Inter-Vehicle Distance Measurement from Partial Vehicle Images
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle detection methods fail to accurately measure inter-vehicle distance when the lower portion of the target vehicle is not included in the driving image, particularly at close distances, posing a risk for autonomous driving and collision detection.
Innovation Solution
A method and apparatus that utilize feature point tracking to detect and measure inter-vehicle distance by identifying feature points in vehicle images, even when the lower portion of the target vehicle is not visible, using machine learning or deep learning for detection and optical flow for tracking, and calculating distance based on feature point changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If shadow-based or learning-based detection methods are used, then detection performance is improved when vehicle lower portion is visible, but detection accuracy deteriorates when the lower portion of the target vehicle is not included in the driving image
Solution Approach 1:
The patent segments the vehicle detection task into two distinct approaches: shadow-based detection for distant vehicles and feature point tracking for close-distance vehicles. This segmentation allows each method to be optimized for its specific operational range, resolving the contradiction between detection accuracy when the vehicle lower portion is visible and detection capability when it is not visible.
Solution Approach 2:
The system dynamically switches between shadow-based detection and feature point tracking methods based on the detected distance to the target vehicle. When the vehicle is detected to be within a predetermined distance, the system transitions to feature point tracking, which does not require the lower portion of the vehicle to be visible, thereby maintaining detection accuracy across varying distances.
2Adaptability or versatility
If feature point tracking is implemented for close-distance detection, then detection capability in partial visibility scenarios is improved, but system complexity increases
Solution Approach 1:
The system employs dynamic method selection, activating feature point tracking only when the target vehicle is detected to be within a predetermined distance. This dynamic approach enables the system to handle close-distance detection scenarios where the lower portion of the vehicle is not visible, while avoiding the complexity of feature point tracking in scenarios where simpler shadow-based detection suffices.
Solution Approach 2:
The patent introduces an intermediary distance detection mechanism that determines when to switch between detection methods. This intermediary layer manages the complexity by automatically selecting the appropriate detection approach based on measured distance, eliminating the need for manual configuration or complex decision-making logic.
3Device complexity
If conventional detection methods are used, then system simplicity is maintained, but inter-vehicle distance measurement accuracy deteriorates at close distances
Solution Approach 1:
The system dynamically adapts its detection methodology based on the distance to the target vehicle. At close distances where conventional methods fail to provide accurate measurements, the system automatically switches to feature point tracking, which maintains measurement precision without requiring the lower portion of the vehicle to be visible.
Solution Approach 2:
The patent changes the detection parameters and methodology based on distance thresholds. When the target vehicle is within a predetermined distance, the system transitions from shadow-based detection to feature point tracking, effectively changing the operational parameters to maintain measurement accuracy in close-proximity scenarios.
Data Source
AI summary
A method for measuring an inter-vehicle distance includes acquiring driving image photographed by a photographing device of a first vehicle which is being driven; detecting a second vehicle from the acquired driving image; detecting first feature points of a second vehicle region in a first frame corresponding to a frame in which the second frame is detected before a frame in which the second vehicle is not detected among a plurality of frames constituting the driving image, when the second vehicle is not detected from the driving image; detecting second feature points in a second frame corresponding to a current frame by tracking the detected first feature points; calculating a feature point change value between the first feature points and the second feature points; and calculating an inter-vehicle distance from the photographing device of the first vehicle to the second vehicle based on the calculated feature point change value.