Adaptable algorithm monitors mental workload of drivers for improved safety

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.