Vanishing Point Estimation via Lane Model Intersection
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Solution Overview
Problem
Current autonomous driving systems face challenges in accurately estimating the vanishing point or horizon from image data, which is essential for localization and navigation, due to computational burdens and the brittleness of low-level features in varying environmental conditions.
Innovation Solution
The system estimates the vanishing point or horizon by constructing lane models from detected lane features in images and extending them to find the horizontal line with the maximum number of intersections, reducing computational resources by not directly recognizing the vanishing point or horizon using computer vision techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer vision techniques are used to directly recognize the vanishing point or horizon, then localization accuracy is improved, but computational burden increases
Solution Approach 1:
The patent uses lane models as an intermediary element to bridge the gap between image data and vanishing point estimation. Instead of directly recognizing the vanishing point from image features, the system first constructs lane models from detected lane markings, then extends these models to intersect and determine the vanishing point. This intermediary approach reduces computational complexity while maintaining localization accuracy.
Solution Approach 2:
The system performs preliminary construction of lane models from lane markings before attempting to estimate the vanishing point. By pre-processing the image data to establish lane models and their geometric relationships, the system reduces the computational burden of the subsequent vanishing point calculation, as the heavy lifting of feature extraction and model construction is completed in advance.
2Extent of automation
If low-level features are used for vanishing point estimation, then direct recognition is achieved, but reliability deteriorates in varying environmental conditions
Solution Approach 1:
Instead of directly recognizing the vanishing point from low-level image features (the conventional approach), the patent inverts the methodology by constructing lane models from lane markings and using the geometric relationships of these models to determine the vanishing point. This inversion transforms the problem from direct feature recognition to geometric reasoning, which is more robust to environmental variations.
Solution Approach 2:
Lane models serve as an intermediary that transforms unreliable low-level image features into stable geometric representations. By modeling the lane markings with parametric equations and using their intersections to define the vanishing point, the system creates a more reliable estimation that is less sensitive to changes in lighting, weather, and camera parameters.
3Measurement precision
If complex computer vision algorithms are applied to estimate horizon, then estimation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the horizon estimation problem into distinct components: lane marking detection, lane model construction, model extension, and intersection calculation. By dividing the complex task into modular steps, each with its own specialized algorithm, the system achieves high estimation accuracy while keeping individual components manageable and the overall system architecture clear.
Solution Approach 2:
The lane models act as an intermediary structure that simplifies the relationship between image data and horizon estimation. Rather than directly computing the horizon from raw pixels using complex algorithms, the system uses the parametric lane models as an intermediate representation, which reduces the complexity of the final horizon calculation while maintaining accuracy.
Data Source
AI summary
An approach is provided for estimating a vanishing point or horizon in an image depicting one or more lanes of a roadway. The approach involves processing the image to construct one or more lane models of the one or more road lanes depicted in the image. The approach also involves extending the one or more road lanes through the image using the one or more lane models. The approach further involves determining a horizontal line in the image at which a maximum number of the one or more extended road lanes crosses over a minimum horizontal extent of the horizontal line. The approach further involves designating the horizontal line as the horizon of the image.