Simulation of Cut-In by Manually Driven Vehicles in Platooning Scenarios Show others and affiliations
2017 (English) In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2017, p. 315-320Conference paper, Published paper (Refereed)
Abstract [en]
In the near future, Cooperative Intelligent Transport System (C-ITS) applications are expected to be deployed. To support this, simulation is often used to design and evaluate the applications during the early development phases. Simulations of C-ITS scenarios often assume a fleet of homogeneous vehicles within the transportation system. In contrast, once C-ITS is deployed, the traffic scenarios will consist of a mixture of connected and non-connected vehicles, which, in addition, can be driven manually or automatically. Such mixed cases are rarely analysed, especially those where manually driven vehicles are involved. Therefore, this paper presents a C-ITS simulation framework, which incorporates a manually driven car through a driving simulator interacting with a traffic simulator, and a communication simulator, which together enable modelling and analysis of C-ITS applications and scenarios. Furthermore, example usages in the scenarios, where a manually driven vehicle cut-in to a platoon of Cooperative Adaptive Cruise Control (CACC) equipped vehicles are presented.
Place, publisher, year, edition, pages IEEE, 2017. p. 315-320
Keywords [en]
Cooperative ITS, Autonomous driving, Traffic mixture, Simulation, Adaptive cruise control, Platooning (electronic)
National Category
Transport Systems and Logistics Electrical Engineering, Electronic Engineering, Information Engineering
Research subject 20 Road: Traffic engineering, 23 Road: ITS och traffic
Identifiers URN: urn:nbn:se:vti:diva-12685 DOI: 10.1109/ITSC.2017.8317806 ISBN: 978-1-5386-1525-6 (electronic) OAI: oai:DiVA.org:vti-12685 DiVA, id: diva2:1167731
Conference 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Kanagawa, Japan, 16-19 October, 2017
Funder Knowledge Foundation 2017-12-192017-12-192025-09-11 Bibliographically approved