IEEE Transactions on Intelligent Vehicles最新文献_第10页

An Efficient and Rapidly Adaptable Lightweight Multi-Destination Urban Path Planning Approach for UAVs Using Q-Learning 基于q -学习的无人机轻型多目的地城市路径规划方法

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-10 DOI: 10.1109/TIV.2024.3387018

Michael R. Jones;Soufiene Djahel;Kristopher Welsh

{"title":"An Efficient and Rapidly Adaptable Lightweight Multi-Destination Urban Path Planning Approach for UAVs Using Q-Learning","authors":"Michael R. Jones;Soufiene Djahel;Kristopher Welsh","doi":"10.1109/TIV.2024.3387018","DOIUrl":"https://doi.org/10.1109/TIV.2024.3387018","url":null,"abstract":"Advancement in UAV technologies have facilitated the development of lightweight airborne platforms capable of fulfilling a diverse range of tasks due to a varied array of mountable sensing and interaction modules available. To further advance UAVs and widen their application spectrum, providing them with fully autonomous operations capability is necessary. To address this challenge, we present Multiple Q-table Path Planning (MQTPP), a novel method specifically tailored for UAV path planning in urban environments. Unlike a conventional Q-learning approach that necessitates relearning in response to dynamic changes in urban landscapes or targets, MQTPP is designed to adaptively re-plan UAV paths with notable efficiency, utilising a singular learning phase executed prior to take-off. Results obtained through simulation demonstrate the exceptional capability of MQTPP to swiftly generate new paths or modify existing ones during flight. This performance significantly surpasses existing state-of-the-art methods in terms of computational efficiency, while still achieving near-optimal path planning results. Thus, demonstrating MQTPP's potential as a robust solution for real-time, adaptive in-flight UAV navigation in complex urban settings.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 10","pages":"6624-6636"},"PeriodicalIF":14.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10496242","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314740","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

Weather and Meteorological Optical Range Classification for Autonomous Driving 用于自动驾驶的天气和气象光学距离分类

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-10 DOI: 10.1109/TIV.2024.3387113

Celso Pereira;Ricardo P. M. Cruz;João N. D. Fernandes;João Ribeiro Pinto;Jaime S. Cardoso

{"title":"Weather and Meteorological Optical Range Classification for Autonomous Driving","authors":"Celso Pereira;Ricardo P. M. Cruz;João N. D. Fernandes;João Ribeiro Pinto;Jaime S. Cardoso","doi":"10.1109/TIV.2024.3387113","DOIUrl":"https://doi.org/10.1109/TIV.2024.3387113","url":null,"abstract":"Weather and meteorological optical range (MOR) perception is crucial for smooth and safe autonomous driving (AD). This article introduces two deep learning-based architectures, employing early and intermediate sensor fusion and multi-task strategies, designed for concurrent weather and MOR classification in AD. Extensive experiments employing the publicly available FogChamber dataset demonstrate that the proposed early fusion architecture, characterized by its lightweight design and simplicity, achieves an accuracy of 98.88% in weather classification and 89.77% in MOR classification, with a competitive memory allocation of 5.33 megabytes (MB) and an inference time of 2.50 milliseconds (ms). In contrast, the proposed intermediate fusion architecture prioritizes performance, achieving higher accuracies of 99.38% in weather classification and 91.88% in MOR classification. However, it requires a more substantial memory allocation of 54.06 MB and exhibits a longer inference time of 15.55 ms. Compared to other state-of-the-art architectures, the proposed methods present a competitive balance between accuracy performance, inference time, and memory allocation, which are crucial parameters for enabling autonomous driving.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 10","pages":"6637-6647"},"PeriodicalIF":14.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10496236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308269","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

UpBEV: Fast and Accurate LiDAR-Based Drivable Region Detection Utilizing Uniform Polar BEV UpBEV：利用均匀极性BEV快速准确的基于激光雷达的可驾驶区域检测

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-10 DOI: 10.1109/TIV.2024.3387330

Hao Wen;Tianci Wang;Yong Chen;Chunhua Liu

{"title":"UpBEV: Fast and Accurate LiDAR-Based Drivable Region Detection Utilizing Uniform Polar BEV","authors":"Hao Wen;Tianci Wang;Yong Chen;Chunhua Liu","doi":"10.1109/TIV.2024.3387330","DOIUrl":"https://doi.org/10.1109/TIV.2024.3387330","url":null,"abstract":"Drivable region detection is a crucial upstream task for autonomous navigation, so speed and accuracy are the most critical indicators for safe driving. In this article, we proposed a novel representation paradigm for LiDAR data, whereby the drivable region can be efficiently detected and transformed into a dense region in the bird's eye view. Our method differs from the conventional spatial feature extraction and deep learning-based computation-intensive methods. Based on the proposed representation paradigm, our method takes full advantage of image-based features and processing to capture the boundaries between drivable and non-drivable regions within 10 ms solely on a CPU clocked at 4.0 GHz, thus suitable for most mobile platforms with various computational resources. Our contributions are fourfold. Firstly, we propose UpBEV, a representation addressing the sparsity of the point cloud from LiDAR. With this representation, the boundaries are projected into a 2D image and become distinguishable. Second, we develop a complete framework for road detection based on UpBEV, directly generating a dense top-view drivable region that is essential for navigation. Third, with comprehensive experiments on KITTI-Road dataset and SemanticKITTI dataset, the accuracy, speed, and robustness of our method are demonstrated well. Particularly, our method outperforms all the state-of-the-art non-learning methods on the KITTI-Road Benchmark in both maximum F1-measure and runtime, regardless of data type.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 10","pages":"6648-6659"},"PeriodicalIF":14.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308465","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

Run-Time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representations 使用学习表征对自动驾驶系统中的二维物体检测进行运行时自省

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-08 DOI: 10.1109/TIV.2024.3385531

Hakan Yekta Yatbaz;Mehrdad Dianati;Konstantinos Koufos;Roger Woodman

{"title":"Run-Time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representations","authors":"Hakan Yekta Yatbaz;Mehrdad Dianati;Konstantinos Koufos;Roger Woodman","doi":"10.1109/TIV.2024.3385531","DOIUrl":"https://doi.org/10.1109/TIV.2024.3385531","url":null,"abstract":"Reliable detection of various objects and road users in the surrounding environment is crucial for the safe operation of automated driving systems (ADS). Despite recent progresses in developing highly accurate object detectors based on Deep Neural Networks (DNNs), they still remain prone to detection errors, which can lead to fatal consequences in safety-critical applications such as ADS. An effective remedy to this problem is to equip the system with run-time monitoring, named as \u0000<italic>introspection</i>\u0000 in the context of autonomous systems. Motivated by this, we introduce a novel introspection solution, which operates at the frame level for DNN-based 2D object detection and leverages neural network activation patterns. The proposed approach pre-processes the neural activation patterns of the object detector's backbone using several different modes. To provide extensive comparative analysis and fair comparison, we also adapt and implement several state-of-the-art (SOTA) introspection mechanisms for error detection in 2D object detection, using one-stage and two-stage object detectors evaluated on KITTI and BDD datasets. We compare the performance of the proposed solution in terms of error detection, adaptability to dataset shift, and, computational and memory resource requirements. Our performance evaluation shows that the proposed introspection solution outperforms SOTA methods, achieving an absolute reduction in the missed error ratio of 9% to 17% in the BDD dataset.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 6","pages":"5033-5046"},"PeriodicalIF":14.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965420","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

Share Your Preprint Research with the World! 与世界分享您的预印本研究成果

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-01 DOI: 10.1109/TIV.2024.3413588

引用次数: 0

Sora for Hierarchical Parallel Motion Planner: A Safe End-to-End Method Against OOD Events 分层平行运动规划器 Sora：针对 OOD 事件的安全端到端方法

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-01 DOI: 10.1109/TIV.2024.3392647

Siyu Teng;Ran Yan;Xiaotong Zhang;Yuchen Li;Xingxia Wang;Yutong Wang;Yonglin Tian;Hui Yu;Lingxi Li;Long Chen;Fei-Yue Wang

{"title":"Sora for Hierarchical Parallel Motion Planner: A Safe End-to-End Method Against OOD Events","authors":"Siyu Teng;Ran Yan;Xiaotong Zhang;Yuchen Li;Xingxia Wang;Yutong Wang;Yonglin Tian;Hui Yu;Lingxi Li;Long Chen;Fei-Yue Wang","doi":"10.1109/TIV.2024.3392647","DOIUrl":"https://doi.org/10.1109/TIV.2024.3392647","url":null,"abstract":"End-to-end motion planners have shown great potential for enabling fully autonomous driving. However, when facing out-of-distribution (OOD) events, these planners might not guarantee the optimal prediction of control commands. To better enhance safety, an end-to-end method that benefits robust and general policy learning from potential OOD events is urgently desirable. In this perspective, Sore4PMP, a hierarchical parallel motion planner, is presented as a suitable solution. Based on raw perception data and descriptive prompts, Sore4PMP can first leverage the advanced generative capabilities of Sora to generate virtual OOD events, and then integrate these events into the decision-making process, thereby enhancing the robustness and generalization of autonomous vehicles (AVs) in emergency scenarios. With a comprehensive outlook, this perspective aims to provide a potential direction for the development of foundation models coupled with autonomous driving and finally promote the safety, efficiency, reliability, and sustainability of AVs.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 4","pages":"4573-4576"},"PeriodicalIF":8.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315146","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

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

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-01 DOI: 10.1109/TIV.2024.3400796

引用次数: 0

IEEE Transactions on Intelligent Vehicles Publication Information 电气和电子工程师学会智能车辆论文集》出版信息

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-01 DOI: 10.1109/TIV.2024.3400798

引用次数: 0

LLM-Based Operating Systems for Automated Vehicles: A New Perspective 基于 LLM 的自动驾驶汽车操作系统：新视角

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-01 DOI: 10.1109/TIV.2024.3399813

Jingwei Ge;Cheng Chang;Jiawei Zhang;Lingxi Li;Xiaoxiang Na;Yilun Lin;Li Li;Fei-Yue Wang

引用次数: 0

Probabilistic Graph-Based Real-Time Ground Segmentation for Urban Robotics 基于概率图的城市机器人实时地面分割技术

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-04-01 DOI: 10.1109/TIV.2024.3383599

Iván del Pino;Angel Santamaria-Navarro;Anaís Garrell Zulueta;Fernando Torres;Juan Andrade-Cetto

{"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}

引用次数: 0