基於共識演算法之聯網自駕車訊息容錯列隊行駛

Consensus-Based Fault-Tolerant Platooning for Connected and Autonomous Vehicles

林家佑(Jia-You Lin)

指導教授 ： 林忠緯

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2022年)

https://doi.org/10.6342/NTU202201050

摘要

在此篇論文中，我們研究聯網自駕車的一個代表性的應用¬:列隊行駛。由於列隊行駛可以維持短車距且高速行駛，它可以提升交通的效率。列隊行駛中車與車之間的訊息交換是關鍵點。Santini et al.[14]提出一種共識演算法，他們多考慮傳輸延遲這項因素。然而他們並沒有考慮到訊息交換之間可能會有錯 誤或是干擾。此篇論文主要就是為了解決錯誤和干擾的問題，基於[14]的方法，我們提出可容錯的方法。經由偵錯和control-gain的調整，在錯誤和干擾影響之下，我們的方法還是成功讓車隊可以收斂到正確的狀態。最後我們進行了模擬實驗來證明方法的有效性。模擬實驗是利用Plexe [15] 來進行。 Plexe是一個開源的工具，結合SUMO和Veins來實作一個擬真的車隊模擬環境。我們呈現了各個方法的steady state error、收斂時間和能量消耗。

關鍵字

聯網自駕車 ； 列隊行駛 ； 協同適應巡航控制 ； 錯誤偵測 ； 共識問題

並列摘要

In this thesis, we study the platoon which is a representative application of connected and autonomous vehicles. It can achieve high traffic efficiency because it can maintain closed following distances and high speeds. The information exchanged between the vehicles is the key. Santini et al. [14] proposed a novel consensus-based control algorithm. They took communication delays into account. However, they did not consider the faulty messages or the noise. To fix the faulty messages or the noise, we propose a Fault-Tolerant approach based on the approach in [14]. By doing fault detection and control-gain adjustment, our approach can let the platoon converge to the correct state even with faulty messages and the noise presented. We have simulation experiments to show the effectiveness of our approach. The simulation is based on Plexe [15]. Plexe is an open-source simulator, it combines SUMO and Veins to implement a realistic simulation of platooning. In the result of the simulation, we present the steady-state error, the settling time, and the power consumption.

並列關鍵字

Connected and Autonomous Vehicle ； Platoon ； Cooperative Adaptive Cruise Control ； Fault Detection ； Consensus Problem

參考文獻

[1] M. Amoozadeh, A. Raghuramu, C. Chuah, D. Ghosal, H. M. Zhang, J. Rowe, and K. Levitt, “Security vulnerabilities of connected vehicle streams and their impact on cooperative driving,” IEEE Communications Magazine, pp. 126–132, June 2015.

[2] N. Bermad, S. Zemmoudj, and M. Omar, “Securing vehicular platooning against vehicle platooning disruption (VPD) attacks,” in International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN), pp. 1–6, Nov 2019.

[3] L. Bertoni, J. Guanetti, M. Basso, M. Masoero, S. Cetinkunt, and F. Borrelli, “An adaptive cruise control for connected energy-saving electric vehicles,” IFAC-PapersOnLine, vol. 50, no. 1, pp. 2359 – 2364, 2017, 20th IFAC World Congress. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S2405896317307759

[4] J. Han, A. Vahidi, and A. Sciarretta, “Fundamentals of energy efficient driving for combustion engine and electric vehicles: An optimal control perspective,” Automatica, vol. 103, pp. 558 – 572, 2019. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0005109819300858

[5] Y. He, Q. Zhou, M. Makridis, K. Mattas, J. Li, H. Williams, and H. Xu, “Multiobjective co-optimization of cooperative adaptive cruise control and energy management strategy for phevs,” IEEE Transactions on Transportation Electrification, vol. 6, no. 1, pp. 346–355, 2020.