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IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-06-01 DOI: 10.1109/TIV.2024.3437221

引用次数: 0

The Transactions on Intelligent Vehicles Information 智能车辆信息论文集

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-06-01 DOI: 10.1109/TIV.2024.3435289

引用次数: 0

Boosting Visual Recognition in Real-World Degradations via Unsupervised Feature Enhancement Module With Deep Channel Prior 基于深度信道先验的无监督特征增强模块增强现实世界退化中的视觉识别

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-30 DOI: 10.1109/TIV.2024.3395455

Zhanwen Liu;Yuhang Li;Yang Wang;Bolin Gao;Yisheng An;Xiangmo Zhao

{"title":"Boosting Visual Recognition in Real-World Degradations via Unsupervised Feature Enhancement Module With Deep Channel Prior","authors":"Zhanwen Liu;Yuhang Li;Yang Wang;Bolin Gao;Yisheng An;Xiangmo Zhao","doi":"10.1109/TIV.2024.3395455","DOIUrl":"https://doi.org/10.1109/TIV.2024.3395455","url":null,"abstract":"The environmental perception of autonomous vehicles in normal conditions have achieved considerable success in the past decade. However, various unfavourable conditions such as fog, low-light, and motion blur will degrade image quality and pose tremendous threats to the safety of autonomous driving. That is, when applied to degraded images, state-of-the-art visual models often suffer performance decline due to the feature content loss and artifact interference caused by statistical and structural properties disruption of captured images. To address this problem, this work proposes a novel Deep Channel Prior (DCP) for degraded visual recognition. Specifically, we observe that, in the deep representation space of pre-trained models, the channel correlations of degraded features with the same degradation type have uniform distribution even if they have different content and semantics, which can facilitate the mapping relationship learning between degraded and clear representations in high-sparsity feature space. Based on this, a novel plug-and-play Unsupervised Feature Enhancement Module (UFEM) is proposed to achieve unsupervised feature correction, where the multi-adversarial mechanism is introduced in the first stage of UFEM to achieve the latent content restoration and artifact removal in high-sparsity feature space. Then, the generated features are transferred to the second stage for global correlation modulation under the guidance of DCP to obtain high-quality and recognition-friendly features. Evaluations of three tasks and eight benchmark datasets demonstrate that our proposed method can comprehensively improve the performance of pre-trained models in real degradation conditions.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 11","pages":"7208-7221"},"PeriodicalIF":14.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Dynamic Context-Aware Approach for Vessel Trajectory Prediction Based on Multi-Stage Deep Learning 基于多阶段深度学习的船舶轨迹预测动态环境感知方法

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-30 DOI: 10.1109/TIV.2024.3395452

Xiaocai Zhang;Xiuju Fu;Zhe Xiao;Haiyan Xu;Wanbing Zhang;Jimmy Koh;Zheng Qin

{"title":"A Dynamic Context-Aware Approach for Vessel Trajectory Prediction Based on Multi-Stage Deep Learning","authors":"Xiaocai Zhang;Xiuju Fu;Zhe Xiao;Haiyan Xu;Wanbing Zhang;Jimmy Koh;Zheng Qin","doi":"10.1109/TIV.2024.3395452","DOIUrl":"https://doi.org/10.1109/TIV.2024.3395452","url":null,"abstract":"Accurate and reliable vessel trajectory prediction is challenging due to the highly non-linear, intricate and stochastic features of maritime transport networks, but its solutions are vital for ensuring maritime safety, intelligence and efficiency. In this study, a dynamic context-aware (DCA) approach based on multi-stage deep learning, termed DCA-MSDL, is proposed to address this challenge and improve prediction accuracy by considering a broader range of influencing factors. An inverted Transformer (iTransformer)-based deep learning architecture is first constructed for vessel turning status prediction. A deep generative framework is then presented for vessel trajectory augmentation. Following that, multiple potential trajectories are predicted using a novel data-driven algorithm based on multiple steps of nearest neighbor selection. Finally, trajectory enhancement considering dynamic traffic context is proposed to further improve prediction accuracy. With the separate steps of vessel turning status prediction, trajectory augmentation, trajectory prediction and trajectory enhancement, our approach allows us to explicitly explain the factors affecting the prediction accuracy and enable targeted improvements correspondingly. Extensive tests on real-world trajectories of vessels in the Singapore Strait have been conducted and the following encouraging results have been obtained: 1) the iTransformer-based turning status prediction achieves high accuracy at 93.37%, beating other state-of-the-art machine learning-based time-series models; 2) the deep generative model-based trajectory augmentation reduces error by 8.26%, and it significantly outperforms other oversampling techniques; 3) DCA-MSDL increases the prediction accuracy by at least 33.75% in comparison to existing benchmarking methods; 4) the devised dynamic context-aware trajectory enhancement in DCA-MSDL significantly improves the accuracy by 3.53%.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 11","pages":"7193-7207"},"PeriodicalIF":14.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved Consensus ADMM for Cooperative Motion Planning of Large-Scale Connected Autonomous Vehicles With Limited Communication 有限通信条件下大规模网联自动驾驶汽车协同运动规划的改进共识ADMM

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-30 DOI: 10.1109/TIV.2024.3395479

Haichao Liu;Zhenmin Huang;Zicheng Zhu;Yulin Li;Shaojie Shen;Jun Ma

{"title":"Improved Consensus ADMM for Cooperative Motion Planning of Large-Scale Connected Autonomous Vehicles With Limited Communication","authors":"Haichao Liu;Zhenmin Huang;Zicheng Zhu;Yulin Li;Shaojie Shen;Jun Ma","doi":"10.1109/TIV.2024.3395479","DOIUrl":"https://doi.org/10.1109/TIV.2024.3395479","url":null,"abstract":"This paper investigates a cooperative motion planning problem for large-scale connected autonomous vehicles (CAVs) under limited communications, which addresses the challenges of high communication and computing resource requirements. Our proposed methodology incorporates a parallel optimization algorithm with improved consensus ADMM considering a more realistic locally connected topology network, and time complexity of <inline-formula><tex-math>$mathcal {O}(N)$</tex-math></inline-formula> is achieved by exploiting the sparsity in the dual update process. To further enhance the computational efficiency, we employ a lightweight evolution strategy for the dynamic connectivity graph of CAVs, and each sub-problem split from the consensus ADMM only requires managing a small group of CAVs. The proposed method implemented with the receding horizon scheme is validated thoroughly, and comparisons with existing numerical solvers and approaches demonstrate the efficiency of our proposed algorithm. Also, simulations on large-scale cooperative driving tasks involving up to 100 vehicles are performed in the high-fidelity CARLA simulator, which highlights the remarkable computational efficiency, scalability, and effectiveness of our proposed development.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 11","pages":"7222-7238"},"PeriodicalIF":14.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Crash-Avoidance Predictive Control Under Multi-Vehicle Dynamic Environment for Intelligent Vehicles 智能汽车多车动态环境下的自适应避碰预测控制

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-30 DOI: 10.1109/TIV.2024.3394845

Yu Zhang;Yuxuan Hu;Xuepeng Hu;Yechen Qin;Zhenfeng Wang;Mingming Dong;Ehsan Hashemi

{"title":"Adaptive Crash-Avoidance Predictive Control Under Multi-Vehicle Dynamic Environment for Intelligent Vehicles","authors":"Yu Zhang;Yuxuan Hu;Xuepeng Hu;Yechen Qin;Zhenfeng Wang;Mingming Dong;Ehsan Hashemi","doi":"10.1109/TIV.2024.3394845","DOIUrl":"https://doi.org/10.1109/TIV.2024.3394845","url":null,"abstract":"Intelligent vehicles (IVs) play a pivotal role within the Intelligent Transportation System (ITS), significantly enhancing transportation efficiency and mitigating the risks of accidents. Nevertheless, the ever-evolving challenge environment, characterized by diverse scenarios with multiple dynamic vehicles and varying road conditions, present a new challenge for IVs' path planning and following algorithms in the adaption improvement under different traffic scenarios, thereby limiting IVs wider integration within ITS. This paper introduces an innovative adaptive integrated predictive control framework, which treats multi-vehicle dynamic interaction as a process of system model reconfiguration, enhancing the versatility of controller under complex scenarios. The dynamic multiple surrounding vehicles' states, the nonlinear tire model, and actuator characteristics are incorporated into the reconfigurable predictive model. Based on the arbitrary driving behavior of multiple vehicles and diverse road conditions, traffic risks are quantitatively assessed, which is applied to optimize the output of actuators within time-varying stability constraints. To assess its effectiveness, robustness, and real-time performance, the adaptive integrated controller is tested in a range of complex scenarios using a driver-in-the-loop platform. The results demonstrate that the adaptive integrated controller can effectively prevent crashes with multiple dynamic vehicles under different road conditions by employing coordinated control among actuators while ensuring driving stability.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 11","pages":"7165-7175"},"PeriodicalIF":14.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Empirical Evidence of Connected Environment Driving on Fuel Consumption and Emissions 互联环境驾驶对油耗和排放的影响

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-30 DOI: 10.1109/TIV.2024.3395500

Yasir Ali;Anshuman Sharma;Zuduo Zheng;Md. Mazharul Haque

引用次数: 0

Imaginative Intelligence for Intelligent Vehicles: Sora Inspired New Directions for New Mobility and Vehicle Intelligence 智能汽车的想象力智能：Sora 为新交通和车辆智能带来的新灵感

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-29 DOI: 10.1109/TIV.2024.3393638

Fei-Yue Wang

引用次数: 0

A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook 自动驾驶数据集的调查：统计、注释质量和未来展望

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-29 DOI: 10.1109/TIV.2024.3394735

Mingyu Liu;Ekim Yurtsever;Jonathan Fossaert;Xingcheng Zhou;Walter Zimmer;Yuning Cui;Bare Luka Zagar;Alois C. Knoll

{"title":"A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook","authors":"Mingyu Liu;Ekim Yurtsever;Jonathan Fossaert;Xingcheng Zhou;Walter Zimmer;Yuning Cui;Bare Luka Zagar;Alois C. Knoll","doi":"10.1109/TIV.2024.3394735","DOIUrl":"https://doi.org/10.1109/TIV.2024.3394735","url":null,"abstract":"Autonomous driving has rapidly developed and shown promising performance due to recent advances in hardware and deep learning techniques. High-quality datasets are fundamental for developing reliable autonomous driving algorithms. Previous dataset surveys either focused on a limited number or lacked detailed investigation of dataset characteristics. To this end, we present an exhaustive study of 265 autonomous driving datasets from multiple perspectives, including sensor modalities, data size, tasks, and contextual conditions. We introduce a novel metric to evaluate the impact of datasets, which can also be a guide for creating new datasets. Besides, we analyze the annotation processes, existing labeling tools, and the annotation quality of datasets, showing the importance of establishing a standard annotation pipeline. On the other hand, we thoroughly analyze the impact of geographical and adversarial environmental conditions on the performance of autonomous driving systems. Moreover, we exhibit the data distribution of several vital datasets and discuss their pros and cons accordingly. Finally, we discuss the current challenges and the development trend of the future autonomous driving datasets.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 11","pages":"7138-7164"},"PeriodicalIF":14.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10509812","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144510803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sora for Smart Mining: Towards Sustainability With Imaginative Intelligence and Parallel Intelligence 用于智能采矿的 Sora：利用想象力智能和并行智能实现可持续发展

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-29 DOI: 10.1109/TIV.2024.3394520

Yuting Xie;Cong Wang;Kunhua Liu;Zhe Xuanyuan;Yuhang He;Hui Cheng;Andreas Nüchter;Lingxi Li;Rouxing Huai;Shuming Tang;Siji Ma;Long Chen

引用次数: 0