United States Department of Transportation
i
Intersection management using in-vehicle speed advisory/adaptation : final report.
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2016-08-30
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[PDF-1.87 MB]
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Alternative Title:Intersection management using in-vehicle speed advisory/adaptation : grant project title : advanced operations focused on connected vehicles/infrastructure (CVI-UTC).
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Edition:Final report
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Abstract:In recent years, connected vehicles (CVs) and automated vehicles (AVs) have emerged as a
realistic and viable transportation option. Research centers and companies have dedicated
substantial efforts to the technology, motivated largely by the potential safety benefits that can be
realized through the elimination of human error, the enhancement of mobility via reduction of
congestion and optimization of trips, and the associated positive environmental impacts. Both
sensors and control mechanisms are needed for this technology to succeed. The goal of this study
is to make use of vehicle connectivity via vehicle-to-vehicle (V2V) (i.e., exchanging information
between vehicles) and vehicle-to-infrastructure (V2I) (i.e., exchanging information with the
infrastructure, including intersection controllers) features, leveraging both connected and
automated capabilities, to develop control algorithms/systems that deliver
solutions/recommendations for connected automated vehicles (CAVs) [1] as they proceed through
intersections. The algorithms developed in this report deliver optimal and/or near-optimal
solutions, which required extensive simulations and field experiments for validation.
In the work described in this report, the research group combined mathematical modeling, optimal
control theory, and optimization into a simulation framework that allows vehicles to cross an
intersection safely, while incurring the least amount of delay. These models feature kinematic,
dynamic and static constraints. Different versions of the model were developed, ranging from exact
solutions that cannot be implemented in real-time to heuristic solutions that are computationally
efficient. The results of the final proposed model were compared to other control techniques
already implemented in the field, and demonstrated that a reduction of at least 50% in delay was
achievable. An interesting byproduct of this model was the reduction in fuel consumption, and
thus emissions, by more than 10%.
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