Bird's Eye View Research Articles

Perspectives on drone journalism in Turkiye: Journalists' views on the use of drones

The subject of this study is the role of drone journalism as a new field within journalism. Drone journalism has emerged due to the rapid development of new communication technologies, making it crucial to define the role of drone technology in journalism. This study aims to investigate journalists' perspectives on drone technology and its effects on their professional lives. The research examines how journalists use drone technology, its contributions to their journalistic processes, and the challenges they face in detail. A qualitative paradigm was adopted for this study, and data were collected through semi-structured interviews within a phenomenological design. The data obtained were analyzed using thematic analysis with MAXQDA qualitative data analysis software. Due to resource and time constraints, the study was limited to 11 journalists working in Türkiye. The findings indicate that the use of drones in journalism is perceived positively for facilitating access, providing bird's-eye view imagery, enhancing aesthetic visuals, reducing physical and material risks in the news-gathering process, increasing the attractiveness of news, promoting objective journalism, and creating new employment opportunities. However, the findings also reveal concerns regarding the violation of personal data protection, privacy, and the boundaries between public and private domains. As a result of these findings, it can be concluded that there are debates about the meaning and use of drone journalism in the industry and that theoretical, practical and legal advances are needed. This study has limitations in terms of research methodology, sampling technique and size. Future research could be improved with new studies that are larger in scale and focus specifically on female drone journalists.

Axes-aligned non-linear optimized PnP algorithm

Pose estimation is an important component of many real-world computer vision systems. Most existing pose estimation algorithms need a large number of point correspondences to accurately determine the pose of an object. Since the number of point correspondences depends on the object’s appearance, lighting and other external conditions, detecting many points may not be feasible. In many real-world applications, the movement of objects is limited, e.g. due to gravity. Hence, detecting objects with only three degrees of freedom is usually sufficient. This allows us to improve the accuracy of pose estimation by changing the underlying equations of the perspective-n-point problem to three variables instead of six. By using the simplified equations, our algorithm is more robust against detection errors with limited point correspondences. In this article, we study three scenarios where such constraints apply. The first one is about parking a vehicle on a specific spot. Here, a stationary camera is detecting the vehicle to assist the driver. The second scenario describes the perspective of a moving camera detecting objects in its environment. This scenario is common for driver assistance systems, autonomous cars or mobile robots. Third, we describe a camera observing objects from a birds-eye view, which occurs in industrial applications. In all three scenarios, observed objects can only move in the ground plane and rotate around the vertical axis. Hence, three degrees of freedom are sufficient to estimate the pose. Experiments with synthetic data and real-world photographs have shown that our algorithm outperforms state-of-the-art pose estimation algorithms. Depending on the scenario, our algorithm is able to achieve 50% better accuracy, while being equally fast.

Recent advances in responsive liquid crystal elastomer‐contained fibrous composites

AbstractResponsive polymers can react to surrounding environments by changing their physical and/or chemical properties. Among them, liquid crystal elastomers (LCEs) have emerged as one of the important branches in the field of applied polymer science due to their significant advantages in flexible mechanics and shape memory. Manufacturing LCE fibers with a large specific surface area and functional fillers has become a research hotspot in recent years. This type of LCE‐contained fibrous composite (LCEF) exhibits not only extremely high response sensitivity but also excellent axial mechanical strength and a high degree of deformation freedom. In this paper, we provide a bird's eye view of recent developments in LCEF, including structural designs, synthesis and forming methods, mechanical response principles and modes. Furthermore, we discuss recent advances of LCEF in artificial muscles, smart textiles, biomimetic systems, intelligent soft machines, followed by challenges and possible routes in fabrications and applications of LCEF. At the end, we aim to provide a perspective for an emerging field of stimulus‐responsive polymeric fiber composites.

A novel hypothesis-generating computational workflow utilizing reverse pharmacophore mapping-A drug repurposing perspective of istradefylline towards major depressive disorder.

Drug repurposing (DR) offers a compelling alternative to traditional drug discovery's lengthy, resource-intensive process. DR is the process of identifying alternative clinical applications for pre-approved drugs as a low-risk and low-cost strategy. Computational approaches are crucial during the early hypothesis-generating stage of DR. However, 'large-scale' data retrieval remains a significant challenge. A computational workflow addressing such limitations might improve hypothesis generation, ultimately benefit patients and advance DR research. We introduce a novel computational workflow (combining free-accessible computational platforms) to provide 'proof-of-concept' of the pre-approved drug's suitability for repurposing. Three key phases are included: target fishing (via reverse pharmacophore mapping), target identification (via disease- and drug-target pathway identification) and retrospective literature and drug-like analysis (via in silico ADMET properties determination). Istradefylline is a Parkinson's disease-approved drug with literature-attributed antidepressant properties remaining unclear. Practically applied, istradefylline's antidepressant activity was assessed in the context of major depressive disorder (MDD). Data mining aided by target identification resulted in istradefylline potentially representing a novel antidepressant drug class. Retrieved drug targets (KYNU, MAO-B, ALOX12 and PLCB2) associated with selected MDD pathways (tryptophan metabolism and serotonergic synapse) generated a hypothesis that istradefylline increased extracellular 5-HT levels (MAO-B inhibition) and reduced inflammation (KYNU, ALOX12 and PLCB2 inhibition). The practically applied workflow's generated hypothesis aligns with known experimental data, validating the effectiveness of this novel computational workflow. It is a low-risk and low-cost DR computational tool providing a bird's-eye view for exploring alternative clinical applications of pre-approved drugs.

AMVFNet: Attentive Multi-View Fusion Network for 3D Object Detection

Pillar-based method is significant in the field of LiDAR-based 3D object detection which could directly make use of efficient 2D backbone and save computational resources during reference. Existing methods usually sequentially project the original point clouds into the cylindrical view or the bird-eye view for feature extraction. However, the former suffers from obscured problems and the scales of instances vary greatly with distance, while the latter leads to considerable confusion problems due to the loss of semantic information caused by the sparsity of the projected point cloud. In this paper, we present a novel and efficient two-stage point-pillar hybrid architecture named Attentive Multi-View Fusion Network (AMVFNet), in which we abstract features from all cylindrical view, bird-eye view, and raw point clouds. Rather than designing more complex modules to solve the problems inherent in the single-view approach, our multi-view fusion architecture effectively combines the strengths of multiple perspectives to improve performance at a more fundamental level. Besides, to compensate for quantization distortion caused by projection operations, we propose attentive feature enhancement layers to further improve the capability of contextual information capturing. Extensive experiments on the KITTI detection benchmark illustrate that our proposed AMVFNet achieves competitive performance compared with other SOTA 3D object detectors.

Rethinking the Non-Maximum Suppression Step in 3D Object Detection from a Bird’s-Eye View

In camera-based bird’s-eye view (BEV) 3D object detection, non-maximum suppression (NMS) plays a crucial role. However, traditional NMS methods become ineffective in BEV scenarios where the predicted bounding boxes of small object instances often have no overlapping areas. To address this issue, this paper proposes a BEV intersection over union (IoU) computation method based on relative position and absolute spatial information, referred to as B-IoU. Additionally, a BEV circular search method, called B-Grouping, is introduced to handle prediction boxes of varying scales. Utilizing these two methods, a novel NMS strategy called BEV-NMS is developed to handle the complex prediction boxes in BEV perspectives. This BEV-NMS strategy is implemented in several existing algorithms. Based on the results from the nuScenes validation set, there was an average increase of 7.9% in mAP when compared to the strategy without NMS. The NDS also showed an average increase of 7.9% under the same comparison. Furthermore, compared to the Scale-NMS strategy, the mAP increased by an average of 3.4%, and the NDS saw an average improvement of 3.1%.

Dense projection fusion for 3D object detection

Fusing information from LiDAR and cameras can effectively enhance the overall perceptivity of autonomous vehicles in various scenarios. Despite the relatively good results achieved by point-wise fusion and Bird’s-Eye-View (BEV) fusion, they still cannot fully leverage the image information and lack of effective depth information. For any fusion methods, the multi-modal features first need to be concatenated along the channel, and then the fused features are extracted using convolutional layers. However, this type of fusion methods is effective, but too coarse which causes that the fused features cannot pay more attention to the regions with important features and suffer from severe noise. To tackle these issues, we propose in this paper a Dense Projection Fusion (DPFusion) approach. It consists of two new modules: dense depth map guided BEV transform (DGBT) module and multi-modal feature adaptive fusion (MFAF) module. The DGBT module first quickly estimates the depth of each pixel and then projects all image features to the BEV space, making full use of the image information. The MFAF module computes the image weights and point cloud weights for each channel in each BEV grid and then adaptively weights and fuses the image BEV features with the point cloud BEV features. It is worth pointing out that the MFAF module makes the fused features pay more attention to background outline and object outline. Our proposed DPFusion demonstrates competitive results in 3D object detection, achieving a mean Average Precision (mAP) of 70.4 and a nuScenes detection score (NDS) of 72.3 on the nuScenes validation set.

A bird's-eye view of avian extinctions.

Conservation should consider species' functional and phylogenetic traits.

Bi-Interfusion: A bidirectional cross-fusion framework with semantic-guided transformers in LiDAR-camera fusion

Multi-sensor modal fusion has shown significant advantages in 3D object detection tasks. However, existing methods for fusing multi-modal features into the bird’s eye view (BEV) space often encounter challenges such as feature misalignment, underutilization of semantic information, and inaccurate depth estimation on the Z-axis, resulting in suboptimal performance. To address these issues, we propose Bi-Interfusion, a novel multi-modal fusion framework based on transformers. Bi-Interfusion incorporates a bidirectional fusion architecture, including components such as Pixel-wise Semantic Painting, Gaussian Depth Prior Distribution module, and Semantic Guidance Align module, to overcome the limitations of traditional fusion methods. Specifically, Bi-Interfusion employs a bidirectional cross-fusion strategy to merge image and LiDAR features, enabling the generation of multi-sensor BEV features. This approach leverages a refined Gaussian Depth Prior Distribution generated from LiDAR points, thereby improving the precision of view transformation. Additionally, we apply a pixel-wise semantic painting technique to embed image semantic information into LiDAR point clouds, facilitating a more comprehensive scene understanding. Furthermore, a transformer-based model is utilized to establish soft correspondences among multi-sensor BEV features, capturing positional dependencies and fully exploiting semantic information for alignment. Through experiments on nuScenes benchmark dataset, Bi-Interfusion demonstrates notable performance improvements, achieving a competitive performance of 72.6% mAP and 75.4% NDS in the 3D object detection task.

Pilot Study for Deciphering Post-Translational Modifications and Proteoforms of Tau Protein by Capillary Electrophoresis-Mass Spectrometry.

Abnormal accumulation of tau protein in the brain is one pathological hallmark of Alzheimer's disease (AD). Many tau protein post-translational modifications (PTMs) are associated with the development of AD, such as phosphorylation, acetylation, and methylation. Therefore, a complete picture of the PTM landscape of tau is critical for understanding the molecular mechanisms of AD progression. Here, we offered a pilot study of combining two complementary analytical techniques, capillary zone electrophoresis (CZE)-tandem mass spectrometry (MS/MS) and reversed-phase liquid chromatography (RPLC)-MS/MS, for bottom-up proteomics of recombinant human tau-0N3R. We identified 50 phosphorylation sites of tau-0N3R in total, which is about 25% higher than that from RPLC-MS/MS alone. CZE-MS/MS provided more PTM sites (i.e., phosphorylation) and modified peptides of tau-0N3R than RPLC-MS/MS, and its predicted electrophoretic mobility helped improve the confidence of the identified modified peptides. We developed a highly efficient capillary isoelectric focusing (cIEF)-MS technique to offer a bird's-eye view of tau-0N3R proteoforms, with 11 putative tau-0N3R proteoforms carrying up to nine phosphorylation sites and lower pI values from more phosphorylated proteoforms detected. Interestingly, under native-like cIEF-MS conditions, we observed three putative tau-0N3R dimers carrying phosphate groups. The findings demonstrate that CE-MS is a valuable analytical technique for the characterization of tau PTMs, proteoforms, and even oligomerization.

Unraveling social undermining at the workplace: A systematic review of past achievements and future promises

AbstractIn the contemporary landscape of organizational dynamics, it is imperative to understand the nuance of social undermining behaviors in the workplace for effective strategy and decision‐making. In this regard, this study reviews, identifies, and critically evaluates the social undermining literature and offers new avenues for future research. The systematic literature review methodology has been used to select and critically review 65 studies on this topic. The analysis and findings of this review are presented in three sections, as follows, (i) research profiling—to highlight the current status of research on social undermining in management literature, (ii) literature synthesis—to highlight key theories and conceptions of social undermining as well as discuss the antecedents and outcomes of social undermining, and (iii) critical analysis—to highlight knowledge gaps in existing research and set future agenda. This review is the first comprehensive review on social undermining at the workplace and serves as a knowledge base by presenting a comprehensive literature review as well as extending further development of knowledge on this topic. A key contribution of our review is the proposal of a comprehensive framework of literature synthesis to provide a bird's eye view on this scholarly domain. Our study contributes to organizational behavior and strategic management discourse by offering crucial insights on human dynamics that shape organizational strategy, and change.

Prevalence of Incidental Findings and Assessment of Maxillary Sinus Pathologies and Dental Diseases Using Cone-Beam Computed Tomography (CBCT) in the Tamil Nadu Population: A Retrospective Study.

Background Cone-beam computed tomography (CBCT), a cross-sectional imaging technique, is valuable for clinical diagnosis and creating effective treatment plans. CBCT can precisely examine the connection between the maxillary sinuses and the maxillary root apices. Oral radiologists must be aware of all potential incidental findings and should be diligent in thoroughly identifying and assessing possible underlying diseases. Aim To determine the prevalence of incidental maxillary sinus pathologies and their relationship to dental diseases. Materials and methods In the present retrospective study, CBCT scans from 300 subjects (encompassing 600 right and left maxillary sinuses), aged 18 to 70, were gathered from various CBCT centers to represent the Tamil Nadu population. The CBCT images were analyzed using proprietary software, which provided both a panoramic reconstruction view and multiplanar reformation modules, including axial, sagittal, and coronal slices. The entire sample size was classified as follows: Group 1, age groups of 18 to 25 years; Group 2, age groups of 26 to 35 years; Group 3, age groups of 36 to 45 years; Group 4, age groups of 46 to 55 years; Group 5, age groups of 56 to 65 years; Group 6, age groups of 66 to 70 years. The prevalence of incidental maxillary sinus findings was analyzed, and their relationship with periapical abscess, periapical granuloma, periapical cyst, and breach was assessed. Results There was a prevalence of 52.05% of cases that had incidental maxillary sinus findings. Among them, 53.43% were males and 50.65% were females. Maxillary sinus pathologies were more common in individuals between 46 and 55 years, i.e., Group 4. In 300 datasets, the frequency of incidental maxillary sinus findings on the right is 21.33%, on the left is 24%, in both is 6.67%, and absent in 48% of the cases. Mucosal thickening was observed in 30% of cases with a periapical abscess, 19.52% with a periapical granuloma, 25% with a periapical cyst, and 51.79% with a breach. Polypoidal mucosal thickening was present in 32.50% of cases with a periapical abscess, 13.79% with a periapical granuloma, 50% with a periapical cyst, and 23.21% with a breach. Opacification occurred in 37.50% of cases with a periapical abscess, 20.69% with a periapical granuloma, 25% with a periapical cyst, and 25% with a breach. Conclusion Dental professionals should have a bird's-eye view in treating chronic odontogenic infections close to the maxillary sinus. Early diagnosis and prompt treatment of odontogenic infections help prevent maxillary sinus pathologies.

Mitochondria and Acute Leukemia: A Clinician's Perspective.

Acute leukemia is a group of aggressive hematological malignancies, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) being the most common types. The biology of acute leukemia involves complex genetic and epigenetic alterations that lead to uncontrolled cell proliferation and resistance to apoptosis. Mitochondrial dysfunction is a feature of acute leukemia that results in altered energy production, unregulated cell death pathways, and increased cancer cell survival. Apoptosis, particularly via the mitochondrial pathway, is crucial for cellular homeostasis and cancer prevention. In acute leukemia, disruption of apoptosis is pivotal in disease development and progression, with elevated levels of anti-apoptotic proteins conferring a survival advantage to leukemia cells and promoting resistance to conventional therapies. Targeting mitochondrial apoptosis using BH3 mimetics and anti-apoptotic protein inhibitors is a viable therapeutic strategy. Alterations in the mitochondrial membrane potential, metabolism, and dynamics also contribute to the pathogenesis of acute leukemia. Continued research is vital for developing novel therapies and enhancing survival outcomes in patients with acute leukemia while minimizing the long-term adverse effects of treatment. In this narrative review, we provide a birds-eye view of the available scientific literature on the importance of mitochondria in acute leukemia, and discuss the role of BH3 mimetics in targeting the mitochondrial internal apoptotic machinery.

The Janus (dual) model of immunoglobulin isotype evolution: Conservation and plasticity are the defining paradigms.

The study of antibodies in jawed vertebrates (gnathostomes) provides every immunologist with a bird's eye view of how human immunoglobulins (Igs) came into existence and subsequently evolved into their present forms. It is a fascinating Darwinian history of conservation on the one hand and flexibility on the other, exemplified by the Ig heavy chain (H) isotypes IgM and IgD/W, respectively. The cartilaginous fish (e.g., sharks) Igs provide a glimpse of "how everything got off the ground," while the amphibians (e.g., the model Xenopus) reveal how the adaptive immune system made an about face with the emergence of Ig isotype switching and IgG-like structure/function. The evolution of mucosal Igs is a captivating account of malleability, convergence, and conservation, and a call to arms for future study! In between there are spellbinding chronicles of antibody evolution in each class of vertebrates and rather incredible stories of how antibodies can adapt to occupy niches, for example, single-domain variable regions, cold-adapted Igs, convergent mechanisms to dampen antibody function, provision of mucosal defense, and many more. The purpose here is not to provide an encyclopedic examination of antibody evolution, but rather to hit the high points and entice readers to appreciate how things "came to be."

Orbital Lesions: A bird's eye view of series of 2068 cases in 27 years in a tertiary care hospital in Pakistan.

To determine the relative frequency of orbital lesions based on the site of origin and histopathology at a Tertiary care hospital (Mayo Hospital, Lahore Pakistan) from 1996 till 2022 (27 years). This descriptive case series included 2651 patients of all age groups presenting with orbital lesions who initially got enrolled at Institute of Ophthalmology Mayo Hospital, Lahore from 1996 till 2022. Of these, 583 patients left against medical advice. So, clinical data of 2068 patients were completely analyzed. Lesions were managed medically and/ or surgically. Final clinical diagnosis, with the help of histopathology, was used to classify the lesions. There were 1258 (60.9%) adults and 810 (39.1%) children, 1358 (65.66%) were neoplastic while 710 (34.33%) non-neoplastic lesions. Amongst the neoplastic lesions, 405 (29.8 %) were benign and 953 (70.2%) malignant. Primary orbital lesions were 1676 (81.04%), Secondary orbital lesions were 300 (14.51%), Endocrine/ hematopoietic reticulo-endothelial system lesions were 84 (4.06%) and Metastatic lesions from distant organs were 08 (0.39%). Retinoblastoma, rhabdomyosarcoma, optic nerve gliomata were common in children. Pleomorphic adenoma & adenocystic carcinoma of lacrimal gland, cavernous hemangioma, optic nerve meningioma, neurofibroma, schwannoma, squamous cell carcinoma of eyelid, carcinoma of maxillary antrum and lymphomas were more common in adults.

Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking With Transformer.

With the prevalent use of LiDAR sensors in autonomous driving, 3D point cloud object tracking has received increasing attention. In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in consecutive frames. Motivated by the success of transformers, we propose Point Tracking TRansformer (PTTR), which efficiently predicts high-quality 3D tracking results in a coarse-to-fine manner with the help of transformer operations. PTTR consists of three novel designs. 1) Instead of random sampling, we design Relation-Aware Sampling to preserve relevant points to the given template during subsampling. 2) We propose a Point Relation Transformer for effective feature aggregation and feature matching between the template and search region. 3) Based on the coarse tracking results, we employ a novel Prediction Refinement Module to obtain the final refined prediction through local feature pooling. In addition, motivated by the favorable properties of the Bird's-Eye View (BEV) of point clouds in capturing object motion, we further design a more advanced framework named PTTR++, which incorporates both the point-wise view and BEV representation to exploit their complementary effect in generating high-quality tracking results. PTTR++ substantially boosts the tracking performance on top of PTTR with low computational overhead. Extensive experiments over multiple datasets show that our proposed approaches achieve superior 3D tracking accuracy and efficiency.

Monocular BEV Perception of Road Scenes via Front-to-Top View Projection.

HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen the view transformation and scene understanding. In addition, we apply multi-scale FTVP modules to propagate the rich spatial information of low-level features to mitigate spatial deviation of the predicted object location. Experiments on public benchmarks show that our method achieves various tasks on road layout estimation, vehicle occupancy estimation, and multi-class semantic estimation, at a performance level comparable to the state-of-the-arts, while maintaining superior efficiency.

LiDAR‐based place recognition for mobile robots in ground/water surface multiple scenes

AbstractLiDAR‐based 3D place recognition is an essential component of simultaneous localization and mapping systems in multi‐scene robotic applications. However, extracting discriminative and generalizable global descriptors of point clouds is still an open issue due to the insufficient use of the information contained in the LiDAR scans in existing approaches. In this paper, we propose a novel spatial‐temporal point cloud encoding network for multiple scenes, dubbed STM‐Net, to fully fuse the multi‐view spatial information and temporal information of LiDAR point clouds. Specifically, we first develop a spatial feature encoding module consisting of the single‐view transformer and multi‐view transformer. The module learns the correlation both within a single view and between two views by utilizing the multi‐layer range images generated by spherical projection and multi‐layer bird's eye view images generated by top‐down projection. Then in the temporal feature encoding module, we exploit the temporal transformer to mine the temporal information in the sequential point clouds, and a NetVLAD layer is applied to aggregate features and generate sub‐descriptors. Furthermore, we use a GeM pooling layer to fuse more information along the time dimension for the final global descriptors. Extensive experiments conducted on unmanned ground/surface vehicles with different LiDAR configurations indicate that our method (1) achieves superior place recognition performance than state‐of‐the‐art algorithms, (2) generalizes well to diverse sceneries, (3) is robust to viewpoint changes, (4) can operate in real‐time, demonstrating the effectiveness and satisfactory capability of the proposed approach and highlighting its promising applications in multi‐scene place recognition tasks.

Native Proteomics by Capillary Zone Electrophoresis-Mass Spectrometry.

Native proteomics measures endogenous proteoforms and protein complexes under a near physiological condition using native mass spectrometry (nMS) coupled with liquid-phase separations. Native proteomics should provide the most accurate bird's-eye view of proteome dynamics within cells, which is fundamental for understanding almost all biological processes. nMS has been widely employed to characterize well-purified protein complexes. However, there are only very few trials of utilizing nMS to measure proteoforms and protein complexes in a complex sample (i.e., a whole cell lysate). Here, we pioneer the native proteomics measurement of large proteoforms or protein complexes up to 400 kDa from a complex proteome via online coupling of native capillary zone electrophoresis (nCZE) to an ultra-high mass range (UHMR) Orbitrap mass spectrometer. The nCZE-MS technique enabled the measurement of a 115-kDa standard protein complex while consuming only about 0.1 ng of protein material. nCZE-MS analysis of an E.coli cell lysate detected 72 proteoforms or protein complexes in a mass range of 30-400 kDa in a single run while consuming only 50-ng protein material. The mass distribution of detected proteoforms or protein complexes agreed well with that from mass photometry measurement. This work represents a technical breakthrough in native proteomics for measuring complex proteomes.

Enhanced 3D object detection for autonomous driving: A spatial-temporal alignment approach in Bird's Eye View scenarios

This paper presents a novel 3D object detection algorithm designed for Bird's Eye View (BEV) scenarios, which significantly improves detection capabilities by integrating spatial and temporal features. The core of our approach is the spatial-temporal alignment module that efficiently processes information across different time steps and spatial locations, enhancing the precision and robustness of object detection. We employ a temporal self-attention mechanism to capture the motion information of objects over time, allowing the model to correlate features across various time steps for identifying and tracking moving objects. Additionally, a spatial cross-attention mechanism is utilized to focus on spatial features within regions of interest, promoting interactions between features extracted from camera views and BEV queries. Our method also implements temporal feature integration and multi-scale feature fusion to enhance detection stability and accuracy for fast-moving objects and to capture multi-scale context information, respectively. The model employs an enriched feature set post alignment for 3D bounding box prediction, ascertaining the position, dimensions, and orientation of objects. We conducted experiments on two public datasets for autonomous driving nuScenes and Waymo Open Dataset, demonstrating that our method outperforms previous BEVFormer and other state-of-the-art methods in terms of detection accuracy and robustness. The paper concludes with potential future directions for optimizing the BEVFormer model's performance and exploring its application in broader scenarios and tasks.