Dynamic Road Shape Recognition via Adaptive Boundary Segmentation
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Solution Overview
Problem
Existing road shape recognition devices face challenges in accurately detecting real road shapes and distinguishing between road surfaces and three-dimensional objects, leading to potential errors in automatic vehicle control, due to the fixed boundary division method used for linear approximation.
Innovation Solution
A road shape recognition device that dynamically adjusts the boundary between near and far groups of distance data using the least square method for approximation line calculation, calculates statistics such as variance for each group, and selects a combination of approximation lines to minimize total sum, generating a road shape model that accurately represents the real road shape.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a region in front of the vehicle is divided into a great number of sections and linear approximation is performed for each section, then the accuracy of road shape recognition is improved, but the processing load increases
Solution Approach 1:
The patent divides the region in front of the vehicle into multiple sections along the traveling direction and performs linear approximation for each section. This segmentation allows accurate road shape recognition while managing processing load by using a moderate number of sections rather than an excessive number.
Solution Approach 2:
The patent dynamically adjusts the boundary positions between sections based on the vehicle's traveling state and detected road shape. This dynamic adjustment optimizes the balance between recognition accuracy and processing efficiency by concentrating sections where needed and reducing them where not necessary.
2Ease of operation
If a fixed boundary division method is used for linear approximation, then the processing is simplified, but the accuracy of distinguishing road surfaces from three-dimensional objects deteriorates
Solution Approach 1:
The patent replaces fixed boundary division with dynamic boundary adjustment based on detected road shapes and vehicle traveling states. This allows the system to maintain processing simplicity while improving accuracy by adapting section boundaries to actual road conditions rather than using predetermined fixed boundaries.
Solution Approach 2:
The patent uses feedback from detected road shapes and vehicle states to adjust boundary positions between sections. This feedback mechanism enables the system to automatically optimize the division points based on actual conditions, improving detection accuracy without significantly increasing processing complexity.
3Productivity
If the boundary position for dividing sections is fixed at a predetermined distance from the vehicle, then the processing load is reduced, but the reliability of road shape recognition deteriorates when the vehicle traveling state changes
Solution Approach 1:
The patent makes the boundary position dynamic by adjusting it according to the vehicle's traveling state (speed, acceleration) and detected road shapes. This dynamic adjustment maintains processing efficiency while ensuring reliable road shape recognition across various driving conditions, unlike fixed boundary methods that fail when conditions change.
Solution Approach 2:
The patent changes the boundary position parameter based on vehicle traveling state parameters (speed, acceleration) and road shape parameters. This parameter adaptation allows the system to maintain both processing efficiency and recognition reliability by adjusting the boundary position to match current operating conditions.
Data Source
AI summary
A road shape recognition device includes: distance and height detecting means for detecting distance data having a distance and height in real space regarding a road surface where a vehicle is traveling at multiple mutually different points; approximation line calculating means for dividing the plurality of distance data into near and far groups as viewed from the vehicle to calculate an approximation line of the distance data for each group each time the distance data of the boundary portion between the two groups is transferred from one of the groups to the other; statistics calculating means for calculating statistics from the corresponding approximation line for each group where the distance data is transferred; and road shape model generating means for selecting one out of combinations of the approximation lines to generate a road shape model using the selected combination.