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Various applications of advanced air mobility (AAM) in urban environmentsfacilitate our daily life and public services. As one of the key issues ofrealizing these applications autonomously, path planning problem has beenstudied with main objectives on minimizing travel distance, flight time andenergy cost. However, AAM operations in metropolitan areas bring safety andsociety issues. Because most of AAM aircraft are unmanned aerial vehicles(UAVs) and they may fail to operate resulting in fatality risk, property damagerisk and societal impacts (noise and privacy) to the public. To quantitativelyassess these risks and mitigate them in planning phase, this paper proposes anintegrated risk assessment model and develops a hybrid algorithm to solve therisk-based 3D path planning problem. The integrated risk assessment methodconsiders probability and severity models of UAV impact ground people andvehicle. By introducing gravity model, the population density and trafficdensity are estimated in a finer scale, which enables more accurate riskassessment. The 3D risk-based path planning problem is first formulated as aspecial minimum cost flow problem. Then, a hybrid estimation of distributionalgorithm (EDA) and risk-based A* (named as EDA-RA*) algorithm is proposed tosolve the problem. To improve computational efficiency, k-means clusteringmethod is incorporated into EDA-RA* to provide both global and local searchheuristic information, which formed the EDA and fast risk-based A* algorithm wecall EDA-FRA*. Case study results show that the risk assessment model cancapture high risk areas and the generated risk map enables safe UAV pathplanning in urban complex environments.

Third Party Risk Modelling and Assessment for Safe UAV Path Planning in Metropolitan Environments