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

Parallel Management of IoV Information Enabled by Blockchain and Decentralized Autonomous Organizations 通过区块链和去中心化自治组织实现 IoV 信息的并行管理

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-02-22 DOI: 10.1109/TIV.2024.3368510

Shuangshuang Han;Yongqiang Bai;Tianrui Zhang;Yueyun Chen;Chintha Tellambura

{"title":"Parallel Management of IoV Information Enabled by Blockchain and Decentralized Autonomous Organizations","authors":"Shuangshuang Han;Yongqiang Bai;Tianrui Zhang;Yueyun Chen;Chintha Tellambura","doi":"10.1109/TIV.2024.3368510","DOIUrl":"https://doi.org/10.1109/TIV.2024.3368510","url":null,"abstract":"With the development of intelligent transportation technologies, the Internet of Vehicles (IoV) faces challenges such as data silos, security and privacy concerns, data quality issues, and collaboration barriers. To address the various challenges, this paper proposes an innovative integration scheme called the IoV Data Management System (IDMS). This system is built upon blockchain and parallel intelligence technologies, aiming to solve the challenges presented in the IoV domain. The proposed system uses the decentralized, immutable and traceable characteristics of blockchain, combined with the incentive mechanism and collaboration model of decentralized autonomous organization (DAO), to build a secure and trusted data sharing platform to solve data problems in IoV. This research combines parallel intelligence, blockchain and DAO technologies to provide an innovative framework for IoV information management. The proposed framework enables parallel management of connected vehicle systems, thereby improving safety, reliability and efficiency. Furthermore, it would promote the development and application of vehicle networking technology, and provide more intelligent, convenient and safe services for people's travel experience. Finally, a parking data sharing case study validates the effectiveness of the designed system and demonstrates its potential to solve IoV data management challenges.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 4","pages":"4759-4768"},"PeriodicalIF":8.2,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315149","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 Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept 基于数字双胞胎的智能交通自动车辆导航系统：概念验证

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-02-21 DOI: 10.1109/TIV.2024.3368109

Kui Wang;Zongdian Li;Kazuma Nonomura;Tao Yu;Kei Sakaguchi;Omar Hashash;Walid Saad

{"title":"Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept","authors":"Kui Wang;Zongdian Li;Kazuma Nonomura;Tao Yu;Kei Sakaguchi;Omar Hashash;Walid Saad","doi":"10.1109/TIV.2024.3368109","DOIUrl":"10.1109/TIV.2024.3368109","url":null,"abstract":"Digital twins (DTs) have driven major advancements across various industrial domains over the past two decades. With the rapid advancements in autonomous driving and vehicle-to-everything (V2X) technologies, integrating DTs into vehicular platforms is anticipated to further revolutionize smart mobility systems. In this paper, a new smart mobility DT (SMDT) platform is proposed for the control of connected and automated vehicles (CAVs) over next-generation wireless networks. In particular, the proposed platform enables cloud services to leverage the abilities of DTs to promote the autonomous driving experience. To enhance traffic efficiency and road safety measures, a novel navigation system that exploits available DT information is designed. The SMDT platform and navigation system are implemented with state-of-the-art products, e.g., CAVs and roadside units (RSUs), and emerging technologies, e.g., cloud and cellular V2X (C-V2X). In addition, proof-of-concept (PoC) experiments are conducted to validate system performance. The performance of SMDT is evaluated from two standpoints: (i) the rewards of the proposed navigation system on traffic efficiency and safety and, (ii) the latency and reliability of the SMDT platform. Our experimental results using SUMO-based large-scale traffic simulations show that the proposed SMDT can reduce the average travel time and the blocking probability due to unexpected traffic incidents. Furthermore, the results record a peak overall latency for DT modeling and route planning services to be 155.15 ms and 810.59 ms, respectively, which validates that our proposed design aligns with the 3GPP requirements for emerging V2X use cases and fulfills the targets of the proposed design.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4348-4361"},"PeriodicalIF":8.2,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10443037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140447458","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

Pre-Stability Control for In-Wheel-Motor-Driven Electric Vehicles With Dynamic State Prediction 具有动态状态预测功能的轮内电机驱动电动汽车预稳定控制系统

IF 8.2 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-02-21 DOI: 10.1109/TIV.2024.3368207

Mengjie Tian;Qixiang Zhang;Duanyang Tian;Liqiang Jin;Jianhua Li;Feng Xiao

{"title":"Pre-Stability Control for In-Wheel-Motor-Driven Electric Vehicles With Dynamic State Prediction","authors":"Mengjie Tian;Qixiang Zhang;Duanyang Tian;Liqiang Jin;Jianhua Li;Feng Xiao","doi":"10.1109/TIV.2024.3368207","DOIUrl":"https://doi.org/10.1109/TIV.2024.3368207","url":null,"abstract":"In-wheel-motor-driven electric vehicles (IWM-EVs) provide more potential to enhance vehicle stability performance. However, traditional stability control relies on the current status fed back by sensors for stability judgment and control, only taking effect after the vehicle has already become unstable. In response to this issue, this paper proposes a pre-stability control strategy based on a hybrid dynamic state prediction method to predict dangerous driving conditions and intervene in vehicle stability control in advance. First, a driver-vehicle model is established to characterize the driver's driving intention and obtain the vehicle's ideal motion responses. Then, the methodology for implementing vehicle pre-stability control is introduced, which mainly includes sideslip angle estimation utilizing the extended Kalman filter, a hybrid dynamic state prediction approach based on vehicle model and data trends, and a vehicle pre-stability judgment method. Subsequently, a vehicle hierarchical controller is designed to achieve pre-stability control. The upper-level controller focuses on calculating the required additional yaw moment, and the lower-level controller aims to optimize torque distribution among the four wheels. Finally, the proposed pre-stability control strategy is validated by the hardware-in-the-loop test bench. The results show that the proposed control strategy can intervene in dangerous driving conditions in advance, and its mean errors of the yaw rate and sideslip angle are reduced by over 17.1% and 23.5%, respectively, compared with the traditional method, which significantly enhances vehicle stability and driving safety.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4541-4554"},"PeriodicalIF":8.2,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820276","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

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