Mono Vision Road Profiling for Vehicle Distance and Width Estimation
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Solution Overview
Problem
Existing methods for estimating the distance and width of a vehicle in front of an ego car using a monocular camera are prone to errors when assumptions about vehicle width and road flatness are violated.
Innovation Solution
A method that calculates the road profile from a video sequence using the camera's mounting position and angles, wheel suspension offsets, and visual odometry, without relying on external sensors, to accurately estimate the distance and width of a vehicle by projecting image pixels onto the calculated road profile.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning is used to train a network to estimate distance and width, then estimation accuracy improves, but device complexity and computational requirements increase
Solution Approach 1:
The patent replaces complex machine learning systems with a geometric computation approach based on camera motion profiles and road profile calculations. Instead of using trained neural networks to estimate distance and width, the system uses mathematical projections of pixel coordinates onto the calculated road profile, substituting mechanical/computational complexity with geometric principles.
2Ease of manufacture
If assumptions about typical vehicle width and flat road are made, then computation is simplified, but measurement accuracy deteriorates when assumptions are violated
Solution Approach 1:
The patent makes the road profile dynamic by calculating it from actual camera motion data rather than assuming a static flat road. The road profile is continuously updated based on the camera's movement through the video sequence, allowing the system to adapt to varying road conditions while maintaining computational efficiency through geometric projections.
Data Source
Figure 1
Figure 2~3
AI summary
A mono vision system (10) for a motor vehicle comprises a mono camera (12) adapted to capture images from a surrounding of the motor vehicle, and a processing device (14) adapted detect object candidates in the surrounding of the motor vehicle by processing images captured by said mono camera (12). The processing device (14) is adapted to perform the following processing with respect to a particular detected object candidate: from a plurality of images spanning a period of time between a time frame m where the object candidate (36) is visible by the camera (12) and a time frame n where the position where the object (31) corresponding to the object candidate (36) was at time m has been reached or passed by the motor vehicle (30), calculate a camera (12) motion profile C(x) between time frame m and time frame n using a visual odometry algorithm; and after time frame n, calculate a road profile R(x) between time frames m and n from said calculated camera (12) motion profile C(x).