Risk-based flight planning and management for urban air mobility

Su, Yu; Xu, Yan

doi:10.2514/6.2022-3619

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…Therefore, in this study, impervious surfaces were further subdivided, as were building roofs with a high sheltering effect and other areas with high exposure risk (shown in Figure 4b). Therefore, to more accurately estimate the PER, based on related research [36], we allocated the population density distribution weight and sheltering factor (shown in Figure 4) according to the land cover area, which portrays the distribution of the population in different types of land cover within the risk map units and the corresponding sheltering factor.…”

Section: Data Processing For Risk Analysismentioning

confidence: 99%

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Data-Driven Insights into Population Exposure Risks: Towards Sustainable and Safe Urban Airspace Utilization by Unmanned Aerial Systems

Liu

et al. 2023

Sustainability

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With the rapid increase in unmanned aerial vehicles (UAVs), ensuring the safety of airspace operations and promoting sustainable development of airspace systems have become paramount concerns. However, research dedicated to investigating the population exposure risks of UAV operations in urban areas and their spatial pattern is still missing. To address this gap, this study evenly divides the urban space into uniform grids and calculates critical areas for two UAV types within each grid. By integrating geospatial data, including buildings, land use, and population, data-driven risk maps are constructed to assess the spatial distribution patterns and potential population exposure risks of two UAV types and compare them with commonly used census units. The results indicate that the mean time between failures (MTBF) for the selected generic and rotary-type UAVs can be up to 9.04 × 108 h and 1.22 × 108 h, respectively, at acceptable risk levels, considering uncertainties. The spatial pattern of population exposure risk exhibits spatial heterogeneity and multi-scale effects in urban areas, aligning with population distribution. High-risk areas concentrate in regions characterized by high population mobility, such as transport hubs, commercial service areas, residential zones, and business districts. Additionally, the comparation emphasizes the potential bias introduced by using census units in risk assessment, especially in regions with significant urban build-up. This framework enables the evaluation of safety and acceptability across diverse urban land use areas and offers guidance for airspace management in megacities, ensuring the safe integration of UAVs in urban environments.

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Section: Data Processing For Risk Analysismentioning

confidence: 99%

“…. Therefore, to more accurately estimate the PER, based on related research[36], we allocated the population density distribution weight and sheltering factor (shown in Figure4) according to the land cover area, which portrays the distribution of the population in different types of land cover within the risk map units and the corresponding sheltering factor.…”

mentioning

confidence: 99%

Data-Driven Insights into Population Exposure Risks: Towards Sustainable and Safe Urban Airspace Utilization by Unmanned Aerial Systems

Liu

et al. 2023

Sustainability

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“…The service called flight plan preparation / optimization can be divided into two parts, one is to perform risk assessment before generating the flight plan and then generate a low-risk flight plan according to the evaluation results [9], and the other is to optimize the existing flight plan with low risk [10]. For the preparation of the flight plan, SORA focuses on assigning to an UAS operation two classes of risk, a ground risk class (GRC) and an air risk class (ARC).…”

Section: B Previous Workmentioning

confidence: 99%

A comprehensive flight plan risk assessment and optimization method considering air and ground risk of UAM

Inalhan

2022

2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC)

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Inspired by risk analysis assistance service and flight plan preparation / optimization service in U-space service, this paper investigates a flight plan risk assessment and optimization method for future urban air mobility. The quantitative risk assessment of the flight plan is divided into two parts: the ground and air risks of the flight plan. After evaluating the risk of the flight plan, optimization suggestions are given to guide the path planning algorithm to optimize the flight plan at low risk. The quantitative risk assessment of the flight plan corresponds to risk analysis assistance service in U-space service, and the procedure to give optimization suggestions correspond to flight plan preparation / optimization service in U-space service. This paper selects the task scenario of logistics drone cargo transportation and carries out risk assessment on the specific flight plan. From the assessment results, when the flight plan crosses the pedestrian intensive area on the ground or the road with high-speed vehicles, the risk value of the corresponding flight plan segment increases significantly. When the flight plan segment approaches the area near the airport or intersects with other UAM participants with the same mission time window, the corresponding risk value is also high. After obtaining the risk assessment results, the targeted optimization suggestions are given to guide the path planning algorithm to optimize the flight plan at low risk. The risk of the optimized flight plan has been significantly reduced.

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“…In [21], effects of mid-air collisions and ground risks to human safety are studied by only considering failures at mid-flight phase. In [22], risk cost map is created using probabilistic impact of an accident to pedestrians and ground traffic and is used for flight planning phase. Another study deals with collision risks of flights with people, vehicles, and manned aviation to create risk-free path planning [23].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Risk Assessment and Utilization for Contingency Management of Future AAM System

Altun

Tsourdos

et al. 2023

AIAA AVIATION 2023 Forum

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This paper presents a risk assessment methodology to be used in the future Advanced Air Mobility (AAM) systems especially for supporting the planning phase and onboard contingency management solutions. Two types of dynamic risk maps are introduced as Contingency Risk Map that includes the probability of observing a contingency onboard and Risk Severity Map which covers various sources of data such as population density, a dense air traffic, obstacles, terrain, no-fly zones, and so forth. Contingency Risk Map is to quantify the probability of having a contingency and decide if the quantified probability is above the threshold. If the contingency risk probability is at unacceptable limit, Risk Severity Map assists to select a pre-defined secure emergency landing zone or non-secure emergency landing zone defined onboard. The developed risk assessment structure is tested through two different use cases. First one is about defining locations as vertiport alternatives based on the generated map, in case of a contingency ending up with an AAM vehicle to do emergency landing. Second case considers minimum risk onboard rerouting of an AAM vehicle to a secure/non-secure emergency landing zone under contingency management process. The main objective of this work is to build a system-wide contingency management concept for the AAM system by supporting with UTM services such as risk analysis assistance.

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Risk-based flight planning and management for urban air mobility

Cited by 8 publications

References 24 publications

Data-Driven Insights into Population Exposure Risks: Towards Sustainable and Safe Urban Airspace Utilization by Unmanned Aerial Systems

Data-Driven Insights into Population Exposure Risks: Towards Sustainable and Safe Urban Airspace Utilization by Unmanned Aerial Systems

A comprehensive flight plan risk assessment and optimization method considering air and ground risk of UAM

Comprehensive Risk Assessment and Utilization for Contingency Management of Future AAM System

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