Pub Date : 2024-09-25DOI: 10.1109/TIV.2024.3462873
{"title":"Proceedings of the IEEE","authors":"","doi":"10.1109/TIV.2024.3462873","DOIUrl":"https://doi.org/10.1109/TIV.2024.3462873","url":null,"abstract":"","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 7","pages":"5241-5241"},"PeriodicalIF":14.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10693702","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320393","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 : 2024-09-25DOI: 10.1109/TIV.2024.3451250
Fei-Yue Wang
I would like to share with you the following information
我想与你们分享以下信息
{"title":"Building Supporting SLAM Community for IEEE TIV: From DHWs to Smart Academic Organizations","authors":"Fei-Yue Wang","doi":"10.1109/TIV.2024.3451250","DOIUrl":"https://doi.org/10.1109/TIV.2024.3451250","url":null,"abstract":"I would like to share with you the following information","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 7","pages":"5119-5123"},"PeriodicalIF":14.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320467","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 : 2024-09-10DOI: 10.1109/TIV.2024.3457759
Bingyi Xia;Peijia Xie;Jiankun Wang
This letter reports the insights gained during a Distributed/Decentralized Hybrid Workshop on Foundation/Infrastructure Intelligence (FII), where we discussed the evolving role of Foundation Models in the field of intelligent vehicles. These models, pre-trained on multimodal data, have emerged as pivotal in the landscape of intelligent vehicles by leveraging their capabilities for high-level reasoning. Ongoing research focuses on these models to further improve scene perception and decision-making, aiming to develop adaptive systems for robot navigation and autonomous driving. However, for smart mobility across the Cyber-Physical-Social space, foundation intelligence should learn human-level knowledge to perform sophisticated interactions and collaborations based on human feedback. Agent-based Foundation Models, as the new training paradigm, can generate cross-domain actions consistent with perception information, paving the way to realize interactive and collaborative agents. This letter discusses the challenges of enhancing and leveraging the scene understanding and spatial reasoning capabilities of the pre-trained foundation model for smart mobility. It also offers insights into the embodied employment of foundation and infrastructure intelligence in enhancing multimodal interactions between robots, environments, and humans.
{"title":"Smart Mobility With Agent-Based Foundation Models: Towards Interactive and Collaborative Intelligent Vehicles","authors":"Bingyi Xia;Peijia Xie;Jiankun Wang","doi":"10.1109/TIV.2024.3457759","DOIUrl":"https://doi.org/10.1109/TIV.2024.3457759","url":null,"abstract":"This letter reports the insights gained during a Distributed/Decentralized Hybrid Workshop on Foundation/Infrastructure Intelligence (FII), where we discussed the evolving role of Foundation Models in the field of intelligent vehicles. These models, pre-trained on multimodal data, have emerged as pivotal in the landscape of intelligent vehicles by leveraging their capabilities for high-level reasoning. Ongoing research focuses on these models to further improve scene perception and decision-making, aiming to develop adaptive systems for robot navigation and autonomous driving. However, for smart mobility across the Cyber-Physical-Social space, foundation intelligence should learn human-level knowledge to perform sophisticated interactions and collaborations based on human feedback. Agent-based Foundation Models, as the new training paradigm, can generate cross-domain actions consistent with perception information, paving the way to realize interactive and collaborative agents. This letter discusses the challenges of enhancing and leveraging the scene understanding and spatial reasoning capabilities of the pre-trained foundation model for smart mobility. It also offers insights into the embodied employment of foundation and infrastructure intelligence in enhancing multimodal interactions between robots, environments, and humans.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 7","pages":"5130-5133"},"PeriodicalIF":14.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320503","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 : 2024-09-04DOI: 10.1109/TIV.2024.3454285
Haoxuan Ma;Yifan Liu;Qinhua Jiang;Brian Yueshuai He;Xishun Liao;Jiaqi Ma
Intelligent vehicles and smart mobility systems are at the forefront of transportation evolution, yet effective management of these new mobility technologies and services are non-trivial. This perspective presents an Intelligent Mobility System Digital Twin (MSDT) framework as a solution. Our framework uniquely maps human beings and vehicles to AI agents, and the mobility systems to AI networks, creating realistic digital simulacra of the physical mobility system. By integrating AI agents and AI networks, this framework offers unprecedented capabilities in prediction and automated simulation of the entire mobility systems, thereby improving planning, operations, and decision-making in smart cities.
{"title":"Mobility AI Agents and Networks","authors":"Haoxuan Ma;Yifan Liu;Qinhua Jiang;Brian Yueshuai He;Xishun Liao;Jiaqi Ma","doi":"10.1109/TIV.2024.3454285","DOIUrl":"https://doi.org/10.1109/TIV.2024.3454285","url":null,"abstract":"Intelligent vehicles and smart mobility systems are at the forefront of transportation evolution, yet effective management of these new mobility technologies and services are non-trivial. This perspective presents an Intelligent Mobility System Digital Twin (MSDT) framework as a solution. Our framework uniquely maps human beings and vehicles to AI agents, and the mobility systems to AI networks, creating realistic digital simulacra of the physical mobility system. By integrating AI agents and AI networks, this framework offers unprecedented capabilities in prediction and automated simulation of the entire mobility systems, thereby improving planning, operations, and decision-making in smart cities.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 7","pages":"5124-5129"},"PeriodicalIF":14.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320478","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 : 2024-07-18DOI: 10.1109/TIV.2024.3430816
Jinwoo Ha;Sesun You;Young-jin Ko;Wonhee Kim
In this paper, we propose an extremum seeking-based algorithm using fuzzy logic for maximizing the braking friction force in anti-lock brake systems (ABSs) without relying on vehicle speed information and slip ratio dynamics. Many current ABSs algorithms utilize slip ratio as a control parameter. If the slip ratio is inaccurately measured, the braking performance of the ABSs may not be optimal. To address this, we propose a method to achieve maximum friction force by designing a reference generator that generates the control inputs for the ABSs without requiring slip ratio information. We design an extended state observer that can estimate the braking friction force and braking friction coefficient. Based on the estimated friction force, the desired wheel cylinder pressure (WCP), which is the control input of the hydraulic brake system, is generated to converge to the maximum friction force using the extremum seeking control algorithm. To achieve improved braking performance, the initial desired WCP is calculated using fuzzy logic for quick convergence to the optimal region. The proposed method is experimentally validated using a hardware-in-the-loop simulation, which includes components such as MATLAB/Simulink, CarSim, SCALEXIO real-time system, and a hydraulic brake system.
{"title":"Extremum Seeking-Based Braking Friction Force Maximization Algorithm Using Fuzzy Logic Without Slip Ratio for ABSs","authors":"Jinwoo Ha;Sesun You;Young-jin Ko;Wonhee Kim","doi":"10.1109/TIV.2024.3430816","DOIUrl":"https://doi.org/10.1109/TIV.2024.3430816","url":null,"abstract":"In this paper, we propose an extremum seeking-based algorithm using fuzzy logic for maximizing the braking friction force in anti-lock brake systems (ABSs) without relying on vehicle speed information and slip ratio dynamics. Many current ABSs algorithms utilize slip ratio as a control parameter. If the slip ratio is inaccurately measured, the braking performance of the ABSs may not be optimal. To address this, we propose a method to achieve maximum friction force by designing a reference generator that generates the control inputs for the ABSs without requiring slip ratio information. We design an extended state observer that can estimate the braking friction force and braking friction coefficient. Based on the estimated friction force, the desired wheel cylinder pressure (WCP), which is the control input of the hydraulic brake system, is generated to converge to the maximum friction force using the extremum seeking control algorithm. To achieve improved braking performance, the initial desired WCP is calculated using fuzzy logic for quick convergence to the optimal region. The proposed method is experimentally validated using a hardware-in-the-loop simulation, which includes components such as MATLAB/Simulink, CarSim, SCALEXIO real-time system, and a hydraulic brake system.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 8","pages":"5272-5283"},"PeriodicalIF":14.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600239","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 : 2024-06-21DOI: 10.1109/TIV.2024.3417512
Ruohong Mei;Wei Sui;Jiaxin Zhang;Xue Qin;Gang Wang;Tao Peng;Tao Chen;Cong Yang
In autonomous driving applications, accurate and efficient road surface reconstruction is paramount. This paper introduces RoMe, a novel framework designed for the robust reconstruction of large-scale road surfaces. Leveraging a unique mesh representation, RoMe ensures that the reconstructed road surfaces are accurate and seamlessly aligned with semantics. To address challenges in computational efficiency, we propose a waypoint sampling strategy, enabling RoMe to reconstruct vast environments by focusing on sub-areas and subsequently merging them. Furthermore, we incorporate an extrinsic optimization module to enhance the robustness against inaccuracies in extrinsic calibration. Our extensive evaluations of both public datasets and wild data underscore RoMe's superiority in terms of speed, accuracy, and robustness. For instance, it costs only 2 GPU hours to recover a road surface of $600times 600$ square meters from thousands of images. Notably, RoMe's capability extends beyond mere reconstruction, offering significant value for auto-labeling tasks in autonomous driving applications.
{"title":"RoMe: Towards Large Scale Road Surface Reconstruction via Mesh Representation","authors":"Ruohong Mei;Wei Sui;Jiaxin Zhang;Xue Qin;Gang Wang;Tao Peng;Tao Chen;Cong Yang","doi":"10.1109/TIV.2024.3417512","DOIUrl":"https://doi.org/10.1109/TIV.2024.3417512","url":null,"abstract":"In autonomous driving applications, accurate and efficient road surface reconstruction is paramount. This paper introduces RoMe, a novel framework designed for the robust reconstruction of large-scale road surfaces. Leveraging a unique mesh representation, RoMe ensures that the reconstructed road surfaces are accurate and seamlessly aligned with semantics. To address challenges in computational efficiency, we propose a waypoint sampling strategy, enabling RoMe to reconstruct vast environments by focusing on sub-areas and subsequently merging them. Furthermore, we incorporate an extrinsic optimization module to enhance the robustness against inaccuracies in extrinsic calibration. Our extensive evaluations of both public datasets and wild data underscore RoMe's superiority in terms of speed, accuracy, and robustness. For instance, it costs only 2 GPU hours to recover a road surface of \u0000<inline-formula><tex-math>$600times 600$</tex-math></inline-formula>\u0000 square meters from thousands of images. Notably, RoMe's capability extends beyond mere reconstruction, offering significant value for auto-labeling tasks in autonomous driving applications.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 7","pages":"5173-5185"},"PeriodicalIF":14.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320504","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 : 2024-06-01DOI: 10.1109/TIV.2024.3426992
Fei-Yue Wang
Dear All
亲爱的各位
{"title":"From RAG/RAT to SAGE for Social Transportation: A CoT and New Perspective on Smart Logistics and Mobility","authors":"Fei-Yue Wang","doi":"10.1109/TIV.2024.3426992","DOIUrl":"https://doi.org/10.1109/TIV.2024.3426992","url":null,"abstract":"Dear All","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 6","pages":"5005-5008"},"PeriodicalIF":14.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965415","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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