Automotive emotions: a human-centred approach towards the measurement and understanding of drivers' emotions and their triggers

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe automotive industry is facing significant technological and sociological shifts, calling for an improved understanding of driver and passenger behaviours, emotions and needs, and a transformation of the traditional automotive design process. This research takes a human-centred approach to automotive research, investigating the users’ emotional states during automobile driving, with the goal to develop a framework for automotive emotion research, thus enabling the integration of technological advances into the driving environment. A literature review of human emotion and emotion in an automotive context was conducted, followed by three driving studies investigating emotion through Facial-Expression Analysis (FEA): An exploratory study investigated whether emotion elicitation can be applied in driving simulators, and if FEA can detect the emotions triggered. The results allowed confidence in the applicability of emotion elicitation to a lab-based environment to trigger emotional responses, and FEA to detect those. An on-road driving study was conducted in a natural setting to investigate whether natures and frequencies of emotion events could be automatically measured. The possibility of assigning triggers to those was investigated. Overall, 730 emotion events were detected during a total driving time of 440 minutes, and event triggers were assigned to 92% of the emotion events. A similar second on-road study was conducted in a partially controlled setting on a planned road circuit. In 840 minutes, 1947 emotion events were measured, and triggers were successfully assigned to 94% of those. The differences in natures, frequencies and causes of emotions on different road types were investigated. Comparison of emotion events for different roads demonstrated substantial variances of natures, frequencies and triggers of emotions on different road types. The results showed that emotions play a significant role during automobile driving. The possibility of assigning triggers can be used to create a better understanding of causes of emotions in the automotive habitat. Both on-road studies were compared through statistical analysis to investigate influences of the different study settings. Certain conditions (e.g. driving setting, social interaction) showed significant influence on emotions during driving. This research establishes and validates a methodology for the study of emotions and their causes in the driving environment through which systems and factors causing positive and negative emotional effects can be identified. The methodology and results can be applied to design and research processes, allowing the identification of issues and opportunities in current automotive design to address challenges of future automotive design. Suggested future research includes the investigation of a wider variety of road types and situations, testing with different automobiles and the combination of multiple measurement techniques

A Multimodal Approach for Monitoring Driving Behavior and Emotions

Studies have indicated that emotions can significantly be influenced by environmental factors; these factors can also significantly influence drivers’ emotional state and, accordingly, their driving behavior. Furthermore, as the demand for autonomous vehicles is expected to significantly increase within the next decade, a proper understanding of drivers’/passengers’ emotions, behavior, and preferences will be needed in order to create an acceptable level of trust with humans. This paper proposes a novel semi-automated approach for understanding the effect of environmental factors on drivers’ emotions and behavioral changes through a naturalistic driving study. This setup includes a frontal road and facial camera, a smart watch for tracking physiological measurements, and a Controller Area Network (CAN) serial data logger. The results suggest that the driver’s affect is highly influenced by the type of road and the weather conditions, which have the potential to change driving behaviors. For instance, when the research defines emotional metrics as valence and engagement, results reveal there exist significant differences between human emotion in different weather conditions and road types. Participants’ engagement was higher in rainy and clear weather compared to cloudy weather. More-over, engagement was higher on city streets and highways compared to one-lane roads and two-lane highways

A user experience‐based toolset for automotive human‐machine interface technology development

The development of new automotive Human-Machine Interface (HMI) technologies must consider the competing and often conflicting demands of commercial value, User Experience (UX) and safety. Technology innovation offers manufacturers the opportunity to gain commercial advantage in a competitive and crowded marketplace, leading to an increase in the features and functionality available to the driver. User response to technology influences the perception of the brand as a whole, so it is important that in-vehicle systems provide a high-quality user experience. However, introducing new technologies into the car can also increase accident risk. The demands of usability and UX must therefore be balanced against the requirement for driver safety. Adopting a technology-focused business strategy carries a degree of risk, as most innovations fail before they reach the market. Obtaining clear and relevant information on the UX and safety of new technologies early in their development can help to inform and support robust product development (PD) decision making, improving product outcomes. In order to achieve this, manufacturers need processes and tools to evaluate new technologies, providing customer-focused data to drive development. This work details the development of an Evaluation Toolset for automotive HMI technologies encompassing safety-related functional metrics and UX measures. The Toolset consists of four elements: an evaluation protocol, based on methods identified from the Human Factors, UX and Sensory Science literature; a fixed-base driving simulator providing a context-rich, configurable evaluation environment, supporting both hardware and software-based technologies; a standardised simulation scenario providing a repeatable basis for technology evaluations, allowing comparisons across multiple technologies and studies; and a technology scorecard that collates and presents evaluation data to support PD decision making processes

Affective scenarios in automotive design: a human-centred approach towards understanding of emotional experience

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe automotive industry is facing a period of significant transformation due to the arrival of many new digital technologies. As the focus of automotive engineering has shifted from hardware to software, the conventional processes of making, buying and owning an automobile have changed. Peoples’ desires for new automotive experiences are increasing; they demand more sophisticated approaches to the automotive experience beyond merely improving functional requirements for advanced automation systems, interfaces and connectivity. Thus, it is essential to understand human experience in order to help people deal with the high degree of complexity in the driving environment and to help them to cope with unanticipated driving events that involve emotional, psychological or sociological issues. This research takes a human-centred approach to investigating real-life scenarios in which people emotionally engage with automobiles with the aim of developing a relevant set of scenarios for this context. An extensive literature review was conducted of human emotion, memory systems, emotional memory characteristics, scenarios, and scenarios with emotional aspects, followed by a discussion defining scenario development process and affective scenarios. This research provides a methodology for in-depth qualitative studies that develop affective design scenarios with automobiles. As a triangulation approach, two independent studies in different settings explored affective scenario themes in automotive contexts of people’s real-life car stories that made them respond emotionally. The themes that were revealed from both studies were consolidated, and exemplary scenarios of 13 consolidated main themes were formulated to illustrate a set of affective scenarios in automotive contexts. This research leads to an enhanced understanding of a set of critical contexts that automotive practitioners should take into account for future automotive design. Suggestions with possible questions based on the research outcome provide opportunities for them to agilely cope with unanticipated future events, whereby highly complex driving environment by connected and autonomous vehicles. This methodology used here can be replicated for future affective scenario studies focusing on specific products, sub-systems or services such as navigation systems or car-sharing services. The results, which have been validated through a triangulation approach, can bolster the automobile design process by addressing potential issues and challenges in automotive experience by facilitating idea generation, enhancing a shared understanding of critical contexts and by assisting decision-making among stakeholders from different departments

Implicit personalization in driving assistance: State-of-the-art and open issues

In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With the consideration of driving preferences and driver characteristics, these systems become more acceptable and trustworthy. This article presents a survey on recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, benefits of personalization, application prospects, and future focal points. Both relevant driving datasets and open issues about personalized driving assistance are discussed to facilitate future research. By creating an organized categorization of the field, we hope that this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the application of these techniques within the driving automation community</h2

MEASURING DUAL TASK COST USING THE PERFORMANCE OPERATING CHARACTERISTIC: THE EFFECT OF EMOTIONAL WORDS ON ONE'S FUNCTIONAL FIELD OF VIEW

Discusses design elements that should be utilized for optimal measurement of dual task performance, and reviews literature suggesting that these elements are underutilized. Participants seem to be able to effectively "tune out" one or the other task in a dual task paradigm, though traditional analyses and POC analyses converge to inform us that under these experimental conditions (which may not require adequate cognitive load), UFOV performance is not as greatly impacted by concurrent verbal tasks as pilot data and theory suggest. While smaller than expected, these dual task costs have implications in an applied setting, as 19% of subjects exhibited UFOV scores under dual task conditions that would predict more than double the risk of injurious accident. Finally, highly arousing negatively valent verbal stimuli may lead to greatest interference with visual attention performance

Classification of Cognitive Load and Expertise for Adaptive Simulation using Deep Multitask Learning

Simulations are a pedagogical means of enabling a risk-free way for healthcare practitioners to learn, maintain, or enhance their knowledge and skills. Such simulations should provide an optimum amount of cognitive load to the learner and be tailored to their levels of expertise. However, most current simulations are a one-type-fits-all tool used to train different learners regardless of their existing skills, expertise, and ability to handle cognitive load. To address this problem, we propose an end-to-end framework for a trauma simulation that actively classifies a participant's level of cognitive load and expertise for the development of a dynamically adaptive simulation. To facilitate this solution, trauma simulations were developed for the collection of electrocardiogram (ECG) signals of both novice and expert practitioners. A multitask deep neural network was developed to utilize this data and classify high and low cognitive load, as well as expert and novice participants. A leave-one-subject-out (LOSO) validation was used to evaluate the effectiveness of our model, achieving an accuracy of 89.4% and 96.6% for classification of cognitive load and expertise, respectively.Comment: 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work