Working in partnership with Jaguar Land Rover (JLR), Cambridge University researchers used a combination of on-road experiments and machine learning, plus Bayesian filtering techniques, to measure driver ‘workload’ reliably and continuously. Driving in an unfamiliar area may translate to a high workload, while a daily commute may mean a lower workload.
The resulting algorithm is said to be highly adaptable and can respond in near real-time to changes the driver’s behaviour and status, road conditions, road type, or driver characteristics.
This information could then be incorporated into in-vehicle systems such as infotainment and navigation, displays, advanced driver assistance systems (ADAS) and others. Any driver vehicle interaction can be then customised to prioritise safety and enhance the user experience, delivering adaptive human machine interactions. The results are reported in IEEE Transactions on Intelligent Vehicles.
In a statement, Dr Bashar Ahmad, from Cambridge’s Department of Engineering, said: “More and more data is made available to drivers all the time. However, with increasing levels of driver demand, this can be a major risk factor for road safety.
“There is a lot of information that a vehicle can make available to the driver, but it’s not safe or practical to do so unless you know the status of the driver.”
A driver’s status – or workload – can change frequently. Driving in a new area, in heavy traffic or in poor road conditions is usually more demanding than a daily commute.
“If you’re in a demanding driving situation, that would be a bad time for a message to pop up on a screen or a heads-up display,” said Ahmad. “The issue for car manufacturers is how to measure how occupied the driver is, and instigate interactions or issue messages or prompts only when the driver is happy to receive them.”
Algorithms exist that measure driver demand by using eye gaze trackers and biometric data from heart rate monitors, but the Cambridge researchers wanted to develop an approach using information that is available in any car, specifically driving performance signals such as steering, acceleration and braking data. It should also be able consume and fuse different unsynchronised data streams that have different update rates, including from biometric sensors if available.
To measure driver workload, the researchers first developed a modified version of the Peripheral Detection Task to collect subjective workload information during driving. For the experiment, a phone showing a route on a navigation app was mounted to the car’s central air vent, next to a small LED ring light that would blink at regular intervals. Participants all followed the same route through a mix of rural, urban and main roads. They were asked to push a finger-worn button whenever the LED light lit up in red and the driver perceived they were in a low workload scenario.
Video analysis of the experiment, paired with the data from the buttons, allowed the researchers to identify high workload situations, such as busy junctions or a vehicle in front or behind the driver behaving unusually.
The on-road data was then used to develop and validate a supervised machine learning framework to profile drivers based on the average workload they experience, and an adaptable Bayesian filtering approach for sequentially estimating, in real-time, the driver’s instantaneous workload, using several driving performance signals including steering and braking.
“For most machine learning applications like this, you would have to train it on a particular driver, but we’ve been able to adapt the models on the go using simple Bayesian filtering techniques,” said Ahmad. “It can easily adapt to different road types and conditions, or different drivers using the same car.”
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