Vehicle Recognition Device Road Surface Detection
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Solution Overview
Problem
Current LIDAR systems for acquiring 3-dimensional point groups face high processing loads and accuracy issues due to the need for time-consuming flatness and normal direction calculations, often misidentifying low-height objects as road surfaces.
Innovation Solution
A recognition device and method that utilize an object detection sensor capable of changing detection directions, determining whether a point of interest is a road surface based on its relation to a reference point with a specific distance and angle criteria, reducing processing load while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If flatness and normal direction calculations are performed for each 3-dimensional point group, then road surface detection accuracy is improved, but processing time increases and processing load increases
Solution Approach 1:
The patent extracts only the necessary geometric relationships (distance and angle relative to reference points) needed for road surface detection, eliminating the need for comprehensive flatness and normal direction calculations. This selective extraction of critical parameters reduces computational complexity while maintaining detection accuracy.
Solution Approach 2:
The detection process is segmented into discrete comparison steps against reference points with known road surface characteristics. By dividing the complex surface analysis into multiple simple point-to-point comparisons, the system achieves accurate road surface identification without requiring heavy computational resources for each point.
2Measurement precision
If clustering sensitivity is increased to improve object detection, then detection accuracy improves, but false recognition of low-height objects as road surfaces increases
Solution Approach 1:
Reference points serve as intermediary elements with known road surface characteristics. By comparing detection points against these intermediary reference points rather than making direct classification decisions, the system mediates between sensitive detection and false recognition, using the reference points as a reliable benchmark for accurate classification.
3Reliability
If clustering sensitivity is lowered to reduce false recognition, then reliability improves, but detection accuracy decreases and low-height objects are misidentified as road surfaces
Solution Approach 1:
The system dynamically adjusts detection criteria based on the geometric relationship between detection points and reference points. By using variable distance and angle thresholds relative to each reference point, the system maintains high sensitivity for actual objects while automatically reducing false positive rates, adapting the detection stringency to local geometric conditions.
Data Source
AI summary
A recognition device includes: an acquirer configured to be connected to an object detection sensor which is mounted in a vehicle and is capable of changing a detection direction of each of upper, lower, right, and left directions and to acquire a detection result including 3-dimensional positional information regarding detection points from the object detection sensor; and a determiner configured to determine whether a point of interest among the detection points is a detection point at which a road surface is detected, based on a relation between the point of interest and a reference point at which a detection direction of the upper and lower directions is further downward than at the point of interest and configured to output a determination result.