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- research-articleJanuary 2024
Rethinking Lightweight Convolutional Neural Networks for Efficient and High-Quality Pavement Crack Detection
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 25, Issue 1Jan. 2024, Pages 237–250https://doi.org/10.1109/TITS.2023.3307286Pixel-level road crack detection has always been a challenging task in intelligent transportation systems. Due to the external environments, such as weather, light, and other factors, pavement cracks often present low contrast, poor continuity, and ...
- research-articleDecember 2023
Single Traffic Image Deraining via Similarity-Diversity Model
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 25, Issue 1Jan. 2024, Pages 90–103https://doi.org/10.1109/TITS.2023.3312633Single traffic image deraining technology based on deep learning is a vital branch of image preprocessing, which is of great help to intelligent monitoring systems and driving navigation system. It is well understood that established deraining methods are ...
- research-articleDecember 2023
Sustainable and Transferable Traffic Sign Recognition for Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 24, Issue 12Dec. 2023, Pages 15784–15794https://doi.org/10.1109/TITS.2022.3215572Traffic Sign Recognition (TSR) is an essential component of Intelligent Transportation Systems (ITS) and intelligent vehicles. TSR systems based on deep learning have grown in popularity in recent years. However, since these models belong to the closed-...
- research-articleDecember 2023
Privacy-Preserving Cross-Area Traffic Forecasting in ITS: A Transferable Spatial-Temporal Graph Neural Network Approach
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 24, Issue 12Dec. 2023, Pages 15499–15512https://doi.org/10.1109/TITS.2022.3215326Traffic forecasting is essential in improving and maintaining safety and orderliness in intelligent transportation systems (ITS). As a deep learning approach, graph neural networks (GNN) based spatial-temporal association mining methods are promising in ...
- research-articleNovember 2023
LTP-Net: Life-Travel Pattern Based Human Mobility Signature Identification
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 24, Issue 12Dec. 2023, Pages 14306–14319https://doi.org/10.1109/TITS.2023.3303835How to effectively extract identifiable information from human mobility data and distinguish different agents is a significant topic for location-based services and intelligent transportation systems, which is described as the Human Mobility Signature ...
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- research-articleNovember 2023
A Smart IoT Enabled End-to-End 3D Object Detection System for Autonomous Vehicles
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 24, Issue 11Nov. 2023, Pages 13078–13087https://doi.org/10.1109/TITS.2022.3210490Integration of advanced signal processing, image processing, deep learning, edge computing, and the Internet of Things (IoT) into vehicles allows intelligent automated vehicles to navigate autonomously in different environments. It is crucial for reliable ...
- research-articleOctober 2023
Deep Unfolding Scheme for Grant-Free Massive-Access Vehicular Networks
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 24, Issue 12Dec. 2023, Pages 14443–14452https://doi.org/10.1109/TITS.2023.3296452Grant-free random access is an effective solution to enable massive access for future Internet of Vehicles (IoV) scenarios based on massive machine-type communication (mMTC). Considering the uplink transmission of grant-free based vehicular networks, ...
- research-articleOctober 2023
Automatic Accident Detection, Segmentation and Duration Prediction Using Machine Learning
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 25, Issue 2Feb. 2024, Pages 1547–1568https://doi.org/10.1109/TITS.2023.3323636Traffic accidents are often inaccurately reported, with incorrect location and disruption duration due to various external factors. This can result in imprecise predictions and inaccurate decision-making in data-driven models. To address these challenges, ...
- research-articleOctober 2023
Robust Perception Under Adverse Conditions for Autonomous Driving Based on Data Augmentation
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 24, Issue 12Dec. 2023, Pages 13916–13929https://doi.org/10.1109/TITS.2023.3297318Many existing advanced deep learning-based autonomous systems have recently been used for autonomous vehicles. In general, a deep learning-based visual perception system heavily relies on visual perception to recognize and localize dynamic interest ...
- research-articleOctober 2023
Detecting Intentional AIS Shutdown in Open Sea Maritime Surveillance Using Self-Supervised Deep Learning
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 25, Issue 2Feb. 2024, Pages 1166–1177https://doi.org/10.1109/TITS.2023.3322690In maritime traffic surveillance, detecting illegal activities, such as illegal fishing or transshipment of illicit products is a crucial task of the coastal administration. In the open sea, one has to rely on Automatic Identification System (AIS) message ...
- research-articleOctober 2023
A Training-Free, Lightweight Global Image Descriptor for Long-Term Visual Place Recognition Toward Autonomous Vehicles
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 25, Issue 2Feb. 2024, Pages 1291–1302https://doi.org/10.1109/TITS.2023.3320489Long-term visual place recognition (VPR) has recently become a popular research topic in the field of autonomous driving. In urban scenarios, variations in scene appearance due to the change in seasons and illumination bring great challenges for scene ...
- research-articleOctober 2023
Multi-Branch Enhanced Discriminative Network for Vehicle Re-Identification
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 25, Issue 2Feb. 2024, Pages 1263–1274https://doi.org/10.1109/TITS.2023.3316068Vehicle re-identification (ReID) is the task of identifying the same vehicle across numerous cameras. This is a complex classification task, and the fine-grained information and strong discrimination features have proven to be effective in handling the re-...
- research-articleOctober 2023
Confidence-Enhanced Mutual Knowledge for Uncertain Segmentation
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 25, Issue 1Jan. 2024, Pages 725–737https://doi.org/10.1109/TITS.2023.3309600It is inevitable to recognize objects in adverse weather conditions where the uncertainty of contour areas is increased. Although some multi-task learning frameworks have gained from the directional supervision between boundary detection and semantic ...
- research-articleOctober 2023
Spatial Deep Deconvolution U-Net for Traffic Analyses With Distributed Acoustic Sensing
- Siyuan Yuan,
- Martijn van den Ende,
- Jingxiao Liu,
- Hae Young Noh,
- Robert Clapp,
- Cédric Richard,
- Biondo Biondi
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 25, Issue 2Feb. 2024, Pages 1913–1924https://doi.org/10.1109/TITS.2023.3322355Distributed Acoustic Sensing (DAS) that transforms city-wide fiber-optic cables into a large-scale strain sensing array has shown the potential to revolutionize urban traffic monitoring by providing a fine-grained, scalable, and low-maintenance monitoring ...
- research-articleOctober 2023
Self-Supervise Reinforcement Learning Method for Vacant Parking Space Detection Based on Task Consistency and Corrupted Rewards
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 25, Issue 2Feb. 2024, Pages 1346–1363https://doi.org/10.1109/TITS.2023.3319531This paper proposes a novel task-consistency learning method that enables us to train a vacant space detection network (target task) based on the logic consistency with the semantic outcomes from a flow-based motion behavior classifier (source task) in a ...
- research-articleOctober 2023
Observer-Informed Deep Learning for Traffic State Estimation With Boundary Sensing
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 25, Issue 2Feb. 2024, Pages 1602–1611https://doi.org/10.1109/TITS.2023.3318299Traffic state estimation (TSE) refers to the inference of macroscopic traffic states, including density, speed, and flow, based on partially observed traffic data and some prior knowledge of traffic dynamics. TSE plays a key role in traffic management ...
- research-articleOctober 2023
A Novel Class-Imbalanced Ship Motion Data-Based Cross-Scale Model for Sea State Estimation
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 24, Issue 12Dec. 2023, Pages 15907–15919https://doi.org/10.1109/TITS.2023.3315674Sea state estimation (SSE) is significant to the development of autonomous ships, which can enhance the sustainable development of maritime transportation. Traditional model-based methods are limited by their drawbacks, such as high costs and inaccurate ...
- research-articleOctober 2023
Privacy-Preserving Federated Deep Reinforcement Learning for Mobility-as-a-Service
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 25, Issue 2Feb. 2024, Pages 1882–1896https://doi.org/10.1109/TITS.2023.3317358Mobility-as-a-service (MaaS) is a new transport model that combines multiple transport modes in a single platform. Dynamic passenger behavior based on past experiences requires reinforcement-based optimization of MaaS services. Deep reinforcement learning ...
- research-articleOctober 2023
Research on Personalized AEB Strategies Based on Self-Supervised Contrastive Learning
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 25, Issue 2Feb. 2024, Pages 1303–1316https://doi.org/10.1109/TITS.2023.3317361In this paper, a driving style recognition method based on self-supervised contrastive learning was developed. Traditional machine learning models cannot directly accept time series data of variables as inputs, and therefore, artificially constructed ...
- research-articleOctober 2023
A Multi-Context Aware Human Mobility Prediction Model Based on Motif-Preserving Travel Preference Learning
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 25, Issue 2Feb. 2024, Pages 2139–2152https://doi.org/10.1109/TITS.2023.3314281Accurately predicting human mobility is crucial for various applications, e.g., transportation services, epidemic control, and advertisement recommendation. Although numerous sequential modeling based methods (e.g., recurrent neural networks) have been ...