Many previous studies have identified that physiological responses of a driver are significantly associated with driving stress. However, research is limited to identifying the effects of traffic conditions (low vs. high traffic) and road types (highway vs. city) on driving stress. The objective of this study is to quantify the relationship between driving stress and traffic conditions, and driving stress and road types, respectively. In this study, electrodermal activity (EDA) signals for a male driver were collected in real road driving conditions for 60 min a day for 21 days. To classify the levels of driving stress (low vs. high), two separate models were developed by incorporating the statistical features of the EDA signals, one for traffic conditions and the other for road types. Both models were based on the application of EDA features with the logistic regression analysis. City driving turned out to be more stressful than highway driving. Traffic conditions, defined as traffic jam also significantly affected the stress level of the driver, when using the criteria of the vehicle speed of 40 km/h and standard deviation of the speed of 20 km/h. Relevance to industry: The classification results of the two models indicate that the traffic conditions and the road types are important features for driving stress and its related applications.

The face measurements of KAF pilots were collected and compared with those of Korean civilians and USAF personnel. The distinct facial features of the populations identified in this study are applicable to custom design of an oxygen mask for prevention of excessive pressure and oxygen leakage.

The posture-prediction models can be used to position computational human models or crash-test dummies for vehicle design and assessment.

Artificial intelligence (AI) is becoming increasingly prevalent in all spheres of society. Still, the perception of AI from users and customers remains the main barrier for its widespread adoption. Previous studies showed that the acceptance of new technologies in society depends on perceived characteristics. This study examined users’ perception of trust, the difficulty of the task, and application performance when using an AI‐based technology. These factors help us to elucidate the mechanisms for building trust in AI technology from the users’ perspective. A total of 18 participants took part in the experiment with the Google AutoDraw software as an AI tool. As a result, the difficulty of the task, perceived performance, and success/failure of the task can be regarded as the influential factors for the perceived trust evaluation. The perceived trust of users in new AI products would be increased by improving product performance and the successful implementation of the tasks. The obtained results and insights can serve AI product developers to increase the level of users’ trust and attraction towards their technologies and applications.

Few statistical models of rear seat passenger posture have been published, and none has taken into account the effects of occupant age. This study developed new statistical models for predicting passenger postures in the rear seats of automobiles. Postures of 89 adults with a wide range of age and body size were measured in a laboratory mock-up in seven seat configurations. Posture-prediction models for female and male passengers were separately developed by stepwise regression using age, body dimensions, seat configurations and two-way interactions as potential predictors. Passenger posture was significantly associated with age and the effects of other two-way interaction variables depended on age. A set of posture-prediction models are presented for women and men, and the prediction results are compared with previously published models. This study is the first study of passenger posture to include a large cohort of older passengers and the first to report a significant effect of age for adults. The presented models can be used to position computational and physical human models for vehicle design and assessment. Practitioner Summary: The significant effects of age, body dimensions and seat configuration on rear seat passenger posture were identified. The models can be used to accurately position computational human models or crash test dummies for older passengers in known rear seat configurations.