Unconstrained design: improving multitasking with in-vehicle information systems through enhanced situation awareness

In the age of information, in-vehicle multitasking is inevitable. The popularity of the automobile in combination with the demands of everyday life presents a demand to do more than simply focus on the road. Situation Awareness (SA) is a theory that allows designers to understand how operators interact in dynamic, complex environments. Unconstrained Design is proposed as a way of enhancing multitasking performance in-vehicle. This paper presents an experimental investigation into human-machine interface concepts that aim to support drivers to multitask in-vehicle when frequent task switching is required. Two SA-based approaches were investigated, one which focussed on supporting preparation for a Non-Driving Related Activity (NDRA), and one which focussed on supporting the Driving Related Activity (DRA) when an NDRA is active. While multitasking, Contextual Cueing, using a Head-up Display, produced significant reductions in NDRA response time while an auditory lane keeping aid increased the amount of time a driver spent in the central region of a lane. This provides evidence to suggest that using SA and Unconstrained Design as a philosophy for the design of IVIS that supports drivers’ ability to multitask in-vehicle, could lead to task performance improvements.Jaguar Land Rove

What situations trigger intense emotions in automobiles?

Conference ProceedingsDriving involves a variety of events and activities that stimulate emotional experiences. The aim of this investigation was to examine automobile experiences and to identify affective themes. 245 UK-based participants were recruited using a purposive sampling strategy. One study consisted of an online questionnaire which inquired about the automotive experiences which proved most emotionally intense. The second consisted of a simulator based immersive driving experience, followed afterwards by a questionnaire which inquired about the automotive experiences which proved most emotionally intense. Questionnaire responses were clustered into themes using a content analysis method. The study identified 13 major themes and 44 sub-themes. The findings provide guidance regarding the triggers of emotional responses which designers can use to better understand and to improve automotive experiences.Jaguar Land Rover as part of project Automotive Habitat Laboratory (AutoHablab)

Multimodal classification of driver glance

—This paper presents a multimodal approach to invehicle classification of driver glances. Driver glance is a strong predictor of cognitive load and is a useful input to many applications in the automotive domain. Six descriptive glance regions are defined and a classifier is trained on video recordings of drivers from a single low-cost camera. Visual features such as head orientation, eye gaze and confidence ratings are extracted, then statistical methods are used to perform failure analysis and calibration on the visual features. Non-visual features such as steering wheel angle and indicator position are extracted from a RaceLogic VBOX system. The approach is evaluated on a dataset containing multiple 60 second samples from 14 participants recorded while driving in a natural environment. We compare our multimodal approach to separate unimodal approaches using both Support Vector Machine (SVM) and Random Forests (RF) classifiers. RF Mean Decrease in Gini Index is used to rank selected features which gives insight into the selected features and improves the classifier performance. We demonstrate that our multimodal approach yields significantly higher results than unimodal approaches. The final model achieves an average F1 score of 70.5% across the six classes

Automotive Habitat Laboratory: a facility for automotive co-design

Owing to the growing sophistication of digital technologies and the increasing complexity of modern social behaviours, the 21st century automobile can no longer be considered as an environment solely characterised by the performance of the driving task. In order to address the opportunities introduced by the increasingly dynamic and socially interactive environment of the modern day automobile, from a Human Centred Design perspective, a series of expert interviews and business discussions were held with motor industry professionals. From discussions of modern design tools which would be helpful in support of motor industry, the concept of a design-driven lab emerged. The Automotive Habitat Laboratory assists the discovery of experiential, psychological, sociological, behavioural and ethical aspects of new automotive product and service concepts. This paper discusses the results of the expert interviews and the preliminary definition of the Automotive Habitat Laboratory in terms of the specification of the human behaviour monitoring technologies, communication protocols and working methods that will allow for creative real-time dialogue between designers and people in automobiles

Analysis of yawning behaviour in spontaneous expressions of drowsy drivers

Driver fatigue is one of the main causes of road accidents. It is essential to develop a reliable driver drowsiness detection system which can alert drivers without disturbing them and is robust to environmental changes. This paper explores yawning behaviour as a sign of drowsiness in spontaneous expressions of drowsy drivers in simulated driving scenarios. We analyse a labelled dataset of videos of sleep-deprived versus alert drivers and demonstrate the correlation between hand-over-face touches, face occlusions and yawning. We propose that face touches can be used as a novel cue in automated drowsiness detection alongside yawning and eye behaviour. Moreover, we present an automatic approach to detect yawning based on extracting geometric and appearance features of both mouth and eye regions. Our approach successfully detects both hand-covered and uncovered yawns with an accuracy of 95%. Ultimately, our goal is to use these results in designing a hybrid drowsiness-detection system

Head-up display with dynamic depth-variable viewing effect

Head-Up Displays (HUDs) can reduce duration and frequency of drivers looking away from traffic scenes, but information contents of different importance are usually displayed at the same time in contemporary HUD models. Such configurations increase the time that a driver searches for more critical information and it is essential that the said information can quickly attract driver’s attention without affecting his focus on the road. We introduce an alternative approach of displaying critical information with a variable depth in a designated local area of a HUD image. The variations are engineered to create a dynamic pop-up effect for hazard warnings, such as a car exceeding the speed-limit or approaching certain road signs. The image depth of the corresponding area is designed to vary by about half a metre and the image size by 1.4 times for a natural viewing experience, using an off-the-shelf liquid lens with electrically tuneable focus depths. The HUD optics can be adjusted to have an extended eye-box to accommodate driver’s head movement and a uniformity image brightness across the eye-box

Developing predictive equations to model the visual demand of in-vehicle touchscreen HMIs

Touchscreen 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 modelling 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 and mean 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 behaviour associated with more complex tasks requiring multiple 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 minimise visual demand

Touchless selection schemes for intelligent automotive user interfaces with predictive mid-air touch

Predictive touch technology aims to improve the usability and performance of in-vehicle displays under the influence of perturbations due to the road and driving conditions. It fundamentally relies on predicting and early in the freehand pointing movement, the interface item the user intends to select, using a novel Bayesian inference framework. This article focusses on evaluating facilitation schemes for selecting the predicted interface component whilst driving, and without physically touching the display, thus touchless. Initially, several viable schemes were identified in a brainstorming session followed by an expert workshop with 12 participants. A simulator study with 24 participants using a prototype predictive touch system was then conducted. A number of collected quantitative and qualitative measures show that immediate mid-air selection, where the system autonomously autoselects the predicted interface component, may be the most promising strategy for predictive touch

Validating Operator Event Sequence Diagrams: The case of an automated vehicle to human driver handovers

Abstract: Predicting what drivers will do as vehicle control is handed over to them from automation is a relatively new challenge for the motor vehicle industry. Operator Event Sequence Diagrams (OESDs) offer a way of modeling the interactions between the driver and vehicle automation in the handover of control. In this paper, two studies are presented in which a range of handover strategies are tested. The anticipated driver strategies were modeled using OESDs to serve as predictions of driver behavior. Drivers were then observed in two separate studies: (1) using a Lower‐Fidelity (vehicle seat and controls) simulator and (2) using a Higher‐Fidelity (whole vehicle) simulator. Driver behavior during a takeover task was categorized according to the signal detection paradigm into hits, misses, false alarms, and correct rejections. The results showed that for all strategies in both sets of studies, the median criterion for validity was exceeded ( φ > 0.8), suggesting that OESDs made good predictions of driver behavior during the handover of the vehicle from automation to manual control