Vehicle Road Surface Model Gradient Handling
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Solution Overview
Problem
Existing vehicle external environment recognition systems face challenges in accurately determining road surfaces, leading to potential misidentification of three-dimensional objects, especially when gradients change or snow cover is present, which can result in incorrect extraction of three-dimensional objects.
Innovation Solution
The system employs a vehicle external environment recognition apparatus with a road surface determination processor that generates first and second road surface models, and a three-dimensional object determination processor that groups blocks vertically upward of these models. If the angle between the models exceeds a predetermined threshold, the second model is canceled, and the first model is extended to accurately represent the road surface, allowing for proper three-dimensional object determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple road surface models with different gradients are generated to cover varying road conditions, then the adaptability to different road surfaces is improved, but the complexity of determining accurate three-dimensional objects increases due to model selection difficulties
Solution Approach 1:
The road surface model is divided into multiple gradient sections (first gradient section and second gradient section) with different slope characteristics. Each section independently represents a specific road surface condition, allowing the system to adapt to varying road gradients without requiring a single complex model that would increase determination difficulty.
Solution Approach 2:
The system dynamically selects which road surface model to use based on detected road gradient conditions. When the gradient exceeds a threshold, the system switches from using the second road surface model to extending the first road surface model, making the determination process adaptive rather than static and reducing unnecessary complexity.
2Device complexity
If the first road surface model is extended to cover areas originally represented by the second model, then the simplicity of the determination process is improved, but the accuracy of representing far road surfaces may deteriorate
Solution Approach 1:
Different gradient sections are assigned different modeling approaches based on local road conditions. The first gradient section uses one modeling method while the second gradient section uses another, allowing each local area to be represented with the most appropriate model for its specific characteristics, thus maintaining accuracy while simplifying the overall process.
Solution Approach 2:
The system changes the gradient parameter threshold to determine which modeling approach to use. When the detected gradient exceeds the threshold, it switches from using the second road surface model to extending the first model, dynamically adjusting the representation parameters based on actual road conditions to maintain accuracy.
3Reliability
If the second road surface model is canceled when the angle exceeds the threshold, then the reliability of three-dimensional object determination is improved by avoiding misidentification, but the loss of information about the farther road surface region occurs
Solution Approach 1:
The system extracts and removes the second road surface model from consideration when the gradient angle exceeds the threshold, eliminating the source of potential misidentification. This selective removal prevents unreliable data from interfering with three-dimensional object determination while the first model's extension compensates for the removed information.
Solution Approach 2:
The first road surface model acts as an intermediary that extends into the region where the second model would have provided information. This extension serves as a mediator that maintains continuous road surface representation without introducing the gradient-related errors present in the second model under steep conditions.
4Productivity
If blocks are grouped based on vertical position relative to road surface models, then the speed of three-dimensional object determination is improved, but the precision of object identification deteriorates when road surface gradients change
Solution Approach 1:
The road surface is segmented into gradient sections with distinct modeling approaches. Blocks in different gradient sections are evaluated against appropriate models, allowing rapid processing within each segment while maintaining identification precision by using the model best suited for that specific gradient condition.
Solution Approach 2:
The system dynamically adjusts which road surface model is used for block grouping based on the detected gradient. When gradients are steep, it switches to the first model extension approach; when gradients are mild, it uses the second model. This dynamic adaptation maintains both processing speed and identification precision across varying road conditions.
Data Source
AI summary
A vehicle external environment recognition apparatus to be applied to a vehicle includes a road surface determination processor and a three-dimensional object determination processor. The road surface determination processor determines a road surface region that corresponds to a road surface in an image, plots representative distances of respective horizontal lines in the road surface region at respective vertical positions of the horizontal lines, and generates first and second road surface models. The second road surface model represents a farther portion of the road surface region from the vehicle than the first road surface model and differs in a gradient from the first road surface model. On a condition that an angle formed by the first and second road surface models is greater than a predetermined angle, the three-dimensional object determination processor cancels the second road surface model and extends far the first road surface model.