This survey paper explores the emergent domain of electric vertical takeoff and landing vehicles (eVTOLs), emphasizing the critical role of autonomous navigation capabilities essential for their effective integration and operation in complex urban environments. Pioneering in this review is the introduction of a novel six-level autonomy concept for eVTOLs, categorizing them based on their degree of intelligence. The paper offers a comprehensive review of state-of-the-art developments that together fortify the autonomous functionality of eVTOLs, with a special focus on enhanced perception, intelligent planning, and advanced control. Perception technologies empower eVTOLs with the environmental awareness crucial for navigating the intricate urban airspace, while advanced planning algorithms navigate their paths through densely populated skies, ensuring optimal routing and safety. Control strategies are developed for their ability to endow these eVTOLs with the stability and agility needed to execute complex flight dynamics. The synthesis of these elements forms the backbone of autonomous navigation for eVTOLs, outlining a clear direction for future eVTOL research. This comprehensive survey can serve as a vital resource for enhancing the autonomy of eVTOLs, steering them towards a future where they integrate effortlessly into intelligent urban transportation systems. 

Autonomous Navigation for eVTOL: Review and Future Perspectives

The zero-trust framework is a potential solution to address complex dynamic behaviors, information interactions, complex network topologies, and environmental security threats to groups, such as the safety of the intelligent connected vehicle (ICV) platoon. In addition, it addresses the challenges faced due to increased traffic of intelligent vehicles, communication in critical infrastructure, and malicious attacks between vehicles. Motivated by these observations, we have investigated the zero-trust theory and framework based on the authorization and confirm processes among connected nodes in a group. Furthermore, we have analyzed and summarized the information perception, full information chain communication, and fault-tolerant control mechanism of ICV systems under a zero-trust framework, in addition to the methods of trust value calculation and heterogeneous vehicle platoons. The results indicated that zero-trust theory has a strong potential in group reliability research.

Overview of Cooperative Fault-Tolerant Control Driven by the Full Information Chain of Intelligent Connected Vehicle Platoons Under the Zero-Trust Framework: Opportunities and Challenges

The advent of the sixth generation of mobile communications (6G) ushers in an era of heightened demand for advanced network intelligence to tackle the challenges of an expanding network landscape and increasing service demands. Deep Learning (DL), as a crucial technique for instilling intelligence into 6G, has demonstrated powerful and promising development. This paper provides a comprehensive overview of the pivotal role of DL in 6G, exploring the myriad opportunities and challenges that arise. Firstly, we present a detailed vision for DL in 6G, emphasizing areas such as adaptive resource allocation, intelligent network management, robust signal processing, ubiquitous edge intelligence, and endogenous security. Secondly, this paper reviews how DL models leverage their unique learning capabilities to solve complex service demands in 6G. The models discussed include Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Graph Neural Networks (GNN), Deep Reinforcement Learning (DRL), Transformer, Federated Learning (FL), and Meta Learning. Additionally, we examine the specific challenges each DL model faces within the 6G context. Moreover, we delve into the rapidly evolving field of Artificial Intelligence Generated Content (AIGC), examining its development and impact within the 6G framework. Finally, this paper culminates in a detailed discussion of ten critical open problems in integrating DL with 6G, setting the stage for future research and development in this field.

Advanced Deep Learning Models for 6G: Overview, Opportunities and Challenges

In underwater wireless networks, unmanned surface vehicles (USVs) can be deployed to assist in long-range communication between underwater nodes, enhancing communication quality and reliability due to their high maneuverability and mobility. However, the open nature of underwater acoustic communication makes underwater wireless networks vulnerable to malicious jamming attacks. To address this issue, this paper proposes an algorithm based on Double Deep Q Network (D2QN) to jointly optimize the USV’s trajectory and transmit power, effectively resists malicious underwater jamming attacks and maximizes the achievable end-to-end throughput of the system. The numerical results obtained from our experiments provide strong evidence of the effectiveness of the proposed joint optimization algorithm. 

Throughput Maximization for USV-Enabled Underwater Wireless Networks Under Jamming Attack

Urbanization is a significant global problem affecting many regions facing climate change. Land use land cover (LULC), land surface temperature (LST), urban sprawl, and significant concerns. The research objective was to evaluate the effects of LULC change, pre-urban expansion, and urban growth on LST for 30 years (1992, 1997, 2002, 2007, 2012, 2017 and 2022) in District Lahore using Landsat (TM, ETM+, and OLI/TIRS) data in Google Earth Engine (GEE). In this study, we concentrate on four major LULC classes identified: urban area, vegetation, barren land, and water bodies through Landsat data and Support Vector Machine (SVM) in GEE. Results showed that 975.6 km2 (196.5%) of the built-up area increased, while vegetation decreased by 579.15 km2 (30.4%) from 1992 to 2002. Additionally, the normalized difference built-up index (NDBI) and the normalized difference vegetation index (NDVI) were retrieved to measure the association with LST. A negative and positive correlation was found between NDVI, NDBI, and LST, respectively. The urban heat island ratio index (UHIRI) was also mapped, and an upward trend was displayed during this research. These results are crucial for the division of development and planning to secure the enduring utilization of land resources for future urbanization growth. 

Monitoring and prediction of the LULC change dynamics using time series remote sensing data with Google Earth Engine

The early trajectory prediction of micro unmanned aerial vehicles (micro-UAVs) with random behavior intentions facilitates the elimination of potential safety hazards. However, due to the property of a small radar cross-section (RCS), the backscattered radar signals from micro-UAVs may be submerged under strong background clutters, leading to distorted tracking and false prediction. To this end, this paper presents a spatial-temporal integrated framework (STIF) for end-to-end micro-UAV trajectory tracking and prediction based on a four-dimensional (4D) multiple-input-multiple-output (MIMO) radar. Especially, to obtain accurate trajectories in low signal-to-noise ratio (SNR) conditions, the target detection and tracking are considered to be interdependent and addressed jointly in this work, rather than treating them as two separate processes in conventional methods. The advantage is that with the assistance of tracking, all consecutive spatial information encoded in raw radar streams can be incorporated to enhance the continuous detection performance, avoiding information loss using only one single scan. Subsequently, to accommodate high maneuvering scenarios, an intention-aware end-to-end transformer-based prediction framework is presented to simultaneously discover both spatial and temporal dependencies hiding in long-term estimated trajectories. Consequently, a 4D frequency modulated continuous wave (FMCW) radar is utilized to evaluate the proposed system. Numerous simulation and experimental results indicate that STIF outperforms competing state-of-the-art methods and achieve superior prediction performance with the accuracy of 0.3851m in low SNR conditions. 

STIF: A Spatial-Temporal Integrated Framework for End-to-End Micro-UAV Trajectory Tracking and Prediction with 4D MIMO Radar

Continuous trajectory tracking and movement estimation of microunmanned aerial vehicles (UAVs) in sensitive areas intended for unauthorized intrusion prevention are challenging due to their low-observable characteristics in urban low-altitude. Consequently, through a 4-D radar with range-Doppler-azimuth-elevation information, this article proposes a joint trajectory tracking and estimation approach (JTEA) toward unauthorized micro-UAV monitoring. First, for continuously realizing the 3-D trajectory tracking of micro-UAVs from clutters and noises, JTEA introduces an integrated detection and tracking strategy to directly accumulate the nonthresholding observations with a sequence of radar frames. The advantage is that it removes the threshold-decision process before tracking and thus avoids information loss in comparison to conventional approaches considering target detection and tracking independently. On this basis, JTEA presents a projection-based long short-term memory-connectionist temporal classification (PLSTM-CTC) network to directly predict movement class labels of micro-UAVs from in-progress motion trajectory without any presegmentation process. Remarkably, instead of directly inputting the tracking trajectory into the recognition network, PLSTM-CTC employs a 3-D projection layer to transform the position vectors into three orthogonal sequences for obtaining superior movement recognition performance of micro-UAVs with complex motions. Finally, simulation and experimental results show unique advantages of JTEA in micro-UAV tracking and movement recognition in contrast to conventional methods, especially under low signal-to-noise ratio (SNR) conditions.

JTEA: A Joint Trajectory Tracking and Estimation Approach for Low-Observable Micro-UAV Monitoring with 4D Radar

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STIF: A Spatial-Temporal Integrated Framework for End-to-End Micro-UAV Trajectory Tracking and Prediction with 4D MIMO Radar

JTEA: A Joint Trajectory Tracking and Estimation Approach for Low-Observable Micro-UAV Monitoring with 4D Radar