 Subjects -> ELECTRONICS (Total: 207 journals)
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- IEEE Access
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Pages: C2 - C2
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- Errata
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Authors:
Changlong Zhang;
Pages: C3 - C3
Abstract: Presents corrections to the paper, A New Cellular Vehicle-to-Everything Application: Daytime Visibility Detection and Prewarning on Expressways
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- Calendar [Calendar]
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Authors:
Martin Lauer;
Pages: C4 - C4
Abstract: Lists future events that should be of interest to practitioners and researchers.
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- TechRxiv: Share Your Preprint Research With the World!
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Pages: 2 - 2
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- In the Year of the Rabbit, Let Us Make a Fast Leap!
[Editor’s Column]-
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Authors:
Yisheng Lv;
Pages: 3 - 4
Abstract: It is with great pleasure and honor that I take the role as the editor-in-chief of IEEE Intelligent Transportation Systems Magazine (ITSM) after my appointment in January 2023. First and foremost, I would like to take this opportunity to express my heartfelt thanks to my predecessor, Dr. Ljubo Vlacic, not only for his great work and efforts but also for the generous and unconditional support that he has provided to me in the smooth handover. I have been learning a lot from Dr. Ljubo Vlacic, who has been a great colleague, mentor, and friend. During his term as editor-in-chief, our magazine was elevated to its current high level of excellence, from four issues per year to six issues per year, the impact factor rising to 5.293. The new editorial team will seek his guidance to continue the success of ITSM.
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- IEEE App
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Pages: 4 - 4
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- Valuable Resources and Opportunities for IEEE ITSS Members
[President’s Message]-
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Authors:
Cristina Olaverri-Monreal;
Pages: 5 - 6
Abstract: The field of intelligent transportation systems (ITS) is rapidly growing and has had a significant impact on society as it aims to make transportation safer, more efficient, and more sustainable. The strong leadership of the IEEE ITS Society (ITSS), made up of a dedicated and experienced group of volunteers who are committed to advancing the field of ITS, is working to provide valuable resources and opportunities for members through its Chapters. The Society’s Chapters provide a local network of professionals and students, who are interested in the field of ITS. ITSS Chapters provide a place for members to connect with others in their area, share ideas, and stay current with the latest developments in their area of expertise.
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- IEEE Feedback
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Pages: 6 - 6
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- IEEE Proceedings
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Pages: 7 - 7
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- Making You Only Look Once Faster: Toward Real-Time Intelligent
Transportation Detection-
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Authors:
Yuan Dai;Weiming Liu;Wei Xie;Ruikang Liu;Zhongxing Zheng;Kejun Long;Liang Wang;Liang Mao;Qisheng Qiu;Guangzheng Ling;
Pages: 8 - 25
Abstract: We present in this article a simple yet efficient algorithm named you only look once: dynamic and stem (YOLO-DS), which can better complete real-time intelligent transportation detection. YOLO-DS is accomplished based on YOLOv5s through the following primary modifications. First, we apply a dynamic mechanism in the backbone. The dynamic mechanism enables the model to be a multibranch model during training and a single-path model during inference and deployment. Hence, our model can enjoy the benefits of fast speed and economical memory while maintaining excellent performance. Second, the original focus module in the backbone is inefficient. Accordingly, we employ the stem module (built entirely with standard convolution) instead of the focus module in the backbone. Finally, we modify the width and depth of the model and redesign a new, lighter detection head to improve the model further. Compared to the original YOLOv5s, YOLO-DS improves the mean average precision (AP) by 2.7 and 0.2 on the University at Albany DETection and tRACking and the high-speed train fault dataset. In addition, experiments conducted on various devices show that the speed of YOLO-DS is highly impressive, far exceeding previous lightweight neural networks. Specifically, the inference speed of YOLO-DS is twice that of the original YOLOv5s, up to 7.98 times. Moreover, our YOLO-DS-Tiny processes an image with 640 × 640 resolution averaging barely 35 ms on a cellphone (the Honor V20).
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- High-Speed Rail Station Location Optimization Using Customized Utility
Functions-
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Authors:
Sandeepan Roy;Avijit Maji;
Pages: 26 - 35
Abstract: High-speed rail station location (HSR-SL) is a complex problem with multiple conflicting factors, such as local transportation network connectivity, regional accessibility, downtown proximity, and feasibility. The proposed novel methodology quantified these factors using customized nonlinear, linear, or integer utility functions. Suitable distance decay models to quantify the local transport access to and from existing bus stops, train stations, and downtown proximity; a gravity model for potential regional accessibility to the residential population or workforce; linear models for connectivity with the existing public transit routes and normalized land cost; and a binary model with a threshold value for geographical feasibility in terms of environmental sensitivity were developed. These models were evaluated using a geospatial and network analysis-based approach, and the overall nonlinear HSR-SL problem was optimized using the particle swarm optimization algorithm. The results of a real-world study area in downtown Tokyo, Japan, revealed that customized utility functions for various factors reduced the possibility of over- or underestimation and the selection of suboptimal SLs. The proposed method improved estimation of the land cost feasibility, access to transfer points, and connectivity by 100%, 185%, and 222%, respectively, for the given case study. It was most sensitive to connectivity and proximity to the downtown area, followed by location cost, transportation access, and population or workforce potential accessibility.
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- Autonomous Collision Avoidance of Unmanned Surface Vehicles Based on
Improved A-Star and Dynamic Window Approach Algorithms-
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Authors:
Wei Guan;Kuo Wang;
Pages: 36 - 50
Abstract: Unmanned surface vessel (USV) autonomous navigation on the open sea involving real-time path planning and collision avoidance is still one of the essential problems to ensure the USV’s safe and efficient navigation. Especially in a congested and uncertain marine traffic environment, not only will static obstacles be taken into account but other target vessels in motion should also be considered. Also, the general requirement of the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) should be satisfied. Hence, an improved A-star algorithm for USV path planning and improved dynamic window approach (IDWA) for collision avoidance were proposed. First, considering the requirement of COLREGs, the velocity search space was filtered again, and the quantity of USV trajectories was reduced. Then, the improved A-star algorithm was introduced to let the USV avoid static obstacles and reach its destination without trapping in local optimization. Moreover the Deep Q-network method was utilized to train weight coefficients of the IDWA objective function. Thereby, the improved algorithm-generated path during the process of collision avoidance was more reasonable and safer. To verify feasibility of the proposed path-planning algorithm, a comparison experiment with the traditional DWA method was carried out. The results showed that whether it was for a single USV to a single target or for multiple USVs to multiple targets, path planning, the proposed method, could work effectively to avoid obstacles safely and reach the destination quickly. The improved algorithm will be expected to provide a reference for USV path planning and collision avoidance as well as contribute to the implementation of autonomous ship navigation.
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- Lane-Level Heterogeneous Traffic Flow Prediction: A Spatiotemporal
Attention-Based Encoder–Decoder Model-
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Authors:
Yan Zheng;Wenquan Li;Wen Zheng;Chunjiao Dong;Shengyou Wang;Qian Chen;
Pages: 51 - 67
Abstract: Urban road traffic flow prediction is the key basis for the development of intelligent transportation systems. Lane-level heterogeneous traffic flow prediction will become a new and important challenge in the future development of intelligent transportation. In this study, a spatiotemporal attention-based encoder–decoder model is proposed to solve the lane-level heterogeneous traffic flow prediction problem. Specifically, the traffic flows of different locations and different types of vehicles are all regarded as traffic flows running in independent spaces. A spatial attention layer is added to the encoder to capture the spatial characteristics of traffic flows, and a temporal attention layer is added to the decoder to analyze the temporal correlation of each time interval. Finally, we validate the model by using real traffic monitoring data in the Shunyi District, Beijing, China, and prove the superior performance of the model. Meanwhile, the output of attention weights can deepen the further interpretation of the spatiotemporal correlation of heterogeneous traffic flows.
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- Unexpected Dynamic Obstacle Monocular Detection in the Driver View
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Authors:
Shenlu Jiang;Zhonghua Hong;
Pages: 68 - 81
Abstract: Dynamic obstacle detection is important for environmental perception in self-driving cars. Instance segmentation using a camera is a major trend in obstacle detection. However, unexpected dynamic obstacles are difficult to detect as their classes are unlabeled in the model. In this study, we combine an understanding of a road scene; optical flow movement tracking; and low-cost online visual tracking to build a system for detecting unexpected dynamic obstacles. To monitor the pixel movement, a mobile recurrent pairwise decoding optical flow deep neural network is employed to rapidly track the pixel flows between two frames. To filter background noises and leave the active region on the road, a mobile DABNet detects the targets (only roads and vehicles) in the scene. To reduce the load on the GPU, a cluster-matching tracker employs multi-tensor CPU resources to follow the estimated unexpected dynamic obstacles extracted by processing based on the road understanding and optical flows and tracks such obstacles one by one in the following frames. A real-time system properly splits the usage of GPU and CPU resources to maximize the performance of the system platform. To evaluate the efficiency, a driver view video dataset is recorded for evaluating real-world obstacles on the urban road scene. Then, animal crash videos are collected from YouTube to evaluate unexpected/rarely labeled objects. Furthermore, a mobile robot platform is used to test the proposed system to avoid obstacles in a complicated indoor scene.
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- Optimization Model of Autonomous Vehicle Parking Facilities, Developed
With the Nondominated Sorting Genetic Algorithm With an Elite Strategy 2
and by Comparing Different Moving Strategies-
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Authors:
Xiaofei Ye;Yilu Wang;Xingchen Yan;Wang Tao;Jun Chen;
Pages: 82 - 100
Abstract: Fully self-driving cars, or autonomous vehicles (AVs), will have self-parking functions, which will revolutionize and reshape parking facilities in several ways. Automated parking technology could realize high-density and intensive parking space efficiency through multirow layouts and with narrower spaces per vehicle, which would completely change traditional parking facilities. Nonetheless, extra movements and congestion are generated by releasing stuck vehicles inside rows of parking spaces. Profits in parking space efficiency might be reduced by losses extra movements and congestion. This article aims to optimize the multirow and multichannel layout of parking facilities for AVs, balancing maximal space utilization and minimal movements while satisfying a designated parking demand. A new moving strategy, “first storage and shortest moving to outdistance (FSSMD),” is put forward first. Then, the geometric attributes, other movement strategies, demand characteristics, and other design elements of parking facilities are comprehensively quantified to describe the profit and loss of self-parking. An optimization model for the multirow layout of parking spaces is constructed with the dual goals of maximizing the number of berths and minimizing the number of movements. The nondominated sorting genetic algorithm with an elite strategy 2 is designed to solve the optimal model. Finally, the optimal zones and layouts of parking facilities are put forward by numerical experiments, and the sensitivity of different moving strategies is discussed. The results show that the moving strategy based on the FSSMD significantly reduces expected movement times, without decreasing the utilization rate of parking spaces. Although having only one zone enables maximizing the number of parking berths and the utilization rate of parking facilities, such benefits and improvements might be compromised by additional movements. Therefore, the multirow optim-l layout with a zoning of 4 and 2 interzone channels should be decided by assessing the paradoxical relationships between the maximal utilization rate and minimal movements.
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- Internet of Vehicles Data-Oriented Arterial Travel Time Estimation
Framework With Dynamic Multigraph Model-
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Authors:
Tony Z. Qiu;Mengyun Xu;Jie Fang;
Pages: 101 - 116
Abstract: The rapid development of Internet of Vehicles (IoV) data powers various online intelligent transportation applications, such as network travel time reporting. However, the accuracy might be severely compromised due to limited probe vehicle sampling frequency. On that account, this article proposes a dynamic multigraph model-enabled framework to estimate reliable network travel time, even in low-IoV-frequency arterial corridors. The proposed framework first develops an improved sparse IoV travel time decomposition method. The segment travel time is further divided into the free-flow running time and static and dynamic delays. Second, a dynamic multigraph traffic network model (DMGTN) is developed to aid the proposed decomposition method. The model analyzes complicated spatiotemporal relevance between segments from multiple perspectives: the real-time travel time, congestion level, signal control (which is frequently neglected in previous research), and segment properties. Additionally, two distinct enhanced modules are designed for handing dense and sparse network graphs, respectively. This allows for a more efficient inspection over large-scale intricate arterial networks while maintaining precision. Field implementation is conducted in the downtown area of Zhangzhou, China. Compared to other high-performance baseline models, the designed DMGTN model as well as the proposed decomposition method demonstrate state-of-the-art accuracy and successfully capture travel time variability. The proposed framework better utilizes available IoV data to provide valuable traffic information for commuters and traffic management agencies.
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- A Roadside Millimeter-Wave Radar Calibration Method Based on Connected
Vehicle Technology-
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Authors:
Changlong Zhang;Jimin Wei;Jingang Dai;Shibo Qu;Xianning She;Zetao Wang;
Pages: 117 - 131
Abstract: Roadside millimeter-wave radar is one of the most important sensors in roadside perception systems, which have been widely used in intelligent traffic systems. It is important to find an effective calibration procedure for roadside radar to obtain the World Geodetic System-1984 (WGS-84) coordinates of detected targets. However, it is difficult to calibrate roadside millimeter-wave radar safely on public roads without road closures. To solve this problem, this article proposes a roadside millimeter-wave radar calibration method based on connected vehicle technology. First, all the trajectory points of vehicles detected by radar, including the connected vehicle, are collected, and the WGS-84 coordinates of the connected vehicle are obtained by vehicle-to-everything (V2X) communication with the roadside unit. Then, the trajectory points of vehicles are clustered with the density-based spatial clustering of applications with noise (DBSCAN) method, and the trajectory of the connected vehicle in the radar coordinate system is identified with the velocity-matching method automatically. The data pairs of the connected vehicle in the radar coordinate system and WGS-84 coordinate system are obtained with time synchronization. Finally, the calibration parameters are solved with five different types of optimization methods. We verify these methods on the beltway in Changsha. The results show that the improved pseudo inverse method (IPIM) and PIM achieve better performance than the extrinsic calibration method (ECM), rotate coordinate method (RCM), and improved RCM, and they can meet lane-level positioning requirements for V2X applications. This study provides an economical and effective way to solve the calibration problem for roadside millimeter-wave radar in an engineering project.
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- Platoon Cooperation Across Carriers: From System Architecture to
Coordination-
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Authors:
Alexander Johansson;Ting Bai;Karl Henrik Johansson;Jonas Mårtensson;
Pages: 132 - 144
Abstract: Truck platooning is a well-studied technology that has the potential to reduce both the environmental impact and operational costs of trucks. The technology has matured over the last 20 years, and the commercial rollout of platooning is approaching. Cooperation across carriers is essential for the viability of platooning; otherwise, many platooning opportunities are lost. We first present a cross-carrier platooning system architecture in which many carriers cooperate in forming platoons through a platoon-hailing service. Then, we present a cross-carrier platoon coordination approach in which each carrier optimizes its platooning plans according to the predicted plans of other carriers. A profit-sharing mechanism to even out the platooning profit in each platoon is embedded in the platoon coordination approach. Finally, a simulation study over the Swedish road network is performed to evaluate the potential of platooning under realistic conditions. The simulation study shows that the energy consumption of trucks in Sweden can be reduced by 5.4% due to platooning and that cooperation across carriers is essential to achieve significant platooning benefits.
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- Connected and Automated Mobility Services in 5G Cross-Border Environments:
Challenges and Prospects-
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Authors:
Konstantinos V. Katsaros;Angelos J. Amditis;Konstantinos Trichias;Oyunchimeg Shagdar;Ahmed Soua;Jose Costa Requena;José Santa;Geerd Kakes;João Almeida;Emanuel Sousa;Nuno Cruz;Gorka Velez;Tahir Sari;Daniel Jáuregui Cortizo;Fernando Correia;Djibrilla Amadou Kountche;Simon Rommel;Eftychia Nikolitsa;Panagiotis Demestichas;
Pages: 145 - 157
Abstract: The next generation of mobile networks, namely 5G, promises significant qualitative and quantitative advances for multiple vertical domains. However, most studies and investigations assess these advances under the implicit assumption of a single network service provider, with typical national coverage. In this article, we take a close look at the automotive sector and highlight a series of challenges emerging in the context of its inherent (inter)national mobility and the corresponding importance of cross-border and/or multioperator environments. Our target is to pinpoint the key influential factors affecting the transition toward seamless (cooperative) connected and automated mobility services within and across national borders. To this end, we identify and analyze a series of challenges in the areas of, networking, application, security, and regulation. We further present and discuss a series of corresponding solutions investigated in the pragmatic context of our experimental activities.
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
- AI+CASE Lab: Advanced Interdisciplinary Research and Education Lab for
Connected, Autonomous, Shared, and Green Transportation Systems [Its
Research Lab]-
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Authors:
Yisheng Lv;
Pages: 158 - C3
Abstract: Please send your proposal on profiling research activities of your or other intelligent transportation systems research groups and labs for the “ITS Research Labs” column to Yisheng Lv at yisheng.lv@ ia.ac.cn.
PubDate:
May-June 2023
Issue No: Vol. 15, No. 3 (2023)
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