Sensor Data Error Detection via Multi-Modal Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles and machines face operational hazards due to sensor data inaccuracies, as environmental conditions and inadequate training can lead to malfunctioning or misclassification of objects, resulting in unsafe operations.
Innovation Solution
A system that compares data from individual sensors with fused sensor data from multiple sources to identify errors, using processors to analyze image, LIDAR, and other sensor data, and initiate responses to correct anomalies, while also using this information for improved training of sensor modalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If sensor data from individual sensors is used for autonomous operation, then the system can operate with simpler processing, but the accuracy and reliability of object detection deteriorates due to environmental conditions, damage, miscalibration, and inadequate training data
Solution Approach 1:
The patent combines data from multiple sensor modalities (e.g., LIDAR, cameras, radar) to form a comprehensive perception of the environment. By merging sensor data, the system compensates for individual sensor limitations caused by environmental conditions, damage, or miscalibration, thereby improving object detection accuracy without requiring overly complex individual sensor systems
2Reliability
If multiple sensors are used to improve detection accuracy, then the reliability of sensor data improves, but the device complexity and computational requirements increase
Solution Approach 1:
The system implements a feedback mechanism where sensor data from multiple modalities is continuously compared and cross-validated. The perception system uses feedback loops to identify discrepancies between different sensor readings and adjusts its interpretation accordingly, improving reliability while managing complexity through intelligent data integration rather than simply adding more sensors
Solution Approach 2:
The patent creates a unified perception system that processes multiple sensor modalities through a common framework. This multi-functional approach allows the same processing architecture to handle data from various sensor types, reducing overall system complexity while maintaining high reliability through diverse sensor input
3Adaptability or versatility
If sensors operate in adverse environmental conditions, then the vehicle can maintain operation, but the accuracy of sensor data deteriorates leading to potential unsafe operations
Solution Approach 1:
The perception system acts as a composite information structure that integrates data from multiple sensor modalities. Just as composite materials combine different substances to achieve superior properties, the system combines information from LIDAR, cameras, radar, and other sensors to create a more accurate and reliable environmental model that maintains precision under adverse conditions where individual sensors would fail
Data Source
AI summary
A method includes receiving a first signal from a first sensor, the first signal including data representing an environment. The method also includes receiving a second signal from a second sensor, the second signal including data representing the environment. The method further includes determining a group of objects based at least in part on the received data, and identifying an error associated with data included in the first signal and/or the second signal.