Sensor Fusion Deep Learning Object Detection
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Solution Overview
Problem
Existing sensor data processing systems for autonomous navigation in vehicles face challenges in accurately detecting and classifying objects due to insufficient responsiveness and high costs, particularly when affected by ambient environmental conditions and sensor device faults.
Innovation Solution
Implementing a sensor data-processing system that fuses sensor data representations using deep learning algorithms, allowing for autonomous feature extraction and classification, which improves detection and classification performance compared to traditional machine learning methods, and incorporates a system to adjust processing based on sensor health and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning methods are used for object detection and classification in sensor data, then the system complexity is lower, but the detection accuracy and responsiveness are insufficient
Solution Approach 1:
The patent transitions from traditional machine learning parameters to deep learning parameters, changing the fundamental approach to feature extraction and classification. This parameter change enables higher detection accuracy by using hierarchical feature learning and automatic feature extraction from raw sensor data, resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The patent replaces traditional mechanical machine learning systems with deep learning systems that automatically learn features from data. This substitution eliminates the need for manual feature engineering and achieves superior detection accuracy while the system manages complexity through automated processes and specialized hardware acceleration.
2Productivity
If deep learning algorithms are implemented for sensor data processing, then detection and classification performance improves, but computational power consumption increases
Solution Approach 1:
The patent segments the deep learning processing into distinct stages: sensor data acquisition, preprocessing, feature extraction, classification, and output. This segmentation allows for optimized resource allocation at each stage, enabling high detection performance while managing computational power consumption through selective processing and hardware acceleration.
Solution Approach 2:
The patent introduces intermediate processing layers including preprocessing modules and feature extraction layers that act as mediators between raw sensor data and the final classification. These intermediaries reduce the computational burden on the deep learning model by preparing and filtering data beforehand, thereby improving detection performance while controlling power consumption.
3Reliability
If sensor data is processed without considering environmental conditions and sensor health, then the processing speed is faster, but the reliability of navigation deteriorates
Solution Approach 1:
The patent performs preliminary assessments of sensor health and environmental conditions before processing sensor data for navigation. By evaluating sensor functionality and environmental factors in advance, the system can adjust processing parameters and select appropriate processing paths, ensuring reliable navigation outcomes while minimizing processing time through pre-configured responses to common conditions.
Data Source
AI summary
Some embodiments provide a sensor data-processing system which detects and classifies objects detected in an environment via fusion of sensor data representations generated by multiple separate sensors. The sensor data-processing system can fuse sensor data representations generated by multiple sensor devices into a fused sensor data representation and can further detect and classify features in the fused sensor data representation. Feature detection can be implemented based at least in part upon utilizing a feature-detection model generated via one or more of deep learning and traditional machine learning. The sensor data-processing system can adjust sensor data processing of representations generated by sensor devices based on external factors including indications of sensor health and environmental conditions. The sensor data-processing system can be implemented in a vehicle and provide output data associated with the detected objects to a navigation system which navigates the vehicle according to the output data.