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In the Early Research Program "Adaptive Automotive Automation", TNO has developed two important algorithms that will improve safety and usefulness of (partially) automated vehicles and create a value proposition for automotive OEMs and increase acceptance of automated driving .
Current automated functions in cars and trucks are generally implemented as a one-size-fits-all solution, at most with a limited number of adjustable settings. This may lead to suboptimal system behaviour and reduced user acceptance for a large number of drivers or driving situations. This may lead to undertrusting the system, and people not using it because it does not fit their expectations and needs. TNO has developed an approach in which driver support functions automatically adapt to the driver, the driver state and driving and traffic conditions. This approach has been implemented in an ACC (Adaptive Cruise Control) algorithm; the P-ACC (Personalized ACC). The P-ACC algorithm uses critical elements from manual driving to tune specific elements in the P-ACC to fit to the user preferences and profile without any manual settings. It has been tested in on-road studies showing increased acceptance and comfort compared to driving with ordinary ACC. This personalized approach can also be applied to numerous other driver support functions, which will increase brand loyalty and brand image, increase actual use and comfort and even lead to safer and more comtable driving.
In truck platooning and highly automated driving, the car or truck temporarilty takes over driving tasks from the driver. Depending on the level of automation, it may drive automatically through traffic in a platoon of vehicles. However, in case of unforeseen situations or limitations in the functional envelope of the systems, the driver will be requested to take over control from the automation and switch back to manual driving.
This requires a complicated interaction between automation and driver. The automation allows the driver to be temporarily out of the loop and do other things but needs the driver to resume control in due time in order to ensure safety. This means that the system not only needs to monitor technical status and driving conditionss, but that there needs to be some information about the time the driver needs to take back control under various conditions. TNO has developed a unique Driver Readiness Model that detects the real time status of the driver and predicts real-time the required time that the driver needs to safely take over control from the automation at any moment. This driver readiness algorithm uses driver indicators like vigilance, eye movements, secondary task, feet position, hands on steering wheel, body posture and many more as an input. This driver readiness times are combined with information about the status of technology and the driving conditions, altogether feeding into a prediction what is the most appropriate level of automation. With this prediction of the time that the driver needs to take over control (for normal take-overs and critical take-overs), automation systems will be able to anticipate on a safe handover from machine to the driver. This will improve the combined driver / automation behaviour and performance.
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