Proceedings of the IEEE最新文献

{"title":"A Taxonomy and Review of Algorithms for Modeling and Predicting Human Driver Behavior","authors":"Raunak Bhattacharyya;Kyle J. Brown;Juanran Wang;Katherine Driggs-Campbell;Mykel J. Kochenderfer","doi":"10.1109/JPROC.2025.3617487","DOIUrl":"10.1109/JPROC.2025.3617487","url":null,"abstract":"An open problem in autonomous driving research is modeling human driving behavior, which is needed for the planning component of the autonomy stack, safety validation through traffic simulation (TS), and causal inference for generating explanations for autonomous driving. Modeling human driving behavior is challenging because it is stochastic, high-dimensional, and involves interaction between multiple agents. This problem has been studied in various communities with a vast body of the literature. Existing reviews have generally focused on one aspect: motion prediction (MP). In this article, we present a unification of the literature that covers intent estimation, trait estimation (TE), and motion prediction. This unification is enabled by modeling multiagent driving as a partially observable stochastic game (POSG), which allows us to cast driver modeling tasks as inference problems. We classify driver models into a taxonomy based on the specific tasks they address and the key attributes of their approach. Finally, we identify open research opportunities in the field of driver modeling.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 4","pages":"550-572"},"PeriodicalIF":30.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145447508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow","authors":"Xiao Wang;Junru Yu;Jun Huang;Qiong Wu;Ljubo Vlacic;Changyin Sun","doi":"10.1109/JPROC.2026.3679470","DOIUrl":"https://doi.org/10.1109/JPROC.2026.3679470","url":null,"abstract":"Despite the recent advancements in artificial intelligence (AI) technologies showing great potential in improving transport efficiency and safety, autonomous vehicles (AVs) still face great challenges when driving in time-varying traffic flow, especially in dense and interactive situations. Meanwhile, humans have free will and usually do not make the same decisions even situated in exactly the same scenarios, leading to data-driven methods suffering from poor migratability and high search cost problems, decreasing the efficiency and effectiveness of the behavior policy. In this article, we propose a safety-first human-like decision-making (SF-HLDM) framework for AVs to drive safely, comfortably, and with social compatibility and efficiency. The framework integrates a hierarchical progressive architecture, which combines a spatial–temporal attention (STA) mechanism for other road users’ intention inference, a social compliance estimation (SCE) module for behavior regulation, and a deep evolutionary reinforcement learning (DERL) model for expanding the search space efficiently and effectively to make avoidance of falling into the local optimal trap and reduce the risk of overfitting, thus make human-like decisions with interpretability and flexibility. The SF-HLDM framework enables autonomous driving AI agents to dynamically adjust decision parameters to maintain safety margins while adhering to contextually appropriate driving behaviors at the same time. Extensive experiments in car learning to act, an open-source autonomous-driving simulator (CARLA) validate the framework’s superior performance, which enlarges the minimum time to worst-case hazards (TWHs) by 41.8% to keep a safer distance away from others, while improving the average velocity by 2.5%, reducing the average acceleration and yaw rate by 23.5% and 60.5%, respectively. The results highlight the potential of SF-HLDM to bridge the gap between machine-driven precision and human-like flexibility in AV systems, paving the way for more interpretable and socially acceptable autonomous driving solutions.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 4","pages":"609-627"},"PeriodicalIF":30.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11520174","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148781630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Future Special Issues/Special Sections of the Proceedings","authors":"","doi":"10.1109/JPROC.2026.3723752","DOIUrl":"https://doi.org/10.1109/JPROC.2026.3723752","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 5","pages":"803-803"},"PeriodicalIF":30.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11672236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148837264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuromorphic Object Detection: An In-Depth Study and Future Directions","authors":"Jianing Li;Dianze Li;Arren Glover;Xiaopeng Fan;Guoqi Li;Chiara Bartolozzi;Ryad B. Benosman;Yonghong Tian","doi":"10.1109/JPROC.2026.3717677","DOIUrl":"10.1109/JPROC.2026.3717677","url":null,"abstract":"Conventional frame-based cameras face significant challenges in detecting objects under high-speed motion blur or in low-light environments. Neuromorphic cameras provide asynchronous visual streams with high temporal resolution and a wide dynamic range, offering a promising solution for object detection under challenging conditions. Despite the development of numerous models and the emergence of various applications in neuromorphic object detection, there is still a lack of deep understanding and standardized benchmarks to assess progress and address key challenges. In this article, we provide a comprehensive survey and benchmark of existing neuromorphic object detection algorithms. Specifically, we first present a problem description, review the available datasets, and revisit the evaluation metrics. We then explore existing neuromorphic object detection approaches from various perspectives, including event representation, temporal modeling, multimodal fusion, asynchronous processing, low-latency processing, and energy-efficient computing. Furthermore, we evaluate a wide range of representative neuromorphic object detection models and offer detailed analyses of the comparative results. Finally, we discuss unresolved issues in neuromorphic object detection and propose potential future research directions. We hope this survey and benchmark will be a valuable resource for researchers and provide guidance for future advancements in neuromorphic object detection.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 5","pages":"712-739"},"PeriodicalIF":30.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148756043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Membership","authors":"","doi":"10.1109/JPROC.2026.3712602","DOIUrl":"https://doi.org/10.1109/JPROC.2026.3712602","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 3","pages":"C3-C3"},"PeriodicalIF":30.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11653031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proceedings of the IEEE: Stay Informed. Become Inspired.","authors":"","doi":"10.1109/JPROC.2026.3723756","DOIUrl":"https://doi.org/10.1109/JPROC.2026.3723756","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 5","pages":"C4-C4"},"PeriodicalIF":30.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11672234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148837326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scanning the Issue","authors":"","doi":"10.1109/JPROC.2026.3723724","DOIUrl":"https://doi.org/10.1109/JPROC.2026.3723724","url":null,"abstract":"Summary form only: Abstracts of articles presented in this issue of the publication.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 5","pages":"710-711"},"PeriodicalIF":30.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11672233","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148837323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proceedings of the IEEE Publication Information","authors":"","doi":"10.1109/JPROC.2026.3712596","DOIUrl":"https://doi.org/10.1109/JPROC.2026.3712596","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 3","pages":"C2-C2"},"PeriodicalIF":30.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11653013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Future Special Issues/Special Sections of the Proceedings","authors":"","doi":"10.1109/JPROC.2026.3712600","DOIUrl":"https://doi.org/10.1109/JPROC.2026.3712600","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 3","pages":"516-516"},"PeriodicalIF":30.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11653042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scanning the Issue","authors":"","doi":"10.1109/JPROC.2026.3713716","DOIUrl":"https://doi.org/10.1109/JPROC.2026.3713716","url":null,"abstract":"Summary form only: Abstracts of articles presented in this issue of the publication.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"114 3","pages":"422-424"},"PeriodicalIF":30.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11653014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148710266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}