Drivers' Evaluation of Different Automated Driving Styles: Is It Both Comfortable and Natural?

OBJECTIVE: This study investigated users' subjective evaluation of three highly automated driving styles, in terms of comfort and naturalness, when negotiating a UK road in a high-fidelity, motion-based, driving simulator. BACKGROUND: Comfort and naturalness play an important role in contributing to users' acceptance and trust of automated vehicles (AVs), although not much is understood about the types of driving style which are considered comfortable or natural. METHOD: A driving simulator study, simulating roads with different road geometries and speed limits, was conducted. Twenty-four participants experienced three highly automated driving styles, two of which were recordings from human drivers, and the other was based on a machine learning (ML) algorithm, termed Defensive, Aggressive, and Turner, respectively. Participants evaluated comfort or naturalness of each driving style, for each road segment, and completed a Sensation Seeking questionnaire, which assessed their risk-taking propensity. RESULTS: Participants regarded both human-like driving styles as more comfortable and natural, compared with the less human-like, ML-based, driving controller. Particularly, between the two human-like controllers, the Defensive style was considered more comfortable, especially for the more challenging road environments. Differences in preference for controller by driver trait were also observed, with the Aggressive driving style evaluated as more natural by the high sensation seekers. CONCLUSION: Participants were able to distinguish between human- and machine-like AV controllers. A range of psychological concepts must be considered for the subjective evaluation of controllers. APPLICATION: Insights into how different driver groups evaluate automated vehicle controllers are important in designing more acceptable systems.

Resonance as a Design Strategy for AI and Social Robots.

Resonance, a powerful and pervasive phenomenon, appears to play a major role in human interactions. This article investigates the relationship between the physical mechanism of resonance and the human experience of resonance, and considers possibilities for enhancing the experience of resonance within human-robot interactions. We first introduce resonance as a widespread cultural and scientific metaphor. Then, we review the nature of "sympathetic resonance" as a physical mechanism. Following this introduction, the remainder of the article is organized in two parts. In part one, we review the role of resonance (including synchronization and rhythmic entrainment) in human cognition and social interactions. Then, in part two, we review resonance-related phenomena in robotics and artificial intelligence (AI). These two reviews serve as ground for the introduction of a design strategy and combinatorial design space for shaping resonant interactions with robots and AI. We conclude by posing hypotheses and research questions for future empirical studies and discuss a range of ethical and aesthetic issues associated with resonance in human-robot interactions.

Can a robot teach me that? Children's ability to imitate robots.

Commensurate with constant technological advances, social robots are increasingly anticipated to enter homes and classrooms; however, little is known about the efficacy of social robots as teaching tools. To investigate children's learning from robots, 1- to 3-year-olds observed either a human or a robot demonstrate two goal-directed object manipulation tasks and were then given the opportunity to act on the objects. Children exhibited less imitation from robotic models that varied with task complexity and age, a phenomenon we term the "robot deficit." In addition, the more children engaged with the robot prior to administration of the imitation task, the more likely they were to replicate the robot's actions. These findings document how children are able to learn from robots but that ongoing design of robotic platforms needs to be oriented to developing more socially engaging means of interacting.

Biometric recognition of newborns and infants by non-contact fingerprinting: lessons learned.

Despite years of effort, reliable biometric identification of newborns and young children has remained elusive. In this paper, we review the importance of trusted identification methods, the biometric landscape for infants and adults, barriers and success stories, and we discuss specific failure modes particular to young children. We then describe our approach to infant identification using non-contact optical imaging of fingerprints. We detail our technology development history, including Human-Centered Design methods, various iterations of our platform, and how these iterations addressed failure modes in the identification process. We close with a brief description of our clinical trial of newborns and infants at an urban hospital in Mexico and report preliminary results that show high accuracy, with matching rates consistent with acceptable field-performance for reliable biometric identification in large populations.

Sensorimotor Decoupling Contributes to Triadic Attention: A Longitudinal Investigation of Mother-Infant-Object Interactions.

Previous developmental accounts of joint object activity identify a qualitative "shift" around 9-12 months. In a longitudinal study of 26 dyads, videos of joint object interactions at 4, 6, 9, and 12 months were coded for all targets of gaze and manual activity (at 10 Hz). At 12 months, infants distribute their sensorimotor modalities between objects handled by the parent and others controlled by the infant. Analyses reveal novel trajectories in distributed joint object activity across the 1st year. At 4 months, infants predominantly look at and manipulate a single object, typically held by their mothers. Between 6 and 9 months, infants increasingly decouple their visual and haptic modalities and distribute their attention between objects held by their mothers and by themselves. These previously unreported developments in the distribution of multimodal object activity might "bridge the gap" to coordinated joint activity between 6 and 12 months.

Physiological parameters governing the action of pancreatic lipase.

The most widely used pharmacological therapies for obesity and weight management are based on inhibition of gastrointestinal lipases, resulting in a reduced energy yield of ingested foods by reducing dietary lipid absorption. Colipase-dependent pancreatic lipase is believed to be the major gastrointestinal enzyme involved in catalysis of lipid ester bonds. There is scant literature on the action of pancreatic lipase under the range of physiological conditions that occur within the human small intestine, and the literature that does exist is often contradictory. Due to the importance of pancreatic lipase activity to nutrition and weight management, the present review aims to assess the current body of knowledge with regards to the physiology behind the action of this unique gastrointestinal enzyme system. Existing data would suggest that pancreatic lipase activity is affected by intestinal pH, the presence of colipase and bile salts, but not by the physiological range of Ca ion concentration (as is commonly assumed). The control of secretion of pancreatic lipase and its associated factors appears to be driven by gastrointestinal luminal content, particularly the presence of acid or digested proteins and fats in the duodenal lumen. Secretion of colipase, bile acids and pancreatic lipase is driven by cholecystokinin and secretin release.