Jiahuan Zhang - IEEE Xplore Author Profile

Scheduled Maintenance on Monday 1/13/2025

Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.

IEEE.org
IEEE Xplore
IEEE SA
IEEE Spectrum
More Sites

- Donate
- Personal Sign In

Access provided by:

MIT Libraries

Access provided by:

MIT Libraries

ADVANCED SEARCH

Author details

Jiahuan Zhang

Publications

2

Citations

4

Publications by Year

20102024

Co-Authors:

Dongfang ChenMenghan LiHengzhen LiuZongqian ZhanYi Zhang

Jiahuan Zhang

Affiliation

School of Geodesy and Geomatics

Wuhan University

Wuhan, China

Publication Topics

Ablation Experiments,
Bounding Box,
Bounding Box Regression,
Complexity Cost,
Convolutional Neural Network,
Detection Model,
Distance Values,
Droplet Motion,
Droplet Shape,
Empirical Model,
Feature Extraction Backbone,
Feature Fusion

Biography

Jiahuan Zhang is currently working toward the master's degree in photogrammetry and remote sensing with the School of Geodesy and Geomatics, Wuhan University, Wuhan, China.,His research interests include deep learning-based object detection/semantic segmentation of remote-sensing images, image retrieval of crowd-source images, and detection of collapsed buildings in UAV images.(Based on document published on 28 November 2023).

Publications

2

Citations

4

Publications by Year

20102024

Co-Authors:

Dongfang Chen
Menghan Li
Hengzhen Liu
Zongqian Zhan
Yi Zhang

Author's Published Works

Search History

Showing 1-25 of 57 results

Conferences (33)

Journals (21)

Early Access Articles (2)

Magazines (1)

Sort

Filter Results

Show

Subscribed Content

Open Access Only

Range
Single Year
Yunfeng Ai(56)
Bin Tian(29)
Long Chen(26)
Fei-Yue Wang(11)
Ruiqi Song(11)
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China(25)
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China(13)
Waytous Inc., Qingdao, China(12)
University of Chinese Academy of Sciences, Beijing, China(6)
Artificial Intelligence Department, University of Chinese Academy of Sciences, Beijing, China(6)
IEEE Transactions on Intelligent Vehicles(15)
2019 Chinese Automation Congress (CAC)(4)
2006 2nd IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications(2)
2012 15th International IEEE Conference on Intelligent Transportation Systems(2)
2018 IEEE Intelligent Vehicles Symposium (IV)(2)
IEEE(57)
Beijing, China(8)
Hangzhou, China(4)
Anchorage, AK, USA(2)
Auckland, New Zealand(2)
Bilbao, Spain(2)
Autonomous Vehicles(18)
Point Cloud(16)
Open-pit(11)
Mining Area(10)
Artificial Systems(7)

Select All on Page

Sort By

Results

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

IEEE Intelligent Transportation Systems Magazine

Year: 2025 | Volume: 17, Issue: 1 | Magazine Article |

HTML

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

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

IEEE Intelligent Transportation Systems Magazine

Year: 2025 | Volume: 17, Issue: 1 | Magazine Article |

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 |

HTML

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

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 |

Recent Advances in Planning for Autonomous Driving and Mining Trucks: A Survey

Jun Fu;Bin Tian;Xiaoyu Xiong;Shi Meng;Yunfeng Ai;Long Chen

2024 China Automation Congress (CAC)

Year: 2024 | Conference Paper |

HTML

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

Recent Advances in Planning for Autonomous Driving and Mining Trucks: A Survey

Jun Fu;Bin Tian;Xiaoyu Xiong;Shi Meng;Yunfeng Ai;Long Chen

2024 China Automation Congress (CAC)

Year: 2024 | Conference Paper |

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 |

HTML

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

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 |

Active Parallel Teacher for Human-in-the-Loop Sim2Real Object Detection in Autonomous Haulage Trucks

Lunfeng Guo;Yinan Guo;Yunfeng Ai;Shirong Ge

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

Active Parallel Teacher for Human-in-the-Loop Sim2Real Object Detection in Autonomous Haulage Trucks

Lunfeng Guo;Yinan Guo;Yunfeng Ai;Shirong Ge

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Early Access Article |

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

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Volume: 9, Issue: 5 | Journal Article |

HTML

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

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

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Volume: 9, Issue: 5 | Journal Article |

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

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2024 | Volume: 54, Issue: 12 | Journal Article |

HTML

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

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

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2024 | Volume: 54, Issue: 12 | Journal Article |

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

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Early Access Article |

Cited by: Papers (3)

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

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

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Early Access Article |

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

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Volume: 9, Issue: 3 | Journal Article |

Cited by: Papers (3)

HTML

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

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

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Volume: 9, Issue: 3 | Journal Article |

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

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Volume: 9, Issue: 2 | Journal Article |

Cited by: Papers (2)

HTML

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

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

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Volume: 9, Issue: 2 | Journal Article |

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

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Volume: 9, Issue: 1 | Journal Article |

Cited by: Papers (11)

HTML

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

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

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Volume: 9, Issue: 1 | Journal Article |

UAV Path Planning With Terrain Constraints for Aerial Scanning

Jinbiao Yuan;Zhenbao Liu;Xiaoyu Xiong;Yunfeng Ai;Long Chen;Bin Tian

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Volume: 9, Issue: 1 | Journal Article |

Cited by: Papers (3)

HTML

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

UAV Path Planning With Terrain Constraints for Aerial Scanning

Jinbiao Yuan;Zhenbao Liu;Xiaoyu Xiong;Yunfeng Ai;Long Chen;Bin Tian

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Volume: 9, Issue: 1 | Journal Article |

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

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Volume: 9, Issue: 1 | Journal Article |

Cited by: Papers (7)

HTML

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

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

IEEE Transactions on Intelligent Vehicles

Year: 2024 | Volume: 9, Issue: 1 | Journal Article |

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 |

HTML

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

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 |

BEVMOT: A Multi-Object Detection and Tracking Method in Bird's-Eye-View via Spatiotemporal Transformers

Chenglin Cui;Ruiqi Song;XinQing Li;Yunfeng Ai

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2023 | Conference Paper |

HTML

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

BEVMOT: A Multi-Object Detection and Tracking Method in Bird's-Eye-View via Spatiotemporal Transformers

Chenglin Cui;Ruiqi Song;XinQing Li;Yunfeng Ai

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2023 | Conference Paper |

Scenarios Engineering driven Autonomous Transportation in Open-Pit Mines

Siyu Teng;Xuan Li;Yuchen Li;Lingxi Li;Yunfeng Ai;Long Chen

2023 IEEE 3rd International Conference on Digital Twins and Parallel Intelligence (DTPI)

Year: 2023 | Conference Paper |

Cited by: Papers (4)

HTML

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

Scenarios Engineering driven Autonomous Transportation in Open-Pit Mines

Siyu Teng;Xuan Li;Yuchen Li;Lingxi Li;Yunfeng Ai;Long Chen

2023 IEEE 3rd International Conference on Digital Twins and Parallel Intelligence (DTPI)

Year: 2023 | Conference Paper |

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

IEEE Transactions on Intelligent Vehicles

Year: 2023 | Volume: 8, Issue: 10 | Journal Article |

Cited by: Papers (7)

HTML

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

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

IEEE Transactions on Intelligent Vehicles

Year: 2023 | Volume: 8, Issue: 10 | Journal Article |

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

IEEE Transactions on Intelligent Vehicles

Year: 2023 | Volume: 8, Issue: 6 | Journal Article |

Cited by: Papers (247)

HTML

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

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

IEEE Transactions on Intelligent Vehicles

Year: 2023 | Volume: 8, Issue: 6 | Journal Article |

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

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2023 | Volume: 53, Issue: 4 | Journal Article |

Cited by: Papers (14)

HTML

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

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

IEEE Transactions on Systems, Man, and Cybernetics: Systems

Year: 2023 | Volume: 53, Issue: 4 | Journal Article |

Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving

Siyu Teng;Long Chen;Yunfeng Ai;Yuanye Zhou;Zhe Xuanyuan;Xuemin Hu

IEEE Transactions on Intelligent Vehicles

Year: 2023 | Volume: 8, Issue: 1 | Journal Article |

Cited by: Papers (70)

HTML

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

Hierarchical Interpretable Imitation Learning for End-to-End Autonomous Driving

Siyu Teng;Long Chen;Yunfeng Ai;Yuanye Zhou;Zhe Xuanyuan;Xuemin Hu

IEEE Transactions on Intelligent Vehicles

Year: 2023 | Volume: 8, Issue: 1 | Journal Article |

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

IEEE Transactions on Intelligent Vehicles

Year: 2023 | Volume: 8, Issue: 3 | Journal Article |

Cited by: Papers (17)

HTML

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

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

IEEE Transactions on Intelligent Vehicles

Year: 2023 | Volume: 8, Issue: 3 | Journal Article |

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)

HTML

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

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 |

Collide Detection, Reaction and Implementation for Excavator Arm

Chao Yang;Houxue Ma;Bin Zhao;Ruiqi Song;Yunfeng Ai

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)

Year: 2022 | Conference Paper |

HTML

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

Collide Detection, Reaction and Implementation for Excavator Arm

Chao Yang;Houxue Ma;Bin Zhao;Ruiqi Song;Yunfeng Ai

2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)

Year: 2022 | Conference Paper |

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

IEEE Transactions on Intelligent Vehicles

Year: 2022 | Volume: 7, Issue: 4 | Journal Article |

Cited by: Papers (98)

HTML

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

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

IEEE Transactions on Intelligent Vehicles

Year: 2022 | Volume: 7, Issue: 4 | Journal Article |

A real-time semantic segmentation method for autonomous driving in surface mine

Qinggong Wei;Ruiqi Song;Xue Yang;Yunfeng Ai

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2022 | Conference Paper |

Cited by: Papers (7)

HTML

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

A real-time semantic segmentation method for autonomous driving in surface mine

Qinggong Wei;Ruiqi Song;Xue Yang;Yunfeng Ai

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

Year: 2022 | Conference Paper |

IEEE Personal Account

Change username/password

Purchase Details

Payment Options
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

Follow

About IEEE Xplore | Contact Us | Help | Accessibility | Terms of Use | Nondiscrimination Policy | IEEE Ethics Reporting | Sitemap | IEEE Privacy Policy

A public charity, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.

© Copyright 2025 IEEE - All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies.

IEEE Account

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests

Need Help?

US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support

About IEEE Xplore
Contact Us
Help
Accessibility
Terms of Use
Nondiscrimination Policy
Sitemap
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Test Whats new message.