Near real-time ego-lane detection in highway and urban streets

Duong, Tin Trung; Pham, Cuong Cao; Tran, Tai Huu-Phuong; Nguyên, Tiên Hiêp; Jeon, Jae Wook

doi:10.1109/icce-asia.2016.7804748

Cited by 16 publications

(9 citation statements)

References 10 publications

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“…The transformation matrix H can be calculated using the equation above. The matrix H is then used to map the input images from the first-person to the output of the bird's eye view perspective using a pixel-by-pixel process [25].…”

Section: Bird's Eye View Transformationmentioning

confidence: 99%

Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time Capability

Cakir¹,

Gauß²,

Häppeler³

et al. 2022

Preprint

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Environmental perception is an important aspect within the field of autonomous vehicles that provides crucial information about the driving domain, including but not limited to identifying clear driving areas and surrounding obstacles. Semantic segmentation is a widely used perception method for self-driving cars that associates each pixel of an image with a predefined class. In this context, several segmentation models are evaluated regarding accuracy and efficiency. Experimental results on the generated dataset confirm that the segmentation model FasterSeg is fast enough to be used in realtime on lowpower computational (embedded) devices in self-driving cars. A simple method is also introduced to generate synthetic training data for the model. Moreover, the accuracy of the first-person perspective and the bird's eye view perspective are compared. For a 320 × 256 input in the first-person perspective, FasterSeg achieves 65.44 % mean Intersection over Union (mIoU), and for a 320×256 input from the bird's eye view perspective, FasterSeg achieves 64.08 % mIoU. Both perspectives achieve a frame rate of 247.11 Frames per Second (FPS) on the NVIDIA Jetson AGX Xavier. Lastly, the frame rate and the accuracy with respect to the arithmetic 16-bit Floating Point (FP16) and 32-bit Floating Point (FP32) of both perspectives are measured and compared on the target hardware.

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Section: Bird's Eye View Transformationmentioning

confidence: 99%

Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time Capability

Cakir¹,

Gauß²,

Häppeler³

et al. 2022

Preprint

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show abstract

“…Perspective transform is very useful in transforming a view of a lane from a vehicle to a birds-eye view [10]. This is important for correctly calculating lane curvatures and identification [11].…”

Section: Perspective Transformmentioning

confidence: 99%

Deep vision pipeline for self-driving cars based on machine learning methods

Ahmed¹

2021

Preprint

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The purpose of this thesis project is to design and implement a vision pipeline useful for self-driving cars, based on computer vision methods and deep learning frameworks. This pipeline is useful for identifying the lane, other cars in the view, as well as traffic signs. A final vision pipeline design is proposed that explores a network that can control steering based on vision input. Firstly, the working model of computer vision techniques used are presented. The mathematical models used are explored, and implementation in source code developed. These models comprise the vision side of the pipeline. Secondly, this report explores the deep learning models implemented as part of the pipeline. The mathematical approach is presented as well as the source code implementation. The models are industry and academia proven and their implementation is developed in detail. The final part provides details on full pipeline architecture, and required hardware. A comprehensive discussion is made on the pipeline, the lessons learned, and future work.

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“…Before lane detection, the camera is generally installed inside the front windshield, so the captured images contain information that is not related to the lane, such as sky, vehicles, trees by the roadside, and so on. Through the warp perspective mapping (WPM) [37] of the images collected by the camera, the obtained bird-view images mainly contain the road surface and lane, further, in the bird-view images, the lane appear in the form of parallel lines, which makes it more convenient for subsequent processing.…”

Section: Lane Detection Algorithmmentioning

confidence: 99%

A Fast Learning Method for Accurate and Robust Lane Detection Using Two-Stage Feature Extraction with YOLO v3

Zhang

Yang

Tang

et al. 2018

Sensors

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To improve the accuracy of lane detection in complex scenarios, an adaptive lane feature learning algorithm which can automatically learn the features of a lane in various scenarios is proposed. First, a two-stage learning network based on the YOLO v3 (You Only Look Once, v3) is constructed. The structural parameters of the YOLO v3 algorithm are modified to make it more suitable for lane detection. To improve the training efficiency, a method for automatic generation of the lane label images in a simple scenario, which provides label data for the training of the first-stage network, is proposed. Then, an adaptive edge detection algorithm based on the Canny operator is used to relocate the lane detected by the first-stage model. Furthermore, the unrecognized lanes are shielded to avoid interference in subsequent model training. Then, the images processed by the above method are used as label data for the training of the second-stage model. The experiment was carried out on the KITTI and Caltech datasets, and the results showed that the accuracy and speed of the second-stage model reached a high level.

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Near real-time ego-lane detection in highway and urban streets

Cited by 16 publications

References 10 publications

Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time Capability

Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time Capability

Deep vision pipeline for self-driving cars based on machine learning methods

A Fast Learning Method for Accurate and Robust Lane Detection Using Two-Stage Feature Extraction with YOLO v3

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