Modeling Slipstreaming Effects in Vehicle Platoons

Author(s):  
Michele Segata ◽  
Andrea Stedile ◽  
Renato Lo Cigno
Keyword(s):  
Author(s):  
Takuma WAKASA ◽  
Yoshiki NAGATANI ◽  
Kenji SAWADA ◽  
Seiichi SHIN

Automatica ◽  
2021 ◽  
Vol 127 ◽  
pp. 109542
Author(s):  
Guilherme Fróes Silva ◽  
Alejandro Donaire ◽  
Aaron McFadyen ◽  
Jason J. Ford

Author(s):  
Changkun Du ◽  
Yougang Bian ◽  
Haikuo Liu ◽  
Wei Ren ◽  
Pingli Lu ◽  
...  

2021 ◽  
pp. 107754632110026
Author(s):  
Zeyu Yang ◽  
Jin Huang ◽  
Zhanyi Hu ◽  
Diange Yang ◽  
Zhihua Zhong

The coupling, nonlinearity, and uncertainty characteristics of vehicle dynamics make the accurate longitudinal and lateral control of an automated and connected vehicle platoon a tough task. Little research has been conducted to fully address the characteristics. By using the ideology of constraint-following control this article proposes an integrated longitudinal and lateral adaptive robust control methodology for a vehicle platoon with a bidirectional communication topology. The platoon control objectives contain the path tracking stability, the platoon internal stability, and the string stability. First, we establish the nonlinear kinematics path tracking model and the coupled vehicle longitudinal and lateral dynamical model that contains time-varying uncertainties. Second, we design a series of nonlinear equality constraints that directly guarantee the control objectives based on the kinematic relations. On this basis, an adaptive robust constraint-following control is proposed. It is shown that the control guarantees the uniform boundedness and the uniform ultimate boundedness of the constraint-following error and the uncertainty estimation error. Finally, simulation results are provided to validate the effectiveness of the proposed methodology.


2021 ◽  
Author(s):  
Eshaan Khanapuri ◽  
Veera Venkata Tarun Kartik Chintalapati ◽  
Rajnikant Sharma ◽  
Ryan Gerdes

<p>The security of cyber-physical systems, such as vehicle platoons, is critical to ensuring their proper operation and acceptance to society. In platooning, vehicles follow one another according to an agreed upon control law that determines vehicle separation. It has been shown that a vehicle within a platoon and under the control of a malicious actor could cause collisions involving, or decrease the efficiency of, surrounding vehicles. In this paper we focus on detecting, identifying and mitigating so called destabilizing attacks that could cause vehicle collisions. Our approach is decentralized and requires only local sensor information for each vehicle to identify the vehicle responsible for the attack and then deploy an appropriate mitigating controller that prevents collisions. A Deep Learning approach (Convolutional Neural Network) with various data preprocessing techniques are used to detect and identify the malicious vehicle. Results indicate that with noise upto 30% in range/relative speed data we achieve an accuracy upto 96.3%. Also, once the adversarial vehicle is localized, we derive conditions for controller gains using Routh Hurwitz criterion to mitigate the attack and ensure stability of the platoon. Realistic simulator CARLA and MATLAB simulation results validate the effectiveness of our proposed approaches</p>


2017 ◽  
Vol 29 (1) ◽  
pp. 35-44 ◽  
Author(s):  
Ligang Wu ◽  
Zibao Lu ◽  
Ge Guo

This paper investigates the analysis and synthesis of networked vehicle platoons with communication delays, packet dropouts and disorders. In order to deal with the effects of the communication constraints, we introduce a novel Smart Data Processor (SDP) for each vehicle, by which the latest data packets from logic Data Packet Processor and the matched data packet from its Buffer can be obtained. Based on this mechanism, a leader-predecessor-follower control strategy is proposed. In order to guarantee the asymptotic and string stability, the platoon control problem is transformed into a multi-objective H∞-type synthesis problem with the multiple time-varying delays. A sufficient condition for designing the controller gain is derived by solving a set of linear matrix inequalities. Numerous simulations and experiments with laboratory scale Arduino cars show the efficiency of the proposed methods.


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