IEEE Transactions on Intelligent Vehicles最新文献_第9页

IDM-Follower: A Model-Informed Deep Learning Method for Car-Following Trajectory Prediction IDM-Follower：用于汽车跟随轨迹预测的模型启发式深度学习方法

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-02-20 DOI: 10.1109/TIV.2024.3367654

Yilin Wang;Yiheng Feng

{"title":"IDM-Follower: A Model-Informed Deep Learning Method for Car-Following Trajectory Prediction","authors":"Yilin Wang;Yiheng Feng","doi":"10.1109/TIV.2024.3367654","DOIUrl":"https://doi.org/10.1109/TIV.2024.3367654","url":null,"abstract":"Model-based and learning-based methods are two main approaches modeling car-following behaviors. To combine advantages from both types of models, this study introduces a novel approach, IDM-Follower, which generates a sequence of the following vehicle's trajectory using a recurrent autoencoder informed by a physical car-following model, the Intelligent Driving Model (IDM). We design an innovative neural network (NN) structure with two independent encoders and an attention-based decoder to predict the trajectory sequence. The loss function accounts for discrepancies from both the physical car-following model and the NN predictions. Numerical experiments are conducted using simulated and real world (i.e., NGSIM) datasets under different data noise levels with varying weights between the learning loss and the model loss. Testing results show the proposed approach outperforms both model-based and learning-based baselines under real and high noise levels. The optimal integrating weight between the model and learning component is significantly influenced by data quality, which affects both prediction accuracy and safety metrics.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 6","pages":"5014-5020"},"PeriodicalIF":14.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965413","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

Data-Driven Traffic Simulation: A Comprehensive Review 数据驱动的交通模拟：全面回顾

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-02-20 DOI: 10.1109/TIV.2024.3367919

Di Chen;Meixin Zhu;Hao Yang;Xuesong Wang;Yinhai Wang

{"title":"Data-Driven Traffic Simulation: A Comprehensive Review","authors":"Di Chen;Meixin Zhu;Hao Yang;Xuesong Wang;Yinhai Wang","doi":"10.1109/TIV.2024.3367919","DOIUrl":"https://doi.org/10.1109/TIV.2024.3367919","url":null,"abstract":"Autonomous vehicles (AVs) have the potential to significantly revolutionize society by providing a secure and efficient mode of transportation. Recent years have witnessed notable advancements in autonomous driving perception and prediction, but the challenge of validating the performance of AVs remains largely unresolved. Data-driven microscopic traffic simulation has become an important tool for autonomous driving testing due to 1) availability of high-fidelity traffic data; 2) its advantages of enabling large-scale testing and scenario reproducibility; and 3) its potential in reactive and realistic traffic simulation. However, a comprehensive review of this topic is currently lacking. This paper aims to fill this gap by summarizing relevant studies. The primary objective of this paper is to review current research efforts and provide a futuristic perspective that will benefit future developments in the field. It introduces the general issues of data-driven traffic simulation and outlines key concepts and terms. After overviewing traffic simulation, various datasets and evaluation metrics commonly used are reviewed. The paper then offers a comprehensive evaluation of imitation learning, reinforcement learning, deep generative and deep learning methods, summarizing each and analyzing their advantages and disadvantages in detail. Moreover, it evaluates the state-of-the-art, existing challenges, and future research directions.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 4","pages":"4730-4748"},"PeriodicalIF":8.2,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315237","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

Smart Mining With Autonomous Driving in Industry 5.0: Architectures, Platforms, Operating Systems, Foundation Models, and Applications 工业 5.0 中的自动驾驶智能采矿：架构、平台、操作系统、基础模型和应用

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-02-19 DOI: 10.1109/TIV.2024.3365997

Long Chen;Yuchen Li;Wushour Silamu;Qingquan Li;Shirong Ge;Fei-Yue Wang

{"title":"Smart Mining With Autonomous Driving in Industry 5.0: Architectures, Platforms, Operating Systems, Foundation Models, and Applications","authors":"Long Chen;Yuchen Li;Wushour Silamu;Qingquan Li;Shirong Ge;Fei-Yue Wang","doi":"10.1109/TIV.2024.3365997","DOIUrl":"https://doi.org/10.1109/TIV.2024.3365997","url":null,"abstract":"The increasing importance of mineral resources in contemporary society is becoming more prominent, playing an indispensable and crucial role in the global economy. These resources not only provide essential raw materials for the global economic system but also play an irreplaceable role in supporting the development of modern industry, technology, and infrastructure. With the rapid development of intelligent technologies such as Industry 5.0 and advanced Large Language Models (LLMs), the mining industry is facing unprecedented opportunities and challenges. The development of smart mines has become a crucial direction for industry progress. This article aims to explore the strategic requirements for the development of smart mines by combining advanced products or technologies such as Chat-GPT (one of the successful applications of LLMs), digital twins, and scenario engineering. We propose a comprehensive architecture consisting of three different levels, the mining industrial Internet of Things (IoT) platform, mining operating systems, and foundation models. The systems and models empower the mining equipment for transportation. The architecture delivers a comprehensive solution that aligns perfectly with the demands of Industry 5.0. The application and validation outcomes of this intelligent solution showcase a noteworthy enhancement in mining efficiency and a reduction in safety risks, thereby laying a sturdy groundwork for the advent of Mining 5.0.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4383-4393"},"PeriodicalIF":8.2,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820277","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

Graph Reinforcement Learning for Multi-Aircraft Conflict Resolution 多飞机冲突解决的图强化学习

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-02-12 DOI: 10.1109/TIV.2024.3364652

Yumeng Li;Yunhe Zhang;Tong Guo;Yu Liu;Yisheng Lv;Wenbo Du

{"title":"Graph Reinforcement Learning for Multi-Aircraft Conflict Resolution","authors":"Yumeng Li;Yunhe Zhang;Tong Guo;Yu Liu;Yisheng Lv;Wenbo Du","doi":"10.1109/TIV.2024.3364652","DOIUrl":"https://doi.org/10.1109/TIV.2024.3364652","url":null,"abstract":"The escalating density of airspace has led to sharply increased conflicts between aircraft. Efficient and scalable conflict resolution methods are crucial to mitigate collision risks. Existing learning-based methods become less effective as the scale of aircraft increases due to their redundant information representations. In this paper, to accommodate the increased airspace density, a novel graph reinforcement learning (GRL) method is presented to efficiently learn deconfliction strategies. A time-evolving conflict graph is exploited to represent the local state of individual aircraft and the global spatiotemporal relationships between them. Equipped with the conflict graph, GRL can efficiently learn deconfliction strategies by selectively aggregating aircraft state information through a multi-head attention-boosted graph neural network. Furthermore, a temporal regularization mechanism is proposed to enhance learning stability in highly dynamic environments. Comprehensive experimental studies have been conducted on an OpenAI Gym-based flight simulator. Compared with the existing state-of-the-art learning-based methods, the results demonstrate that GRL can save much training time while achieving significantly better deconfliction strategies in terms of safety and efficiency metrics. In addition, GRL has a strong power of scalability and robustness with increasing aircraft scale.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4529-4540"},"PeriodicalIF":8.2,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820422","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

LCFNets: Compensation Strategy for Real-Time Semantic Segmentation of Autonomous Driving LCFNets：自动驾驶实时语义分割的补偿策略

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-02-08 DOI: 10.1109/TIV.2024.3363830

Lu Yang;Yiwen Bai;Fenglei Ren;Chongke Bi;Ronghui Zhang

{"title":"LCFNets: Compensation Strategy for Real-Time Semantic Segmentation of Autonomous Driving","authors":"Lu Yang;Yiwen Bai;Fenglei Ren;Chongke Bi;Ronghui Zhang","doi":"10.1109/TIV.2024.3363830","DOIUrl":"https://doi.org/10.1109/TIV.2024.3363830","url":null,"abstract":"Semantic segmentation is an important research topic in the environment perception of intelligent vehicles. Many semantic segmentation networks based on bilateral architecture have been proven effective. However, semantic segmentation networks based on this architecture has the risk of pixel classification errors and small objects being overwhelmed. In this paper, we solve the problem by proposing a novel three-branch architecture network called LCFNets. Compared to existing bilateral architecture, LCFNets introduce compensation branch for the first time to preserve the features of original images. Through two efficient modules, Lightweight Detail Guidance Fusion Module (L-DGF) and Lightweight Semantic Guidance Fusion Module (L-SGF), detail and semantic branches are allowed to selectively extract features from this branch. To balance the three-branch features and guide them to fuse effectively, a novel aggregation layer is designed. Depth-wise Convolution Pyramid Pooling module (DCPP) and Total Guidance Fusion Module (TGF) enable the aggregation layer to extract the global receptive field and realize multi-branch aggregation with fewer calculation complexity. Extensive experiments on Cityscapes and CamVid datasets have shown that our family of LCFNets provide a better trade-off between speed and accuracy. With the full resolution input and no ImageNet pre-training, LCFNet-slim achieves 76.86% mIoU at 114.36 FPS and LCFNet achieves 77.96% mIoU at 92.37 FPS on Cityscapes. On the other hand, LCFNet-super achieves 79.10% mIoU at 47.46 FPS.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 4","pages":"4715-4729"},"PeriodicalIF":8.2,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315184","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

Towards the Next Level of Vehicle Automation Through Cooperative Driving: A Roadmap From Planning and Control Perspective 通过协同驾驶实现更高级别的车辆自动化：从规划和控制角度看路线图

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-02-08 DOI: 10.1109/TIV.2024.3363873

Haoran Wang;Yongwei Feng;Yonglin Tian;Ziran Wang;Jia Hu;Masayoshi Tomizuka

{"title":"Towards the Next Level of Vehicle Automation Through Cooperative Driving: A Roadmap From Planning and Control Perspective","authors":"Haoran Wang;Yongwei Feng;Yonglin Tian;Ziran Wang;Jia Hu;Masayoshi Tomizuka","doi":"10.1109/TIV.2024.3363873","DOIUrl":"https://doi.org/10.1109/TIV.2024.3363873","url":null,"abstract":"Cooperative Driving Automation (CDA) stands at the forefront of the evolving landscape of vehicle automation, elevating driving capabilities within intricate real-world environments. This research aims to navigate the path toward the future of CDA by offering a thorough examination from the perspective of Planning and Control (PnC). It classifies state-of-the-art literature according to the CDA classes defined by the Society of Automotive Engineers (SAE). The strengths, weaknesses, and requirements of PnC for each CDA class are analyzed. This analysis helps identify areas that need improvement and provides insights into potential research directions. The research further discusses the evolution directions for CDA, providing valuable insights into the potential areas for further enhancement and enrichment of CDA research. The suggested areas include: Control robustness against disturbance; Risk-aware planning in a mixed environment of Connected and Automated Vehicles (CAVs) and Human-driven Vehicles (HVs); Vehicle-signal coupled modeling for coordination enhancement; Vehicle grouping to enhance the mobility of platooning.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4335-4347"},"PeriodicalIF":8.2,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820419","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

ChatGPT-Based Scenario Engineer: A New Framework on Scenario Generation for Trajectory Prediction 基于 ChatGPT 的场景工程师：用于轨迹预测的情景生成新框架

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-02-07 DOI: 10.1109/TIV.2024.3363232

Xuan Li;Enlu Liu;Tianyu Shen;Jun Huang;Fei-Yue Wang

{"title":"ChatGPT-Based Scenario Engineer: A New Framework on Scenario Generation for Trajectory Prediction","authors":"Xuan Li;Enlu Liu;Tianyu Shen;Jun Huang;Fei-Yue Wang","doi":"10.1109/TIV.2024.3363232","DOIUrl":"https://doi.org/10.1109/TIV.2024.3363232","url":null,"abstract":"The latest developments in parallel driving foreshadow the possibility of delivering intelligence across organizations using foundation models. As is well-known, there are limitations in scenario acquisition in the field of intelligent vehicles (IV), such as efficiency, diversity, and complexity, which hinder in-depth research of vehicle intelligence. To address this issue, this manuscript draws inspiration from scenarios engineering, parallel driving and introduces a pioneering framework for scenario generation, leveraging the ChatGPT, denoted as SeGPT. Within this framework, we define a trajectory scenario and design prompts engineering to generate complex and challenging scenarios. Furthermore, SeGPT, in combination with “Three Modes”, foundation models, vehicle operating system, and other advanced infrastructure, holds the potential to achieve higher levels of autonomous driving. Experimental outcomes substantiate SeGPT's adeptness in producing a spectrum of varied scenarios, underscoring its potential to augment the development of trajectory prediction algorithms. These findings mark significant progress in the domain of scenario generation, also pointing towards new directions in the research of vehicle intelligence and scenarios engineering.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4422-4431"},"PeriodicalIF":8.2,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820329","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

Hierarchical Control for Cooperative Teams in Competitive Autonomous Racing 竞技自主赛车中合作团队的分层控制

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-02-07 DOI: 10.1109/TIV.2024.3363177

Rishabh Saumil Thakkar;Aryaman Singh Samyal;David Fridovich-Keil;Zhe Xu;Ufuk Topcu

{"title":"Hierarchical Control for Cooperative Teams in Competitive Autonomous Racing","authors":"Rishabh Saumil Thakkar;Aryaman Singh Samyal;David Fridovich-Keil;Zhe Xu;Ufuk Topcu","doi":"10.1109/TIV.2024.3363177","DOIUrl":"https://doi.org/10.1109/TIV.2024.3363177","url":null,"abstract":"We investigate the problem of autonomous racing among teams of cooperative agents that are subject to realistic racing rules. Our work extends previous research on hierarchical control in head-to-head autonomous racing by considering a generalized version of the problem while maintaining the two-level hierarchical control structure. A high-level tactical planner constructs a discrete game that encodes the complex rules using simplified dynamics to produce a sequence of target waypoints. The low-level path planner uses these waypoints as a reference trajectory and computes high-resolution control inputs by solving a simplified formulation of a racing game with a simplified representation of the realistic racing rules. We explore two approaches for the low-level path planner: training a multi-agent reinforcement learning (MARL) policy and solving a linear-quadratic Nash game (LQNG) approximation. We evaluate our controllers on simple and complex tracks against three baselines: an end-to-end MARL controller, a MARL controller tracking a fixed racing line, and an LQNG controller tracking a fixed racing line. Quantitative results show our hierarchical methods outperform the baselines in terms of race wins, overall team performance, and compliance with the rules. Qualitatively, we observe the hierarchical controllers mimic actions performed by expert human drivers such as coordinated overtaking, defending against multiple opponents, and long-term planning for delayed advantages.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 5","pages":"4845-4860"},"PeriodicalIF":14.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964746","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

Teleoperation Enhancement for Autonomous Vehicles Using Estimation Based Predictive Display 利用基于估计的预测显示增强自动驾驶汽车的遥控操作功能

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-02-07 DOI: 10.1109/TIV.2024.3360410

Gaurav Sharma;Rajesh Rajamani

{"title":"Teleoperation Enhancement for Autonomous Vehicles Using Estimation Based Predictive Display","authors":"Gaurav Sharma;Rajesh Rajamani","doi":"10.1109/TIV.2024.3360410","DOIUrl":"https://doi.org/10.1109/TIV.2024.3360410","url":null,"abstract":"Teleoperation is increasingly used in the operation of delivery robots and is beginning to be utilized for certain autonomous vehicle intervention applications. This paper addresses the challenges in teleoperation of an autonomous vehicle due to latencies in wireless communication between the remote vehicle and the teleoperator station. Camera video images and Lidar data are typically delayed during wireless transmission but are critical for proper display of the remote vehicle's real-time road environment to the teleoperator. Data collected with experiments in this project show that a 0.5 second delay in real-time display makes it extremely difficult for the teleoperator to control the remote vehicle. This problem is addressed in the paper by using a predictive display (PD) system which provides intermediate updates of the remote vehicle's environment while waiting for actual camera images. The predictive display utilizes estimated positions of the ego vehicle and of other vehicles on the road computed using model-based extended Kalman filters. A crucial result presented in the paper is that vehicle motion models need to be inertial rather than relative and so tracking of other vehicles requires accurate localization of the ego vehicle itself. An experimental study using 5 human teleoperators is conducted to compare teleoperation performance with and without predictive display. A 0.5 second time-delay in camera images makes it impossible to control the vehicle to stay in its lane on curved roads, but the use of the developed predictive display system enables safe remote vehicle control with almost as accurate a performance as the delay-free case.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4456-4469"},"PeriodicalIF":8.2,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820325","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

Safety-Critical Parallel Trajectory Tracking Control of Maritime Autonomous Surface Ships Based on Integral Control Barrier Functions 基于积分控制障碍函数的海上自主水面舰艇安全临界并行轨迹跟踪控制

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-02-02 DOI: 10.1109/TIV.2024.3361477

Jiaxue Xu;Nan Gu;Dan Wang;Tieshan Li;Bing Han;Zhouhua Peng

{"title":"Safety-Critical Parallel Trajectory Tracking Control of Maritime Autonomous Surface Ships Based on Integral Control Barrier Functions","authors":"Jiaxue Xu;Nan Gu;Dan Wang;Tieshan Li;Bing Han;Zhouhua Peng","doi":"10.1109/TIV.2024.3361477","DOIUrl":"https://doi.org/10.1109/TIV.2024.3361477","url":null,"abstract":"This article investigates the parallel trajectory tracking control of fully-actuated maritime autonomous surface ships (MASSs) in the presence of multiple stationary/moving ocean obstacles. A safety-critical parallel control architecture is proposed for the trajectory tracking control of MASSs. Specifically, an artificial MASS system is constructed based on a data-driven learning predictor where real-time and historical navigation data are both utilized to achieve the estimation of the unknown weights of Taylor polynomials and Fourier series. Then, a parallel trajectory tracking control law is designed based on the artificial system such that the MASS is capable of track the reference trajectory positively. Finally, integral control barrier functions are employed to encode input and safety constraints. A safety optimization signal is augmented to the designed parallel control law to achieve the collision avoidance of all ocean obstacles. Based on the stability and safety analyses, the tracking errors of the actual MASS system are verified to be uniformly ultimately bounded and the MASS system is safe. Numerical examples confirm the effectiveness of the designed safety-critical parallel trajectory tracking control scheme for the MASS.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 5","pages":"4979-4988"},"PeriodicalIF":14.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964760","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