Investigating what level of visual information inspires trust in a user of a highly automated vehicle

The aim of this research is to investigate whether visual feedback alone can affect a driver’s trust in an autonomous vehicle, and in particular, what level of feedback (no feedback vs. moderate feedback vs. high feedback) will evoke the appropriate level of trust. Before conducting the experiment, the Human Machine Interfaces (HMI) were piloted with two sets of six participants (before and after iterations), to ensure the meaning of the displays can be understood by all. A static driving simulator experiment was conducted with a sample of 30 participants (between 18 and 55). Participants completed two pre-study questionnaires to evaluate previous driving experience, and attitude to trust in automation. During the study, participants completed a trust questionnaire after each simulated scenario to assess their trust level in the autonomous vehicle and HMI displays, and on intention to use and acceptance. The participants were shown 10 different driving scenarios that lasted approximately 2 minutes each. Results indicated that the ‘high visual feedback’ group recorded the highest trust ratings, with this difference significantly higher than for the ‘no visual feedback’ group (U = .000; p = <0.001 < α) and the ‘moderate visual feedback’ group (U = .000; p = <0.001 < α). There is an upward inclination of trust in all groups due to familiarity to both the interfaces and driving simulator over time. Participants’ trust level was also influenced by the driving scenario, with trust reducing in all displays during safety verses non-safety-critical situations

Perceptions of an automotive load space in a virtual environment

A study was conducted to investigate the accuracy of perceptions of a car load space in a CAVE virtual environment. A total of 46 participants rated load space width, height, depth, usability and overall capacity after viewing either a virtual Range Rover Evoque in the CAVE or the real car. Participants were also asked to estimate how many 100mm3 blocks could fit in the load space in width, depth or height. The only significant difference was in usability, which was rated higher in the CAVE. There was no systematic over- or under-estimation of distances in the virtual environment. Equivalence was demonstrated for width and depth in the block estimation task. The results suggest that virtual environments can be used for car load space design, particularly for estimates of size, but further work is required to be confident that subjective ratings of virtual properties are equivalent to those of real vehicles

The use of virtual reality and physical tools in the development and validation of ease of entry and exit in passenger vehicles

Ease of entry and exit is important for creating a positive first impression of a car and to increase customer satisfaction. Several methods are used within vehicle development to optimise ease of entry and exit, including CAD reviews, benchmarking and buck trials. However, there is an industry trend towards digital methods to reduce the costs and time associated with developing physical prototypes. This paper reports on a study of entry strategy in three properties (buck, car, CAVE) in which inconsistencies were demonstrated by people entering a vehicle representation in the CAVE. In a second study industry practitioners rated the CAVE as worse than physical methods for identifying entry and exit issues, and having lower perceived validity and reliability. However, the resource issues associated with building bucks were recognised. Recommendations are made for developing the CAVE and for combinations of methods for use at different stages of a vehicle’s development