Towards Secure and Safe Appified Automated Vehicles

The advancement in Autonomous Vehicles (AVs) has created an enormous market for the development of self-driving functionalities,raising the question of how it will transform the traditional vehicle development process. One adventurous proposal is to open the AV platform to third-party developers, so that AV functionalities can be developed in a crowd-sourcing way, which could provide tangible benefits to both automakers and end users. Some pioneering companies in the automotive industry have made the move to open the platform so that developers are allowed to test their code on the road. Such openness, however, brings serious security and safety issues by allowing untrusted code to run on the vehicle. In this paper, we introduce the concept of an Appified AV platform that opens the development framework to third-party developers. To further address the safety challenges, we propose an enhanced appified AV design schema called AVGuard, which focuses primarily on mitigating the threats brought about by untrusted code, leveraging theory in the vehicle evaluation field, and conducting program analysis techniques in the cybersecurity area. Our study provides guidelines and suggested practice for the future design of open AV platforms

Research on the Construction of Agro-Ecological Park Under the Background of Smart Agriculture - Take Zengcheng Chuangxian Smart Agriculture Model Park as an Example

[EN] In the process of agricultural modernization in China, smart. agriculture is the new direction of rural development, and scientific development of agroecological parks is an important starting point to promote agricultural transformation and upgrading, as well as an effective way to promote rural revitalization. At present, the application of smart agriculture in agricultural ecological parks in China is still in the experimental stage, and the pilots have been carried out in various regions, but most of them are almost stagnant at the level of agricultural production, failing to maximize the use of smart agricultural resources to build agroecological parks. This paper analyzes the case of Zengcheng Chuangxian Smart Agriculture Model Park on the basis of describing its current development and the existing problems after the introduction of smart agriculture, and puts forward relevant suggestions.Chen, J.; Cai, Y.; Gaoge, Y.; Lv, H. (2022). Research on the Construction of Agro-Ecological Park Under the Background of Smart Agriculture - Take Zengcheng Chuangxian Smart Agriculture Model Park as an Example. En 4th International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. 283-283. http://hdl.handle.net/10251/18976228328

Digital technologies for enhancing crane safety in construction: a combined quantitative and qualitative analysis

A digital-enabled safety management approach is increasingly crucial for crane operations, which are common yet highly hazardous activities sensitive to environmental dynamics on construction sites. However, there exists a knowledge gap regarding the current status and developmental trajectory of this approach. Therefore, this paper aims to provide a comprehensive overview of digital technologies for enhancing crane safety, drawing insights from articles published between 2008 and 2021. Special emphasis is placed on the sensing devices currently in use for gathering “man-machine-environment” data, as well as the communication networks, data processing algorithms, and intuitive visualization platforms employed. Through qualitative and quantitative analysis of the literature, it is evident that while notable advancements have been made in digital-enabled crane safety management, these achievements remain largely confined to the experimentation stage. Consequently, a framework is proposed in this study to facilitate the practical implementation of digital-enabled crane safety management. Furthermore, recommendations for future research directions are presented. This comprehensive review offers valuable guidance for ensuring safe crane operations in the construction industry

M2fNet: Multi-modal Forest Monitoring Network on Large-scale Virtual Dataset

Forest monitoring and education are key to forest protection, education and management, which is an effective way to measure the progress of a country's forest and climate commitments. Due to the lack of a large-scale wild forest monitoring benchmark, the common practice is to train the model on a common outdoor benchmark (e.g., KITTI) and evaluate it on real forest datasets (e.g., CanaTree100). However, there is a large domain gap in this setting, which makes the evaluation and deployment difficult. In this paper, we propose a new photorealistic virtual forest dataset and a multimodal transformer-based algorithm for tree detection and instance segmentation. To the best of our knowledge, it is the first time that a multimodal detection and segmentation algorithm is applied to large-scale forest scenes. We believe that the proposed dataset and method will inspire the simulation, computer vision, education, and forestry communities towards a more comprehensive multi-modal understanding.Comment: 5 page

LiDAR-Forest Dataset: LiDAR Point Cloud Simulation Dataset for Forestry Application

The popularity of LiDAR devices and sensor technology has gradually empowered users from autonomous driving to forest monitoring, and research on 3D LiDAR has made remarkable progress over the years. Unlike 2D images, whose focused area is visible and rich in texture information, understanding the point distribution can help companies and researchers find better ways to develop point-based 3D applications. In this work, we contribute an unreal-based LiDAR simulation tool and a 3D simulation dataset named LiDAR-Forest, which can be used by various studies to evaluate forest reconstruction, tree DBH estimation, and point cloud compression for easy visualization. The simulation is customizable in tree species, LiDAR types and scene generation, with low cost and high efficiency.Comment: 5 page