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IEEE transactions on intelligent transportation systems
Institute of Electrical and Electronics Engineers
IEEE transactions on intelligent transportation systems

Institute of Electrical and Electronics Engineers

季刊

1524-9050

IEEE transactions on intelligent transportation systems/Journal IEEE transactions on intelligent transportation systemsEISCIISTP
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    Table of Contents

    C1-C4页
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    IEEE Intelligent Transportation Systems Society Information

    C3-C3页
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    IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY

    C2-C2页
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    Scanning the Issue - May 2025

    Simona Sacone
    5627-5648页
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    Toward Human-Vehicle Collaboration for Automated Vehicles: A Review and Perspective

    Tao HuangRui FuQinyu SunZejian Deng...
    5649-5673页
    查看更多>>摘要：The human-vehicle collaboration in automated vehicles is an effective transitional means to overcome the difficulty of rapidly transitioning to a highly automated level of intelligence. Furthermore, it can fully leverage the strengths of both drivers and autonomous driving systems, embodying a design philosophy of human-centered. Therefore, this paper provides a review and perspectives of the human-vehicle collaboration for automated vehicles. First, the concept, forms and methods of human-vehicle collaboration are reviewed. Then, a human-vehicle mutual trust collaboration framework based on complementary advantages of humans and vehicles and brain-like intelligence is proposed. Specifically, the framework focuses on driver behavior understanding and brain-like cognitive decision planning. After that, the methods of driver behavior understanding and brain-like cognitive decision planning are summarized. Finally, challenges and future works are analyzed to contribute the develop of understandable, trustable, and acceptable human-vehicle collaboration systems.
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    Integrating LLMs With ITS: Recent Advances, Potentials, Challenges, and Future Directions

    Doaa MahmudHadeel HajmohamedShamma AlmentheriShamma Alqaydi...
    5674-5709页
    查看更多>>摘要：Intelligent Transportation Systems (ITS) are crucial for the development and operation of smart cities, addressing key challenges in efficiency, productivity, and environmental sustainability. This paper comprehensively reviews the transformative potential of Large Language Models (LLMs) in optimizing ITS. Initially, we provide an extensive overview of ITS, highlighting its components, operational principles, and overall effectiveness. We then delve into the theoretical background of various LLM techniques, such as GPT, T5, CTRL, and BERT, elucidating their relevance to ITS applications. Following this, we examine the wide-ranging applications of LLMs within ITS, including traffic flow prediction, vehicle detection and classification, autonomous driving, traffic sign recognition, and pedestrian detection. Our analysis reveals how these advanced models can significantly enhance traffic management and safety. Finally, we explore the challenges and limitations LLMs face in ITS, such as data availability, computational constraints, and ethical considerations. We also present several future research directions and potential innovations to address these challenges. This paper aims to guide researchers and practitioners through the complexities and opportunities of integrating LLMs in ITS, offering a roadmap to create more efficient, sustainable, and responsive next-generation transportation systems.
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    • IEEE

    Lane Detection for Autonomous Driving: Comprehensive Reviews, Current Challenges, and Future Predictions

    Jiping BiYongchao SongYahong JiangLijun Sun...
    5710-5746页
    查看更多>>摘要：Lane detection is crucial for autonomous driving systems (ADS), utilizing sensors like cameras and LiDAR to identify lanes and understand vehicle position, direction, and lane shape. It provides data support for the control system to make informed driving decisions. In this survey, we review recent advancements in lane detection, focusing on both 2D techniques and emerging 3D methods. We begin with an overview of the significance of lane detection in ADS, followed by an analysis of the evolution of 2D techniques over the past decade, covering traditional and deep learning approaches. We also examine recent advancements in 3D lane detection. Additionally, we summarize evaluation metrics and popular datasets in the field. Finally, we discuss current challenges and future directions in lane detection, aiming to provide valuable insights for researchers and developers in this technology.
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    Driving Safety Risk Analysis and Assessment in a Mixed Driving Environment of Connected and Non-Connected Vehicles: A Systematic Survey

    Zeyang ChengJinyang ZhuZhongxiang FengMengmeng Yang...
    5747-5781页
    查看更多>>摘要：With the continuous development of intelligent networks and autonomous driving technologies, heterogeneous traffic flow represented by conventional vehicles (CV), autonomous vehicles (AV), and connected and autonomous vehicles (CAV) have emerged, and consequently, the driving safety risk issues in this mixed driving environment have become increasingly complex. In a mixed and connected environment, different traffic streams in complex driving scenarios are intertwined with each other, and driving behaviours such as steering and lane-changing between various traffic streams are frequent, thus increasing the crash risk of vehicles. To understand the research methods, theoretical models, and system architectures in the field of driving safety analysis in this mixed-connected environment, this article reviews the driving safety risk progress from four major aspects: driving safety risk perception and identification, driving safety risk prediction, driving safety risk quantification, and driving safety risk early warning. By summarizing the existing research, it can be found that academics have achieved many achievements in urban driving safety risk evaluation in mixed-connected environments. Still, there are several problems and challenges that need to be solved, such as the stability and reliability of collaborative sensing systems of AV and CAV in complex traffic environments, the accuracy of object detection, the vehicle information security, the limitations of a single factor analysis used in driving safety risk assessment, and the accuracy of trajectory prediction for both the CV, AV, and CAV. By analyzing the limitations of the existing research, this article proposes a future research direction, which provides a reference for the development of driving safety risk research.
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    Cross-Granularity Network for Vehicle Make and Model Recognition

    Shi Hao TanJoon Huang ChuahChee-Onn ChowJeevan Kanesan...
    5782-5791页
    查看更多>>摘要：Vehicle Make and Model Recognition (VMMR) is a fine-grained classification task in Intelligent Transportation System (ITS). Recent works address VMMR through feature encoding schemes, part-based methods or attention modules. Despite their astounding results, these techniques concentrate on the high-level semantic features. This practice cripples the feature expressive ability of the networks as the granular traits of the vehicle distilled from the early convolution layers are not embedded into the final feature representations. In this work, by contrast, a Cross-Granularity (CG) module which is responsible for the integration of macroscopic and microscopic components is proposed. By incorporating the CG module into a Convolutional Neural Network (CNN), the resultant network i.e. CGNet reinforces the feature extraction ability by amalgamating the feature maps from different scales to render a balanced mix between local contextual information and global semantic details. To validate the proposed framework, experiments are conducted on four publicly available datasets. We report competitive performance on web-nature Comprehensive Cars, Stanford Cars, Car-FG3K and surveillance-nature Comprehensive Cars datasets with 98.3%, 95.4% 86.4% and 99.1% accuracies. Furthermore, we demonstrate the ability of the CGNet to pinpoint distinctive fine-grained details via the Gradient-Weighted Class Activation Mapping (Grad-CAM) technique and compare it against the baseline which learns on deep features alone. The generalization ability of the CG module on other CNNs is also examined and the results suggest a high compatibility between the two.
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    Intelligent Offloading Balance for Vehicular Edge Computing and Networks

    Yu WuXuming FangGeyong MinHongyang Chen...
    5792-5803页
    查看更多>>摘要：With the explosion of connected devices and Internet-of-Things (IoT) services in the smart city, the challenge to meet the demands of urban computing is increasingly prominent. Recent advances in vehicle-to-everything (V2X) communications promote urban Internet-connected vehicles to become excellent candidates for computing tasks. However, due to the limited computing capacity of vehicles, conducting computation in the vehicular networks themselves is insufficient to satisfy the demands of smart city applications. Edge computing, which delivers computing tasks to edge servers (e.g., base stations, BSs, or roadside units, RSUs) with plenty of computing resources, could be a possible solution. However, the static deployment of edge servers may cause severe load unbalance among servers in both real-time communication and computation, thereby decreasing the system performance. This paper explores a two-hop vehicle-assisted edge computing network framework in which vehicles are able to offload the tasks beyond their capabilities to underloaded edge servers relaying via neighbor vehicles. According to the state of the time-varying vehicular environment and the dynamic traffic loads among RSUs, we formulate the task offloading, relay node selection, and resources allocations problem as a Markov decision process (MDP) aiming at maximizing the performance of the computation offloading capacity with the considerations of load balancing and latency constraints. We propose a deep reinforcement learning (DRL) algorithm with a DNN as Q action-value function approximator to solve this problem. Extensive simulation results reveal that the proposed scheme can significantly improve the system performance compared to other state-of-the-art algorithms.
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