Road Incline Determination via Non-linear Path Line Interpolation
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Solution Overview
Problem
Current methods for determining relative road incline using camera images are inaccurate and susceptible to errors, especially when relying on differences in road markings at limited distances, and alternative methods like radar or lidar are costly.
Innovation Solution
The method involves interpolating road path lines in a non-linear manner relative to the horizon ahead, calculating the distance between the current and ahead horizons to derive the relative incline, using techniques like the generalized Hough transform, and optionally correcting for vehicle pitch using sensors or map data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If road incline is determined using map data, then the method is simple, but the accuracy is insufficient and data is not available for all roads
Solution Approach 1:
The patent introduces road path lines (markings) as an intermediary element to determine road incline. Instead of relying on map data or direct horizon comparison, the system uses the geometric properties of road markings captured in images as a mediator to calculate incline angles, achieving both accuracy and broader applicability
Solution Approach 2:
The patent replaces traditional mechanical/instrumental methods (radar, lidar, horizon-based camera methods) with a computational image processing approach. By substituting physical measurement systems with algorithmic analysis of road marking geometry, the system achieves accurate incline measurement without expensive hardware
2Measurement precision
If radar or lidar is used to determine distances, then measurement precision is high, but device complexity and cost increase significantly
Solution Approach 1:
The patent creates a 2D geometric model (copy) of the 3D road scene by analyzing road marking patterns in camera images. Instead of using expensive 3D sensing hardware, the system reconstructs distance and incline information from 2D image data through mathematical modeling of perspective projection and road marking geometry
Solution Approach 2:
The patent substitutes radar and lidar systems with a computer vision-based approach. By replacing active sensing hardware (radar/lidar) with passive optical sensing and computational geometry, the system achieves comparable measurement precision without the associated complexity and cost
3Device complexity
If horizon distance is determined directly from camera images, then device complexity is low, but measurement precision deteriorates
Solution Approach 1:
The patent introduces road path lines as an intermediary reference system. Instead of directly measuring horizon distance from camera images (which is imprecise), the system uses the known geometric properties of road markings as a mediator to indirectly determine distance and incline with higher accuracy
Solution Approach 2:
The patent transitions from 2D image space to 3D geometric interpretation by modeling road markings in three-dimensional space. By analyzing the perspective projection of 3D road markings onto the 2D image plane and reversing this transformation through geometric calculations, the system extracts accurate distance and incline information
4Ease of manufacture
If linear interpolation of road path lines is used, then calculation is simple, but the result is affected by vehicle pitch
Solution Approach 1:
The patent applies non-linear (curved) interpolation to road path lines instead of straight-line interpolation. By modeling the natural curvature and perspective convergence of road markings, the method accurately accounts for perspective distortion without being affected by vehicle pitch, while maintaining computational feasibility
Solution Approach 2:
The patent changes the interpolation parameter from linear distance to angular/perspective-based parameters. By transforming the interpolation approach to work with angular relationships and perspective geometry rather than simple linear distances, the method becomes invariant to vehicle pitch while remaining computationally tractable
Data Source
AI summary
A method for determining an incline in a road that is some distance ahead of a motor vehicle relative to the section of road that is currently being driven on by the motor vehicle, wherein images of the road that is ahead of the motor vehicle are recorded by a camera, road path lines are identified ahead of the motor vehicle in the recorded images and the relative incline is calculated with reference to the differences in the courses of the road path lines at different distances.