Document Type
Article
Publication Date
6-2023
Department
Department of Mechanical Engineering-Engineering Mechanics; Department of Applied Computing; Department of Electrical and Computer Engineering
Abstract
Artificial intelligence is gaining tremendous attractiveness and showing great success in solving various problems, such as simplifying optimal control derivation. This work focuses on the application of Neuroevolution to the control of Connected and Autonomous Vehicle (CAV) cohorts operating at uncontrolled intersections. The proposed method implementation’s simplicity, thanks to the inclusion of heuristics and effective real-time performance are demonstrated. The resulting architecture achieves nearly ideal operating conditions in keeping the average speeds close to the speed limit. It achieves twice as high mean speed throughput as a controlled intersection, hence enabling lower travel time and mitigating energy inefficiencies from stop-and-go vehicle dynamics. Low deviation from the road speed limit is hence continuously sustained for cohorts of at most 50 m long. This limitation can be mitigated with additional lanes that the cohorts can split into. The concept also allows the testing and implementation of fast-turning lanes by simply replicating and reconnecting the control architecture at each new road crossing, enabling high scalability for complex road network analysis. The controller is also successfully validated within a high-fidelity vehicle dynamic environment, showing its potential for driverless vehicle control in addition to offering a new traffic control simulation model for future autonomous operation studies.
Publication Title
Eng
Recommended Citation
Jacquelin, F.,
Bae, J.,
Chen, B.,
&
Robinette, D.
(2023).
Neuroevolution Application to Collaborative and Heuristics-Based Connected and Autonomous Vehicle Cohort Simulation at Uncontrolled Intersection.
Eng,
4(2), 1320-1336.
http://doi.org/10.3390/eng4020077
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/47
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Included in
Computer Sciences Commons, Electrical and Computer Engineering Commons, Mechanical Engineering Commons
Publisher's Statement
Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. Publisher’s version of record: https://doi.org/10.3390/eng4020077