3D Lane Generation Using Neural Depth and Spatial Offsets
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Solution Overview
Problem
Existing deep learning-based lane recognition technologies struggle to accurately identify and generate three-dimensional lane information in diverse road environments, lacking effective methods for integrating topographical features and depth information.
Innovation Solution
A neural network-based approach that generates lane probability maps, feature information, depth distributions, and spatial information to produce accurate three-dimensional lane data by applying multiple neural networks for lane feature extraction, depth estimation, and spatial transformation using calibration parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning-based lane recognition is applied to diverse road environments, then lane recognition potential increases, but accuracy in identifying topographical features deteriorates
Solution Approach 1:
The patent transforms 2D lane detection into 3D lane information by introducing depth estimation through neural networks. The system generates three-dimensional lane data including height and distance information, moving from planar image space to volumetric space to capture topographical features accurately
Solution Approach 2:
The patent divides the lane detection task into multiple independent neural network components: one network for lane probability mapping, another for depth estimation, and a third for 3D lane generation. This segmentation allows each component to specialize in specific features while maintaining overall system adaptability
2Measurement precision
If multiple neural networks are used for feature extraction and depth estimation, then lane recognition accuracy improves, but system complexity increases
Solution Approach 1:
The patent combines multiple neural network outputs (lane probability maps and depth estimates) into a unified 3D lane representation. By merging these separate feature extractions into a single volumetric output, the system achieves high accuracy while managing complexity through integrated processing
Data Source
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AI summary
A method of generating lane information using a neural network includes generating a lane probability map based on an input image, generating lane feature information and depth feature information by applying the lane probability map to a second neural network, generating depth distribution information by applying the depth feature information to a third neural network, generating spatial information based on the lane feature information and the depth distribution information, generating offset information including a displacement between a position of a lane and a reference line by applying the spatial information to a fourth neural network, and generating three-dimensional (3D) lane information using the offset information.