IEEE Transactions on Intelligent Transportation Systems最新文献

IEEE Intelligent Transportation Systems Society Information

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3527807

引用次数: 0

Scanning the Issue

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3528298

Simona Sacone

引用次数: 0

IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3527805

引用次数: 0

Corrections to “Toward Infotainment Services in Vehicular Named Data Networking: A Comprehensive Framework Design and Its Realization”

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-02-04 DOI: 10.1109/TITS.2025.3527256

Huhnkuk Lim;Sajjad Ahmad Khan

引用次数: 0

Proactive Assignment Strategy With Human Choice Models for Boosting Pooled Rideshare Service

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-01-16 DOI: 10.1109/TITS.2024.3515074

Joseph Paul;Krishna Murthy Gurumurthy;Taner Cokyasar;Haotian Su;Nazmul Khan;Josh Auld;Yunyi Jia

引用次数: 0

IEEE Intelligent Transportation Systems Society Information IEEE智能交通系统学会信息

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-01-14 DOI: 10.1109/TITS.2024.3518293

引用次数: 0

IEEE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY Ieee智能交通系统学会

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-01-14 DOI: 10.1109/TITS.2024.3518292

引用次数: 0

Scanning the Issue 扫描问题

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-01-14 DOI: 10.1109/TITS.2024.3518135

Simona Sacone

引用次数: 0

Predicting Motion Incongruence Ratings in Closed- and Open-Loop Urban Driving Simulation 闭环和开环城市驾驶仿真中运动不一致等级的预测

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-01-14 DOI: 10.1109/TITS.2024.3503496

Maurice Kolff;Joost Venrooij;Elena Arcidiacono;Daan M. Pool;Max Mulder

{"title":"Predicting Motion Incongruence Ratings in Closed- and Open-Loop Urban Driving Simulation","authors":"Maurice Kolff;Joost Venrooij;Elena Arcidiacono;Daan M. Pool;Max Mulder","doi":"10.1109/TITS.2024.3503496","DOIUrl":"https://doi.org/10.1109/TITS.2024.3503496","url":null,"abstract":"This paper presents a three-step validation approach for subjective rating predictions of driving simulator motion incongruences based on objective mismatches between reference vehicle and simulator motion. This approach relies on using high-resolution rating predictions of open-loop driving (participants being driven) for ratings of motion in closed-loop driving (participants driving themselves). A driving simulator experiment in an urban scenario is described, of which the rating data of 36 participants was recorded and analyzed. In the experiment’s first phase, participants actively drove themselves (i.e., closed-loop). By recording the drives of the participants and playing these back to themselves (open-loop) in the second phase, participants experienced the same motion in both phases. Participants rated the motion after each maneuver and at the end of each drive. In the third phase they again drove open-loop, but rated the motion continuously, only possible in open-loop driving. Results show that a rating model, acquired through a different experiment, can well predict the measured continuous ratings. Second, the maximum of the measured continuous ratings correlates to both the maneuver-based (<inline-formula> <tex-math>$rho =0.94$ </tex-math></inline-formula>) and overall (<inline-formula> <tex-math>$rho =0.69$ </tex-math></inline-formula>) ratings, allowing for predictions of both rating types based on the continuous rating model. Third, using Bayesian statistics it is then shown that both the maneuver-based and overall ratings between the closed-loop and open-loop drives are equivalent. This allows for predictions of maneuver-based and overall ratings using the high-resolution continuous rating models. These predictions can be used as an accurate trade-off method of motion cueing settings of future closed-loop driving simulator experiments.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 1","pages":"517-528"},"PeriodicalIF":7.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976138","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

LiDAR-BEVMTN: Real-Time LiDAR Bird’s-Eye View Multi-Task Perception Network for Autonomous Driving

IF 7.9 1区工程技术

IEEE Transactions on Intelligent Transportation Systems Pub Date : 2025-01-10 DOI: 10.1109/TITS.2024.3510642

Sambit Mohapatra;Senthil Yogamani;Varun Ravi Kumar;Stefan Milz;Heinrich Gotzig;Patrick Mäder

{"title":"LiDAR-BEVMTN: Real-Time LiDAR Bird’s-Eye View Multi-Task Perception Network for Autonomous Driving","authors":"Sambit Mohapatra;Senthil Yogamani;Varun Ravi Kumar;Stefan Milz;Heinrich Gotzig;Patrick Mäder","doi":"10.1109/TITS.2024.3510642","DOIUrl":"https://doi.org/10.1109/TITS.2024.3510642","url":null,"abstract":"LiDAR is crucial for robust 3D scene perception in autonomous driving. LiDAR perception has the largest body of literature after camera perception. However, multi-task learning across tasks like detection, segmentation, and motion estimation using LiDAR remains relatively unexplored, especially on automotive-grade embedded platforms. We present a real-time multi-task convolutional neural network for LiDAR-based object detection, semantics, and motion segmentation. The unified architecture comprises a shared encoder and task-specific decoders, enabling joint representation learning. We propose a novel Semantic Weighting and Guidance (SWAG) module to transfer semantic features for improved object detection selectively. Our heterogeneous training scheme combines diverse datasets and exploits complementary cues between tasks. The work provides the first embedded implementation unifying these key perception tasks from LiDAR point clouds achieving 3ms latency on the embedded NVIDIA Xavier platform. We achieve state-of-the-art results for two tasks, semantic and motion segmentation, and close to state-of-the-art performance for 3D object detection. By maximizing hardware efficiency and leveraging multi-task synergies, our method delivers an accurate and efficient solution tailored for real-world automated driving deployment. Qualitative results can be seen at <uri>https://youtu.be/H-hWRzv2lIY</uri>.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"26 2","pages":"1547-1561"},"PeriodicalIF":7.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10838288","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184005","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