Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Aerospace aerospace	2.1	3.4	2014	21.3 Days	CHF 2400	Submit
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Drones drones	4.4	5.6	2017	19.2 Days	CHF 2600	Submit
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
Eng eng	-	2.1	2020	21.5 Days	CHF 1200	Submit
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700	Submit
Sensors sensors	3.4	7.3	2001	18.6 Days	CHF 2600	Submit

22 pages, 23754 KiB

Open AccessArticle

A Low-Latency Dynamic Object Detection Algorithm Fusing Depth and Events

by Duowen Chen, Liqi Zhou and Chi Guo

Drones 2025, 9(3), 211; https://doi.org/10.3390/drones9030211 - 15 Mar 2025

Viewed by 439

Existing RGB image-based object detection methods achieve high accuracy when objects are static or in quasi-static conditions but demonstrate degraded performance with fast-moving objects due to motion blur artifacts. Moreover, state-of-the-art deep learning methods, which rely on RGB images as input, necessitate training [...] Read more.

Existing RGB image-based object detection methods achieve high accuracy when objects are static or in quasi-static conditions but demonstrate degraded performance with fast-moving objects due to motion blur artifacts. Moreover, state-of-the-art deep learning methods, which rely on RGB images as input, necessitate training and inference on high-performance graphics cards. These cards are not only bulky and power-hungry but also challenging to deploy on compact robotic platforms. Fortunately, the emergence of event cameras, inspired by biological vision, provides a promising solution to these limitations. These cameras offer low latency, minimal motion blur, and non-redundant outputs, making them well suited for dynamic obstacle detection. Building on these advantages, a novel methodology was developed through the fusion of events with depth to address the challenge of dynamic object detection. Initially, an adaptive temporal sampling window was implemented to selectively acquire event data and supplementary information, contingent upon the presence of objects within the visual field. Subsequently, a warping transformation was applied to the event data, effectively eliminating artifacts induced by ego-motion while preserving signals originating from moving objects. Following this preprocessing stage, the transformed event data were converted into an event queue representation, upon which denoising operations were performed. Ultimately, object detection was achieved through the application of image moment analysis to the processed event queue representation. The experimental results show that, compared with the current state-of-the-art methods, the proposed method has improved the detection speed by approximately 20% and the accuracy by approximately 5%. To substantiate real-world applicability, the authors implemented a complete obstacle avoidance pipeline, integrating our detector with planning modules and successfully deploying it on a custom-built quadrotor platform. Field tests confirm reliable avoidance of an obstacle approaching at approximately 8 m/s, thereby validating practical deployment potential. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

► Show Figures

Figure 1

19 pages, 3025 KiB

Open AccessArticle

Two-Step Robust Fault-Tolerant Controller Design Based on Nonlinear Extended State Observer (NESO) for Unmanned Aerial Vehicles (UAVs) with Actuator Faults and Disturbances

by Wei Wang, Yiming Chen, Zhang Ren and Huanhua Liu

Drones 2025, 9(3), 183; https://doi.org/10.3390/drones9030183 - 1 Mar 2025

Viewed by 572

Abstract

This paper presents a two-step robust fault-tolerant controller of incorporating disturbances and actuator faults rejection for a UAV flight control system, which is challenging due to its complex and nonlinear dynamics. First, the main controller, which is designed separately, considers all the design [...] Read more.

This paper presents a two-step robust fault-tolerant controller of incorporating disturbances and actuator faults rejection for a UAV flight control system, which is challenging due to its complex and nonlinear dynamics. First, the main controller, which is designed separately, considers all the design parameters giving the desired closed loop system response. Second, a method to design a standalone disturbance/fault compensator is suggested, which is integrated into the original system to ensure stability. The degraded system stability and performance are compensated by the compensator, which comes into effect only after the disturbance/fault residual error increases to a certain level. The disturbance/fault compensator is designed based on a nonlinear extended state observer (NESO), which cannot only estimate the system’s states but also the unknown disturbances and fault. In the faultless system, only the main controller is active. When an actuator fault/disturbance occurs, the compensator is automatically activated. This controller scheme solves the traditional control conflict between high performance and robustness. It also guarantees the stability of the system in the presence of the disturbances/faults. A civil fixed-wing unmanned aerial vehicle (UAV) that is equipped with a thrust vector control (TVC) with actuator faults and disturbance is chosen for the simulations, and the results prove the efficacy of the new approach. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

► Show Figures

Figure 1

22 pages, 13198 KiB

Open AccessArticle

UAV Localization in Urban Area Mobility Environment Based on Monocular VSLAM with Deep Learning

by Mutagisha Norbelt, Xiling Luo, Jinping Sun and Uwimana Claude

Drones 2025, 9(3), 171; https://doi.org/10.3390/drones9030171 - 26 Feb 2025

Cited by 1 | Viewed by 1015

Abstract

Unmanned Aerial Vehicles (UAVs) play a major role in different applications, including surveillance, mapping, and disaster relief, particularly in urban environments. This paper presents a comprehensive framework for UAV localization in outdoor environments using monocular ORB-SLAM3 integrated with optical flow and YOLOv5 for [...] Read more.

Unmanned Aerial Vehicles (UAVs) play a major role in different applications, including surveillance, mapping, and disaster relief, particularly in urban environments. This paper presents a comprehensive framework for UAV localization in outdoor environments using monocular ORB-SLAM3 integrated with optical flow and YOLOv5 for enhanced performance. The proposed system addresses the challenges of accurate localization in dynamic outdoor environments where traditional GPS methods may falter. By leveraging the capabilities of ORB-SLAM3, the UAV can effectively map its environment while simultaneously tracking its position using visual information from a single camera. The integration of optical flow techniques allows for accurate motion estimation between consecutive frames, which is critical for maintaining accurate localization amidst dynamic changes in the environment. YOLOv5 is a highly efficient model utilized for real-time object detection, enabling the system to identify and classify dynamic objects within the UAV’s field of view. This dual approach of using both optical flow and deep learning enhances the robustness of the localization process by filtering out dynamic features that could otherwise cause mapping errors. Experimental results show that the combination of monocular ORB-SLAM3, optical flow, and YOLOv5 significantly improves localization accuracy and reduces trajectory errors compared to traditional methods. In terms of absolute trajectory error and average tracking time, the suggested approach performs better than ORB-SLAM3 and DynaSLAM. For real-time SLAM applications in dynamic situations, our technique is especially well-suited due to its potential to achieve lower latency and greater accuracy. These improvements guarantee more dependable performance in a variety of scenarios in addition to increasing overall efficiency. The framework effectively distinguishes between static and dynamic elements, allowing for more reliable map construction and navigation. The results show that our proposed method (U-SLAM) produces a considerable decrease of up to 43.47% in APE and 26.47% RPE in S000, and its accuracy is higher for sequences with moving objects and more motion inside the image. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

► Show Figures

Figure 1

20 pages, 907 KiB

Open AccessArticle

Task-Offloading Optimization Using a Genetic Algorithm in Hybrid Fog Computing for the Internet of Drones

by Mohamed Amine Attalah, Sofiane Zaidi, Naçima Mellal and Carlos T. Calafate

Sensors 2025, 25(5), 1383; https://doi.org/10.3390/s25051383 - 24 Feb 2025

Viewed by 599

Abstract

Research and development on task offloading over the Internet of Drones (IoD) has expanded rapidly in the last few years. Task offloading in a fog IoD environment is very challenging due to the high dynamics of the IoD topology, which cause intermittent connections, [...] Read more.

Research and development on task offloading over the Internet of Drones (IoD) has expanded rapidly in the last few years. Task offloading in a fog IoD environment is very challenging due to the high dynamics of the IoD topology, which cause intermittent connections, as well as the stringent requirements of task offloading, such as reduced delay. To overcome these challenges, in this paper, we propose a task-offloading optimization strategy using a heuristic genetic algorithm (GA) with hybrid fog computing technology for the Internet of Drones, named GA Hybrid-Fog. The proposed solution employs a GA for task offloading from edge Unmanned Aerial Vehicles (UAVs) to both fog base stations (FBSs) and fog UAVs (FUAVs) in order to optimize offloading delays (transmission and fog computing delays) and guarantee higher storage and processing capacity. Experimental results show that GA Hybrid-Fog achieves greater improvements in task-offloading delays compared to other IoD technologies (GA BS-Fog, GA UAV-Fog, and GA UAV-Edge). Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

► Show Figures

Figure 1

17 pages, 25856 KiB

Open AccessArticle

An Independent UAV-Based Mobile Base Station

by Sung-Chan Choi and Sung-Yeon Kim

Sensors 2025, 25(5), 1349; https://doi.org/10.3390/s25051349 - 22 Feb 2025

Viewed by 583

Abstract

In disaster scenarios, e.g., earthquakes, tsunamis, and wildfires, communication infrastructure often becomes severely damaged. To rapidly restore damaged communication systems, we propose a UAV-based mobile base station equipped with Public Safety LTE (PS-LTE) technology to provide standalone communication capabilities. The proposed system includes [...] Read more.

In disaster scenarios, e.g., earthquakes, tsunamis, and wildfires, communication infrastructure often becomes severely damaged. To rapidly restore damaged communication systems, we propose a UAV-based mobile base station equipped with Public Safety LTE (PS-LTE) technology to provide standalone communication capabilities. The proposed system includes PS-LTE functionalities, mission-critical push-to-talk, proximity-based services, and isolated E-UTRAN operation to ensure the reliable and secure communication for emergency services. We provide a simulation result to achieve the radio coverage of mobile base station. By using this radio coverage, we find an appropriate location of the end device for performing the outdoor experiments. We develop a prototype of a proposed mobile base station and test its operation in an outdoor environment. The experimental results provide a sufficient data rate to make an independent mobile base station to restore communication infrastructure in areas that experienced environmental disasters. This prototype and experimental results offer a significant step forward in creating agile and efficient communication solutions for emergency scenarios. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

► Show Figures

Figure 1

26 pages, 3057 KiB

Open AccessReview

Multi-Dimensional Research and Progress in Parking Space Detection Techniques

by Xi Wang, Haotian Miao, Jiaxin Liang, Kai Li, Jianheng Tan, Rui Luo and Yueqiu Jiang

Electronics 2025, 14(4), 748; https://doi.org/10.3390/electronics14040748 - 14 Feb 2025

Cited by 1 | Viewed by 1270

Abstract

Due to the increase in the number of vehicles and the complexity of parking spaces, parking space detection technology has emerged. It is capable of automatically identifying vacant parking spaces in parking lots or on streets, and delivering this information to drivers or [...] Read more.

Due to the increase in the number of vehicles and the complexity of parking spaces, parking space detection technology has emerged. It is capable of automatically identifying vacant parking spaces in parking lots or on streets, and delivering this information to drivers or parking management systems in real time, which has a significant impact on improving urban parking efficiency, alleviating traffic congestion, optimizing driving experience, and promoting the development of intelligent transportation systems. This paper firstly describes the research significance of parking space detection technology and its research background, and then systematically reviews different types of parking spaces and detection technologies, covering a variety of technical means such as ultrasonic sensors, infrared sensors, magnetic sensors, other sensors, methods based on traditional computer vision, and methods based on deep learning. At the end of the paper, the article summarizes the current research progress in parking space detection technology, analyzes the existing challenges, and provides an outlook on future research directions. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

► Show Figures

Figure 1

28 pages, 4431 KiB

Open AccessArticle

Parking Trajectory Planning for Autonomous Vehicles Under Narrow Terminal Constraints

by Yongxing Cao, Bijun Li, Zejian Deng and Xiaomin Guo

Electronics 2024, 13(24), 5041; https://doi.org/10.3390/electronics13245041 - 22 Dec 2024

Cited by 1 | Viewed by 1218

Abstract

Trajectory planning in tight spaces presents a significant challenge due to the complex maneuvering required under kinematic and obstacle avoidance constraints. When obstacles are densely distributed near the target state, the limited connectivity between the feasible states and terminal state can further decrease [...] Read more.

Trajectory planning in tight spaces presents a significant challenge due to the complex maneuvering required under kinematic and obstacle avoidance constraints. When obstacles are densely distributed near the target state, the limited connectivity between the feasible states and terminal state can further decrease the efficiency and success rate of trajectory planning. To address this challenge, we propose a novel Dual-Stage Motion Pattern Tree (DS-MPT) algorithm. DS-MPT decomposes the trajectory generation process into two stages: merging and posture adjustment. Each stage utilizes specific heuristic information to guide the construction of the trajectory tree. Our experimental results demonstrate the high robustness and computational efficiency of the proposed method in various parallel parking scenarios. Additionally, we introduce an enhanced driving corridor generation strategy for trajectory optimization, reducing computation time by 54% to 84% compared to traditional methods. Further experiments validate the improved stability and success rate of our approach. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

► Show Figures

Figure 1

16 pages, 2350 KiB

Open AccessArticle

Connectivity-Enhanced 3D Deployment Algorithm for Multiple UAVs in Space–Air–Ground Integrated Network

by Shaoxiong Guo, Li Zhou, Shijie Liang, Kuo Cao and Zhiqun Song

Aerospace 2024, 11(12), 969; https://doi.org/10.3390/aerospace11120969 - 25 Nov 2024

Viewed by 860

Abstract

The space–air–ground integrated network (SAGIN) can provide extensive access, continuous coverage, and reliable transmission for global applications. In scenarios where terrestrial networks are unavailable or compromised, deploying unmanned aerial vehicles (UAVs) within air network offers wireless access to designated regions. Meanwhile, ensuring the [...] Read more.

The space–air–ground integrated network (SAGIN) can provide extensive access, continuous coverage, and reliable transmission for global applications. In scenarios where terrestrial networks are unavailable or compromised, deploying unmanned aerial vehicles (UAVs) within air network offers wireless access to designated regions. Meanwhile, ensuring the connectivity between UAVs as well as between UAVs and ground users (GUs) is critical for enhancing the quality of service (QoS) in SAGIN. In this paper, we consider the 3D deployment problem of multiple UAVs in SAGIN subject to the UAVs’ connection capacity limit and the UAV network’s robustness, maximizing the coverage of UAVs. Firstly, the horizontal positions of the UAVs at a fixed height are initialized using the k-means algorithm. Subsequently, the connections between the UAVs are established based on constraint conditions, and a fairness connection strategy is employed to establish connections between the UAVs and GUs. Following this, an improved genetic algorithm (IGA) with elite selection, adaptive crossover, and mutation capabilities is proposed to update the horizontal positions of the UAVs, thereby updating the connection relationships. Finally, a height optimization algorithm is proposed to adjust the height of each UAV, completing the 3D deployment of multiple UAVs. Extensive simulations indicate that the proposed algorithm achieves faster deployment and higher coverage under both random and clustered distribution scenarios of GUs, while also enhancing the robustness and load balance of the UAV network. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

► Show Figures

Figure 1

21 pages, 11869 KiB

Open AccessArticle

Quantifying Well Clear Thresholds for UAV in Conjunction with Trajectory Conformity

by Linghang Meng, Hongyang Zhang, Yifei Zhao and Kin Huat Low

Drones 2024, 8(11), 624; https://doi.org/10.3390/drones8110624 - 30 Oct 2024

Cited by 1 | Viewed by 1401

Abstract

The rapid advancement of unmanned aerial vehicles (UAVs) has introduced new challenges in overseeing and managing their flight operations due to their diverse flight dynamics and performance metrics. To address these complexities, this study introduces a concept of trajectory conformity aimed at enhancing [...] Read more.

The rapid advancement of unmanned aerial vehicles (UAVs) has introduced new challenges in overseeing and managing their flight operations due to their diverse flight dynamics and performance metrics. To address these complexities, this study introduces a concept of trajectory conformity aimed at enhancing the supervision and control of UAV flights. Trajectory conformity, from a regulatory perspective, is defined as the distribution of deviations between a UAV’s actual flight path and its intended trajectory, offering a measure of system-wide operational error. The concept of conformity is hoped to simplify and strengthen the monitoring process to ensure conflict-free drone flying. The present work is also concerned with the development of a comprehensive UAV collision risk model grounded in trajectory conformity analysis. The normality and homogeneity of UAV trajectory deviations are validated by evaluating the trajectory data obtained from real-world UAV flights. Well clear thresholds between two UAVs operating in three orthogonal directions within the same airspace have been established by the developed model. The results obtained demonstrate the effectiveness in omni-encounter scenarios, underscoring the potential to strengthen safety measures. The present work is expected to enhance UAV safety systems, such as detect and avoid (DAA) and unmanned aircraft system traffic management (UTM), by enabling real-time collision warnings within predefined safety thresholds, supporting proactive risk mitigation. Furthermore, the model’s versatility allows it to be applied to various UAV operational aspects in future works, including route planning, flight procedure design, airspace capacity assessments, and establishment of separation minima. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

► Show Figures

Figure 1

17 pages, 5286 KiB

Open AccessArticle

U-Space Contingency Management Based on Enhanced Mission Description

by Jose L. Munoz-Gamarra, Juan J. Ramos and Zhiqiang Liu

Aerospace 2024, 11(11), 876; https://doi.org/10.3390/aerospace11110876 - 24 Oct 2024

Viewed by 799

Abstract

Loss of communication, low battery, or bad weather conditions (like high-speed wind) are currently managed by performing a return to launch (RTL) point maneuver. However, the execution of this procedure can pose a safety threat since it has not been considered within the [...] Read more.

Loss of communication, low battery, or bad weather conditions (like high-speed wind) are currently managed by performing a return to launch (RTL) point maneuver. However, the execution of this procedure can pose a safety threat since it has not been considered within the mission planning process. This work proposes an advanced management of contingency events based on the integration of a new U-space service that enhances mission description. The proposed new service, deeply linked to demand capacity balance and strategic deconfliction services, assigns alternative safe landing spots by analyzing the planned mission. Two potential solutions are characterized (distinguished primarily by the number of contingency vertiports assigned): contingency management based on the assignment of a single alternative vertiport to each mission (static) or the allocation of a set of different contingency vertiports that are valid during certain time intervals. It is proven that this enhanced mission planning could ensure that U-space volumes operate in an ultra-safe system conditions while facing these unforeseen events, highlighting its importance in high-risk scenarios like urban air mobility deployments. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

► Show Figures

Figure 1

19 pages, 11959 KiB

Open AccessArticle

Learning Autonomous Navigation in Unmapped and Unknown Environments

by Naifeng He, Zhong Yang, Chunguang Bu, Xiaoliang Fan, Jiying Wu, Yaoyu Sui and Wenqiang Que

Sensors 2024, 24(18), 5925; https://doi.org/10.3390/s24185925 - 12 Sep 2024

Cited by 1 | Viewed by 1743

Abstract

Autonomous decision-making is a hallmark of intelligent mobile robots and an essential element of autonomous navigation. The challenge is to enable mobile robots to complete autonomous navigation tasks in environments with mapless or low-precision maps, relying solely on low-precision sensors. To address this, [...] Read more.

Autonomous decision-making is a hallmark of intelligent mobile robots and an essential element of autonomous navigation. The challenge is to enable mobile robots to complete autonomous navigation tasks in environments with mapless or low-precision maps, relying solely on low-precision sensors. To address this, we have proposed an innovative autonomous navigation algorithm called PEEMEF-DARC. This algorithm consists of three parts: Double Actors Regularized Critics (DARC), a priority-based excellence experience data collection mechanism, and a multi-source experience fusion strategy mechanism. The algorithm is capable of performing autonomous navigation tasks in unmapped and unknown environments without maps or prior knowledge. This algorithm enables autonomous navigation in unmapped and unknown environments without the need for maps or prior knowledge. Our enhanced algorithm improves the agent’s exploration capabilities and utilizes regularization to mitigate the overestimation of state-action values. Additionally, the priority-based excellence experience data collection module and the multi-source experience fusion strategy module significantly reduce training time. Experimental results demonstrate that the proposed method excels in navigating the unmapped and unknown, achieving effective navigation without relying on maps or precise localization. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

► Show Figures

Figure 1

37 pages, 10534 KiB

Open AccessArticle

Optimization of Urban Target Area Accessibility for Multi-UAV Data Gathering Based on Deep Reinforcement Learning

by Zhengmiao Jin, Renxiang Chen, Ke Wu, Tengwei Yu and Linghua Fu

Drones 2024, 8(9), 462; https://doi.org/10.3390/drones8090462 - 5 Sep 2024

Viewed by 1124

Abstract

Unmanned aerial vehicles (UAVs) are increasingly deployed to enhance the operational efficiency of city services. However, finding optimal solutions for the gather–return task pattern under dynamic environments and the energy constraints of UAVs remains a challenge, particularly in dense high-rise building areas. This [...] Read more.

Unmanned aerial vehicles (UAVs) are increasingly deployed to enhance the operational efficiency of city services. However, finding optimal solutions for the gather–return task pattern under dynamic environments and the energy constraints of UAVs remains a challenge, particularly in dense high-rise building areas. This paper investigates the multi-UAV path planning problem, aiming to optimize solutions and enhance data gathering rates by refining exploration strategies. Initially, for the path planning problem, a reinforcement learning (RL) technique equipped with an environment reset strategy is adopted, and the data gathering problem is modeled as a maximization problem. Subsequently, to address the limitations of stationary distribution in indicating the short-term behavioral patterns of agents, a Time-Adaptive Distribution is proposed, which evaluates and optimizes the policy by combining the behavioral characteristics of agents across different time scales. This approach is particularly suitable for the early stages of learning. Furthermore, the paper describes and defines the “Narrow-Elongated Path” Problem (NEP-Problem), a special spatial configuration in RL environments that hinders agents from finding optimal solutions through random exploration. To address this, a Robust-Optimization Exploration Strategy is introduced, leveraging expert knowledge and robust optimization to ensure UAVs can deterministically reach and thoroughly explore any target areas. Finally, extensive simulation experiments validate the effectiveness of the proposed path planning algorithms and comprehensively analyze the impact of different exploration strategies on data gathering efficiency. Full article

(This article belongs to the Topic Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation)

► Show Figures

Figure 1

Topic Menu

Topic Editors

Unmanned Vehicles Technology and Embodied Intelligence Systems for Intelligent Transportation

Topic Information

Keywords

Participating Journals

Published Papers (12 papers)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI