Lane change scheduling for connected and autonomous vehicles

Date

2023-02-01

Author

Atagoziev, Maksat
Güran Schmidt, Ece
Schmidt, Klaus Verner

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The execution of lane changes has a strong impact on driving safety and traffic throughput. Therefore, it is highly desired to automate and coordinate lane changes. The subject of this paper is the scheduling of lane changes of a group of connected and autonomous vehicles (CAVs) with the aim of minimizing the time during which all lane changes are completed, while keeping small inter-vehicle distances. To this end, the paper first develops an algorithm for minimizing the lane change time of a single CAV. This algorithm is then successively applied to all lane-changing CAVs. As a special feature, the proposed algorithm analytically computes CAV reference trajectories based on a second-order vehicle model such that all computations can be carried out in real time. In addition, the developed method supports the implementation of the computed CAV trajectories using cooperative adaptive cruise control in order to ensure safe driving in a tight vehicle formation. Detailed simulation experiments and a comparison to a benchmark method demonstrate the superior performance of our method.

Subject Keywords

Autonomous vehicles, Connected vehicles, Cooperative adaptive cruise control, Coordination, Lane changes, Real time, Scheduling

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85144617495&origin=inward
https://hdl.handle.net/11511/101695

Journal

Transportation Research Part C: Emerging Technologies

DOI

https://doi.org/10.1016/j.trc.2022.103985

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

M. Atagoziev, E. Güran Schmidt, and K. V. Schmidt, “Lane change scheduling for connected and autonomous vehicles,” Transportation Research Part C: Emerging Technologies, vol. 147, pp. 0–0, 2023, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85144617495&origin=inward.