Real-Time Mine Road Boundary Detection and Tracking for Autonomous Truck

Lu, Xiaowei; Ai, Yunfeng; Tian, Bin

doi:10.3390/s20041121

Cited by 23 publications

(6 citation statements)

References 28 publications

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“…Xiao et al [16] designed RATT-UNet network to extract open-pit mine roads information. Liu et al [17] used point cloud data to track mine road.…”

Section: Open-pit Mining Perceptionmentioning

confidence: 99%

MineSDS: A Unified Framework for Small Object Detection and Drivable Area Segmentation for Open-Pit Mining Scenario

Liu¹,

Cheng²,

Huang³

et al. 2023

Sensors

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To tackle the challenges posed by dense small objects and fuzzy boundaries on unstructured roads in the mining scenario, we proposed an end-to-end small object detection and drivable area segmentation framework for open-pit mining. We employed a convolutional network backbone as a feature extractor for both two tasks, as multi-task learning yielded promising results in autonomous driving perception. To address small object detection, we introduced a lightweight attention module that allowed our network to focus more on the spatial and channel dimensions of small objects without impeding inference time. We also used a convolutional block attention module in the drivable area segmentation subnetwork, which assigned more weight to road boundaries to improve feature mapping capabilities. Furthermore, to improve our network perception accuracy of both tasks, we used weighted summation when designing the loss function. We validated the effectiveness of our approach by testing it on pre-collected mining data which were called Minescape. Our detection results on the Minescape dataset showed 87.8% mAP index, which was 9.3% higher than state-of-the-art algorithms. Our segmentation results surpassed the comparison algorithm by 1 percent in MIoU index. Our experimental results demonstrated that our approach achieves competitive performance.

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“…Xiao et al [16] designed RATT-UNet network to extract open-pit mine roads information. Liu et al [17] used point cloud data to track mine road.…”

Section: Open-pit Mining Perceptionmentioning

confidence: 99%

MineSDS: A Unified Framework for Small Object Detection and Drivable Area Segmentation for Open-Pit Mining Scenario

Liu¹,

Cheng²,

Huang³

et al. 2023

Sensors

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“…Then, the false facts are filtered depending on whether they are inside and outside the lane. Xiaowei Lu [111] suggested an unstructured road curb detection method using a 3D-LiDAR sensor. The road boundary detection algorithm extracts the spatial distance and angular features that remove cloud points above ground.…”

Section: A Short Range Target Identification and Trackingmentioning

confidence: 99%

Blockchain and Autonomous Vehicles: Recent Advances and Future Directions

et al. 2021

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Blockchain is an underlying technology for securing many real-time applications and their data. The automobile is one such sector in which auto-manufacturers are looking forward to accepting the advantages of distributed ledger technology in autonomous vehicles or systems and improving their products, customer satisfaction, and other valuable experiences. This work aims to find the significance of blockchain technology in Autonomous Vehicles, including Autonomous Electric Vehicles (AEV), Autonomous Underwater Vehicles (AUV), Autonomous Guided Vehicles (AGV), Autonomous Aerial Vehicles (AAeV), and Autonomous Driving. In this work, a comparative analysis of blockchain-integrated autonomous vehicle systems is explored to identify the present scenario and futuristic challenges. In addition to blockchain technology, the uses and importance of sensors, architectures and infrastructure requirements, vehicle types, driving modes, vehicles target and tracking approaches, intelligent contracts, intelligent data handling, and industry-specific use cases are also explored. This study is based on the exploration of recent technologies and practices. As autonomous vehicles are expected to be the future of intelligent transportation, this paper surveys recent advances in autonomous vehicles and systems and how blockchain can help in improving user experiences and improving industry practices. Finally, limitations of work, future research directions, and challenges associated with different autonomous vehicles and systems are presented.

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“…However, part of the three-dimensional spatial information disappeared [8][9][10][11] . The grid-based methods reduce data dimensionality, and the accuracy of the road extraction could reach 95.61% in a city scenario [12][13][14][15][16][17] . Due to the availability of regular data, the voxel-based road extraction methods require few calculations with limited adaptability, while their accuracy could exceed 93% [18,19] .…”

Section: Introduction mentioning

confidence: 99%

Extraction of straight field roads between farmlands based on agricultural vehicle-mounted LiDAR

Yang

Liang

et al. 2022

International Journal of Agricultural and Biological Engineering

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The application of autonomous agricultural vehicles is gaining popularity as a way to increase production efficiency and lower operational costs. To achieve high performance, perception tasks (such as obstacle detection, road extraction, and drivable area extraction) are of great importance. Compared with structured roads, field roads between farmlands, including unstructured roads and semi-structured roads, are unfavorable for autonomous agricultural vehicle driving due to their bumpiness and unstructured nature. This study proposed an extraction method for the straight field roads between farmlands. The proposed method was based on the point cloud data acquired by LiDAR (Velodyne VLP-16) mounted on a John Deere 1204 6B-1204 tractor. The proposed method has three aspects: Euclidean Clustering-based extraction, boundary-based extraction, and road point cloud curve segment modification. Firstly, Euclidean Clustering with K-Dimensional (KD)-Tree data structure was adopted to extract the road curve segments close to the LiDAR composed of road points. Secondly, the boundary lines constraint was constructed to extract the distant road curve segments. Thirdly, the local distance ratio was used to modify the extracted road curve segments. The average extraction accuracy for both semi-structured and unstructured roads exceeded 98%, and the false positive rate (FPR) was less than 0.5%. These experimental findings demonstrated that the proposed road extraction method was precise and effective. The proposed method of this study can be applied to enhance the perception ability of autonomous agricultural vehicles thereby increasing the efficiency and safety of field road driving.

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Real-Time Mine Road Boundary Detection and Tracking for Autonomous Truck

Cited by 23 publications

References 28 publications

MineSDS: A Unified Framework for Small Object Detection and Drivable Area Segmentation for Open-Pit Mining Scenario

MineSDS: A Unified Framework for Small Object Detection and Drivable Area Segmentation for Open-Pit Mining Scenario

Blockchain and Autonomous Vehicles: Recent Advances and Future Directions

Extraction of straight field roads between farmlands based on agricultural vehicle-mounted LiDAR

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