The rapid development of the drone industry has facilitated the emergence of concepts such as urban air mobility (UAM), driving a wave of air logistics in urban very low-level (VLL) airspace. However, existing trajectory planning algorithms do not adequately consider the ground risks and secondary conflicts arising from high-density operations in urban VLL airspace. To address these challenges, this paper proposes a two-stage hierarchical 4D trajectory planning method to minimize multiple risks. Specifically, the method consists of a risk-aware global planning module (RAGPM) for preflight trajectory planning and a non-secondary conflict local planning module (NCLPM) for in-flight conflict avoidance. Consequently, low-risk trajectory without secondary conflict can be found in complex environments with high-density operations, as illustrated by extensive experiments.

With the accelerated growth of the UAV industry, researchers are paying close attention to the flight safety of UAVs. When a UAV loses its GPS signal or encounters unusual conditions, it must perform an emergency landing. Therefore, real-time recognition of emergency landing zones on the ground is an important research topic. This paper employs a semantic segmentation approach for recognizing emergency landing zones. First, we created a dataset of UAV aerial images, denoted as UAV-City. A total of 600 UAV aerial images were densely annotated with 12 semantic categories. Given the complex backgrounds, diverse categories, and small UAV aerial image targets, we propose the STDC-CT real-time semantic segmentation network for UAV recognition of emergency landing zones. The STDC-CT network is composed of three branches: detail guidance, small object attention extractor, and multi-scale contextual information. The fusion of detailed and contextual information branches is guided by small object attention. We conducted extensive experiments on the UAV-City, Cityscapes, and UAVid datasets to demonstrate that the STDC-CT method is superior for attaining a balance between segmentation accuracy and inference speed. Our method improves the segmentation accuracy of small objects and achieves 76.5% mIoU on the Cityscapes test set at 122.6 FPS, 68.4% mIoU on the UAVid test set, and 67.3% mIoU on the UAV-City dataset at 196.8 FPS on an NVIDIA RTX 2080Ti GPU. Finally, we deployed the STDC-CT model on Jetson TX2 for testing in a real-world environment, attaining real-time semantic segmentation with an average inference speed of 58.32 ms per image.

The corridor network serves as an effective solution for the airspace structure safety design of UAM. However, current studies rarely account for the ground risk posed by the corridor operation and typically consider a single design objective with limited variables. In this paper, we address these gaps by considering three key factors: demand, safety, and implementation costs. The corridor network design is formulated as a multi-objective optimization problem. In practice, firstly, we define the travel time-saving rate, average population density, and total length of corridors as optimization objectives. Then, we propose a straightforward and efficient corridor network encoding scheme that supports a variable number of corridors, significantly enhancing the diversity and flexibility of corridor network designs. Finally, based on this encoding scheme, we solve the corridor network problem using the unified non-dominated sorting genetic algorithm III (U-NSGA-III). Based on a detailed analysis of the obtained Pareto front, a relatively optimal design scheme across three optimization objectives is determined. The case study conducted in Chengdu illustrates that the corridor network obtained by our method not only achieves a 37.8% reduction in ground risk and a 69.9% decrease in implementation costs, but also saves a comparable 4.7% in time relative to traditional methods.

Tourist destinations thrive on sustainable development. Electric vertical take-off and landing (eVTOL) aircraft, representing energy-efficient advancements in aviation that are pivotal to advanced air mobility (AAM), have garnered attention. Yet, the discourse on eVTOLs’ role in tourism remains scant. This study, drawing from 450 samples in the Mogan Mountain Scenic Area, introduces the AAM-tourism acceptance model (ATAM). It integrates the theory of planned behavior (TPB) and technology acceptance model (TAM) theoretical frameworks, incorporating environmental consciousness, perceived safety, hedonic motivation, and personal innovativeness, assessing their influence on tourists’ eVTOL usage intention through a structural equation model (SEM). The results reveal that environmental consciousness significantly impacts hedonic motivation and perceived usefulness, driving eVTOL adoption. Furthermore, personal innovativeness influences intention through hedonic motivation and perceived behavioral control. Therefore, environmental consciousness and hedonic motivation align deeply with eVTOL attributes, both significantly positively influencing intention to use. Thus, the study validates eVTOL’s viability in tourism and its potential for sectoral expansion. Moreover, it offers insights into how psychological factors shape eVTOL adoption, guiding the promotion of eVTOL sightseeing services and informing research on AAM acceptance across various domains.