PFI-TT: Local Sensing on Automated Vehicles
This Partnerships for Innovation - Technology Translation (PFI-TT) project creates new technologies for automated vehicles that can improve traffic for everyone. The majority of vehicles available for sale today in the United States have adaptive cruise control as a standard or optional feature. These systems are designed for safety and driver comfort, but they do not have the ability to smooth out traffic jams created by other drivers. This project creates a vehicle technology that allows automated vehicles to automatically and safely change their speeds based on traffic conditions ahead, thereby unlocking the potential for the adaptive cruise control system to minimize traffic jams rather than contribute to them. The societal benefits include reduced energy use and increased safety. The commercial impact of the project includes new on-board safety features that will be part of the expanding market for vehicle-to-infrastructure connectivity.
This project creates a technology to automatically reject erroneous traffic information used in traffic wave-smoothing adaptive cruise control systems, thereby enabling the systems to be safely deployed. The project demonstrates an ability to measure traffic conditions around the vehicle and estimate a safety envelope that determines safe and socially acceptable speeds for the vehicle to travel. The goals of the research include verification and documentation of the capability, in sufficient detail to allow commercialization by vehicle manufacturers. The approach includes the use of testing data from smart freeway infrastructure to validate the system design. The project also implements the developed technologies on a commercial vehicle with stock sensors, which drives on a multi-lane United States freeway in open-road tests to demonstrate the effectiveness of the implementation on larger vehicles.
The period of performance for this award is March 1, 2024 – February 28, 2027.
This is collaborative research with:
- Dan Work (PI, Vanderbilt University)
- Alexandre M. Bayen (University of California, Berkeley)
- Jonathan Sprinkle (Vanderbilt University)
This work is supported by the National Science Foundation under award TI-2329820. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.