Optimal lane-free crossing of CAVs through intersections

Amouzadi, Mahdi, Olawumi Orisatoki, Mobolaji and Moradinegade Dizqah, Arash (2022) Optimal lane-free crossing of CAVs through intersections. IEEE Transactions on Vehicular Technology. pp. 1-13. ISSN 0018-9545

[img] PDF - Published Version
Available under License Creative Commons Attribution.

Download (3MB)


Connected and autonomous vehicles (CAVs), unlike conventional cars, will utilise the whole space of intersections and cross in a lane-free order. This paper formulates such a lanefree crossing of intersections as a multi-objective optimal control problem (OCP) that minimises the overall crossing time, as well as the energy consumption due to the acceleration of CAVs. The constraints that avoid collision of vehicles with each other and with road boundaries are smoothed by applying the dual problem theory of convex optimisation. The developed algorithm is capable of finding the lower boundary of the crossing time of a junction which can be used as a benchmark for comparing other intersection crossing algorithms. Simulation results show that the lane-free crossing time is better by an average of 40% as compared to the state-of-the-art reservation-based method, whilst consuming the same amount of energy. Furthermore, it is shown that the lane-free crossing time through intersections is fixed to a constant value regardless of the number of CAVs.

Item Type: Article
Additional Information: © 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: signal-free intersection, path planning, connected and autonomous vehicles, dual problem theory
Schools and Departments: School of Engineering and Informatics > Engineering and Design
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 12 Sep 2022 09:22
Last Modified: 27 Apr 2023 10:47
URI: http://sro.sussex.ac.uk/id/eprint/107860

View download statistics for this item

📧 Request an update