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PMScenes: A Parallel Mine Dataset for Autonomous Driving in Surface Mines
Yunfeng Ai;Xinqing Li;Ruiqi Song;Chenglin Cui;Bin Tian;Long Chen;Fei-Yue Wang
Synthetic data, complementary with real-world datasets, have shown their effectiveness for autonomous driving in urban scenarios. However, for specific and complex environments, such as surface mines, which often involve extreme weather and corner cases, synthetic datasets are notably scarce. In this article, PMScenes, the first synthetic dataset for autonomous driving in surface mines, is built u...Show More
A Generative Active Learning Framework for Semantic Segmentation in Autonomous Driving
Ye Wu;Qinggong Wei;Ruiqi Song;Chenglin Cui;Xinqing Li;Fenghua Zhu;Yunfeng Ai
2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2024 | Conference Paper |
Active learning is a machine learning or artificial intelligence method that aims to achieve optimal model performance by actively selecting and labeling the most valuable samples, using the fewest high-quality labeled samples. Relevant works have already been conducted using information uncertainty to select valuable samples. However, its effectiveness for semantic segmentation tasks is relativel...Show More
Recent Advances in Planning for Autonomous Driving and Mining Trucks: A Survey
2024 China Automation Congress (CAC)
Year: 2024 | Conference Paper |
Autonomous driving technology, which enhances driving safety, comfort, and reduces energy consumption, has become a prominent research focus in recent years. Path planning is a crucial component of autonomous driving technology, focusing on the optimal route for a vehicle to travel from one point to another. As a category of autonomous driving technology, the modular methods are highly favored for...Show More
V2X-BGN: Camera-based V2X-Collaborative 3D Object Detection with BEV Global Non-Maximum Suppression
Caiji Zhang;Bin Tian;Shi Meng;Shuangying Qi;Yang Sun;Yunfeng Ai;Long Chen
2024 IEEE Intelligent Vehicles Symposium (IV)
Year: 2024 | Conference Paper |
In recent years, research on Vehicle-to-Everything (V2X) cooperative perception algorithms mainly focuses on the fusion of intermediate features from LiDAR point clouds. Since the emergence of excellent single-vehicle visual perception models like BEVFormer, collaborative perception schemes based on camera and late-fusion have become feasible approaches. This paper proposes a V2X-collaborative 3D ...Show More
Active Parallel Teacher for Human-in-the-Loop Sim2Real Object Detection in Autonomous Haulage Trucks
IEEE Transactions on Intelligent Vehicles
Year: 2024 | Early Access Article |
Object detection for autonomous haulage trucks in open-pit mines is challenging due to unstructured environments. Collecting and annotating large-scale real-world images in these harsh conditions is extremely expensive. Inspired by parallel vision, virtual images have been used to supplement real-world data. However, models trained on virtual data exhibit poor performance in real mines due to the ...Show More
Embodied Intelligence in Mining: Leveraging Multi-Modal Large Language Models for Autonomous Driving in Mines
Luxi Li;Yuchen Li;Xiaotong Zhang;Yuhang He;Jianjian Yang;Bin Tian;Yunfeng Ai;Lingxi Li;Andreas Nüchter;Zhe Xuanyuan
With advancements in computer technology, the benefits of embodied intelligence are increasingly evident. This interactive learning model allows AI to be more flexibly deployed across diverse fields. Recent developments in multi-modal large language models (LLMs) have accelerated AI progress, especially in autonomous driving. This perspective highlights how embodied intelligence can enhance LLM ap...Show More
iMAPeM: A New Paradigm for Implementing Intelligent Mining With Humans in the Loop
Yunfeng Ai;Siyu Teng;Qingyuan Yang;Ye Wu;Yuchen Li;Yu Gao;Feng Meng;Shengli Yang;Bin Tian;Long Chen;Fei-Yue Wang
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 alle...Show More
MiningLLM: Towards Mining 5.0 via Large Language Models in Autonomous Driving and Smart Mining
Yuchen Li;Luxi Li;Zizhang Wu;Zhenshan Bing;Yunfeng Ai;Bin Tian;Zhe Xuanyuan;Alois Christian Knoll;Long Chen
Minerals serve as the cornerstone of contemporary societal infrastructure and diverse industries. However, the mining sector perennially confronts transportation challenges marked by elevated expenses, diminished efficiency, and significant environmental ramifications. Although autonomous driving has been accomplished in open scenarios, its development in specialized areas remains limited. The ope...Show More
Scenario Engineering for Autonomous Transportation: A New Stage in Open-Pit Mines
Siyu Teng;Xuan Li;Yuchen Li;Lingxi Li;Zhe Xuanyuan;Yunfeng Ai;Long Chen
In recent years, open-pit mining has seen significant advancement, the cooperative operation of various specialized machinery substantially enhancing the efficiency of mineral extraction. However, the harsh environment and complex conditions in open-pit mines present substantial challenges for the implementation of autonomous transportation systems. This research introduces a novel paradigm that i...Show More
LiDAR-Camera Fusion in Perspective View for 3D Object Detection in Surface Mine
Yunfeng Ai;Xue Yang;Ruiqi Song;Chenglin Cui;Xinqing Li;Qi Cheng;Bin Tian;Long Chen
LiDAR-Camera fusion can effectively provide the complementary geometric and appearance information for 3D object detection task of autonomous driving system. However, current dominant methods designed for urban scenes often fuse multi-modal information into a Bird's-Eye-View (BEV) feature representation and have a relatively short perception range, which limits their application in surface mine. T...Show More
PMWorld: A Parallel Testing Platform for Autonomous Driving in Mines
Yunfeng Ai;Yuhang Liu;Yu Gao;Chen Zhao;Xiang Cheng;Jinpeng Han;Bin Tian;Long Chen;Fei-Yue Wang
Autonomous driving will be widely deployed in the near future which can efficiently reduce the workload on human drivers. Unlike complicated urban driving conditions, fully autonomous driving is expected to be implemented in closed and relatively simple environments, e.g., mines. Autonomous driving in mines holds substantial practical value that can significantly reduce mining costs and enhance op...Show More
UAV Path Planning With Terrain Constraints for Aerial Scanning
Jinbiao Yuan;Zhenbao Liu;Xiaoyu Xiong;Yunfeng Ai;Long Chen;Bin Tian
A global path planning method constrained by dynamics, kinematics, and terrain is proposed for Vertical Takeoff and Landing (VTOL) Unmanned Aerial Vehicles (UAVs). Firstly, new constant-altitude waypoints are interpolated above the terrain based on altitude information and terrain resolution. Account for the coverage of airborne sensors over the surface area, a horizontal waypoint interpolation op...Show More
A Real-Time Road Boundary Detection Approach in Surface Mine Based on Meta Random Forest
Yunfeng Ai;Ruiqi Song;Chongqing Huang;Chenglin Cui;Bin Tian;Long Chen
Efficient and accurate road boundary detection is a fundamental building component of the perception system for autonomous driving. Specially, the challenges for road boundary detection in surface mine are high generalization error of model and difficulty in boundary generation, which caused by diversity of samples along with scarcity for corresponding samples and complexity of terrain respectivel...Show More
VSSeg: A Point Cloud Segmentation Approach for Unstructured Region in Surface Mine
Chongqing Huang;Ruiqi Song;Chenglin Cui;Xinqing Li;Yunfeng Ai
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2023 | Conference Paper |
Point cloud segmentation for unstructured regions is challenging. Especially, the diversity and complexity of the terrains in surface mine make point cloud segmentation more difficult, which brings potential risks for autonomous driving. To solve this problem, a Variance and Slope ratio based Segmentation method, named VSSeg was proposed. The proposed VSSeg method takes advantage of the different ...Show More
BEVMOT: A Multi-Object Detection and Tracking Method in Bird's-Eye-View via Spatiotemporal Transformers
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2023 | Conference Paper |
Camera-based Bird'View (BEV) 3D object detection is thoroughly challenging and essential in autonomous driving perception system for alleviating the impression caused by object overlap and occlusion effectively. 3D multi-object tracking is one of the most important perception tasks in autonomous driving system, which suffers from the narrow view provided by only one single camera. In this paper, w...Show More
Scenarios Engineering driven Autonomous Transportation in Open-Pit Mines
2023 IEEE 3rd International Conference on Digital Twins and Parallel Intelligence (DTPI)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
One critical bottleneck that impedes the development and deployment of autonomous transportation in open-pit mines is guaranteed robustness and trustworthiness in prohibitively extreme scenarios. In this research, a novel scenarios engineering (SE) methodology for the autonomous mining truck is proposed for open-pit mines. SE increases the trustworthiness and robustness of autonomous trucks from f...Show More
Decoupled Real-Time Trajectory Planning for Multiple Autonomous Mining Trucks in Unloading Areas
Qingyuan Yang;Yunfeng Ai;Siyu Teng;Yu Gao;Chenglin Cui;Bin Tian;Long Chen
Cooperative trajectory planning for autonomous vehicles has garnered significant attention in structured environments, but corresponding methodologies for unstructured environments remains relatively underexplored. The unloading area, an integral component of open-pit mines, exemplifies a quintessential unstructured environment. Implementing cooperative planning for autonomous mining trucks (AMTs)...Show More
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives
Siyu Teng;Xuemin Hu;Peng Deng;Bai Li;Yuchen Li;Yunfeng Ai;Dongsheng Yang;Lingxi Li;Zhe Xuanyuan;Fenghua Zhu;Long Chen
Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being essential prerequisites for IVs. This article reviews state-of-the-art motion plan...Show More
RadarVerses in Metaverses: A CPSI-Based Architecture for 6S Radar Systems in CPSS
Yuhang Liu;Yu Shen;Yonglin Tian;Yunfeng Ai;Bin Tian;Er Wu;Long Chen
Metaverses have caused significant changes in the industry and their academic foundation can be traced back to the term cyber–physical–social systems (CPSS), which was proposed in 2010. Radar is an important sensor in sensing systems that are widely applied in many fields, especially, in autonomous driving. To deal with the complex environment, smart radars with real-time information processing ca...Show More
Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving
Siyu Teng;Long Chen;Yunfeng Ai;Yuanye Zhou;Zhe Xuanyuan;Xuemin Hu
End-to-end autonomous driving provides a simple and efficient framework for autonomous driving systems, which can directly obtain control commands from raw perception data. However, it fails to address stability and interpretability problems in complex urban scenarios. In this paper, we construct a two-stage end-to-end autonomous driving model for complex urban scenarios, named HIIL (Hierarchical ...Show More
MSFANet: A Light Weight Object Detector Based on Context Aggregation and Attention Mechanism for Autonomous Mining Truck
Ruiqi Song;Yunfeng Ai;Bin Tian;Long Chen;Fenghua Zhu;Fei Yao
Accurate and reliable object detection is a fundamental component of perception system for autonomous driving. Specially, in some circumstances like autonomous driving in surface mine, there is a fact that the particularity of scene brings tremendous challenges for object detection with a series of problems caused by the multi-scale and camouflaged objects. In this paper, a multi-scale feature fus...Show More
An Architecture and Key Technologies of Autonomous Truck Dispatching System in Open-pit Mines
Jianlin Tang;Bin Zhao;Chao Yang;Changcheng Zhou;Shichao Chen;Xiaochun Ni;Yunfeng Ai
2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
Dispatching system plays an important role in intelligent mines, and the current research on the dispatching system is mainly aimed at manned vehicles. In the new scenario of unmanned transportation in open-pit mines, traditional dispatching systems are not fully appropriate. To meet the requirements of the unmanned transportation in open-pit mines, a new dispatching system framework being suitabl...Show More
Collide Detection, Reaction and Implementation for Excavator Arm
2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)
Year: 2022 | Conference Paper |
Collision detection and proper motion reaction strategy for avoiding the collision is a common problem for excavator arms to ensure the safety of the surrounding and itself. In this paper, a method consisting two parts with the implementation for the hydraulic excavator was proposed for this problem. Firstly, collision was detected based on forward kinematics of an excavator model. Secondly, ‘enab...Show More
Parallel Driving OS: A Ubiquitous Operating System for Autonomous Driving in CPSS
Long Chen;Yunqing Zhang;Bin Tian;Yunfeng Ai;Dongpu Cao;Fei-Yue Wang
With the rapid development of autonomous driving technologies, a vast array of autonomous driving algorithms and platforms have emerged. These algorithms and platforms are usually designed for different tasks in specific scenarios, which makes them incompatible with each other. To solve the problem, we propose a ubiquitous operating system named Parallel Driving Operating System (PDOS) based on th...Show More
A real-time semantic segmentation method for autonomous driving in surface mine
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)
Year: 2022 | Conference Paper |
Cited by: Papers (7)
Semantic segmentation for perception of autonomous vehicles has made great progress with the development of deep learning technique. However, existing related studies mainly focus on urban scenes, thus semantic segmentation for perception of autonomous mining trucks in surface mine remains to be further explored. Considering the importance of dataset in deep learning technique, we first built a se...Show More
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