Learning from examples of manual control of a central plant refrigerated cabinet

Semi-autonomous vehicles still require drivers to occasionally resume manual control. However, drivers of these vehicles may have different mental states. For example, drivers may be engaged in non-driving related tasks or may exhibit mind wandering behavior. Also, monitoring monotonous driving environments can result in passive fatigue. Given the potential for different types of mental states to negatively affect takeover performance, it will be critical to highlight how mental states affect semi-autonomous takeover. A systematic review was conducted to synthesize the literature on mental states (such as distraction, fatigue, emotion) and takeover performance. This review focuses specifically on five fatigue studies. Overall, studies were too few to observe consistent findings, but some suggest that response times to takeover alerts and post-takeover performance may be affected by fatigue. Ultimately, this review may help researchers improve and develop real-time mental states monitoring systems for a wide range of application domains.

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Towards a Framework of Operational-Risk Assessment for a Maritime Autonomous Surface Ship

Energies ◽

10.3390/en14133879 ◽

2021 ◽

Vol 14 (13) ◽

pp. 3879

Author(s):

Cunlong Fan ◽

Jakub Montewka ◽

Di Zhang

Keyword(s):

Expert Knowledge ◽

Safety Evaluation ◽

Manual Control ◽

Operational Mode ◽

Surface Ship ◽

Open Questions ◽

Study Results ◽

Operational Modes ◽

Mass Case

Global research interest in the domain of maritime autonomous surface ships (MASS) is dramatically increasing. With new prototypes planned to be set to the seas where various operational modes (OMs) are claimed, the issue of the safety evaluation of an MASS, and criteria for selecting the appropriate OM for given conditions remain open questions. This paper proposes a four-step risk-informed framework to assess risk in a scenario for an MASS operating at one of three OMs: manual control (MC), remote control (RC), and autonomous control (AC). To this end, the concept of risk priority numbers (RPNs), adopted from failure mode and effects analysis (FMEA), is utilized. The required parameters to defined RPNs are obtained in the course of analyzing a model MASS accident with expert knowledge. The applicability of the proposed framework is demonstrated via a model MASS case study. Results reveal that, in the same scenario, the risk of MASS varied across the analyzed OMs. On the basis of the aggregated results for each operational mode, suggestions for OM switching are put forward.

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