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1.	Hägele, Georg, et al. (författare) Situational risk assessment within safety-driven behavior management in the context of UAS 2020 Ingår i: 2020 International Conference on Unmanned Aircraft Systems (ICUAS). - : IEEE. - 9781728142784 - 9781728142791 ; , s. 1407-1415 Konferensbidrag (refereegranskat)abstract This paper addresses the problem of hazard recognition and risk assessment in open and non-predictive environments to support decision making and action selection for UAS. Decision making and action selection incorporate decreasing situational risks and maintain safety as operational constraints. Commonly, neither existing safety standards nor the situation modeling or knowledge representation is considered in that context. This contribution applies a novel approach denoted as a Safety-Driven Behavior Management for UAS focusing on situation modeling, and the problem of knowledge representation in the context of situational risks. It combines the safety standards-oriented hazards analysis and the risk assessment approach with the machine learning-based situation recognition. The illustrative scenario and first experimental results underline the feasibility of the novel approach.

Hägele, Georg, et al. (författare)
Situational risk assessment within safety-driven behavior management in the context of UAS
2020
Ingår i: 2020 International Conference on Unmanned Aircraft Systems (ICUAS). - : IEEE. - 9781728142784 - 9781728142791 ; , s. 1407-1415
Konferensbidrag (refereegranskat)abstract
- This paper addresses the problem of hazard recognition and risk assessment in open and non-predictive environments to support decision making and action selection for UAS. Decision making and action selection incorporate decreasing situational risks and maintain safety as operational constraints. Commonly, neither existing safety standards nor the situation modeling or knowledge representation is considered in that context. This contribution applies a novel approach denoted as a Safety-Driven Behavior Management for UAS focusing on situation modeling, and the problem of knowledge representation in the context of situational risks. It combines the safety standards-oriented hazards analysis and the risk assessment approach with the machine learning-based situation recognition. The illustrative scenario and first experimental results underline the feasibility of the novel approach.

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