A Lane-Level Road Marking Map Using a Monocular Camera

Jang, Wonje; Hyun, Junhyuk; An, Jhonghyun; Cho, Minho; Kim, Euntai

doi:10.1109/jas.2021.1004293

Cited by 27 publications

(5 citation statements)

References 52 publications

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“…Jang et al [27] used the idea of graph optimization to abstract lane elements into nodes to build lane level HD map. Then in [28], they further abstracted the markings into nodes in the graph and optimized them together with the poses of the vehicle. Qin et al [29] proposed a lightweight and highly feasible method for building HD maps.…”

Section: B Hd Map Constructionmentioning

confidence: 99%

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V2HDM-Mono: A Framework of Building a Marking-Level HD Map with One or More Monocular Cameras

Liu¹,

Zheng²,

Yan³

et al. 2022

Preprint

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Marking-level high-definition maps (HD maps) are of great significance for autonomous vehicles, especially in large-scale, appearance-changing scenarios where autonomous vehicles rely on markings for localization and lanes for safe driving. In this paper, we propose a highly feasible framework for automatically building a marking-level HD map using a simple sensor setup (one or more monocular cameras). We optimize the position of the marking corners to fit the result of marking segmentation and simultaneously optimize the inverse perspective mapping (IPM) matrix of the corresponding camera to obtain an accurate transformation from the front view image to the bird's-eye view (BEV). In the quantitative evaluation, the built HD map almost attains centimeter-level accuracy. The accuracy of the optimized IPM matrix is similar to that of the manual calibration. The method can also be generalized to build HD maps in a broader sense by increasing the types of recognizable markings. The supplementary materials and videos are available at http://liuhongji.site/V2HDM-Mono/.

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Section: B Hd Map Constructionmentioning

confidence: 99%

“…To minimize the displacement of the inverse projected markings, instead of directly fitting the contours [28] after projecting them to the ground plane, we pursue to estimate an optimal homography matrix H and the position of markings' corners at the same time to minimize the projection error.…”

Section: Optimizationmentioning

confidence: 99%

V2HDM-Mono: A Framework of Building a Marking-Level HD Map with One or More Monocular Cameras

Liu¹,

Zheng²,

Yan³

et al. 2022

Preprint

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show abstract

“…Accordingly, awareness of road markings has enabled the derivation of various driving scenarios and prediction of vehicle driving movements. Studies [12][13][14] are being conducted on various road marking datasets enabling road and lane information for autonomous vehicles. However, the datasets constructed to date mainly include directions of straight, left, or right turns and do not include detailed directions information for marking merging sections, which limits the ability to derive diversified associative relationships with other objects.…”

Section: Introductionmentioning

confidence: 99%

DataSet for Unflappable Driving : UNFLAPSet

Kim,

Kang,

Yoon

2024

Preprint

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Autonomous vehicles must be aware of dynamic and static objects, road lanes, road signs and road markings. Recent autonomous vehicles awareness studies adaptable to various road environments continues, it is necessary to construct datasets that accurately reflect the real driving environment. The existing datasets consist of annotations that focus on dynamic and static objects, lanes, and road signs in the driving environment. These annotations enable management of object distance and avoidance, lane recognition and maintenance and awareness of road signs. Although road markings on the road surface provide the traffic regulations and guidance for driving lanes in the driving environment, there is a lack of the road marking datasets containing various types of directions and regulations. When driving without recognizing road markings, difficulty in recognizing lane information poses challenges in determining the appropriate lanes for the driving route and the limitation in predicting the movement of surrounding cars presents difficulties in maintaining stable driving responses. This paper presents a road marking dataset, UNFLAPSet(UNFLAPpable Set). UNFLAPSet is able to be aware of multidimensional information by including the various types of direction and regulations compared to existing road marking datasets. UNFLAPSet consists of three classes - 1. Driving Direction Centric class(DDCclass) 2. Capable of Lane Maneuver class(CLMclass) 3. Specific Condition Caution class(SCCclass), which are based on the meaning of road markings for emphasizing the primary implication of labels unlike the existing road marking datasets. Especially, Merge Line, Merge Arrow and Uturn Dot Line of CLMclass enable the prediction of surrounding vehicles’ movement and subsequently allow for stable responses. Furthermore, restricted direction road markings of SCCclass helps mitigate the risk of accidents caused by driving in unsuitable lanes, thereby facilitating the maintenance of smooth traffic flow. The validation results of UNFLAPSet showed high recognition accuracy for each label, enabling predictable driving integrated perception of driving lanes and surrounding lanes based on road surface marking recognition using UNFLAPSet.

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“…In addition, they evaluate the proposed model for semantic segmentation and object detection. Road marking is an essential requirement of the self-driving car; in [21], Jang et al built a lane-level road marking segmentation using a monocular camera. Though the camera is the most commonly used sensor for collecting high-resolution visual data for the perception of autonomous vehicles, it has some limitations.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Point Clouds and Images for Autonomous Vehicles: Trends, Challenges, and Opportunities

Munia,

Kunjumon,

Khosravi

et al. 2024

Preprint

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A Lane-Level Road Marking Map Using a Monocular Camera

Cited by 27 publications

References 52 publications

V2HDM-Mono: A Framework of Building a Marking-Level HD Map with One or More Monocular Cameras

V2HDM-Mono: A Framework of Building a Marking-Level HD Map with One or More Monocular Cameras

DataSet for Unflappable Driving : UNFLAPSet

Semantic Segmentation of Point Clouds and Images for Autonomous Vehicles: Trends, Challenges, and Opportunities

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