Tunnel Decision Apparatus Using Vanishing Point Segmentation
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Solution Overview
Problem
Vehicle sensors face challenges in accurately recognizing objects in tunnel environments due to sudden changes in illumination, making it difficult to determine whether a vehicle is entering or exiting a tunnel.
Innovation Solution
A tunnel decision apparatus and method that divides the vehicle's view into non-road and road areas using a horizontal line with a vanishing point, detects lamp and lane patterns, and considers curvature, gradient, and heading direction to determine tunnel entry or exit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle sensors are used to recognize objects in tunnel environments, then object detection capability is provided, but sudden changes in illumination cause imprecise recognition
Solution Approach 1:
The image is divided into road area and non-road area based on vanishing point detection. The non-road area (upper portion) is specifically analyzed for lamp patterns while the road area (lower portion) is analyzed for lane patterns, allowing separate processing of different environmental cues to improve tunnel detection accuracy under varying illumination
Solution Approach 2:
The patent introduces an intermediary decision-making process that combines multiple features (lamp pattern, lane pattern, curvature, gradient, heading direction) to determine tunnel entry/exit. This intermediary analysis layer processes sensor data through multiple verification steps before final tunnel status determination, reducing the impact of illumination changes on direct object recognition
2Measurement precision
If only lamp pattern detection is used to determine tunnel entry, then tunnel detection capability is provided, but detection accuracy is reduced due to false positives from other light sources
Solution Approach 1:
The patent merges multiple detection approaches into a unified tunnel decision system. Lamp pattern detection in the non-road area is combined with lane pattern detection in the road area, along with curvature, gradient, and heading direction analysis. This combination of multiple detection methods increases measurement precision by cross-validating tunnel entry signals while managing complexity through integrated processing
Solution Approach 2:
The system employs feedback mechanisms where the tunnel decision unit continuously evaluates multiple parameters (lamp pattern, lane pattern, curvature, gradient, heading direction) and adjusts tunnel status determination based on the consistency of these signals. This feedback-based decision process reduces false positives by requiring multiple corroborating indicators before confirming tunnel entry
3Productivity
If the image is divided into road area and non-road area using vanishing point, then simultaneous detection of lamp and lane patterns is enabled, but processing complexity increases
Solution Approach 1:
The image is segmented into road area and non-road area using vanishing point detection. This segmentation enables parallel processing of lamp patterns in the non-road area and lane patterns in the road area, improving detection productivity by allowing simultaneous analysis of multiple features without requiring sequential processing of the entire image
Solution Approach 2:
Different processing approaches are applied to different regions: the non-road area receives lamp pattern detection with brightness threshold filtering, while the road area receives lane pattern detection with curvature and gradient analysis. This local quality approach optimizes processing for each region's specific characteristics, improving overall detection efficiency while managing complexity through region-specific algorithms
Data Source
AI summary
The present invention provides a tunnel decision apparatus, including: a camera which outputs an image of a view in front of a vehicle; a road area classifying unit which detects a vanishing point from the image of the view in front of a vehicle to output at least one of a road area and a non-road area; a pattern detecting unit which detects at least one of a lamp pattern from the non-road area and a lane pattern from the road area; and a tunnel decision unit which determines whether the vehicle enters a tunnel in consideration of at least one of the lamp pattern and the lane pattern.