Eco-Cooperative Adaptive Cruise Control algorithm demonstrates average savings of 15 percent in fuel consumption while reducing average delay by 80 percent within the traffic signalized intersection vicinity.

Research provides a novel approach to managing automated vehicles at intersections.

Nationwide; United States

Summary Information

This 2014 study out of Virginia Tech presents an algorithmic development of an Eco-Cooperative Adaptive Cruise Control system. The modeling of the system constitutes a modified state-of-the-art path- finding algorithm within a dynamic programming framework to find near-optimal and near-real-time solutions to a complex non-linear programming problem that involves minimizing vehicle fuel consumption in the vicinity of signalized intersections.


The system leveraged dedicated short-range communication (DSRC) capabilities between the roadway infrastructure and vehicles. The optimization was conducted in two steps: (1) Computation of a proposed time to intersection based on available intersection data (queued vehicle information), lead-vehicle information (if any) and signal change information (Time to Red or Time to Green); and (2) Computation of a fuel-optimal speed profile using the computed time to intersection, vehicle acceleration model, roadway characteristics and microscopic fuel consumption models.

The proposed system was tested in an agent-based environment developed in MATLAB using the Rakha-Pasumarthy-Adjerid (RPA) car-following model as well as the Society of Automobile Engineers (SAE) J2735 message set standards for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications. The final testing of the algorithm was done in an enhanced Traffic Experimental Simulation tool (eTEXAS).

The approach volumes were ranged from 500 to 2000 vehicles per hour per approach so that the volume-capacity ratio will range between 0.2 and 0.9. Measures of effectiveness compared were fuel consumption and delay incurred per vehicle. A case-based classification was also made so as to compare the individual vehicles that had a positive speed change with the ones that had a negative speed change upstream of the intersection.


Results showed that the eco-speed control algorithm was able to reduce the overall fuel consumption of autonomous vehicles passing through an intersection by 15 percent while maintaining a 80 percent saving in delay when compared to a traditional signalized intersection control.

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Eco-Driving in the Vicinity of Roadway Intersections - Algorithmic Development, Modeling, and Testing

Author: Kamalanathsharma, R.

Published By: Virginia Polytechnic Institute and State University

Source Date: 03/28/2014



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intelligent cruise control, ICC, ACC, Intelligent Speed Adaptation, ISA

Benefit ID: 2017-01139