

The analysis provides a systematic approach to selecting the appropriate CTHP controller gains for predecessor acceleration, velocity error, and spacing error to ensure robust string stability with communication delays.

Numerical simulations are provided to corroborate the main results and demonstrate the benefits of CACC+ over CACC in terms of reducing the length of the platoon.

The paper aims to address the key challenges in connected and autonomous vehicle platoons, including handling of imperfect information due to communication delays, and ensuring internal and string stability of the platoon in the presence of such delays.

Stats

The following sentences contain key metrics or figures:
The parasitic actuation lag τ can take any value in the interval (0, τ0], where τ0 is a positive real constant.
The communication delay is denoted by ℓ.
The time headway is denoted by hw.
The control gains are denoted by ka, kv, kp for CACC, and ¯ka, ¯kv, ¯kp for CACC+.

Citations

"Given the latency in V2V communication, what is a lower bound of the employable time headway for CACC and CACC+ based vehicle platooning as a function of the level of latency?"
"The analysis is conducted to facilitate systematic and step wise co-design procedure for the selection of the control gains as well as the time headway under a given latency and parasitic actuation lag."

Idées clés tirées de

Benefits of V2V communication in connected and autonomous vehicles in the presence of delays in communicated signals

by Guoqi Ma,Pra... à arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.08879.pdf

Questions plus approfondies

How can the proposed control design be extended to handle stochastic packet losses in the V2V communication

To extend the proposed control design to handle stochastic packet losses in V2V communication, we can incorporate robust control techniques. One approach is to design a controller that can adapt to the varying communication conditions by incorporating predictive models of packet loss rates. By using predictive models, the controller can adjust its parameters in real-time to compensate for the expected packet losses. Additionally, implementing redundancy in the communication system can help mitigate the impact of packet losses. This redundancy can involve retransmission mechanisms, error correction coding, or even alternative communication paths to ensure the reliability of the information exchange. Furthermore, incorporating feedback mechanisms that can detect and react to packet losses can enhance the system's resilience to communication disruptions.

What are the potential trade-offs between the level of communication delay, the number of predecessor vehicles considered in CACC+, and the achievable string stability performance

The trade-offs between the level of communication delay, the number of predecessor vehicles considered in CACC+, and the achievable string stability performance are crucial in designing an effective cooperative control system.

Communication Delay: A higher communication delay can lead to slower response times and reduced system performance. However, a certain level of delay may be acceptable depending on the application and the system's robustness to handle it.
Number of Predecessor Vehicles: Increasing the number of predecessor vehicles considered in CACC+ can provide more comprehensive information for control decisions. This can lead to improved coordination and smoother platoon behavior. However, it can also introduce complexity and potential instability if not properly managed.
String Stability Performance: Achieving robust string stability is essential for the safe and efficient operation of connected and autonomous vehicle platoons. Balancing the communication delay, the number of predecessor vehicles, and the control design is crucial to ensure optimal string stability performance.
Finding the right balance between these factors involves a trade-off between system complexity, performance, and stability. It requires careful consideration of the specific requirements of the application and the capabilities of the communication infrastructure.

How can the insights from this work on robust control design under communication delays be applied to other cooperative control problems in autonomous systems, such as multi-robot coordination or distributed optimization

The insights from this work on robust control design under communication delays can be applied to other cooperative control problems in autonomous systems, such as multi-robot coordination or distributed optimization, in the following ways:

Adaptive Control: Similar adaptive control strategies can be employed to handle uncertainties and delays in communication channels in multi-robot systems. By incorporating predictive models and feedback mechanisms, the control system can adapt to changing communication conditions.
Redundancy: Implementing redundancy in communication channels can enhance the reliability of information exchange in distributed systems. Redundant communication paths and error correction mechanisms can improve the robustness of the control system.
Distributed Optimization: The principles of robust control design can be applied to distributed optimization problems in autonomous systems. By considering communication delays and uncertainties in the optimization process, more resilient and efficient solutions can be achieved. Additionally, cooperative control strategies can be designed to ensure coordination and synchronization among multiple agents in distributed optimization tasks.