A report published in 2018 (206) gives the results of an analysis performed on data about the events and related factors that led to crashes of small road vehicles from 2005 to 2007 across the USA. It indicates that the critical reasons for these crashes are likely attributable to the driver (in 94% of the cases), the vehicle (2%), the environment (2%), and the rest to unknown causes (2%). An overwhelming proportion of these crashes is thus due to human error. It is widely recognized that most of them could be avoided by constantly monitoring the driver (236; 4), and by taking proper, timely actions when necessary.
Monitoring the driver is thus critically important, and this applies to all vehicles, with the exception of those that are fully autonomous, i.e., where the driver does not control the vehicle under any circumstances. Given that the average driver will not own a fully-autonomous vehicle for decades to come, “driver monitoring (DM)1 ” will remain critically important during all this time.
This paper focuses on the topic of DM, which is usefully viewed as consisting of two successive steps. In the first, one characterizes the driver, or more precisely the state of the driver, and, in the second, one decides what safety actions to take based on this characterization. For example, in the monitoring of drowsiness, the first step might compute the level of drowsiness, whereas the second might check whether this level is at, or will soon reach, a critical level. More generally, the decision process should ideally fuse the various characterization parameters available and predict the future state of the driver based on them. The paper focuses almost exclusively on the characterization of the state of the driver, i.e., on the first step in DM, which is also the one that is almost exclusively considered in the literature.
By “state of the driver” or “driver state”, we mean, in a loose way, the state, or situation, that the driver is in from various perspectives, in particular physical, physiological, psychological, and behavioral. To deal with this driver state in a manageable, modular way, we consider a specific number of distinct facets (such as drowsiness) of this driver state, which we call “driver (sub)states”. In the sequel, “state” thus refers either to the global state of the driver or to one of its facets, or substates. The paper covers the main (sub)states of drowsiness, mental workload, distraction, emotions, and under the influence, which emerge as being the most significant ones in the literature.
The core of the paper focuses on the characterization of each of these (sub)states, using indicators (of this state) and sensors (to access the values of these indicators in real time and in real driving conditions). In the example of the (sub)state of drowsiness, an indicator thereof is the eye-blink rate, and it can be accessed using a camera.
DM is important whether the vehicle is equipped with some form of “driving automation (DA)” (except for full automation) or not. In future vehicles, DA and DM will need to increasingly interact, and they will need to be designed and implemented in a synergistic way. While the paper focuses on DM (and, more precisely, on its characterization part), it considers and describes, at a high-level, how DM and DA interact at the various, standard levels of DA.
As suggested by its title, the paper comprises two main phases: (1) it reports on a systematic survey of the state of the art of DM (as of early 2021), (2) it provides a synthesis of the many characterization techniques of DM. This synthesis leads up to an innovative, structured, polychotomous view of the recent developments in the characterization part of DM. In a nutshell, this view is provided by two interlocked tables that involve the main driver (sub)states, the indicators of these states, and the sensors allowing access to the values of these indicators. The polychotomy presented should prove useful to researchers, equipment providers, and vehicle manufacturers in organizing their approach concerning the characterization and monitoring of the state of the driver.
Section 2 describes the standard levels of DA, and the role played by DM for each. Section 3 indicates the strategy for, and results of, our survey of the literature on DM. Section 4 describes the rationale and strategy for expressing the characterization of the driver state as much as possible in terms of the triad of the (sub)states, indicators, and sensors. Section 5 provides our innovative, structured, polychotomous view of the characterization part of DM. Sections 6 to 10 successively describe the five driver (sub)states that the survey revealed as being the most important. Section 11 summarizes and concludes.