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Touchscreen human-machine interfaces (HMIs) are commonly employed as the primary control interface and touch-point of vehicles. However, there has been very little theoretical work to model the demand associated with such devices in the automotive domain. Instead, touchscreen HMIs intended for deployment within vehicles tend to undergo time-consuming and expensive empirical testing and user trials, typically requiring fully functioning prototypes, test rigs, and extensive experimental protocols. While such testing is invaluable and must remain within the normal design/development cycle, there are clear benefits, both fiscal and practical, to the theoretical modeling of human performance. We describe the development of a preliminary model of human performance that makes a priori predictions of the visual demand (total glance time, number of glances, andmean glance duration) elicited by in-vehicle touchscreen HMI designs, when used concurrently with driving. The model incorporates information theoretic components based on Hick-Hyman Law decision/search time and Fitts' Law pointing time and considers anticipation afforded by structuring and repeated exposure to an interface. Encouraging validation results, obtained by applying the model to a real-world prototype touchscreen HMI, suggest that it may provide an effective design and evaluation tool, capable of making valuable predictions regarding the limits of visual demand/performance associated with in-vehicle HMIs, much earlier in the design cycle than traditional design evaluation techniques. Further validation work is required to explore the behavior associated withmore complex tasks requiringmultiple screen interactions, as well as other HMI design elements and interaction techniques. Results are discussed in the context of facilitating the design of in-vehicle touchscreen HMI to minimize visual demand. [ABSTRACT FROM AUTHOR]

Although at present legislation does not allow drivers in a Level 3 autonomous vehicle to engage in a secondary task, there may become a time when it does. Monitoring the behaviour of drivers engaging in various non-driving activities (NDAs) is crucial to decide how well the driver will be able to take over control of the vehicle. One limitation of the commonly used face-based head tracking system, using cameras, is that sufficient features of the face must be visible, which limits the detectable angle of head movement and thereby measurable NDAs, unless multiple cameras are used. This paper proposes a novel orientation sensor based head tracking system that includes twin devices, one of which measures the movement of the vehicle while the other measures the absolute movement of the head. Measurement error in the shaking and nodding axes were less than 0.4°, while error in the rolling axis was less than 2°. Comparison with a camera-based system, through in-house tests and on-road tests, showed that the main advantage of the proposed system is the ability to detect angles larger than 20° in the shaking and nodding axes. Finally, a case study demonstrated that the measurement of the shaking and nodding angles, produced from the proposed system, can effectively characterise the drivers' behaviour while engaged in the NDAs of chatting to a passenger and playing on a smartphone. [ABSTRACT FROM AUTHOR]

Although drivers gain experience with age, many older drivers are faced with age-related deteriorations that can lead to a higher crash risk. Head-Up Displays (HUDs) have been linked to significant improvements in driving performance for older drivers by tackling issues related to aging. For this study, two Augmented Reality (AR) HUD virtual car navigation solutions were tested (one screen-fixed, one world-fixed), aiming to improve navigation performance and reduce the discrepancy between younger and older drivers by aiding the appropriate allocation of attention and easing interpretation of navigational information. Twenty-five participants (12 younger, 13 older) undertook a series of drives within a medium-fidelity simulator with three different navigational conditions (virtual car HUD, static HUD arrow graphic, and traditional head-down satnav). Results showed that older drivers tended to achieve navigational success rates similar to the younger group, but experienced higher objective mental workload. Solely for the static HUD arrow graphic, differences in most workload questionnaire items and objective workload between younger and older participants were not significant. The virtual car led to improved navigation performance of all drivers, compared to the other systems. Hence, both AR HUD systems show potential for older drivers, which needs to be further investigated in a real-world driving context. [ABSTRACT FROM AUTHOR]