researcher says its driver workload estimator, still in development, could play a role in eliminating distracted driving.
Driver workload estimator monitors surroundings and adjusts vehicle settings.
DEARBORN, MI –hasn’t found the solution to driver distraction, but it may have developed an integral technology to help solve the problem, a top researcher says.
Jeff Greenberg, senior technical leader-Research and Innovation, says the auto maker is developing a technology called the driver-workload estimator that could play a key role in mitigating distracted driving.
“Vehicle control inputs, sensors, road conditions and biometric information such as a driver’s pulse and breathing can all be used to create a driver workload estimation that can then help manage certain functions in demanding situations,” he says at a media event here.
While still in the research phase, Greenberg says early findings are promising.
The system collects data from the sensing systems used by the vehicle’s driver-assist technologies, such as lane-keeping assist and the blind-spot information system.
The data helps determine the amount of concentration needed by a driver, which Ford calls workload, caused by traffic and road conditions.
The driver-workload estimator essentially is an algorithm that uses real-time data from existing sensors such as radar and cameras, combined with input from the driver, such as his use of the throttle, brakes and steering wheel.
The result is a so-called intelligent system that can determine the workload of a driver in any given situation and automatically adjust vehicle systems, including blocking incoming calls or texts and adjusting the sensitivity of safety warning systems to give the driver extra time to react.
For the system to be production-ready, Greenberg says massive amounts of data must be collected from thousands of drivers. Ford is collecting the stats from volunteers paired with professional drivers who observe the test drivers’ reactions to certain stimuli.
For example, some drivers are nervous while merging onto an expressway. If the vehicle’s sensor detects this, action is taken to avoid distraction, such as automatically forwarding in-coming calls to voicemail, inhibiting text messages or adjusting the vehicle’s safety systems.
But for any of this to work, the car must be equipped with biometric systems that monitor the driver’s vital statistics. Greenberg says such systems are not slated for production, but are being tested in laboratory research.
“The biometrics comes into play because there are variations in different people’s responses,” he says. “Someone on an icy road may be cool as a cucumber, no heart rate or respiration change, but another person could be very stressed out.
“That’s an extra level of accuracy we can add on that tailors the (vehicle’s) response to (the individual), Greenberg says.
Near-term, the driver-workload estimator could use existing sensors to allow the vehicle to automatically adapt its systems based on driver input and road conditions.
For example, the side-looking radar sensors used by the blind-spot information system and the forward-looking camera for the lane-keeping system constantly monitor the vehicle’s surroundings.
Without action from the driver, the signals from the sensors and camera could indicate heavy traffic in a merging lane and block incoming calls to prevent the driver from being distracted.
Greenberg declines to reveal when the technology will make its way into production vehicles, but vows it’s not just a pie-in-the-sky research project.
“This isn’t Buck Rogers stuff you’re never going to see, but I hesitate to put a timeframe on it,” he says. “One of the reasons this is in the research lab right now, is I can’t just take a couple of hundred drivers and believe I’m going to produce a system that’s going to seem intelligent across the population.
“We really do need a very large database across a wide range of conditions in order to come up with estimates that drivers agree are stressful situations.”
Even when the intelligent system reaches production, it won’t be a silver bullet. Greenberg says it will take a combination of in-vehicle technology and a change in social norms regarding the use of intelligent technology while operating a vehicle.
“Any problem involving human beings doesn’t have just one dimension of solutions, and this is a human problem,” he says. “But (the driver workload estimator) is part of the solution, absolutely.”