Adaptive Cruise Control | Compositional Systems Lab

PFI-TT: Local Sensing on Automated Vehicles

Fri, 01 Mar 2024 00:00:00 +0000

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 . 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.

Safer adaptive cruise control for traffic wave dampening

Fri, 01 Jan 2021 00:00:00 +0000

This project aims to develop an adaptive cruise controller for vehicles at low speeds in stop-and-go traffic. Current adaptive cruise controllers can use RADAR sensors to follow a vehicle at high speeds (greater than 18 mph), but reach their limits if the lead vehicle’s velocity dips below threshold, requiring the driver of the host vehicle to resume control over the car’s speed. Some cruise controllers adapt to stop-and-go traffic, but these are mostly experimental and have yet to see widespread commercial implementation. These experimental models often have issues because of their limited data; consequently, the acceleration and deceleration can be jarring and uncomfortable to passengers. In contrast, because of our reliable sensor data, and the sensor configuration unique to the CAT Vehicle, our cruise controller will be capable of following cars at low speeds and functioning continuously, even when the car is stopped.

Are commercially implemented adaptive cruise control systems string stable?

Wed, 01 Jan 2020 00:00:00 +0000

In this article, we assess the string stability of seven 2018 model year adaptive cruise control (ACC) equipped vehicles that are widely available in the US market. Seven distinct vehicle models from two different vehicle makes are analyzed using data collected from more than 1,200 miles of driving in car-following experiments with ACC engaged by the follower vehicle. The resulting dataset is used to identify the parameters of a linear second order delay differential equation model that approximates the behavior of the black box ACC systems. The string stability of the data-fitted model associated with each vehicle is assessed, and the main finding is that all seven vehicle models have string unstable ACC systems. For one commonly available vehicle model that offers ACC as a standard feature on all trim levels, we validate the string stability finding with a multi-vehicle homogeneousplatoon experiment in which all vehicles are the same year, make, and model. In this test, an initial disturbance of 6 mph is amplified to a 25 mph disturbance, at which point the last vehicle in the platoon is observed to disengage the ACC. The data collected in the driving experiments is made available, representing the largest publicly available comparative driving dataset on ACC equipped vehicles.