In recent years, intelligent vehicles operating in urban environments have demonstrated the capability to autonomously execute various tasks, such as object detection, lane detection, segmentation, etc. This advancement is facilitated by the extensive datasets accumulated by researchers, alongside advancements in intelligent algorithms, as well as significant breakthroughs in software and hardware. However, within the autonomous driving community, there is a scarcity of data regarding scenarios encountered in mining environments. This scarcity presents challenges and bottlenecks for the advancement of comprehensive autonomous driving systems and autonomousoperations. Although we previously released our dataset, AutoMine, which includes over 18 hours of driving data in open-pit mines, its scope is limited to two specific tasks. This scope limitation impedes the training and validation of the majority of algorithms for different tasks in this particular scenario. To broaden the scope of autonomous driving visual tasks in mining environments, we have curated a diverse collection encompassing multiple tasks, including detection, segmentation, tracking, etc. Additionally, we have established benchmarks and set up baselines for the aforementioned multiple tasks. By comparing the performance differences of visual algorithms between mining areas and other scenarios, we demonstrate the distinctive characteristics of mining regions in an intuitive manner. We have developed a suite of tools for converting annotated data into the standardized format used in existing driving datasets. Our aspiration is to establish data and benchmark foundations, supporting research endeavors in intelligent transportation within mining environments and autonomous driving in comprehensive scenarios. Our project website can be seen in AutoMine, and the dataset can be downloaded via AutoMine-Benchmark.

Aiming to boost production efficiency and reduce human workload, human-centricity has emerged as the core concept of Industry 5.0 (I5.0). However, current works have not established a unified automation and autonomous framework for human-centric smart manufacturing across various real world applications. Addressing this gap, this research introduces an innovative automated framework, ParallelWorkforce, which integrates blockchain intelligence and decentralized autonomous organizations and operations (DAOs) to drive the evolution from digital twins to parallel intelligence. First, this research conducts a comprehensive investigation into smart manufacturing in I5.0, summarizing the ongoing evolution. Next, a detailed exploration of ParallelWorkforce is provided to offer customized strategies for managing different levels of out-of-distribution events, significantly alleviating the workload on biological workers and maximizing the potential of both digital and robotic workers. Finally, the development of ParallelWorkforce across various key applications of smart manufacturing is demonstrated, including autonomous transportation, task assignment, and worker management. This research provides a viable solution for the further development of human-centered smart manufacturing and paves the way for the realization of “6S” goals in I5.0.

Since the 1990s, the automated process within open-pit mines has been rapidly advanced. However, mineral transportation, as the most costly and dangerous production process, has not witnessed efficient achievements. Adverse weather conditions and complex work environments are two critical bottlenecks that impede the development and deployment of autonomous transportation of open-pit mines. To alleviate this issue, this research proposes a novel paradigm, named IMAPeM, designed to enable safe and efficient autonomous transportation with humans in the loop. IMAPeM includes three categories of miners: 1) biological miners; 2) digital miners; and 3) robotic miners, as well as three operational modes: 1) autonomous model (AM); 2) parallel model (PM); and 3) expert/emergency model (EM). IMAPeM employs these miners and modes depend on the complexity of the task, optimizing the utilization of digital and robotic miners while reducing the workload for biological miners. In addition, we developed the YUGONG system based on IMAPeM. The empirical implementation demonstrates the exceptional performance of the YUGONG system in autonomous transportation across diverse open-pit mines. This system contributes to the advancement of sustainable mining practices, which also carries profound significance for achieving long-term environmentally responsible mining operations.

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks and lack systematic summaries and research directions in the future. Our work is divided into three independent articles and the first part is a survey of surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. This is the second part (Part I for this technical survey) to review the development of control, computing system design, communication, high-definition map (HD map), testing, and human behaviors in IVs. In addition, the third part (Part II for this technical survey) is to review the perception and planning sections. The objective of this article is to involve all the sections of AD, summarize the latest technical milestones, and guide abecedarians to quickly understand the development of AD and IVs. Combining the SoS and Part II, we anticipate that this work will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future.

A growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is fueled by their promise for enhanced safety, efficiency, and economic benefits. While previous surveys have captured progress in this field, a comprehensive and forward-looking summary is needed. Our work fills this gap through three distinct articles. The first part, a “survey of surveys” (SoS), outlines the history, surveys, ethics, and future directions of AD and IV technologies. The second part, “Milestones in AD and IVs Part I: Control, Computing System Design, Communication, high-definition map (HD map), Testing, and Human Behaviors” delves into the development of control, computing system, communication, HD map, testing, and human behaviors in IVs. This part, the third part, reviews perception and planning in the context of IVs. Aiming to provide a comprehensive overview of the latest advancements in AD and IVs, this work caters to both newcomers and seasoned researchers. By integrating the SoS and Part I, we offer unique insights and strive to serve as a bridge between past achievements and future possibilities in this dynamic field.

Autonomous driving datasets have played an important role in validating the advancement of intelligent vehicle algorithms including localization, perception and prediction in academic areas. However, current existing datasets pay more attention to the structured urban road, which hampers the exploration on unstructured special scenarios. Moreover, the open-pit mine is one of the typical representatives for them. Therefore, we introduce the Autonomous driving dataset on the Mining scene (AutoMine) for positioning and perception tasks in this paper. The AutoMine is collected by multiple acquisition platforms including an SUV, a wide-body mining truck and an ordinary mining truck, depending on the actual mine operation scenarios. The dataset consists of 18+ driving hours, 18K annotated lidar and image frames for 3D perception with various mines, time-of-the-day and weather conditions. The main contributions of the AutoMine dataset are as follows: I.The first autonomous driving dataset for perception and localization in mine scenarios. 2.There are abundant dynamic obstacles of 9 degrees of freedom with large dimension difference (mining trucks and pedestrians) and extreme climatic conditions (the dust and snow) in the mining area. 3.Multi-platform acquisition strategies could capture mining data from multiple perspectives that fit the actual operation. More details can be found in our website(https://automine.cc).