Lane Boundary Recognition Device with Adaptive Uncertainty Suppression
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Solution Overview
Problem
Existing lane boundary line recognition devices face challenges in stably suppressing recognition when road conditions are unsuitable and in quickly responding to changes in road environments, such as snowy or gravel roads, leading to inaccurate detections and delayed recognition restarts.
Innovation Solution
A lane boundary line recognition device with a calculation section to assess uncertainty, a learning section to update learning values, a recognition suppression section to halt recognition when uncertainty exceeds a threshold, and a learning resetting section to revert to previous values when environment changes are detected, allowing for stable suppression and quick response to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a filter is used to perform stable recognition operation, then stability of recognition suppression is improved, but response speed to road condition changes deteriorates
Solution Approach 1:
The patent applies dynamics by making the recognition suppression state adjustable and reversible. The learning resetting section dynamically changes the suppression state based on detected road condition changes, allowing the system to transition from a filtered stable state back to an active recognition state when conditions improve, thus resolving the contradiction between stability and response speed.
Solution Approach 2:
The patent changes the parameter of recognition suppression by introducing a learnable threshold that can be adjusted based on road conditions. When road conditions are poor, the threshold increases to suppress recognition; when conditions improve, the threshold decreases to allow recognition again, balancing stability and responsiveness.
2Speed
If instantaneous images are used for judgment, then response speed to road condition changes is improved, but stability of recognition suppression deteriorates
Solution Approach 1:
The patent implements feedback by continuously monitoring road conditions through the in-vehicle camera and adjusting the recognition suppression state based on this feedback. The learning section accumulates information about road conditions and uses this feedback to maintain stable suppression during poor conditions while allowing quick response when conditions improve.
Solution Approach 2:
The patent applies preliminary action by proactively suppressing recognition when poor road conditions are detected, rather than waiting for recognition errors to occur. This prevents unstable or incorrect recognition before they happen, maintaining stability while responding quickly to condition changes.
3Measurement precision
If recognition suppression is performed based on calculated degree of uncertainty, then detection accuracy under poor conditions is improved, but recognition restart delay occurs
Solution Approach 1:
The patent introduces an intermediary mechanism - the learning resetting section - that mediates between the suppression decision (based on uncertainty) and the recognition restart. This intermediary detects road condition changes and triggers the reset, allowing the system to maintain high detection accuracy during suppression while minimizing restart delays when conditions improve.
Solution Approach 2:
The patent replaces a purely mechanical or fixed-threshold suppression system with a learning-based system that uses environmental detection and adaptive threshold adjustment. This substitution allows the system to maintain detection accuracy by using learned patterns of poor conditions while reducing restart delays through intelligent detection of condition improvements.
Data Source
AI summary
In a lane boundary line recognition device, a calculation section calculates a degree of uncertainty which affects a correct recognition of white lines on a roadway of a vehicle. A learning section updates a learning value of the degree of uncertainty. A recognition suppression section suppresses execution of a recognition process of recognizing white lines on the roadway when the updated learning value is more than a threshold value. An environment change judgment section judges whether or not a road environment has changed. A learning resetting section resets the learning value of the degree of uncertainty to a previous learning value when the detection result of the environment change judgment section indicates an occurrence of change of the road environment.