  Recent advances in AI and intelligent vehicle technology hold promise to
revolutionize mobility and transportation, in the form of advanced driving
assistance (ADAS) interfaces. Although it is widely recognized that certain
cognitive factors, such as impulsivity and inhibitory control, are related to
risky driving behavior, play a significant role in on-road risk-taking,
existing systems fail to leverage such factors. Varying levels of these
cognitive factors could influence the effectiveness and acceptance of driver
safety interfaces.
  We demonstrate an approach for personalizing driver interaction via driver
safety interfaces that are triggered based on a learned recurrent neural
network. The network is trained from a population of human drivers to infer
impulsivity and inhibitory control from recent driving behavior. Using a
high-fidelity vehicle motion simulator, we demonstrate the ability to deduce
these factors from driver behavior. We then use these inferred factors to make
instantaneous determinations on whether or not to engage a driver safety
interface. This interface aims to decrease a driver's speed during yellow
lights and reduce their inclination to run through them.
