Road Curvature Detection Using Gradient-Aware Method Selection
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Solution Overview
Problem
Conventional road curvature detection devices face challenges in accurately distinguishing between shape variations of lane boundary lines and road curvature due to varying road gradients, leading to reduced detection accuracy.
Innovation Solution
A road curvature detection device equipped with a computer system that includes an image acquiring section, estimation section, information acquiring section, accuracy detection section, gradient judgment section, and calculation section, which uses special detection methods based on gradient accuracy to suppress the influence of road gradients, selecting appropriate methods to calculate curvature accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional curvature detection methods are used on roads with gradients, then the detection process remains simple, but the detection accuracy deteriorates due to inability to distinguish lane boundary shape variations from road curvature
Solution Approach 1:
The system performs preliminary detection of road gradients using inclination sensors before curvature detection. This preliminary information about road gradient is then used to correct and compensate for gradient-induced distortions in the curvature detection process, thereby improving accuracy without requiring complete system redesign
Solution Approach 2:
The patent introduces an intermediary correction mechanism that uses detected road gradient information as a mediator to adjust the curvature detection algorithm. This intermediary step allows the system to account for gradient effects and improve curvature measurement accuracy without fundamentally changing the core detection methodology
2Measurement precision
If multiple special detection methods are selected based on gradient accuracy, then detection precision improves, but computational complexity and processing time increase
Solution Approach 1:
The system dynamically selects from multiple detection methods based on real-time assessment of gradient accuracy. Rather than using a fixed method, the system adapts its detection approach according to the reliability of available gradient information, optimizing the balance between precision and processing efficiency for each specific driving condition
Solution Approach 2:
The patent changes the parameter of detection method selection based on gradient accuracy parameters. When gradient accuracy is high, more sophisticated gradient-compensated methods are employed; when accuracy is low, simpler methods are used to avoid excessive processing time, thus dynamically adjusting detection precision based on information quality
3Adaptability or versatility
If gradient information with low accuracy is used to suppress gradient influence, then curvature calculation error increases, but using no gradient information reduces detection adaptability
Solution Approach 1:
The system incorporates feedback mechanisms that assess the accuracy of gradient information before using it in curvature detection. Based on this feedback about gradient information quality, the system determines whether to apply gradient compensation and selects appropriate detection methods, thereby adapting to varying information quality without compromising accuracy
Solution Approach 2:
The patent applies partial gradient compensation based on the assessed accuracy of gradient information. Rather than always applying full compensation or never compensating, the system applies gradient correction to the extent that the gradient information reliability warrants, optimizing the balance between adaptability and precision
Data Source
AI summary
An in-vehicle system as a road curvature detection device calculates a curvature of a road in front of a vehicle based on an acquired front scene image. The in-vehicle system receives gradient information from the data map. The gradient information corresponds to a current road section on the road on which the vehicle drives. The in-vehicle system detects a gradient accuracy of the received gradient information. When the current road section has a gradient, i.e. the road is an uphill or downhill road, the in-vehicle system selects an appropriate special detection methods based on the gradient accuracy of the received gradient information, and calculates a road curvature by using the selected special detection method. Each of the special detection methods calculates a road curvature while effectively suppressing influence of a road gradient indicated by the received gradient information.