Connected vehicle prototype uses automated longitudinal control capabilities to optimize speed and approach at signalized intersections.
McLean, Virginia, United States
AERIS Capstone Executive Summary
Summary Information
Employing a multimodal approach, the USDOT’s Applications for the Environment: Real-Time Information Synthesis (AERIS) Research Program aims to encourage the development of technologies and applications that support a more sustainable relationship between transportation and the environment, chiefly through fuel use reductions and resulting emissions reductions.
In 2014, the AERIS team undertook the GlidePath prototype application project and developed an application of a cooperative adaptive cruise control (CACC) that automatically communicates wirelessly with a traffic signal and controls a vehicle’s approaching and departing speed in an eco-friendly manner.
The prototype consists of an onboard application and control software that provides a tablet-based driver interface and computes an optimum speed trajectory through Turner Fairbank Highway Research Center (TFHRC’s) intelligent intersection. When the vehicle approaches an intelligent intersection, it receives two distinct standard dedicated short range communications (DSRC) messages describing the Signal Phase and Timing (SPaT) and intersection geometry. The application then performs calculations to determine the vehicle’s optimal speed to pass the next traffic signal on a green light or to decelerate to a stop in the most ecofriendly manner. It then provides speed recommendations to the driver using the tablet or (with activation by the driver) sends the speed recommendations directly to the vehicle’s longitudinal control system to support partial automation.
In August 2015, Federal Highway Administration’s (FHWA) Saxton Transportation Operations Laboratory had a single equipped vehicle demonstrate the GlidePath prototype’s automated longitudinal control application at TFHRC by interacting with one signalized traffic signal. Performance of the algorithm and automated prototype was then evaluated in terms of energy savings and environmental benefits.
Findings from the August 2015 field experiment:
- Driver-vehicle interface (DVI) based driving provided a seven percent fuel economy benefit
- Partially automated driving provided a 22 percent fuel economy benefit
