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A better understanding of automation disengagements can lead to improved safety and efficiency of automated systems. This study investigates the factors contributing to automation disengagements initiated by human operators and the automation itself by analyzing semi-structured interviews with 103 users of Tesla's Autopilot and FSD Beta. The factors leading to automation disengagements are represented by categories. In total, we identified five main categories, and thirty-five subcategories. The main categories include human operator states (5), human operator's perception of the automation (17), human operator's perception of other humans (3), the automation's perception of the human operator (3), and the automation incapability in the environment (7). Human operators disengaged the automation when they anticipated failure, observed unnatural or unwanted automation behavior (e.g., erratic steering, running red lights), or believed the automation is not capable to operate safely in certain environments (e.g., inclement weather, non-standard roads). Negative experiences of human operators, such as frustration, unsafe feelings, and distrust represent some of the adverse human operate states leading to automation disengagements initiated by human operators. The automation, in turn, monitored human operators and disengaged itself if it detected insufficient vigilance or speed rule violations by human operators. Moreover, human operators can be influenced by the reactions of passengers and other road users, leading them to disengage the automation if they sensed discomfort, anger, or embarrassment due to the automation's actions. The results of the analysis are synthesized into a conceptual framework for automation disengagements, borrowing ideas from the human factor's literature and control theory. This research offers insights into the factors contributing to automation disengagements, and highlights not only the concerns of human operators but also the social aspects of this phenomenon. The findings provide information on potential edge cases of automated vehicle technology, which may help to enhance the safety and efficiency of such systems.
RESUMO
BACKGROUND: Under physiological conditions, the melanocortin system is a crucial part of the complex network regulating food intake and energy expenditure. In pathological states, like cachexia, these two parameters are deregulated, i.e., food intake is decreased and energy expenditure is increased-a vicious combination leading to catabolism. Agouti-related protein (AgRP), the endogenous antagonist at the melanocortin-4 receptor (MC-4R), was found to increase food intake and to reduce energy expenditure. This qualifies MC-4R blockade as an attractive mode of action for the treatment of cachexia. Based on this rationale, a novel series of small-molecule MC-4R antagonists was designed, from which the orally active compound BL-6020/979 (formerly known as SNT207979) emerged as the first promising development candidate showing encouraging pre-clinical efficacy and safety properties which are presented here. METHODS AND RESULTS: BL-6020/979 is an orally available, selective and potent MC-4R antagonist with a drug-like profile. It increased food intake and decreased energy expenditure in healthy wild-type but not in MC-4R deficient mice. More importantly, it ameliorated cachexia-like symptoms in the murine C26 adenocarcinoma model; with an effect on body mass and body composition and on the expression of catabolic genes. Moreover, BL-6020/979 showed antidepressant-like properties in the chronic mild stress model in rats and exhibits a favorable safety profile. CONCLUSION: The properties of BL-6020/979 demonstrated in animal models and presented here make it a promising candidate suitable for further development towards a first-in-class treatment option for cachexia that potentially opens up the opportunity to treat two hallmarks of the disease, i.e., decreased food intake and increased energy expenditure, with one drug.
