Learning-Based Driver Workload Estimation

Abstract

A popular definition of workload is given by O’Donnell and Eggmeir, which states that “The term workload refers to that portion of the operator’s limited capacity actually required to perform a particular task” [1]. In the vehicle environment, the “particular task” refers to both the vehicle control, which is the primary task, and other secondary activities such as listening to the radio. Three major types of driver workload are usually studied, namely, visual, manual, and cognitive. Auditory workload is not treated as a major type of workload in the driving context because the auditory perception is not considered as a major requirement to perform a driving task. Even when there is an activity that involves audition, the driver is mostly affected cognitively. Lately, the advanced computer and telecommunication technology is introducing many new in-vehicle information systems (IVISs), which give drivers more convenient and pleasant driving experiences. Active research is being conducted to provide IVISs with both high functionality and high usability. On the usability side, driver’s workload is a heated topic advancing in at least two major directions. One is the offline assessment of the workload imposed by IVISs, which can be used to improve the design of IVISs. The other effort is the online workload estimation, based on which IVISs can provide appropriate service at appropriate time, which is usually termed as Workload Management . For example, the incoming phone call may be delayed if the driver is engaged in a demanding maneuver. Among the three major types of driver workload, cognitive workload is the most difficult to measure. For example, withdrawing hands from the steering wheel to reach for a coffee cup requires extra manual workload. It also may require extra visual workload in that the position of the cup may need to be located. Both types of workload are directly measurable through such observations as hands-off-wheel and eyes-offroad time. On the other hand, engaging in thinking (the so-called minds-off-road phenomenon) is difficult to detect. Since the cognitive workload level is internal to the driver, it can only be inferred based on the information that is observable. In this chapter, we report some of our research results on driver’s cognitive workload estimation. After the discussion of the existing practices, we propose a new methodology to design driver workload estimation systems, that is, using machine-learning techniques to derive optimized models to index workload. The advantage of this methodology will be discussed, followed by the presentation of some experimental results. This chapter concludes with discussion of future work.