Obstacle Recognition Device Sensor Bias Error Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing obstacle recognition devices face challenges in accurately identifying the same object detected by multiple sensors due to bias errors, which can lead to erroneous recognition of separate objects, especially in varying travel environments.
Innovation Solution
An obstacle recognition device and method that utilize a combination of sensors to calculate an index value for determining whether detected objects are the same, with a determination unit comparing this value to a threshold and a correction unit generating corrected data to remove detection errors, incorporating prediction units for future object movement analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors are used to detect objects, then detection coverage and reliability are improved, but bias errors cause the same object to be erroneously recognized as separate objects
Solution Approach 1:
The system calculates an index value representing the degree of coincidence between detection results from multiple sensors, uses this index to determine whether detected objects are the same, and feeds back this determination to correct detection errors. This closed-loop feedback mechanism resolves the contradiction by using the index value to identify and correct bias errors while maintaining multi-sensor detection benefits
Solution Approach 2:
The system changes the parameter of detection data by calculating an index value that transforms raw detection data into a comparable metric for determining object identity. This parameter transformation enables accurate comparison across different sensor types despite their inherent bias errors, resolving the identification accuracy problem while preserving detection reliability
2Measurement precision
If bias error correction is implemented, then object identification accuracy is improved, but system complexity increases due to additional calculation and determination units
Solution Approach 1:
The calculation unit and determination unit serve multiple functions: they calculate index values for object identity determination, detect bias errors between sensors, and generate corrected detection data. This multi-functionality reduces the need for separate dedicated components, achieving accurate object identification without proportionally increasing system complexity
3Measurement precision
If corrected detection data is generated to remove bias errors, then detection accuracy is improved, but processing time increases due to additional calculation steps
Solution Approach 1:
The system performs preliminary calculation of index values and determination of object identity before final obstacle recognition. By pre-calculating the degree of coincidence between detection results, the system prepares correction data in advance, reducing the processing time burden during critical real-time operation while maintaining high detection accuracy
Data Source
AI summary
An obstacle recognition device includes: a first sensor and a second sensor, which are configured to detect an object near a vehicle; a calculation unit configured to calculate, based on first detection data on a first object detected by the first sensor and second detection data on a second object detected by the second sensor, an index value for identifying whether the two objects are the same object; a determination unit configured to determine whether the two objects are the same object by comparing the index value with a threshold value set in advance; and a correction unit configured to calculate, when the determination unit has determined that the two objects are the same object, a detection error between the two sensors based on the two detection data, and generate corrected detection data so as to remove the detection error.