China
Africa
Explore chapters and articles related to this topic
Perspectives on the Nature of Intuitive Interaction
Published in Blackler Alethea, Intuitive Interaction, 2018
Alethea Blackler, Shital Desai, Mitchell McEwan, Vesna Popovic, Sarah Diefenbach
For NMCIs, the level of natural mapping determines the Pathways to intuitive use, Characteristics of features, and the type of Knowledge leveraged. For example, NMCIs in a racing video game might range from a realistic arcade-style racing wheel controller to a traditional dual-analogue stick controller (as explored in McEwan, 2017). The racing wheel is more naturally mapped since it has higher realism, bandwidth, and naturalness in terms of the correspondence between the control interface and the real-life activity (racing) that is simulated by the game. The racing wheel leverages physical affordances and population stereotypes (sensorimotor and cultural Knowledge) since it is shaped and manipulated like the equivalent real-life interface (a steering wheel). This may cause features to be transparent and not consciously recognized, since the mapping is literal and transfer distance is low. In contrast, the traditional Xbox 360 controller uses familiar features to help players control the car (using directional correspondence through the analogue sticks), relying more heavily on less ubiquitous Knowledge (or higher GTF). This means that transfer distance is higher and the source of intuitive interaction may be more discoverable due to the mental effort required. To some extent, NMCIs for video games always leverage metaphor; the correspondence of natural mapping to the simulated activity is a type of metaphor, even if the natural mapping is high. As such, magical experiences may be possible with any control interfaces that leverage natural mapping, yet greater use of metaphor may also increase transfer distance and require a higher level of TF for intuitive use. In all, higher natural mapping in the control interface provides another tool that is complementary to prior experience and can compensate for a lack of relevant familiarity.
Class-imbalanced crash prediction based on real-time traffic and weather data: A driving simulator study
Published in Traffic Injury Prevention, 2020
Zouhair Elamrani Abou Elassad, Hajar Mousannif, Hassan Al Moatassime
A total of 62 volunteers (43 males and 19 females) between the ages of 20 and 51 (M = 40.25; SD = 2.20) participated in the study. All participants had a full driver’s license and had been driving for at least a year. Average years of driving experience ranged from 1 to 17 years (M = 10.45; SD = 6.78) with an average hour of driving per day ranging from 1 to 6 h (M = 3.20, SD = 2.39). All were in a good health, and had (corrected to) normal vision. In reference to the provided information about the experiment’s general intentions, all participants were naïve to the purpose of the study and gave informed consent form about data recording of their driving performance. The study was carried out using a fixed-based driving simulator located at the University of Cadi Ayyad (UCA) facility. Simulator driving studies hold a major advantage of simulating conduct in a safe environment with a full experimental control over driving conditions including all types of weather, terrain, and traffic (Elamrani Abou Elassad and Hajar 2019). Surely, it would be very dangerous to carry out trials on real road environment. The driving simulation was run through the Project Cars 2 simulator by (Slightly Mad Studios) using a Logitech® G27 Racing Wheel set (steering wheel, accelerator pedal, and brake pedal) with the adjustable Logitech Evolution® Playseat, simulations were conducted with automatic gear selection, thus gear shifter was not needed. Figure 1 illustrates the hardware setup.
Effects of Non-Driving-Related Task Attributes on Takeover Quality in Automated Vehicles
Published in International Journal of Human–Computer Interaction, 2021
Seul Chan Lee, Sol Hee Yoon, Yong Gu Ji
A driving simulator comprising a steering wheel, gas and brake pedals, a seat, a front driving monitor, and a tablet PC was used. The STISIM M100K driving simulator software was used to generate the driving scenarios, and the steering wheel and pedals were purchased from Logitech Racing Wheel G27. The driver’s seat, with adjustable seat positions, was obtained from a Genesis model developed by Hyundai Motors. Driving scenes were presented on a 50-in Samsung TV. An Apple iPad Pro 12.9-in model was used as the center console display. Tobii Pro Glasses 2 and Tobii Pro Lab software were used to observe the glance behaviors. Figure 2 shows the experimental settings.
Control Interface for Next Generation Vehicles: What Is the Best Way to Drive Four-Wheel Independent Steering Vehicles?
Published in International Journal of Human–Computer Interaction, 2022
Young Woo Kim, Yong Gu Ji, Sol Hee Yoon
The experimental settings consisted of a driver’s seat, a monitor, a steering wheel, center console buttons, a joystick, and a touch display. Participants were asked to sit in an RS1 seat (RSeat) during the experiment. A 32-inch Dell monitor was placed in front of the participant to provide maneuvering scenarios. A Logitech G27 Racing Wheel with force feedback was used as the steering wheel and center console buttons. The joystick was a Thrustmaster t16000, and a Hansung Portable Touch Monitor was used as the touch display.