Adjustable automation and manoeuvre control in automated driving

Current implementations of automated driving rely on the driver to monitor the vehicle and be ready to assume control in situations that the automation cannot successfully manage. However, research has shown that drivers are not able to monitor an automated vehicle for longer periods of time, as the monotonous monitoring task leads to attention reallocation or fatigue. Driver involvement in the automated driving task promises to counter this effect. The authors researched how the implementation of a haptic human–vehicle interface, which allows the driver to adjust driving parameters and initiate manoeuvres, influences the subjective experience of drivers in automated vehicles. In a simulator study, they varied the level of control that drivers have over the vehicle, between manual driving, automated driving without the possibility to adjust the automation, as well as automated driving with the possibility to initiate manoeuvres and adjust driving parameters of the vehicle. Results show that drivers have a higher level of perceived control and perceived level of responsibility when they have the ability to interact with the automated vehicle through the haptic interface. The authors conclude that the possibility to interact with automated vehicles can be beneficial for driver experience and safety

Defining, measuring, and modeling passenger's in-vehicle experience and acceptance of automated vehicles

Automated vehicle acceptance (AVA) has been measured mostly subjectively by questionnaires and interviews, with a main focus on drivers inside automated vehicles (AVs). To ensure that AVs are widely accepted by the public, ensuring the acceptance by both drivers and passengers is key. The in-vehicle experience of passengers will determine the extent to which AVs will be accepted by passengers. A comprehensive understanding of potential assessment methods to measure the passenger experience in AVs is needed to improve the in-vehicle experience of passengers and thereby the acceptance. The present work provides an overview of assessment methods that were used to measure a driver's behavior, and cognitive and emotional states during (automated) driving. The results of the review have shown that these assessment methods can be classified by type of data-collection method (e.g., questionnaires, interviews, direct input devices, sensors), object of their measurement (i.e., perception, behavior, state), time of measurement, and degree of objectivity of the data collected. A conceptual model synthesizes the results of the literature review, formulating relationships between the factors constituting the in-vehicle experience and AVA acceptance. It is theorized that the in-vehicle experience influences the intention to use, with intention to use serving as predictor of actual use. The model also formulates relationships between actual use and well-being. A combined approach of using both subjective and objective assessment methods is needed to provide more accurate estimates for AVA, and advance the uptake and use of AVs.Comment: 22 pages, 1 figur

Measuring Drivers’ Physiological Response to Different Vehicle Controllers in Highly Automated Driving (HAD): Opportunities for Establishing Real-Time Values of Driver Discomfort

This study investigated how driver discomfort was influenced by different types of automated vehicle (AV) controllers, compared to manual driving, and whether this response changed in different road environments, using heart-rate variability (HRV) and electrodermal activity (EDA). A total of 24 drivers were subjected to manual driving and four AV controllers: two modelled to depict “human-like” driving behaviour, one conventional lane-keeping assist controller, and a replay of their own manual drive. Each drive lasted for ~15 min and consisted of rural and urban environments, which differed in terms of average speed, road geometry and road-based furniture. Drivers showed higher skin conductance response (SCR) and lower HRV during manual driving, compared to the automated drives. There were no significant differences in discomfort between the AV controllers. SCRs and subjective discomfort ratings showed significantly higher discomfort in the faster rural environments, when compared to the urban environments. Our results suggest that SCR values are more sensitive than HRV-based measures to continuously evolving situations that induce discomfort. Further research may be warranted in investigating the value of this metric in assessing real-time driver discomfort levels, which may help improve acceptance of AV controllers

Designing for Appropriate Trust in Automated Vehicles

Automated vehicles (AVs) have become a popular area of research due to, among others, claims of increased traffic safety and user comfort. However, before a user can reap the benefits, they must first trust the AV. Trust in AVs has gained a greater interest in recent years due to being a prerequisite for user acceptance, adoption as well as important for good user experience. However, it is not about creating trust in AVs, as much as creating an appropriate level of trust in relation to the actual performance of the AV. However, little research has presented a systematic and holistic approach that may assist developers in the design process to understand what to primarily focus on and how, when developing AVs that assist users to generate an appropriate level of trust.\ua0This thesis presents two mixed-method studies (Study I and II). The first study considers what factors affect users trust in the AV and is primarily based on a literature review as well as a complementary user study. The second study, a user study, is built upon Study I and uses a Wizard of Oz (WOz) approach with the purpose to understand how the behaviour of an AV affects users trust in a simulated but realistic context, including seven day-to-day traffic situations.The results show that trust is primarily affected by information from and about the AV. Furthermore, results also show that trust in AVs have primarily four different phases, before the user’s first physical interaction with the AV (i), during usage and whilst learning how the AV performs (ii), after the user has learned how the AV performs in a specific context (iii) and after the user has learned how the AV performs in a specific context but that context changes (iv). It was also found that driving behaviour affects the user’s trust in the AV during usage and whilst learning how the AV performs. This was primarily due to how well the driving behaviour communicated intentions for the users’ to be able to predict upcoming AV actions. The users’ were also affected by the perceived benevolence of the AV, that is how respectful the driving behaviour was interpreted by the user. Finally, the results also showed that the user’s trust in the AV also is affected by aspects relating to different traffic situations such as perceived task difficulty, perceived risk for oneself (and others) and how well the AV conformed to the user’s expectations. Thus, it is not only how the AV performs but rather how the AV performs in relation to different traffic situations. Finally, since design research not only considers how things are, but also how things ought to be, a tentative explanatory and prescriptive model was developed based on the results presented above. The model of trust information exchange and gestalt explains how information affecting user trust, travels from a trust information sender to a trust information receiver and highlights the important aspects for developers to consider designing for appropriate trust in AVs, such as the design space and related variables. The design variables are a) the message (the type and amount of information), b) the artefact (the AV, including communication channels and properties) and c) the information gestalt, which is based on the combination of signals communicated from the properties (and communication channels). In this case, the gestalt is what the user ultimately perceives; the combined result of all signals. Therefore, developers need to consider not only how individual signals are perceived and interpreted, but also how different signals are perceived and interpreted together, as a whole, an information gestalt

A psychophysiological insight into driver state during highly automated driving

The aim of this research was to investigate and validate the usage of physiological measures as an objective indicator of driver state in dynamic driving environments, and understand if such a methodology can be used to measure driver discomfort, and high workload. The work addressed questions relating to: (i) detecting and removing motion artefacts from electrodermal activity (EDA) signals in dynamic driving environments; (ii) primary factors contributing to driver discomfort during automation, measured in terms of their physiological state; (iii) understanding changes in drivers’ workload levels at different stages of automation, as indicated by electrocardiogram (ECG) and EDA-based measures and; (iv) how drivers’ attentional demands and workload levels are affected at different stages of automation, measured using eye tracking-based metrics. A series of experiments were developed to manipulate drivers’ discomfort and workload levels. The analysis around driver discomfort focused on automated driving, whereas drivers’ workload levels were investigated during automation, and during resumption of control from automation, in a series of car-following scenarios. Our results indicated that phasic EDA was able to pick up discomfort experienced by the driver during automation, and correlated to drivers’ subjective ratings of discomfort. Narrower roads, higher resultant acceleration forces and how the automated vehicle negotiated different road geometries all influenced driver discomfort. We observed that drivers’ workload levels were captured by ECG and EDA-based signals, with phasic component of EDA signal being more sensitive to short term variations in driver workload. Similar results were observed in drivers’ pupil diameter values, as well as subjective ratings of workload. Factors such as engagement in a non-driving related task (NDRT), presence of a lead vehicle while maintaining a short time headway, and takeovers, all seemed to increase drivers’ workload levels. Future work can build on this research by incorporating sensor fusion of ECG and EDA-based data, along with eye tracking, to help improve the accuracy and capabilities of future driver state monitoring systems