  Road-vehicle accidents are mostly due to human errors, and many such
accidents could be avoided by continuously monitoring the driver. Driver
monitoring (DM) is a topic of growing interest in the automotive industry, and
it will remain relevant for all vehicles that are not fully autonomous, and
thus for decades for the average vehicle owner. The present paper focuses on
the first step of DM, which consists in characterizing the state of the driver.
Since DM will be increasingly linked to driving automation (DA), this paper
presents a clear view of the role of DM at each of the six SAE levels of DA.
This paper surveys the state of the art of DM, and then synthesizes it,
providing a unique, structured, polychotomous view of the many characterization
techniques of DM. Informed by the survey, the paper characterizes the driver
state along the five main dimensions--called here "(sub)states"--of drowsiness,
mental workload, distraction, emotions, and under the influence. The
polychotomous view of DM is presented through a pair of interlocked tables that
relate these states to their indicators (e.g., the eye-blink rate) and the
sensors that can access each of these indicators (e.g., a camera). The tables
factor in not only the effects linked directly to the driver, but also those
linked to the (driven) vehicle and the (driving) environment. They show, at a
glance, to concerned researchers, equipment providers, and vehicle
manufacturers (1) most of the options they have to implement various forms of
advanced DM systems, and (2) fruitful areas for further research and
innovation.
