CMU study shows autonomous vehicle algorithms can have considerable effect on fuel economy; need for new approaches in testing
Autonomous vehicle driving behavior can have a considerable effect on fuel economy. Researchers in the College of Engineering at Carnegie Mellon University have determined that fuel efficiency for self-driving cars—within the bounds of current fuel economy testing—could improve by up to 10% under efficiency-focused control strategies when following another vehicle.
However, the study also showed that autonomous vehicle (AV) technology following algorithms designed without considering efficiency can degrade fuel economy by up to 3%. In a paper published in the journal Transportation Research Part C: Emerging Technologies, Assistant Professor of Civil & Environmental Engineering Constantine Samaras and Ph.D. student Avi Chaim Mersky suggest the need for a new near-term approach in fuel economy testing to account for connected and autonomous vehicles.
The results of this study have shown that following control algorithms designed without considering fuel economy performance can perform significantly worse, while more intelligently designed control schemes may equal or exceed the base driver performance assumed by the EPA fuel economy tests. At present, with no incentive to design more fuel efficient autonomous rulesets, manufacturers may not design for increased fuel economy. They may design a system to maximize speed and/or acceleration, by default or as an option. … In addition, this study found more advanced connected features can improve performance consistently and significantly, by improving the amount of time a vehicle can predict actions in the future.
While the basic testing method outlined here would have to be expanded to meet US regulatory requirements in order to test automated vehicles, it does show the need for a new testing procedure. Additionally, while this study did not attempt to find an optimal control function, it is seen that attempting to significantly improve fuel economy without any predictive or connected features is challenging and inconsistent. This is because the lead vehicle’s behavior in the EPA tests is fairly non-aggressive, and the rules tested did not account for the full range of behaviors exhibited by the EPA drive cycles.
In particular, none of the rulesets explicitly distinguished between abrupt emergency stops and general city stop-and-go traffic. The inability to account for this caused poor performance on the urban cycles, where such actions are common, and may have caused poorer performance than could be expected of vehicles following more robust control sets designed for stop-and-go traffic. Additionally the fuel consumption model used precluded any testing of grade-based optimization or broader fuel economy benefits of automation such as platooning or reduced congestion.
… As technology and adoption increases and the system becomes more efficient, the driving behavior of the lead vehicle as well as the entire system will change. Hence, car following algorithms will have less predictive power. What is clear is that rapid progress is being made in the development of autonomous and connected vehicles and that AV technology affects individual vehicle fuel economy. Given this, stakeholders can use the methods outlined here as a starting point in the discussions for the best path forward.—Mersky and Samaras (2016)
The proposed standardized method for testing the fuel economy effects of autonomous vehicle behavior when following another vehicle consisted of two steps. This approach is applicable for the near-term, when AVs will travel in traffic with primarily conventional vehicles, the researchers noted.
They first abstracted the driverless vehicle’s control strategy for simulation to a simple one lane and one-dimensional road, with only one leading vehicle and perfect visibility; they then ran it following a vehicle obeying the EPA’s FTP and HWFET drive cycles.
These derived drive cycles were then tested with a dynamometer, similar to current testing. They then developed a series of simplified rulesets for adaptive cruise control (ACC) behavior and simulated the car following behavior for EPA’s drive cycles. They estimated fuel economy using the Virginia Tech Comprehensive Fuel Consumption model.
Because existing standardized tests don’t consider AV technologies, there are limited incentives for car manufacturers to design cars for optimum fuel efficiency. The EPA can use our research as a starting point in redesigning fuel economy testing for autonomous vehicles.—Constantine Samaras
The researchers also looked at connected vehicle scenarios in which information about a lead car’s travel behavior was communicated to an AV following this lead car. The study found that more advanced connectivity could enhance a vehicle’s performance by providing the vehicle with more time to plan future actions. The longer the vehicle plans into the future, the greater the fuel economy benefits.
What we have quantified is that fuel economy testing will need to account for AV technologies in the not-so-distant future.—Avi Mersky
To start these discussions, the study provided suggestions on how current EPA fuel economy tests could be modified to address AV technologies.
Avi Chaim Mersky, Constantine Samaras (2016) “Fuel economy testing of autonomous vehicles” Transportation Research Part C: Emerging Technologies, Volume 65, Pages 31-48 doi: 10.1016/j.trc.2016.01.001