Autonomous vehicles have been highly anticipated because of the possibility that they will greatly reduce or perhaps eliminate the collisions that cause more than one million deaths each year. But safety isn’t the only potential benefit self-driving cars can offer: Teams of researchers around the world are showing that autonomous vehicles can also drive more efficiently than humans can. A U.S. Department of Energy program called NEXTCAR (Next-Generation Energy Technologies for Connected and Automated On-Road Vehicles), for example, is betting that a mix of new smart vehicle technologies can boost fuel efficiency by as much as 30 percent.
As part of the NEXTCAR program, San Antonio, Texas-based Southwest Research Institute (SwRI) showcased advances in autonomous vehicle technology that will improve vehicles’ fuel economy—including the fuel efficiency of non-autonomous automobiles that just so happen to be in traffic with autonomous ones. The demonstration was held at the ARPA-E Energy Inovation Summit in Dallas in late May.
Making an Efficient Autonomous Vehicle
The SwRI team retrofitted a 2021 Honda Clarity hybrid with basic autonomous features such as perception and localization. On the day of the summit, they drove the vehicle along a route encircling the parking lot of the convention center where the summit was held. SWRI’s Ranger localization system, which the researchers installed on the Honda, has a downward-facing camera that captures images of the ground. By initially mapping the driving surface, Ranger can later localize the vehicle with centimeter-level accuracy, using the ground’s unique “fingerprint” combined with GPS data. This precision ensures the vehicle drives with exceptional control.
“It’s almost like riding on rails,” says Stas Gankov, a researcher in SwRI’s powertrain engineering group. For this project, his group collaborated with other divisions at the institute, such as the intelligence systems division, which developed the autonomy software stack added to the Honda Clarity.
Just as important, however, was the addition of an eco-driving module, a key innovation by SwRI. The eco-mode determines the most economical driving speed by considering various factors such as traffic lights and surrounding vehicles. This system employs predictive control algorithms to help solve a tricky optimization problem: How can cars minimize energy consumption while maintaining efficient traffic flow? SwRI’s eco-mode aims to reduce unnecessary acceleration and deceleration in order to optimize energy usage without impeding other vehicles.
“Autonomous vehicles operating in eco-mode influence the driving behavior of all the cars behind them.” —Stas Gankov, Southwest Research Institute
To illustrate how the technology works, the team installed a traffic signal along the demonstration pathway. Gankov says an actual traffic light timer from a traffic signal cabinet was connected to a TV screen, providing a visual for attendees. A dedicated short range communications (DRSC) radio was also attached, broadcasting the signal’s phase and timing information to the vehicle. This setup enabled the vehicle to anticipate the traffic light’s actions far more accurately than a human driver could.
For instance, Gankov says, if the Honda Clarity was approaching a red light that was about to turn green, it would know the light was due to change and so avoid wasting energy by braking and then accelerating again. Conversely, if the car was approaching the signal as it was about to turn from green to yellow to red, the vehicle would release the accelerator and let friction slow it to a crawl, avoiding unnecessary acceleration in an attempt to beat the light.
These autonomous driving strategies can lead to significant energy savings, benefiting not just the autonomous vehicles themselves, but also the entire traffic ecosystem.
“In a regular traffic situation, autonomous vehicles operating in eco-mode influence the driving behavior of all the cars behind them,” says Gankov. “The result is that even vehicles with Level 0 autonomy use fuel more sparingly.”
The Grand Vehicle Energy Plan
SwRI has been a participant in the NEXTCAR initiative since 2017. The program’s initial phase involved 11 teams, including SwRI, Michigan Technological University, Ohio State University, and the University of California Berkeley. SwRI, in collaboration with the University of Michigan, focused on optimizing a Toyota Prius Prime, already known for its fuel efficiency, to achieve a 20 percent improvement in energy usage through optimization algorithms and wireless communicating with its surroundings. This was accomplished without modifying the Toyota’s powertrain or compromising its emissions. The team utilized power split optimization, balancing the use of the gas engine and battery propulsion system for maximum efficiency.
Building on the success of NEXTCAR’s first phase, the program entered its second phase in 2021, with just SwRI, UC Berkeley, Michigan Tech and Ohio State remaining. The focus of NEXTCAR 2 has been determining how much automation could further enhance energy efficiency. Gankov explains that while the first phase demonstrated a 20 percent energy efficiency improvement over a baseline 2016 or 2017 model year vehicle with no autonomous driving capabilities, through the addition of vehicle-to-everything connectivity alone, the second phase is exploring the potential for an additional 10 percent improvement by incorporating autonomous features.
Gankov says SwRI initially intended to partner with Honda for NEXTCAR’s second phase, but when contracting issues arose, the nonprofit proceeded independently. Utilizing an autonomy platform developed by SwRI’s intelligence systems division, the NEXTCAR team equipped the Honda Clarity with what amounted to Level 4 autonomy in a box. This autonomy system features a drive-by-wire system, allowing the vehicle to automatically adjust its speed and steering based on inputs from the autonomy software stack and the eco-driving module. This ensures the vehicle prioritizes safety while optimizing for energy efficiency.
Employing techniques like efficient highway merging were key strategies in their approach to making the most of each tank of fuel or battery charge. “For example, in heavy traffic on the highway, calculating the most optimal way to merge onto the highway without negatively affecting the energy efficiency of the vehicles already on the highway is crucial,” Gankov noted.
As NEXTCAR 2 enters its final year, the demonstration at the ARPA-E Summit served as a testament to the progress made in autonomous vehicle technology and its potential to dramatically improve energy efficiency in transportation.
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