Top-down View Classification for Clear Path Detection
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Solution Overview
Problem
Existing vehicle vision-imaging systems for autonomous or semi-autonomous driving face challenges in efficiently distinguishing clear path features from non-clear path features due to intense data processing and difficulty in distinguishing between them.
Innovation Solution
A method utilizing a top-down view classification technique, where an input image is segmented into potential clear path and non-clear path regions, and then classified using a classifier, with additional confidence gained through a top-down view approach focusing on ground plane features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional perspective view image processing is used for clear path detection, then the system can capture the full scene, but the data processing becomes intense and slow due to difficulty in distinguishing clear path features from non-clear path features
Solution Approach 1:
The image processing is segmented into two distinct views: a perspective view for capturing the full scene and a top-down view for analyzing ground plane features. This segmentation allows the system to process different types of information separately, reducing the overall processing complexity and improving detection speed by focusing computational resources on the most relevant features for clear path identification
Solution Approach 2:
The top-down view extraction isolates ground plane features from the full scene by removing non-ground plane elements. This extraction process filters out irrelevant information (non-clear path features) and retains only the essential data needed for clear path detection, thereby reducing data processing intensity while maintaining detection accuracy
2Measurement precision
If traditional perspective view classification is used, then all scene features are available for analysis, but the system cannot effectively distinguish clear path features from non-clear path features
Solution Approach 1:
The system transitions from analyzing only the perspective view to incorporating a top-down view dimension. This dimensional change provides a different perspective on the scene that naturally separates ground plane features (clear path) from non-ground plane features (non-clear path), making feature distinction significantly easier and improving identification accuracy
Solution Approach 2:
The top-down view acts as an intermediary representation that bridges the gap between the captured perspective image and the final clear path classification. By introducing this intermediate top-down representation, the system can more easily identify and distinguish clear path features before mapping the results back to the original perspective view for navigation
Data Source
AI summary
A method of detecting a clear path in a road of travel for a vehicle utilizing a top-down view classification technique. An input image of a scene exterior of the vehicle is captured. The captured input image represents a perspective view of the road of travel. The captured input image is analyzed. A segmented top-down image that includes potential clear path regions and potential non-clear path regions are generated. The segmented top-down image represents a viewing angle perpendicular to a ground plane. The segmented regions of the segmented top-down view are input to a classifier for identifying the clear path regions of travel. The identified clear path regions are utilized for navigating the road of travel.