Input modality matters: A comparison of touch, speech, and gesture based in-vehicle interaction

Comparison of proactive and reactive interaction modes in a mobile robotic telecare study

Mobile robotic telepresence systems require that information about the environment, the task, and the robot be presented to a remotely located user (operator) who controls the robot for a specific task. In this study, two interaction modes, proactive and reactive, that differ in the way the user receives information from the robot, were compared in an experimental system simulating a healthcare setting. The users controlled a mobile telepresence robot that delivered and received items (medication, food, or drink), and also obtained metrics (vital signs) from a simulated patient while the users performed a secondary healthcare-related task (they compiled health records which were displayed to them on the screen and answered related questions). The effect of the two interaction modes on overall performance and user perception was evaluated through a within-participant study design conducted with 50 participants belonging to two different types of populations (with and without a technological background). Efficiency, effectiveness, understanding, satisfaction, and situation awareness were defined as the dependent variables measured both objectively and subjectively. The proactive mode increased user performance, and understanding of the system and reduced the workload compared to the reactive mode. However, several of the users valued the option of increased user control experienced in the reactive mode. We, therefore, proposed design suggestions to highlight some of the benefits of factoring the reactive mode into the design as a hybrid mode.

An empirical study on new-energy vehicle users' preferences of in-vehicle interaction input methods

New energy sources are transforming the automotive market. This shift has also expanded the possibilities for in-vehicle interaction. Through a literature review, this study categorizes the in-vehicle interaction activities into three types: driving tasks, comfort tasks, and entertainment tasks. This study conducted empirical survey of 377 users to understand their preferences of in-vehicle interaction input methods inside new energy vehicles. The results show that gender, educational level, income, driver's license type and driving experience have significant influence on the perception and preference of the in-vehicle interaction input methods. However, age and experience with new energy vehicle didn't show significant results. The findings of this study can assist manufacturers in developing targeted solutions and meeting the personalized needs of users in future vehicle market segments.

Assessment of Drivers' Mental Workload by Multimodal Measures during Auditory-Based Dual-Task Driving Scenarios

Assessing drivers' mental workload is crucial for reducing road accidents. This study examined drivers' mental workload in a simulated auditory-based dual-task driving scenario, with driving tasks as the main task, and auditory-based N-back tasks as the secondary task. A total of three levels of mental workload (i.e., low, medium, high) were manipulated by varying the difficulty levels of the secondary task (i.e., no presence of secondary task, 1-back, 2-back). Multimodal measures, including a set of subjective measures, physiological measures, and behavioral performance measures, were collected during the experiment. The results showed that an increase in task difficulty led to increased subjective ratings of mental workload and a decrease in task performance for the secondary N-back tasks. Significant differences were observed across the different levels of mental workload in multimodal physiological measures, such as delta waves in EEG signals, fixation distance in eye movement signals, time- and frequency-domain measures in ECG signals, and skin conductance in EDA signals. In addition, four driving performance measures related to vehicle velocity and the deviation of pedal input and vehicle position also showed sensitivity to the changes in drivers' mental workload. The findings from this study can contribute to a comprehensive understanding of effective measures for mental workload assessment in driving scenarios and to the development of smart driving systems for the accurate recognition of drivers' mental states.