Pub Date : 2024-02-01DOI: 10.1109/TIV.2024.3394589
{"title":"Share Your Preprint Research with the World!","authors":"","doi":"10.1109/TIV.2024.3394589","DOIUrl":"https://doi.org/10.1109/TIV.2024.3394589","url":null,"abstract":"","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 2","pages":"4232-4232"},"PeriodicalIF":8.2,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10510218","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140813984","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}
Pub Date : 2024-02-01DOI: 10.1109/TIV.2024.3376575
Hongkai Yu;Xinyu Liu;Yonglin Tian;Yutong Wang;Chao Gou;Fei-Yue Wang
There are a large number of functional sensors installed on the modern intelligent vehicles. Many Artificial Intelligence based foundation models have been proposed for smart sensing to recognize the known object classes in the new but similar scenarios. However, it is still challenging for the foundation models of smart sensing to detect all the object classes in both seen and unseen scenarios. This letter aims at pushing the boundary of smart sensing research for intelligent vehicles. We first summarize the current widely-used foundation models and the foundation intelligence needed for smart sensing of intelligent vehicles. We then explain Sora-based Parallel Vision to boost the foundation models of smart sensing from basic intelligence (1.0) to enhanced intelligence (2.0) and final generalized intelligence (3.0). Several representative case studies are discussed to show the potential usages of Sora-based Parallel Vision, followed by its future research direction.
现代智能车辆上安装了大量功能传感器。人们提出了许多基于人工智能的智能传感基础模型，以便在新的但相似的场景中识别已知的物体类别。然而，智能传感的基础模型要在看到和看不到的场景中检测到所有物体类别，仍然具有挑战性。这封信旨在推动智能车辆的智能传感研究。我们首先总结了目前广泛使用的基础模型以及智能车辆智能感知所需的基础智能。然后，我们解释了基于 Sora 的并行视觉如何将智能感知的基础模型从基本智能（1.0）提升到增强智能（2.0）和最终的通用智能（3.0）。我们还讨论了几个具有代表性的案例研究，以展示基于 Sora 的并行视觉的潜在用途，以及其未来的研究方向。
{"title":"Sora-Based Parallel Vision for Smart Sensing of Intelligent Vehicles: From Foundation Models to Foundation Intelligence","authors":"Hongkai Yu;Xinyu Liu;Yonglin Tian;Yutong Wang;Chao Gou;Fei-Yue Wang","doi":"10.1109/TIV.2024.3376575","DOIUrl":"https://doi.org/10.1109/TIV.2024.3376575","url":null,"abstract":"There are a large number of functional sensors installed on the modern intelligent vehicles. Many Artificial Intelligence based foundation models have been proposed for smart sensing to recognize the known object classes in the new but similar scenarios. However, it is still challenging for the foundation models of smart sensing to detect all the object classes in both seen and unseen scenarios. This letter aims at pushing the boundary of smart sensing research for intelligent vehicles. We first summarize the current widely-used foundation models and the foundation intelligence needed for smart sensing of intelligent vehicles. We then explain Sora-based Parallel Vision to boost the foundation models of smart sensing from basic intelligence (1.0) to enhanced intelligence (2.0) and final generalized intelligence (3.0). Several representative case studies are discussed to show the potential usages of Sora-based Parallel Vision, followed by its future research direction.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 2","pages":"3123-3126"},"PeriodicalIF":8.2,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140814020","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}
Pub Date : 2024-02-01DOI: 10.1109/TIV.2024.3376461
Zhibin Shuai;Zheng Hu;Jiangtao Gai;Yijie Chen;Jicheng Chen;Hui Zhang;Fei-Yue Wang
Open-terrain field vehicle (OTFV) fleets, including mining trucks, construction machinery, and agricultural machinery, often encounter significantly more intricate scenarios and unique challenges than road vehicles. Enhancing the intelligence level of OTFV fleets can significantly enhance their operational effectiveness and improve energy efficiency. This perspective paper introduces a metaverse-enabled framework to improve the intelligence levels of OTFV fleets. The metaverse-enabled framework consists of the parallel intelligence-based vehicle fleet control and edge computing-based vehicle dynamics control levels. We first delve into the framework's specifics, covering open-terrain field metaverse, parallel intelligence, edge computing, and human-vehicle cooperation. We further discuss critical issues such as artificial general intelligence (AGI) enabled large control models, vehicle teleoperation, communication privacy, and edge scenario engineering. Additionally, we provide a detailed account of edge computing and integrated domain control within the vehicle dynamics control level, illustrating the interactions among human drivers, domain controllers, vehicle systems and open-terrain field metaverse. Ultimately, the proposed framework can potentially empower intelligence to OTFV fleets and other equipment clusters with complicated system compositions and challenging missions in complex environments.
{"title":"Metaverse-Enabled Intelligence for Open-Terrain Field Vehicle Fleets: Leveraging Parallel Intelligence and Edge Computing","authors":"Zhibin Shuai;Zheng Hu;Jiangtao Gai;Yijie Chen;Jicheng Chen;Hui Zhang;Fei-Yue Wang","doi":"10.1109/TIV.2024.3376461","DOIUrl":"https://doi.org/10.1109/TIV.2024.3376461","url":null,"abstract":"Open-terrain field vehicle (OTFV) fleets, including mining trucks, construction machinery, and agricultural machinery, often encounter significantly more intricate scenarios and unique challenges than road vehicles. Enhancing the intelligence level of OTFV fleets can significantly enhance their operational effectiveness and improve energy efficiency. This perspective paper introduces a metaverse-enabled framework to improve the intelligence levels of OTFV fleets. The metaverse-enabled framework consists of the parallel intelligence-based vehicle fleet control and edge computing-based vehicle dynamics control levels. We first delve into the framework's specifics, covering open-terrain field metaverse, parallel intelligence, edge computing, and human-vehicle cooperation. We further discuss critical issues such as artificial general intelligence (AGI) enabled large control models, vehicle teleoperation, communication privacy, and edge scenario engineering. Additionally, we provide a detailed account of edge computing and integrated domain control within the vehicle dynamics control level, illustrating the interactions among human drivers, domain controllers, vehicle systems and open-terrain field metaverse. Ultimately, the proposed framework can potentially empower intelligence to OTFV fleets and other equipment clusters with complicated system compositions and challenging missions in complex environments.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 2","pages":"3111-3116"},"PeriodicalIF":8.2,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140814069","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}
Pub Date : 2024-02-01DOI: 10.1109/TIV.2024.3373614
Fei-Yue Wang
This issue comprises 2 Perspectives, 2 Letters, and 82 Regular Papers. After Scanning the Issue , I would like to initiate a broad discussion about Intelligent Vehicles (IVs), with an emphasis on smart logistics and autonomous mobility (SLAM) for smart societies.
{"title":"Intelligent Vehicles From Your HomePorts to Underwaters and Low Attitude Airspaces: SLAM for Smart Societies","authors":"Fei-Yue Wang","doi":"10.1109/TIV.2024.3373614","DOIUrl":"https://doi.org/10.1109/TIV.2024.3373614","url":null,"abstract":"This issue comprises 2 Perspectives, 2 Letters, and 82 Regular Papers. After \u0000<bold>Scanning the Issue</b>\u0000, I would like to initiate a broad discussion about Intelligent Vehicles (IVs), with an emphasis on smart logistics and autonomous mobility (SLAM) for smart societies.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 2","pages":"3092-3105"},"PeriodicalIF":8.2,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140814073","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}
Pub Date : 2024-02-01DOI: 10.1109/TIV.2024.3377464
Jingqiu Guo;Long Chen;Lingxi Li;Xiaoxiang Na;Ljubo Vlacic;Fei-Yue Wang
Advanced Air Mobility (AAM) is an innovative transportation system that will provide safe and efficient mobility of cargo, passenger and emergency services in low-altitude airspace. This paper highlights AAM's vision and addresses fundamental research challenges and opportunities, e.g., vertical lift aircraft technologies, infrastructure and operation, unmanned traffic management and system security. The overall structure of the AAM ecosystem is discussed from a technology and service perspective. The SEEDS evaluation criteria for the AAM system are explored in terms of promoting safety, sustainability and service quality during the development, verification and validation, and operation phases. As a key sector of the Mobility 5.0 paradigm, new air mobility needs to be integrated into multimodal transport and logistics systems to deliver diverse, sustainable and trustworthy solutions in the near future.
{"title":"Advanced Air Mobility: An Innovation for Future Diversified Transportation and Society","authors":"Jingqiu Guo;Long Chen;Lingxi Li;Xiaoxiang Na;Ljubo Vlacic;Fei-Yue Wang","doi":"10.1109/TIV.2024.3377464","DOIUrl":"https://doi.org/10.1109/TIV.2024.3377464","url":null,"abstract":"Advanced Air Mobility (AAM) is an innovative transportation system that will provide safe and efficient mobility of cargo, passenger and emergency services in low-altitude airspace. This paper highlights AAM's vision and addresses fundamental research challenges and opportunities, e.g., vertical lift aircraft technologies, infrastructure and operation, unmanned traffic management and system security. The overall structure of the AAM ecosystem is discussed from a technology and service perspective. The SEEDS evaluation criteria for the AAM system are explored in terms of promoting safety, sustainability and service quality during the development, verification and validation, and operation phases. As a key sector of the Mobility 5.0 paradigm, new air mobility needs to be integrated into multimodal transport and logistics systems to deliver diverse, sustainable and trustworthy solutions in the near future.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 2","pages":"3106-3110"},"PeriodicalIF":8.2,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140813868","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}
Pub Date : 2024-02-01DOI: 10.1109/TIV.2024.3379989
Xuan Li;Qinghai Miao;Lingxi Li;Qinghua Ni;Lili Fan;Yutong Wang;Yonglin Tian;Fei-Yue Wang
The advent of Scenarios Engineering (SE) paves the way to a new era of intelligent vehicles (IVs), driven by Artificial Intelligence (AI)-enabled strategies. It aims at shaping the IVs to be a form that is more relevant to the underlying scenario, thereby accomplishing validation, verification (V&V), and calibration, certification (C&C) of each vehicle. However, such improved capabilities relies on the accumulation and analysis of an unprecedented volume of scenarios. Recently, Sora and other video generation models have opened up new horizons for Imaginative Intelligence. As an extension of TIV-DHW (Distributed/Decentralized Hybrid Workshop) on SE, this letter discusses the potential of Sora to change the scenario generation process by reducing physical shooting, increasing extreme scenario generation, thereby enabling more comprehensive training and testing of IVs. This letter also analyzes the limitations of Sora in accurately model physics and understand cause and effect, which may affect its effectiveness in SE applications. Last, through a comprehensive outlook, this letter aims to provide a potential direction for the development of Sora-like AI technology, thereby promoting the safety, efficiency, reliability, and sustainability of IVs.
场景工程（SE）的出现为人工智能（AI）战略驱动的智能车辆（IV）新时代铺平了道路。其目的是将智能车辆（IVs）塑造成与基础场景更加相关的形式，从而完成每辆车的验证、核查（V&V）以及校准和认证（C&C）。然而，这种能力的提高有赖于前所未有的大量场景的积累和分析。最近，索拉和其他视频生成模型为想象智能开辟了新天地。作为 TIV-DHW（分布式/分散式混合研讨会）在 SE 上的延伸，这封信讨论了 Sora 通过减少物理拍摄、增加极端场景生成来改变场景生成过程的潜力，从而实现更全面的 IV 培训和测试。本信还分析了 Sora 在准确建立物理模型和理解因果关系方面的局限性，这些局限性可能会影响其在 SE 应用中的有效性。最后，这封信通过全面的展望，旨在为类似 Sora 的人工智能技术的发展提供一个潜在的方向，从而促进 IV 的安全性、效率、可靠性和可持续性。
{"title":"Sora for Senarios Engineering of Intelligent Vehicles: V&V, C&C, and Beyonds","authors":"Xuan Li;Qinghai Miao;Lingxi Li;Qinghua Ni;Lili Fan;Yutong Wang;Yonglin Tian;Fei-Yue Wang","doi":"10.1109/TIV.2024.3379989","DOIUrl":"https://doi.org/10.1109/TIV.2024.3379989","url":null,"abstract":"The advent of Scenarios Engineering (SE) paves the way to a new era of intelligent vehicles (IVs), driven by Artificial Intelligence (AI)-enabled strategies. It aims at shaping the IVs to be a form that is more relevant to the underlying scenario, thereby accomplishing validation, verification (V&V), and calibration, certification (C&C) of each vehicle. However, such improved capabilities relies on the accumulation and analysis of an unprecedented volume of scenarios. Recently, Sora and other video generation models have opened up new horizons for Imaginative Intelligence. As an extension of TIV-DHW (Distributed/Decentralized Hybrid Workshop) on SE, this letter discusses the potential of Sora to change the scenario generation process by reducing physical shooting, increasing extreme scenario generation, thereby enabling more comprehensive training and testing of IVs. This letter also analyzes the limitations of Sora in accurately model physics and understand cause and effect, which may affect its effectiveness in SE applications. Last, through a comprehensive outlook, this letter aims to provide a potential direction for the development of Sora-like AI technology, thereby promoting the safety, efficiency, reliability, and sustainability of IVs.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 2","pages":"3117-3122"},"PeriodicalIF":8.2,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140814019","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}
Pub Date : 2024-01-31DOI: 10.1109/TIV.2024.3360418
Huihui Pan;Mao Luo;Jue Wang;Tenglong Huang;Weichao Sun
Accurate trajectory tracking is unrealistic in real-world scenarios, however, which is commonly assumed to facilitate motion planning algorithm design. In this paper, a safe and reliable motion planning and control framework is proposed to handle the tracking errors caused by inaccurate tracking by coordinating the motion planning layer and controller. Specifically, motion space is divided into safe regions and risky regions by designing the movement restraint size dependent on tracking error to construct the repulsive potential field. The collision-free waypoint set can then be obtained by combining global search and the proposed waypoint set filtering method. The planned trajectory is fitted by an optimization-based approach which minimizes the acceleration of the reference trajectory. Then, the planned trajectory is checked and modified by the designed anti-collision modification to ensure safety. Using invertible transformation and adaptive compensation allows the transient trajectory tracking errors to be limited within the designed region even with actuator faults. Because tracking error is considered and margined at the planning level, safety and reliability can be guaranteed by the coordination between the planning and control levels under inaccurate tracking and actuator faults. The advantages and effectiveness of the proposed motion planning and control method are verified by simulation and experimental results.
{"title":"A Safe Motion Planning and Reliable Control Framework for Autonomous Vehicles","authors":"Huihui Pan;Mao Luo;Jue Wang;Tenglong Huang;Weichao Sun","doi":"10.1109/TIV.2024.3360418","DOIUrl":"https://doi.org/10.1109/TIV.2024.3360418","url":null,"abstract":"Accurate trajectory tracking is unrealistic in real-world scenarios, however, which is commonly assumed to facilitate motion planning algorithm design. In this paper, a safe and reliable motion planning and control framework is proposed to handle the tracking errors caused by inaccurate tracking by coordinating the motion planning layer and controller. Specifically, motion space is divided into safe regions and risky regions by designing the movement restraint size dependent on tracking error to construct the repulsive potential field. The collision-free waypoint set can then be obtained by combining global search and the proposed waypoint set filtering method. The planned trajectory is fitted by an optimization-based approach which minimizes the acceleration of the reference trajectory. Then, the planned trajectory is checked and modified by the designed anti-collision modification to ensure safety. Using invertible transformation and adaptive compensation allows the transient trajectory tracking errors to be limited within the designed region even with actuator faults. Because tracking error is considered and margined at the planning level, safety and reliability can be guaranteed by the coordination between the planning and control levels under inaccurate tracking and actuator faults. The advantages and effectiveness of the proposed motion planning and control method are verified by simulation and experimental results.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 4","pages":"4780-4793"},"PeriodicalIF":8.2,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315280","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}
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