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IEEE Transactions on Intelligent Transportation Systems
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Year: 2022 | Volume: 23 | Issue: 1
IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY
Year: 2022 | Volume: 23 | Issue: 1
Scanning the Issue
Year: 2022 | Volume: 23 | Issue: 1
Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey
Publication Year: 2022,Page(s):11 - 32
Cited by: Papers (287)
Latest technological improvements increased the quality of transportation. New data-driven approaches bring out a new research direction for all control-based systems, e.g., in transportation, robotics, IoT and power systems. Combining data-driven applications with transportation systems plays a key role in recent transportation applications. In this paper, the latest deep reinforcement learning (...Show More
Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review
Sajjad Mozaffari;Omar Y. Al-Jarrah;Mehrdad Dianati;Paul Jennings;Alexandros Mouzakitis
Publication Year: 2022,Page(s):33 - 47
Cited by: Papers (365)
Behaviour prediction function of an autonomous vehicle predicts the future states of the nearby vehicles based on the current and past observations of the surrounding environment. This helps enhance their awareness of the imminent hazards. However, conventional behavior prediction solutions are applicable in simple driving scenarios that require short prediction horizons. Most recently, deep learn...Show More
A Review on Swarm Intelligence and Evolutionary Algorithms for Solving the Traffic Signal Control Problem
Palwasha W. Shaikh;Mohammed El-Abd;Mounib Khanafer;Kaizhou Gao
Publication Year: 2022,Page(s):48 - 63
Cited by: Papers (92)
The rapid development of urban cities coupled with the rise in population has led to an exponentially growing number of vehicles on the roads for the latter to commute. This is adding to the already overbearing problem of traffic congestion. Short term, costly and short-sighted solutions of road infrastructure expansions are no longer suitable. One effective method of road resource allocation is f...Show More
Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review
Sarang Thombre;Zheng Zhao;Henrik Ramm-Schmidt;José M. Vallet García;Tuomo Malkamäki;Sergey Nikolskiy;Toni Hammarberg;Hiski Nuortie;M. Zahidul H. Bhuiyan;Simo Särkkä;Ville V. Lehtola
Publication Year: 2022,Page(s):64 - 83
Cited by: Papers (124)
Autonomous ships are expected to improve the level of safety and efficiency in future maritime navigation. Such vessels need perception for two purposes: to perform autonomous situational awareness and to monitor the integrity of the sensor system itself. In order to meet these needs, the perception system must fuse data from novel and traditional perception sensors using Artificial Intelligence (...Show More
Two-Echelon Dispatching Problem With Mobile Satellites in City Logistics
Yu-Lin Lan;Fa-Gui Liu;Zhixing Huang;Wing W. Y. Ng;Jinghui Zhong
Publication Year: 2022,Page(s):84 - 96
Cited by: Papers (9)
At present, city logistics mostly adopts a two-echelon dispatching model which combines distribution centers located in suburbs and fixed satellites located in urban areas for distribution. However, both expensive rental fees and daily changes of customer demand in metropolitan areas make dispatching route generated by fixed satellites inefficient. Moreover, the existing mobile depot model needs a...Show More
Driver Identification and Verification From Smartphone Accelerometers Using Deep Neural Networks
Sara Hernández Sánchez;Rubén Fernández Pozo;Luis Alfonso Hernández Gómez
Publication Year: 2022,Page(s):97 - 109
Cited by: Papers (23)
This paper addresses driver identification and verification using Deep Learning (DL) on tri-axial accelerometer signals from drivers’ smartphones. The proposed driver identification architecture includes ResNet-50 followed by two Stacked Gated Recurrent Units (SGRUs). ResNet provides a deep layer model, thanks to shortcut connections, is able to extract rich features from accelerometers, and GRU l...Show More
A BRB-Based Effective Fault Diagnosis Model for High-Speed Trains Running Gear Systems
Chao Cheng;Jiuhe Wang;Zhijie Zhou;Wanxiu Teng;Zhongbo Sun;Bangcheng Zhang
Publication Year: 2022,Page(s):110 - 121
Cited by: Papers (31)
Fault diagnosis is a key way to improve the efficient, safe and stable operation of high-speed trains. This paper proposes a fault diagnosis method based on belief rule base with mixed reliability (BRB-mr). Different from the traditional BRB, this method considers two kinds of interference factors that affect the observation data in engineering practice, including the performance of sensors and th...Show More
Dynamic Driving Risk Potential Field Model Under the Connected and Automated Vehicles Environment and Its Application in Car-Following Modeling
Publication Year: 2022,Page(s):122 - 141
Cited by: Papers (99)
This paper proposes a new dynamic driving risk potential field model under the connected and automated vehicles environment that fully considers the dynamic effect of the vehicle’s acceleration and steering angle. The statistical analysis of the model’s parameter reveals that acceleration and steering angle will directly affect the distribution of the driving risk potential field and that this str...Show More
Short-Term Prediction of Level of Service in Highways Based on Bluetooth Identification
Mark Richard Wilby;Ana Belén Rodríguez González;Rubén Fernández Pozo;Juan José Vinagre Díaz
Publication Year: 2022,Page(s):142 - 151
Cited by: Papers (7)
A precise knowledge about future traffic will eventually open a new era in traffic management. Research has focused on the still unresolved problem of predicting travel time (TT). However, practitioners favor the level of service (LOS) as a meaningful metric that avoids the continuous fluctuations and link-specificity of TT. Evolving from TT to LOS opens a new research line in the field, moving th...Show More
A Multi-Scale Attributes Attention Model for Transport Mode Identification
Publication Year: 2022,Page(s):152 - 164
Cited by: Papers (14)
Transport mode identification (TMI), which infers the travel modes of user trajectories, is essential to facilitate an understanding of urban mobility patterns and passengers’ choice behaviors with the goal of improving urban transportation systems. To achieve higher accuracy, existing TMI methods usually rely on mobility features obtained from densely sampled GPS trajectory points (e.g. 1 second ...Show More
Global Context Assisted Structure-Aware Vehicle Retrieval
Yan Tian;Tao Chen;Guohua Cheng;Shihao Yu;Xi Li;Jianyuan Li;Bailin Yang
Publication Year: 2022,Page(s):165 - 174
Cited by: Papers (13)
In vehicle retrieval, the vehicle patch should first be localized to remove the irrelevant background information. Moreover, the negative samples are much more prevalent than the positive samples, and the information from the negative samples is not fully exploited in the triple loss. What we need is a way to incorporate global knowledge and structure information to address these two issues. There...Show More
Condition-Based Maintenance for Traction Power Supply Equipment Based on Partially Observable Markov Decision Process
Sheng Lin;Ruidong Fan;Ding Feng;Chao Yang;Qi Wang;Shibin Gao
Publication Year: 2022,Page(s):175 - 189
Cited by: Papers (16)
Actual condition-based maintenance for traction power supply equipment (TPSE) is almost based on completely observable equipment state. However, it is unpractical to accurately reveal the equipment state due to the inescapably uncertainty of state assessment. In order to optimize the maintenance of TPSE, a maintenance model based on partially observable Markov decision process is proposed in this ...Show More
Analysis of Classifier Training on Synthetic Data for Cross-Domain Datasets
Andoni Cortés;Clemente Rodríguez;Gorka Vélez;Javier Barandiarán;Marcos Nieto
Publication Year: 2022,Page(s):190 - 199
Cited by: Papers (7)
A major challenges of deep learning (DL) is the necessity to collect huge amounts of training data. Often, the lack of a sufficiently large dataset discourages the use of DL in certain applications. Typically, acquiring the required amounts of data costs considerable time, material and effort. To mitigate this problem, the use of synthetic images combined with real data is a popular approach, wide...Show More
Routing With Traffic Awareness and Link Preference in Internet of Vehicles
Chen Chen;Lei Liu;Tie Qiu;Jiange Jiang;Qingqi Pei;Houbing Song
Publication Year: 2022,Page(s):200 - 214
Cited by: Papers (26)
Considering the high mobility and uneven distribution of vehicles, an efficient routing protocol should avoid that the sent packets are forwarded within road segments with ultra-low density or serious data congestion in vehicular networks. To this end, in this paper, we propose a Traffic aware and Link Quality sensitive Routing Protocol (TLRP) for urban Internet of Vehicles (IoV). First, we design...Show More
Improved Vehicle LiDAR Calibration With Trajectory-Based Hand-Eye Method
Xuan Yuwen;Long Chen;Fengjun Yan;Hui Zhang;Jianlin Tang;Bin Tian;Yunfeng Ai
Publication Year: 2022,Page(s):215 - 224
Cited by: Papers (22)
In unmanned vehicles, LiDARs and GPS/INSs are the most popular sensors to achieve perception and positioning. Precise calibration of the extrinsic parameters between the LiDAR and the GPS/INS is necessary for a successful implementation of sensor fusion. The extrinsic transformation between the LiDAR and GPS/INS is 6D ( $x$ , $y$ , $z$ , $yaw$ , $pitch$ , $roll$ ), but the motion of a vehicle...Show More
Short-Term Traffic Flow Forecasting Method With M-B-LSTM Hybrid Network
Publication Year: 2022,Page(s):225 - 235
Cited by: Papers (51)
Deep learning has achieved good performance in short-term traffic forecasting recently. However, the stochasticity and distribution imbalance are main characteristics to traffic flow, and these will bring the uncertainty and induce the network overfitting problem during deep learning. To deal with the problems, a new end-to-end hybrid deep learning network model, named M-B-LSTM, is proposed for sh...Show More
Multi-Vehicle Collaborative Learning for Trajectory Prediction With Spatio-Temporal Tensor Fusion
Yu Wang;Shengjie Zhao;Rongqing Zhang;Xiang Cheng;Liuqing Yang
Publication Year: 2022,Page(s):236 - 248
Cited by: Papers (52)
Accurate behavior prediction of other vehicles in the surroundings is critical for intelligent transportation systems. Common practices to reason about the future trajectory are through their historical paths. However, the impact of traffic context is ignored, which means the beneficial environment information is deserted. Although a few methods are proposed to exploit the surrounding vehicle info...Show More
Multi-Robot Source Location of Scalar Fields by a Novel Swarm Search Mechanism With Collision/Obstacle Avoidance
Publication Year: 2022,Page(s):249 - 264
Cited by: Papers (15)
This paper focuses on source location for scalar fields through multiple mobile robots with sensors. Combined with field strength measurements and swarm evolution mechanisms, the robots are guided to move toward the source of the field. Upon introducing an adaptive weight strategy, a swarm timely update mechanism and a leading-following behavior into quantum particle swarm optimization (QPSO) algo...Show More
Data-Driven Models Support a Vision for Over-the-Air Vehicle Emission Inspections
Publication Year: 2022,Page(s):265 - 279
Cited by: Papers (2)
Emissions inspection and maintenance (I/M) programs for light-duty motor vehicles manage ambient air quality by enforcing emissions standards and requiring non-compliant vehicles to be repaired or retired. I/M programs in the United States typically identify over-emitters through on-board diagnostics (OBD) systems and vehicles’ proprietary firmware (i.e., indirect tests), rather than through physi...Show More
Optimal Driving Strategies for Two Successive Trains on Level Track With Safe Separation
Publication Year: 2022,Page(s):280 - 295
Cited by: Papers (1)
When two trains travel along the same track in the same direction it is a common safety requirement that they should always be separated by at least two signals. If the signals are located at fixed positions that divide the track into sections, then safe separation means there must always be one clear section of track between the two trains. It is desirable to find optimal driving strategies that ...Show More
Continuous Finger Gesture Spotting and Recognition Based on Similarities Between Start and End Frames
Gibran Benitez-Garcia;Muhammad Haris;Yoshiyuki Tsuda;Norimichi Ukita
Publication Year: 2022,Page(s):296 - 307
Cited by: Papers (8)
Touchless in-car devices controlled by single and continuous finger gestures can provide comfort and safety on driving while manipulating secondary devices. Recognition of finger gestures is a challenging task due to (i) similarities between gesture and non-gesture frames, and (ii) the difficulty in identifying the temporal boundaries of continuous gestures. In addition, (iii) the intraclass varia...Show More
Unsupervised Learning of Depth, Optical Flow and Pose With Occlusion From 3D Geometry
Guangming Wang;Chi Zhang;Hesheng Wang;Jingchuan Wang;Yong Wang;Xinlei Wang
Publication Year: 2022,Page(s):308 - 320
Cited by: Papers (39)
In autonomous driving, monocular sequences contain lots of information. Monocular depth estimation, camera ego-motion estimation and optical flow estimation in consecutive frames are high-profile concerns recently. By analyzing tasks above, pixels in the middle frame are modeled into three parts: the rigid region, the non-rigid region, and the occluded region. In joint unsupervised training of dep...Show More
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