Article
Engineering, Civil
B. Ravi Kiran, Ibrahim Sobh, Victor Talpaert, Patrick Mannion, Ahmad A. Al Sallab, Senthil Yogamani, Patrick Perez
Summary: This paper summarizes deep reinforcement learning algorithms, provides a taxonomy of automated driving tasks, discusses key computational challenges in real world deployment of autonomous driving agents, and explores adjacent domains as well as the role of simulators in training agents.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Wu Deng, Junjie Xu, Huimin Zhao, Yingjie Song
Summary: The paper introduces a three-objective gate allocation model to optimize passenger walking distances, balanced idle time, and efficient use of gates. An IPOQEA algorithm is proposed to efficiently solve the model, with validation on the effectiveness at Baiyun Airport.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Hongtian Chen, Bin Jiang, Steven X. Ding, Biao Huang
Summary: This paper provides a systematic review and categorization of data-driven FDD methods for traction systems in high-speed trains. It analyzes the challenges in implementing FDD and proposes several promising solutions.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Review
Engineering, Civil
Sajjad Mozaffari, Omar Y. Al-Jarrah, Mehrdad Dianati, Paul Jennings, Alexandros Mouzakitis
Summary: This article provides a comprehensive review of deep learning-based approaches for vehicle behavior prediction. It discusses the challenges and issues in behavior prediction and categorizes and reviews the most recent solutions based on input representation, output type, and prediction method. The article also evaluates the performance of several solutions and outlines potential future research directions.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Ammar Haydari, Yasin Yilmaz
Summary: Latest technological improvements have enhanced the quality of transportation. The emergence of new data-driven approaches has opened up new research directions for control-based systems in various domains, including transportation, robotics, IoT, and power systems. This paper presents a survey of traffic control applications based on deep reinforcement learning (RL). It extensively discusses different problem formulations, RL parameters, and simulation environments for traffic signal control (TSC) applications. The survey also covers autonomous driving applications studied with deep RL models, categorizing them based on application types, control models, and algorithms studied. The paper concludes with a discussion on challenges and open questions in deep RL-based transportation applications.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Seyed Mojtaba Marvasti-Zadeh, Li Cheng, Hossein Ghanei-Yakhdan, Shohreh Kasaei
Summary: This survey systematically investigates current deep learning-based visual tracking methods, benchmark datasets, and evaluation metrics, while extensively evaluating leading visual tracking methods.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Szilard Aradi
Summary: Academic research in the field of autonomous vehicles has gained popularity in recent years, covering various topics such as sensor technologies, communication, safety, decision making, and control. Artificial Intelligence and Machine Learning methods have become integral parts of this research. Motion planning, with a focus on strategic decision-making, trajectory planning, and control, has also been studied. This article specifically explores Deep Reinforcement Learning (DRL) as a field within Machine Learning. The paper provides insights into hierarchical motion planning and the basics of DRL, including environment modeling, state representation, perception models, reward mechanisms, and neural network implementation. It also discusses vehicle models, simulation possibilities, and computational requirements. The paper surveys state-of-the-art solutions, categorized by different tasks and levels of autonomous driving, such as car-following, lane-keeping, trajectory following, merging, and driving in dense traffic. Lastly, it raises open questions and future challenges.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Yongze Song, Peng Wu, Qindong Li, Yuchen Liu, Lalinda Karunaratne
Summary: Strategic maintenance is crucial for sustainable road infrastructure development. Accurate estimation of road maintenance effects can support the assessment of maintenance strategies and reasonable allocation of budgets and resources. The study developed a dynamic trade-off model (DTOM) to quantify the impacts of different factors, and found that 12 years of maintenance activities at the network level have effectively reduced roughness deterioration and improved road performance.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Zhihan Lv, Yuxi Li, Hailin Feng, Haibin Lv
Summary: The study aims to enhance the security performance of digital twins in the Cooperative Intelligent Transportation System in a deep learning environment. By combining Convolutional Neural Network with Support Vector Regression, a model is constructed and analyzed through simulation experiments. Results show that the proposed algorithm has significant advantages in security performance and data transmission speed.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Jinchao Chen, Chenglie Du, Ying Zhang, Pengcheng Han, Wei Wei
Summary: Unmanned aerial vehicles (UAVs) are widely utilized in civilian and military applications for their high autonomy and strong adaptability. This paper addresses the coverage path planning problem of autonomous heterogeneous UAVs on a bounded number of regions by proposing an exact formulation based on mixed integer linear programming and a clustering-based algorithm inspired from density-based clustering methods to achieve optimal flight paths and efficient coverage tasks. Experiments demonstrating the efficiency and effectiveness of the proposed approach with randomly generated regions are conducted.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Transportation
Yuxin He, Yang Zhao, Kwok-Leung Tsui
Summary: Short-term travel demand forecasting is a critical step in transportation system management, but it faces challenges due to the complex relevance among OD pairs, temporal dependencies, and external factors. This study proposes an innovative deep learning approach called MF-ResNet, which converts the complex relevance among OD pairs into graphical-based spatial dependencies. Experimental results show that MF-ResNet robustly captures multiple complex dependencies and outperforms traditional methods in terms of forecasting accuracy.
TRANSPORTMETRICA A-TRANSPORT SCIENCE
(2023)
Review
Engineering, Civil
Yaodong Cui, Ren Chen, Wenbo Chu, Long Chen, Daxin Tian, Ying Li, Dongpu Cao
Summary: The development of autonomous vehicles has been rapid in recent years, yet achieving full autonomy poses challenges due to the complex and dynamic driving environments. The fusion of camera and LiDAR sensors using deep learning is an emerging research theme. Despite the lack of critical reviews on deep-learning-based camera-LiDAR fusion methods, recent research has focused on leveraging image and point cloud data processing for improved environmental perception and object detection.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Jiexia Ye, Juanjuan Zhao, Kejiang Ye, Chengzhong Xu
Summary: This article surveys graph-based deep learning architectures in the traffic domain, providing guidelines for problem formulation and graph construction, discussing shared deep learning techniques, and presenting graph neural network solutions for traffic challenges.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Wei Sun, Guangzhao Dai, Xiaorui Zhang, Xiaozheng He, Xuan Chen
Summary: In this study, a novel vehicle re-identification method, TBE-Net, is proposed which integrates global appearance and local region features through a multi-branch embedding network. By utilizing feature complementary learning and part-aware ability, the proposed TBE-Net improves the accuracy of vehicle re-identification.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Review
Energy & Fuels
Xin Lai, Quanwei Chen, Xiaopeng Tang, Yuanqiang Zhou, Furong Gao, Yue Guo, Rohit Bhagat, Yuejiu Zheng
Summary: This study reviews the framework and methods of life cycle assessment (LCA) and evaluates the entire lifespan of lithium-ion batteries (LIBs). The results show that battery production significantly impacts the environment and resources, while battery materials recycling and remanufacturing have considerable environmental and economic values. Moreover, greening of electricity is critical to reducing carbon emissions during the battery life cycle.
Article
Engineering, Civil
Liang Tan, Keping Yu, Long Lin, Xiaofan Cheng, Gautam Srivastava, Jerry Chun-Wei Lin, Wei Wei
Summary: Speech emotion recognition (SER) is becoming an important aspect of human-computer interaction for autonomous vehicles in the next generation of transportation systems. However, current vehicle-mounted SER systems have limitations in terms of communication network capacity and accuracy. To address these issues, a solution is proposed using a 5G-enabled space-air-ground integrated network to improve traffic efficiency and enhance the performance and user experience of autonomous vehicles.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Ze Liu, Yingfeng Cai, Hai Wang, Long Chen, Hongbo Gao, Yunyi Jia, Yicheng Li
Summary: The study utilizes radar and camera fusion sensing methods, matching observed values through Mahalanobis distance and performing data fusion to enhance environmental perception performance in severe weather conditions.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Changxi Ma, Guowen Dai, Jibiao Zhou
Summary: This paper proposes a short-term traffic flow prediction model based on traffic flow time series analysis and an improved long short-term memory network. By integrating bidirectional long-term memory network, the model shows better accuracy and stability compared to other models.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Guibing Zhu, Yong Ma, Zhixiong Li, Reza Malekian, M. Sotelo
Summary: This study investigates the tracking control problem of marine surface vessels (MSVs) in the presence of uncertain dynamics and external disturbances, considering undesirable faults and input saturation of actuators. A novel control scheme is proposed using a saturation function, event-triggered mechanism, and neural network technique, which is robust, adaptive, tolerant, and guarantees stable tracking of MSVs without prior knowledge of dynamics or faults. Simulation results demonstrate the effectiveness of the proposed scheme.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Ricardo Roriz, Jorge Cabral, Tiago Gomes
Summary: This article presents the recent advances and technologies behind LiDAR sensors for the automotive industry, while highlighting the current and future challenges, providing insights on how to step towards better LiDAR solutions.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
