Multi-Sensor Fusion Error Correction for Obstacle Detection
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Solution Overview
Problem
Current obstacle detection systems using multiple sensors, such as cameras and radars, face reliability issues due to high detection errors, making precise tracking of obstacles challenging, especially in autonomous vehicle applications.
Innovation Solution
A method for analyzing error and existence probability in multi-sensor fusion systems, involving obstacle sensing, error modeling, and data fusion using predetermined error-average distributing functions and a Kalman filter to generate corrected and fused obstacle datasets, with a simulating obstacle to simulate real-world scenarios and correct error variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple sensors are used for obstacle detection, then the coverage and detection capability are improved, but the detection error increases and reliability decreases
Solution Approach 1:
The patent combines multiple sensor data sources (camera, radar, LIDAR) into a unified obstacle detection system through data fusion. The processor integrates observations from different sensors to generate comprehensive obstacle datasets, allowing the system to leverage the strengths of each sensor type while compensating for individual weaknesses, thereby improving overall detection capability and reliability
Solution Approach 2:
The patent implements feedback mechanisms through error modeling and correction. The system generates error-average distributing functions based on sensor performance characteristics and uses these to correct detection errors in real-time. The error accumulation and correction process continuously refines detection accuracy by feeding back correction information to adjust subsequent detections
2Measurement precision
If error correction methods are applied, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The patent performs preliminary error analysis and modeling before actual obstacle detection. Error-average distributing functions are pre-calculated based on sensor characteristics and environmental factors. This preliminary preparation allows the system to apply straightforward correction algorithms during real-time operation, improving tracking precision without adding excessive complexity during critical detection phases
Solution Approach 2:
The patent adjusts detection parameters dynamically based on error models. The system modifies observation weights, fusion parameters, and correction factors according to pre-established error distributions. By changing these parameters rather than fundamentally altering the detection architecture, the system achieves higher precision while controlling complexity
Data Source
AI summary
The present disclosure provides a method for analyzing an error and an existence probability of a multi-sensor fusion. The method includes the ab obstacle sensing step, an obstacle predicting step, an error-model providing step, an existence-probability step, a tracking and fusing step and an error accumulating and correcting step. Therefore, by using the method, a plurality of fused obstacle datasets can be obtained, and an accumulation of error variations thereof can be corrected, which can improve the reliability for judging whether the obstacle exist or not.