Multiview cooperative perception and multimodal fusion are essential for reliable 3D spatiotemporal understanding in autonomous driving, especially in cases with occlusions, limited viewpoints, and communication delays in vehicle-to-everything (V2X) scenarios. In this paper, XET-V2X, a multimodal fused end-to-end tracking framework for V2X collaboration that unifies multiview multimodal sensing within a shared spatiotemporal representation, is proposed. To efficiently align heterogeneous viewpoints and modalities, XET-V2X introduces a dual-layer spatial cross-attention module based on multiscale deformable attention. Multiview image features are aggregated to enhance semantic consistency, followed by point cloud fusion guided by the updated spatial queries, enabling effective cross-modal interaction while reducing computational overhead. Experiments based on the real-world V2X-Seq-SPD dataset and the simulated V2X-Sim-V2V and V2X-Sim-V2I benchmarks demonstrate consistent improvements in detection and tracking performance under varying communication delays, with XET-V2X achieving up to 15–20% relative gains in mAP and AMOTA over single-view or single-modal baselines, while also outperforming representative tracking-by-detection cooperative perception methods.

Combinational equivalence checking (CEC) is essential for verifying the correctness of circuit designs. With the growing complexity of circuits, effective verification techniques have become increasingly critical. Recently, a conjunctive normal form (CNF)-based approach, converting circuits to CNF for Boolean satisfiability (SAT) solvers, has shown competitive performance compared to state-of-the-art hybrid SAT sweeping approaches. The capability of this CNF-based approach depends on effective CNF conversion. This work presentsCirOPT, which is the first CNF conversion method using compiler optimization to equivalently simplify circuits. Extensive experiments are conducted on a broad range of real-world benchmarks, which are far more than the number of benchmarks typically used in empirical studies. The results reveal that, when paired with the state-of-the-art CNF SAT solverKissat,CirOPTconsiderably outperforms existing approaches in CEC.

Cooperatively utilizing both ego-vehicle and infrastructure sensor data via V2X communication has emerged as a promising approach for advanced autonomous driving. However, current research mainly focuses on improving individual modules, rather than taking end-to-end learning to optimize final planning performance, resulting in underutilized data potential. In this paper, we introduce UniV2X, a pioneering cooperative autonomous driving framework that seamlessly integrates all key driving modules across diverse views into a unified network. We propose a sparse-dense hybrid data transmission and fusion mechanism for effective vehicle-infrastructure cooperation, offering three advantages: 1) Effective for simultaneously enhancing agent perception, online mapping, and occupancy prediction, ultimately improving planning performance. 2) Transmission-friendly for practical and limited communication conditions. 3) Reliable data fusion with interpretability of this hybrid data. We implement UniV2X, as well as reproducing several benchmark methods, on the challenging DAIR-V2X, the real-world cooperative driving dataset. Experimental results demonstrate the effectiveness of UniV2X in significantly enhancing planning performance, as well as all intermediate output performance.

Temporal perception, defined as the capability to detect and track objects across temporal sequences, serves as a fundamental component in autonomous driving systems. While single-vehicle perception systems encounter limitations, stemming from incomplete perception due to object occlusion and inherent blind spots, cooperative perception systems present their own challenges in terms of sensor calibration precision and positioning accuracy. To address these issues, we introduce LET-VIC, a LiDAR-based End-to-End Tracking framework for vehicle-infrastructure cooperation (VIC). First, we employ Temporal Self-Attention and VIC cross-attention modules to effectively integrate temporal and spatial information from both vehicle and infrastructure perspectives. Then, we develop a novel calibration error compensation (CEC) module to mitigate sensor misalignment issues and facilitate accurate feature alignment. Experiments on the vehicle-to-everything-Seq-SPD dataset demonstrate that LET-VIC significantly outperforms baseline models. Compared to LET-V, LET-VIC achieves +15.0% improvement in mean average precision (mAP) and a +17.3% improvement in average multiobject tracking accuracy (AMOTA). Furthermore, LET-VIC surpasses representative Tracking by Detection models, including V2VNet, FFNet, and PointPillars, with at least a +13.7% improvement in mAP and a +13.1% improvement in AMOTA without considering communication delays, showcasing its robust detection and tracking performance. The experiments demonstrate that the integration of multiview perspectives, temporal sequences, or CEC in end-to-end training significantly improves both detection and tracking performance. All code will be open-sourced.

In the big data era, the healthcare sector grapples with increased data volumes and the push for more intelligent medical devices. This challenge is marked by data silos, higher data processing needs, and the quest for personalized medicine, emphasizing the importance of integrating and analyzing diverse big data from medical devices. This study employs bibliometric analysis to thoroughly analyze the research trends in big data and medical device research, aiming to identify key developments, patterns, and potential future directions in the field. This study employed the Web of Science Core Collection, BIOSIS Citation Index, and Derwent Innovations Index for conducting keyword searches on ’big data’ and ’medical devices’, focusing on English-language articles, reviews, and patents, while excluding duplicates. Quantitative analyses and visualizations were facilitated using tools such as R 4.3.1, VOSviewer, CiteSpace, and Tableau. The research assessed the impact of journals and academic contributions through metrics like the impact factor and G-Index. Our analysis covered 592 articles and 795 patents. Notably, the annual growth rate of articles reached 62.19%, with the primary contributions originating from China, the United States, India, and England. Among the identified publications, “IEEE Access” emerged as the most prominent journal. The research identified key trends in the application of big data within medical devices, including the extensive use of artificial intelligence (AI), deep learning, Internet of Things (IoT) technologies, genomic data analysis, and bioinformatics. Case studies show how big data and medical device integration, exemplified by Electronic Health Records (EHRs), Clinical Decision Support Systems (CDSS), and smart wearables, enhance diagnostic efficiency and care quality. The study further identified intensive care units, radiology departments, and oncology as key application departments leveraging this integration. Big data and medical device integration have led to smart healthcare and personalized treatment, improving medical decision accuracy and inspiring new research directions. Future research efforts should explore the potential for incorporating even more expansive datasets and multilingual resources to further maximize the potential of this integration and advance medical technology in disease prevention, diagnosis, treatment, and management.

Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of surrounding traffic participants can significantly improve decision-making and safety in autonomous driving. However, the lack of real-world sequential datasets limits research in this area. To address this issue, we introduce V2X-Seq, the first large-scale sequential V2X dataset, which includes data frames, trajectories, vector maps, and traffic lights captured from natural scenery. V2X-Seq comprises two parts: the sequential perception dataset, which includes more than 15,000 frames captured from 95 scenarios, and the trajectory forecasting dataset, which contains about 80,000 infrastructure-view scenarios, 80,000 vehicle-view scenarios, and 50,000 cooperative-view scenarios captured from 28 intersections' areas, covering 672 hours of data. Based on V2X-Seq, we introduce three new tasks for vehicle-infrastructure cooperative (VIC) autonomous driving: VIC3D Tracking, Online-VIC Forecasting, and Offline-VIC Forecasting. We also provide benchmarks for the introduced tasks. Find data, code, and more up-to-date information at https://github.com/AIR-THU/DAIR-V2X-Seq.