Cooperative Eco-driving Automation Improves Energy Efficiency, Safety on City Streets

Automated driving, location-based traffic control devices and road geometry improve driving safety, energy efficiency and

Automated driving, location-based traffic control devices and road geometry improve
driving safety, energy efficiency and driver comfort.

Imagine you’re driving up a hill toward a traffic light. The light is still green
so you’re tempted to accelerate to make it through the intersection before the light
changes. Then, a device in your car receives a signal from the controller mounted
on the intersection alerting you that the light will change in two seconds — clearly
not enough time to beat the light. You take your foot off the gas pedal and decelerate,
saving on fuel. You feel safer, too, knowing you didn’t run a red light and potentially
cause a collision in the intersection.

Connected and automated vehicles, which can interact vehicle to vehicle (V2V) and
between vehicles and roadway infrastructure like traffic signals and stop signs (V2I),
promise to save energy and improve safety. In a new study published in Transportation Research Part B, engineers from Michigan Technological
University propose a modeling framework for V2V and V2I cooperative driving.

Energy Efficiency and Other Benefits

Cooperative driving helps cars and their drivers safely and efficiently navigate.
The framework uses an eco-driving algorithm that prioritizes saving fuel and reducing
emissions. The automated algorithm calculates location-based traffic control devices
and roadway constraints using maps and geographic information. The research is led
by Kuilin Zhang, associate professor of civil and environmental engineering and affiliated associate professor of computer science at Michigan Tech, along with Shuaidong Zhao ’18, now a senior quantitative analyst
at National Grid.

For the past three years, Houghton, Michigan, has been home to roadside units installed on five of the city’s traffic signals that make V2I communication possible. Zhang conducted a simulation analysis using
real traffic signal phasing and timing messages from the Ann Arbor connected vehicle test environment and plans to expand testing in the Houghton area.

“The whole idea of cooperative driving automation is that the signals in the intersection
tell your car what’s happening ahead,” Zhang said. “The sensor at the intersection
can benefit all connected vehicles passing through the intersection. The automated
eco-driving algorithm improves the driving decisions of the connected and automated
vehicles.”

Stop, Go, Stop, Go

The simulation results show that the cooperative automated eco-driving algorithm saves
energy — 7% under light traffic and 23% under heavy traffic along the corridor.

Cooperative, Data-Driven Automated Driving

Zhang’s NSF CAREER Award-funded research includes a recent study that shows how connected and automated vehicles can adapt to road hazards at longer
range, increasing safety and preventing slowdowns.

“The stop and go, stop and go, it may use a lot of energy,” Zhang said. “The concept
of eco-driving incorporates how the vehicle makes driving decisions using data not
only from vehicles in front of it, but also with information given from a traffic
signal.”

Zhang’s model pulls in high-definition (HD) maps, which use a connected vehicle’s
hardware and software to provide down-to-the-centimeter accuracy in navigation. HD
maps incorporate multiple types of environmental sensing: long-range radar, lidar,
camera footage, short/medium-range radar and ultrasound.

Zhang said for autonomous driving, it’s important to know landmarks to control the
car’s driving, as well as hill grades; using a hill to slow or accelerate a car can
also increase energy savings. It’s easy to conserve energy on a straight highway;
on busy arterial streets with traffic and stoplights, energy conservation isn’t so
simple. On city streets, Zhang and Zhao’s online predictive connected and automated
eco-driving model considers traffic control devices and road geometry constraints
under light and heavy traffic conditions.

Michigan Technological University is a public research university, home to more than
7,000 students from 54 countries. Founded in 1885, the University offers more than
120 undergraduate and graduate degree programs in science and technology, engineering,
forestry, business and economics, health professions, humanities, mathematics, and
social sciences. Our campus in Michigan’s Upper Peninsula overlooks the Keweenaw Waterway
and is just a few miles from Lake Superior.