Feedback-based platoon control for connected autonomous vehicles under different communication network topologies

Author(s):  
Yongfu Li ◽  
Kezhi Li ◽  
Linqin Cai ◽  
Hao Zhu ◽  
Fenglan Sun
2017 ◽  
Vol 50 (1) ◽  
pp. 1199-1204 ◽  
Author(s):  
Holger Zipper ◽  
Christian Diedrich

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Roberto Merco

This research focuses on the identification and mitigation of malicious vehicles in cooperative tasks between automated connected vehicles. The approach aims to design estimators which employ the number and diversity of sensors that autonomous vehicles are equipped with in order to enrich the knowledge of the surrounding environment of each vehicle in the wireless communication network range. Since an event-triggered communication network is considered to increase the overall performance of the communication channels, the estimators have to be designed to take into account aperiodic and asynchronous measurements.


2021 ◽  
Author(s):  
Anna Arestova ◽  
Wojciech Baron

The rapid development in information and communication technology confronts designers of real-time systems with new challenges that have arisen due to the increasing amount of data and an intensified interconnection of functions. This is e.g. driven by recent trends such as automated driving in the automotive field and digitization in factory automation. For distributed safety-critical systems, this progression has the impact that the complexity of scheduling tasks with precedence constraints organized in so-called task chains increases the more data has to be exchanged between tasks and the more functions are involved. Especially when data has to be transmitted over an Ethernet-based communication network, the coordination between the processing tasks running on different end-devices and the communication network has to be ensured to meet strict end-to-end deadlines of task chains. In this work, we present a heuristic approach that computes schedules for distributed and data-dependent task chains consisting of preemptive and periodic tasks, taking into account the network communication delays of time-sensitive networks. Our algorithm is able to solve large problems for synthetic network topologies with randomized data dependencies in a few seconds. A high success rate was achieved, which can also be further enhanced by relaxing the deadline conditions.


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