Vehicle Parking Frame Estimation via White Line Orientation
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Solution Overview
Problem
Existing vehicle parking assistance systems are inefficient in quickly and reliably estimating parking frames on a road surface due to time-consuming pattern matching and limitations in detecting white line widths.
Innovation Solution
An image processing device that includes a marker detector to identify white lines, a parking frame detector to compute adjacent markers, and a shape estimator to determine the extending directions of white lines, allowing for rapid and accurate estimation of parking frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pattern matching is used to estimate parking frames by patterning the extending directions of white lines at corner portions, then the parking frame can be detected, but it takes a considerable amount of time for the pattern matching
Solution Approach 1:
The parking frame detection process is segmented into three independent modules: marker detector (detects markers including white lines), parking frame detector (computes adjacent markers and detects parking frames based on distances), and shape estimator (detects extending directions of white lines and estimates shape). This segmentation allows each module to perform a specific function efficiently, avoiding the time-consuming full pattern matching while maintaining detection accuracy.
Solution Approach 2:
The marker detector first detects markers including white lines extending in two directions before the parking frame detection. This preliminary detection of markers and their positions provides foundation data for the subsequent parking frame detector and shape estimator, enabling faster processing by avoiding redundant detection steps.
2Measurement precision
If pattern matching is used to estimate parking frames, then the parking frame can be detected, but there is a certain limit to the widths of the white lines that can be detected
Solution Approach 1:
The shape estimator detects the extending directions of white lines and uses this information to estimate the shape of the parking frame. By changing from fixed pattern matching to parameter-based estimation (using extending directions and distances), the system can adapt to various white line widths without being constrained by predetermined patterns.
Solution Approach 2:
The system transitions from static pattern matching to dynamic parameter-based detection. The marker detector, parking frame detector, and shape estimator work dynamically with detected parameters (positions, distances, extending directions) rather than fixed patterns, enabling the system to adapt to different white line widths and parking frame configurations.
Data Source
AI summary
An image processing device includes a marker detector configured to detect markers including white lines extending in two directions on a road surface based on an image signal from an imager that takes an image of the road surface around a vehicle, a parking frame detector configured to compute adjacent markers on the road surface among the detected markers, and to detect a parking frame defined by the adjacent markers based on a distance between the adjacent markers, and a shape estimator configured to detect extending directions of the white lines of the markers that are included in the detected parking frame, and to estimate a shape of the parking frame based on the extending directions of the detected white lines.