TR2022-059
Coordination of Autonomous Vehicles and Dynamic Traffic Rules in Mixed Automated/Manual Traffic
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- "Coordination of Autonomous Vehicles and Dynamic Traffic Rules in Mixed Automated/Manual Traffic", American Control Conference (ACC), June 2022.BibTeX TR2022-059 PDF
- @inproceedings{Firoozi2022jun,
- author = {Firoozi, Roya and Quirynen, Rien and Di Cairano, Stefano},
- title = {Coordination of Autonomous Vehicles and Dynamic Traffic Rules in Mixed Automated/Manual Traffic},
- booktitle = {American Control Conference (ACC)},
- year = 2022,
- month = jun,
- url = {https://www.merl.com/publications/TR2022-059}
- }
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- "Coordination of Autonomous Vehicles and Dynamic Traffic Rules in Mixed Automated/Manual Traffic", American Control Conference (ACC), June 2022.
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MERL Contacts:
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Research Areas:
Abstract:
We consider the coordination of multiple con- nected automated vehicles (CAVs) by a central coordinator (CC) in mixed traffic where also non-controlled vehicles (NCVs) are present. The CC directly provides motion target commands to the CAVs and affects the behavior of NCVs by controlling dynamic traffic rules, such as changing traffic lights. We model the traffic rules for these scenarios by mixed-logical constraints, and the response of the NCVs to the changing traffic rules by a switched system with switches triggered by logic conditions. The proposed approach is implemented as a mixed-integer programming (MIP) problem to be solved in the central coordinator, which also handles timing constraint
Related News & Events
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NEWS MERL researchers presented 9 papers at the American Control Conference (ACC) Date: June 8, 2022 - June 10, 2022
Where: Atlanta, GA
MERL Contacts: Karl Berntorp; Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Christopher R. Laughman; Marcel Menner; Rien Quirynen; Abraham P. Vinod; Avishai Weiss
Research Areas: Control, Machine Learning, OptimizationBrief- At the American Control Conference in Atlanta, GA, MERL presented 9 papers on subjects including autonomous-vehicle decision making and motion planning, realtime Bayesian inference and learning, reference governors for hybrid systems, Bayesian optimization, and nonlinear control.