This paper focuses on the development and analysis of an innovative unmanned soil excavating and transporting system, utilizing three-dimensional point cloud technology to enhance automation and productivity in earthwork operations. The study introduces a 3D point cloud-based volume estimation technique for real-time monitoring and measurement of exca-vation sites, as well as a model for controlling soil unloading volumes in conjunction with vehicle speed to ensure precise and efficient soil handling. Earthmoving in construction sites, particularly soil excavation and placement, is a critical yet risky endeavor due to manual operation inefficiencies. Therefore, the creation of a safer, unmanned soil handling system is vital for augmenting the safety and efficiency of construction operations.

Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending and drawing extensive attention both from industry and academia. Conventional approaches for most autonomous driving algorithms perform detection, segmentation, tracking, etc., in a front or perspective view. As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance. BEV perception inherits several advantages, as representing surrounding scenes in BEV is intuitive and fusion-friendly; and representing objects in BEV is most desirable for subsequent modules as in planning and/or control. The core problems for BEV perception lie in (a) how to reconstruct the lost 3D information via view transformation from perspective view to BEV; (b) how to acquire ground truth annotations in BEV grid; (c) how to formulate the pipeline to incorporate features from different sources and views; and (d) how to adapt and generalize algorithms as sensor configurations vary across different scenarios. In this survey, we review the most recent works on BEV perception and provide an in-depth analysis of different solutions. Moreover, several systematic designs of BEV approach from the industry are depicted as well. Furthermore, we introduce a full suite of practical guidebook to improve the performance of BEV perception tasks, including camera, LiDAR and fusion inputs. At last, we point out the future research directions in this area. We hope this report will shed some light on the community and encourage more research effort on BEV perception.

The state detection of mechanical equipment is one of the important application fields of intelligent diagnosis. In this article, a multiscale dense residual network is developed to solve the problem of inefficient diagnosis of mechanical equipment in complex operating environments. The model improves the performance of the model from three aspects: pixel level, feature level, and decision level. At the pixel level, the introduction of data input frameworks with different granularities reduces the limitation of traditional models that only extract fault features on a single time scale, and enriches the utilization of state information of mechanical equipment. At the feature level, multiscale dense residual units are used to perform layer-by-layer explicit amplification and densification learning of global and local features. Make the model learn the fault information of different levels and depths to the maximum extent. In addition, the residual connection of interval sampling is used to correct the coding abnormal behavior to stabilize the diagnostic performance of the model. At the decision level, the original feature decision-making mode is changed, and the proposed regional mean strategy can efficiently aggregate multiscale features. The expression demands of each feature of model learning are considered. Through the state recognition of different mechanical equipment, it is identified that the model has excellent generalization ability. Finally, the general relationship between spectrum characteristics and filter parameters is explored to obtain a microscopic representation that the deep learning model extracts fault signal features.

Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments. However, existing benchmarks tend to oversimplify the scene by solely focusing on lane perception tasks. Observing that human drivers rely on both lanes and traffic signals to operate their vehicles safely, we present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. The objective of the presented dataset is to advance research in understanding the structure of road scenes by examining the relationship between perceived entities, such as traffic elements and lanes. Leveraging existing datasets, OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. It comprises three primary sub-tasks, including the 3D lane detection inherited from OpenLane, accompanied by corresponding metrics to evaluate the model’s performance. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.

Although the deep learning diagnosis model has been widely used in the fault diagnosis of rotating machinery. However, these methods lack the interpretability of the diagnostic process. In other words, it is still a difficult problem to understand that the structural function and the diagnosis process in the model correspond to each other. Therefore, this article discusses how to add multiscale and multiattention mechanism to lightweight network. From different scales, different dimensions, combined with the fault signal characteristics of centrifugal fan, the attention structure of cross layer fusion is designed. How to integrate different functions continuously and effectively to achieve better diagnostic performance is answered. The proposed lightweight multiscale multiattention feature fusion network adaptively recalibrates feature weights, which effectively enhances the fault feature learning ability and antinoise ability. Experimental results show that this network is stronger than other advanced diagnostic models.

The production of aircraft structure parts is featured by multiple varieties and small batches, which imposes significant challenges for the representation of process knowledge. Feature based method is an effective way as the process knowledge carrier. Although the parts are different from each other, they are composed of similar geometric features with similar machining processes. This paper introduces the concept of "dynamic machining feature" which is formed in the machining process and influenced by various real operations. In this paper, the process knowledge and interim geometric information are associated based on dynamic machining features. An ontology-based method has been adopted to represent relevant information of dynamic machining features. The proposed approach can speed up process decision and facilitate process optimization.