Adaptive Cruise Control Tar Strip Detection
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Solution Overview
Problem
Current driver assistance systems face challenges in accurately distinguishing tar strips or joints on roadways from regular road markings, especially under varying lighting conditions and vehicle movement, which can lead to misinterpretation and compromised safety.
Innovation Solution
A method for evaluating sensor images based on brightness, geometric extent, contrast, and reflectivity to differentiate tar strips from road markings by calculating the width and length of irregularities, using sub-pixel measurements and look-up tables for enhanced accuracy, and considering exposure states and light angles to correctly identify tar strips.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brightness-based evaluation is used to distinguish tar strips from road markings, then detection accuracy improves, but reliability deteriorates under varying lighting conditions
Solution Approach 1:
The patent changes the evaluation parameters from simple brightness values to multiple parameters including brightness, geometric extent (width, length, area), contrast, and reflectivity. This multi-parameter approach allows the system to distinguish tar strips from road markings more reliably under varying lighting conditions by considering the physical properties of the road surface irregularities rather than relying solely on brightness intensity.
Solution Approach 2:
The patent introduces look-up tables as an intermediary component that stores pre-calculated relationships between brightness values, geometric extents, and road surface characteristics. These look-up tables serve as a reference system that mediates between the raw sensor data and the final classification, enabling consistent identification of tar strips regardless of lighting variations by comparing against stored reference patterns.
2Measurement precision
If sub-pixel measurements are used to calculate width of irregularities, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical or hardware-based measurement systems with computational image processing methods. Sub-pixel measurements are achieved through algorithmic analysis of pixel intensity gradients and edge detection in the sensor images, rather than through physical measurement devices. This substitution of computational methods for mechanical systems achieves high precision while avoiding the complexity of specialized hardware.
3Measurement precision
If multiple parameters (brightness, geometric extent, contrast, reflectivity) are evaluated, then detection accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing reference data in look-up tables during system initialization or offline processing. These pre-computed references include expected brightness patterns, geometric extent ranges, contrast values, and reflectivity characteristics for different road surface features. During actual operation, the system quickly compares sensor data against these pre-established references, significantly reducing real-time processing requirements while maintaining high detection accuracy.
Data Source
Figure 1~2
AI summary
The invention relates to a method and a system for evaluating sensor images of an image-evaluating adaptive cruise control system on a moving support, especially a vehicle (1) which moves on a roadway (2). According to the invention, irregularities, especially tar strips (8) or tar joints on the roadway (2) are evaluated due to their geometric shapes, their brightness, their contrast and/or their reflectivity to distinguish them from markings (7) relating to the predetermined trajectory.