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

Lu, Xiaowei; Ai, Yunfeng; Tian, Bin

doi:10.3390/s20041121

Cited by 20 publications

(4 citation statements)

References 28 publications

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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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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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“…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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“…It divides the road area and off-road area, thus it can constrain the vehicle within appropriate regions, which prevents possible collision and ensures safety. Early works usually detect road boundaries with vehicle-mounted sensors, such as LiDARs and cameras [1], [2]. However, perceiving the accurate location of road boundaries with high robustness is a challenging task due to their irregular long-and-thin shape.…”

Section: Introductionmentioning

confidence: 99%

csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps

Xu¹,

Liu²,

Gan³

et al. 2021

Preprint

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High-Definition (HD) maps can provide precise geometric and semantic information of static traffic environments for autonomous driving. Road-boundary is one of the most important information contained in HD maps since it distinguishes between road areas and off-road areas, which can guide vehicles to drive within road areas. But it is labor-intensive to annotate road boundaries for HD maps at the city scale. To enable automatic HD map annotation, current work uses semantic segmentation or iterative graph growing for road-boundary detection. However, the former could not ensure topological correctness since it works at the pixel level, while the latter suffers from inefficiency and drifting issues. To provide a solution to the aforementioned problems, in this letter, we propose a novel system termed csBoundary to automatically detect road boundaries at the city scale for HD map annotation. Our network takes as input an aerial image patch, and directly infers the continuous road-boundary graph (i.e., vertices and edges) from this image. To generate the city-scale road-boundary graph, we stitch the obtained graphs from all the image patches. Our csBoundary is evaluated and compared on a public benchmark dataset. The results demonstrate our superiority. The accompanied demonstration video is available at our project page https://sites.google.com/view/csboundary/.

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

Cited by 20 publications

References 28 publications

Blockchain and Autonomous Vehicles: Recent Advances and Future Directions

Blockchain and Autonomous Vehicles: Recent Advances and Future Directions

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

csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps

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