Simulation experiments show that connected and autonomous vehicle signalized intersection coordination systems can reduce fuel consumption by 22.1 to 52.0 percent.

Simulated intersection coordination method for connected and autonomous vehicles with network from Guangzhou, China.

Guangzhou; China

Summary Information

This paper presents a coordinated intersection control method in a connected vehicle environment and improves the autonomous intersection management strategy on a simulation test-bed and typical road network in Guangzhou, China, is used to evaluate the proposed method.


The road network is divided into three areas: buffer area, core area and free driving area. A buffer-assignment mechanism is developed to cooperatively assign a specific crossing span for an individual autonomous vehicle (AV) and guide each AV to adjust its entry time and corresponding speed in the core area. A set-projection algorithm and a three-segment linear speed profile are used to control the trajectories of the AVs in the buffer area. The performance of the proposed method is evaluated by simulating various traffic conditions on an actual urban network.


The simulation experiments and sensitivity analyses demonstrate that the proposed intersection coordination method can reduce fuel consumption by 22.1 percent to 52.0 percent deployment-wide.

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Autonomous Vehicle-Intersection Coordination Method in a Connected Vehicle Environment

Author: Lin, Peiqun, et al.

Published By: IEEE Intelligent Transportation Systems Magazine

Source Date: 10/25/2017

Other Reference Number: Volume 9, Issue 4, Winter 2017.



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Typical Deployment Locations

Metropolitan Areas


coordinated signals, signal coordination, centralized signal control, signal synchronization, traffic signals, advanced signal control, signal timing optimization, coordinated signal control, advanced signal controller, traffic signal retiming, retiming, advanced signal systems, connected vehicles, autonomous vehicles, fuel consumption, intersection management, signal coordination

Benefit ID: 2017-01207