IEEE Transactions on Intelligent Vehicles最新文献

{"title":"The Transactions on Intelligent Vehicles Information","authors":"","doi":"10.1109/TIV.2024.3400796","DOIUrl":"https://doi.org/10.1109/TIV.2024.3400796","url":null,"abstract":"","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 4","pages":"C3-C3"},"PeriodicalIF":8.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10555389","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315161","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}

{"title":"IEEE Transactions on Intelligent Vehicles Publication Information","authors":"","doi":"10.1109/TIV.2024.3400798","DOIUrl":"https://doi.org/10.1109/TIV.2024.3400798","url":null,"abstract":"","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 4","pages":"C2-C2"},"PeriodicalIF":8.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10555433","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315145","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}

{"title":"LLM-Based Operating Systems for Automated Vehicles: A New Perspective","authors":"Jingwei Ge;Cheng Chang;Jiawei Zhang;Lingxi Li;Xiaoxiang Na;Yilun Lin;Li Li;Fei-Yue Wang","doi":"10.1109/TIV.2024.3399813","DOIUrl":"https://doi.org/10.1109/TIV.2024.3399813","url":null,"abstract":"The deployment of large language models (LLMs) brings challenges to intelligent systems because its capability of integrating large-scale training data facilitates contextual reasoning. This paper envisions a revolution of the LLM based (Artificial) Intelligent Operating Systems (IOS, or AIOS) to support the core of automated vehicles. We explain the structure of this LLM-OS and discuss the resulting benefits and implementation difficulties.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 4","pages":"4563-4567"},"PeriodicalIF":8.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315157","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}

{"title":"Probabilistic Graph-Based Real-Time Ground Segmentation for Urban Robotics","authors":"Iván del Pino;Angel Santamaria-Navarro;Anaís Garrell Zulueta;Fernando Torres;Juan Andrade-Cetto","doi":"10.1109/TIV.2024.3383599","DOIUrl":"https://doi.org/10.1109/TIV.2024.3383599","url":null,"abstract":"Terrain analysis is of paramount importance for the safe navigation of autonomous robots. In this study, we introduce GATA, a probabilistic real-time graph-based method for segmentation and traversability analysis of point clouds. In the method, we iteratively refine the parameters of a ground plane model and identify regions imaged by a LiDAR as traversable and non-traversable. The method excels in delivering rapid, high-precision obstacle detection, surpassing existing state-of-the-art methods. Furthermore, our method addresses the need to distinguish between surfaces with varying traversability, such as vegetation or unpaved roads, depending on the specific application. To achieve this, we integrate a shallow neural network, which operates on features extracted from the ground model. This enhancement not only boosts performance but also maintains real-time efficiency, without the need for GPUs. The method is rigorously evaluated using the SemanticKitti dataset and its practicality is showcased through real-world experiments with an urban last-mile delivery autonomous robot.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 5","pages":"4989-5002"},"PeriodicalIF":14.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10487036","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964761","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}

{"title":"VistaRAG: Toward Safe and Trustworthy Autonomous Driving Through Retrieval-Augmented Generation","authors":"Xingyuan Dai;Chao Guo;Yun Tang;Haichuan Li;Yutong Wang;Jun Huang;Yonglin Tian;Xin Xia;Yisheng Lv;Fei-Yue Wang","doi":"10.1109/TIV.2024.3396450","DOIUrl":"https://doi.org/10.1109/TIV.2024.3396450","url":null,"abstract":"Autonomous driving based on foundation models has recently garnered widespread attention. However, the risk of hallucinations inherent in foundation models could compromise the safety and reliability of autonomous driving systems. This letter, as part of a series of reports from the Distributed/Decentralized Hybrid Workshop on Foundation/Infrastructure Intelligence (DHW-FII), aims to tackle these issues. We introduce VistaRAG, which integrates retrieval-augmented generation (RAG) technologies into autonomous driving systems based on foundation models, to address the inherent reliability challenges in decision-making. VistaRAG employs a dynamic retrieval mechanism to access highly relevant driving experience, real-time road network status, and other contextual information from external databases. This aids foundation models in informed reasoning and decision-making, thereby enhancing the safety and trustworthiness of foundation-model-based autonomous driving systems under complex traffic scenarios.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 4","pages":"4579-4582"},"PeriodicalIF":8.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315147","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}

{"title":"Scenario-Function System for Automotive Intelligent Cockpits: Framework, Research Progress and Perspectives","authors":"Hongchang Chen;Ruiyang Gao;Lili Fan;Erxuan Liu;Wenbo Li;Ruichen Tan;Ying Li;Lei He;Dongpu Cao","doi":"10.1109/TIV.2024.3382995","DOIUrl":"https://doi.org/10.1109/TIV.2024.3382995","url":null,"abstract":"The innovative development of intelligent cockpit scenarios and functions brings increasingly enhanced user experiences to drivers and passengers in intelligent vehicles. However, existing research lacks a precise definition of intelligent cockpit scenarios and functions, let alone an understanding of their relationship. In this article, we first define concepts related to scenario and function. Then, we construct the scenario-function system framework. Specifically, the scenarios are divided based on the spatial-temporal dimension, and both scenarios and functions are stratified by their attributes. Finally, the progress and perspectives on scenario understanding are discussed in relation to existing research, especially for emotion and motion sickness recognition.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 5","pages":"4890-4904"},"PeriodicalIF":14.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964756","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}

{"title":"Progressive Growth for Point Cloud Completion by Surface-Projection Optimization","authors":"Ben Fei;Rui Zhang;Weidong Yang;Zhijun Li;Wen-Ming Chen","doi":"10.1109/TIV.2024.3383108","DOIUrl":"https://doi.org/10.1109/TIV.2024.3383108","url":null,"abstract":"Point cloud completion concentrates on completing geometric and topological shapes from incomplete 3D shapes. Nevertheless, the unordered nature of point clouds will hamper the generation of high-quality point clouds without predicting structured and topological information of the complete shapes and introducing noisy points. To effectively address the challenges posed by missing topology and noisy points, we introduce SPOFormer, a novel topology-aware model that utilizes surface-projection optimization in a progressive growth manner. SPOFormer consists of three distinct steps for completing the missing topology: (1) \u0000<bold>Missing Keypoints Prediction.</b>\u0000 A topology-aware transformer auto-encoder is integrated for missing keypoint prediction. (2) \u0000<bold>Skeleton Generation.</b>\u0000 The skeleton generation module produces a new type of representation named skeletons with the aid of keypoints predicted by topology-aware transformer auto-encoder and the partial input. (3) \u0000<bold>Progressively Growth.</b>\u0000 We design a progressive growth module to predict final output under \u0000<bold>Multi-scale Supervision</b>\u0000 and \u0000<bold>Surface-projection Optimization</b>\u0000. Surface-projection Optimization is firstly devised for point cloud completion, aiming to enforce the generated points to align with the underlying object surface. Experimentally, SPOFormer model achieves an impressive Chamfer Distance-\u0000<inline-formula><tex-math>$ell _{1}$</tex-math></inline-formula>\u0000 (CD) score of 8.11 on PCN dataset. Furthermore, it attains average CD-\u0000<inline-formula><tex-math>$ell _{2}$</tex-math></inline-formula>\u0000 scores of 1.13, 1.14, and 1.70 on ShapeNet-55, ShapeNet-34, and ShapeNet-Unseen21 datasets, respectively. Additionally, the model achieves a Maximum Mean Discrepancy (MMD) of 0.523 on the real-world KITTI dataset. These outstanding qualitative and quantitative performances surpass previous approaches by a significant margin, firmly establishing new state-of-the-art performance across various benchmark datasets.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 5","pages":"4931-4945"},"PeriodicalIF":14.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964750","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}

{"title":"Continuous Decision-Making in Lane Changing and Overtaking Maneuvers for Unmanned Vehicles: A Risk-Aware Reinforcement Learning Approach With Task Decomposition","authors":"Sifan Wu;Daxin Tian;Xuting Duan;Jianshan Zhou;Dezong Zhao;Dongpu Cao","doi":"10.1109/TIV.2024.3380074","DOIUrl":"https://doi.org/10.1109/TIV.2024.3380074","url":null,"abstract":"Reinforcement learning methods have shown the ability to solve challenging scenarios in unmanned systems. However, solving long-time decision-making sequences in a highly complex environment, such as continuous lane change and overtaking in dense scenarios, remains challenging. Although existing unmanned vehicle systems have made considerable progress, minimizing driving risk is the first consideration. Risk-aware reinforcement learning is crucial for addressing potential driving risks. However, the variability of the risks posed by several risk sources is not considered by existing reinforcement learning algorithms applied in unmanned vehicles. Based on the above analysis, this study proposes a risk-aware reinforcement learning method with driving task decomposition to minimize the risk of various sources. Specifically, risk potential fields are constructed and combined with reinforcement learning to decompose the driving task. The proposed reinforcement learning framework uses different risk-branching networks to learn the driving task. Furthermore, a low-risk episodic sampling augmentation method for different risk branches is proposed to solve the shortage of high-quality samples and further improve sampling efficiency. Also, an intervention training strategy is employed wherein the artificial potential field (APF) is combined with reinforcement learning to speed up training and further ensure safety. Finally, the complete intervention risk classification twin delayed deep deterministic policy gradient-task decompose (IDRCTD3-TD) algorithm is proposed. Two scenarios with different difficulties are designed to validate the superiority of this framework. Results show that the proposed framework has remarkable improvements in performance.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 4","pages":"4657-4674"},"PeriodicalIF":8.2,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315154","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}

{"title":"4D mmWave Radar for Autonomous Driving Perception: A Comprehensive Survey","authors":"Lili Fan;Junhao Wang;Yuanmeng Chang;Yuke Li;Yutong Wang;Dongpu Cao","doi":"10.1109/TIV.2024.3380244","DOIUrl":"https://doi.org/10.1109/TIV.2024.3380244","url":null,"abstract":"The rapid development of autonomous driving technology has driven continuous innovation in perception systems, with 4D millimeter-wave (mmWave) radar being one of the key sensing devices. Leveraging its all-weather operational characteristics and robust perception capabilities in challenging environments, 4D mmWave radar plays a crucial role in achieving highly automated driving. This review systematically summarizes the latest advancements and key applications of 4D mmWave radar in the field of autonomous driving. To begin with, we introduce the fundamental principles and technical features of 4D mmWave radar, delving into its comprehensive perception capabilities across distance, speed, angle, and time dimensions. Subsequently, we provide a detailed analysis of the performance advantages of 4D mmWave radar compared to other sensors in complex environments. We then discuss the latest developments in target detection and tracking using 4D mmWave radar, along with existing datasets in this domain. Finally, we explore the current technological challenges and future directions. This review offers researchers and engineers a comprehensive understanding of the cutting-edge technology and future development directions of 4D mmWave radar in the context of autonomous driving perception.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 4","pages":"4606-4620"},"PeriodicalIF":8.2,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315194","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}

{"title":"Evaluation of Range Sensing-Based Place Recognition for Long-Term Urban Localization","authors":"Weixin Ma;Huan Yin;Lei Yao;Yuxiang Sun;Zhongqing Su","doi":"10.1109/TIV.2024.3380083","DOIUrl":"https://doi.org/10.1109/TIV.2024.3380083","url":null,"abstract":"Place recognition is a critical capability for autonomous vehicles. It matches current sensor data with a pre-built database to provide coarse localization results. However, the effectiveness of long-term place recognition may be degraded by environment changes, such as seasonal or weather changes. To have a deep understanding of this issue, we conduct a comprehensive evaluation study on several state-of-the-art range sensing-based (i.e., LiDAR and radar) place recognition methods on the Borease dataset, which encapsulates long-term localization scenarios with stark seasonal variations and adverse weather conditions. In addition, we design a novel metric to evaluate the influence of matching thresholds on place recognition performance for long-term localization. Our results and findings provide fresh insights to the community and potential directions for future study.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 5","pages":"4905-4916"},"PeriodicalIF":14.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964749","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}