Vehicle Perception Fault Diagnosis Using Predicted Sensor Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle perception systems face challenges in diagnosing faults in sensors and cameras, which can lead to inaccurate object detection and vehicle control issues, as existing methods lack effective predictive and diagnostic capabilities to identify and mitigate such faults in real-time.
Innovation Solution
A perception system comprising a prediction module using convolutional long short-term memory networks to generate predicted sensor data and perception results based on historical data, and a diagnostic module that compares these predictions with actual data to selectively identify faults in external sensors and cameras, enabling timely fault detection and mitigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fault detection methods are used in perception systems, then the system structure remains simple, but the ability to identify faults in real-time deteriorates
Solution Approach 1:
The system performs preliminary actions by generating predicted sensor data and perception results before actual faults occur. The prediction module uses historical data to forecast expected sensor readings and perception outcomes, enabling the system to detect deviations that indicate faults before they significantly impact vehicle operation. This proactive approach enhances reliability without requiring complex real-time intervention mechanisms.
Solution Approach 2:
The patent introduces intermediary elements including a prediction module that generates expected sensor data, a comparison module that identifies deviations between predicted and actual data, and a diagnostic module that determines fault conditions. These intermediary components act as mediators between raw sensor data and fault detection, enabling real-time monitoring while maintaining a structured system architecture that balances complexity and effectiveness.
2Measurement precision
If no predictive capabilities are implemented, then the system remains simple, but the accuracy of fault identification deteriorates
Solution Approach 1:
The prediction module performs preliminary computations by analyzing historical sensor data to generate expected future sensor readings and perception results. This preliminary action enables accurate fault identification by providing a baseline for comparison, allowing the system to detect deviations that indicate faults with high precision while using computational methods that balance accuracy with processing efficiency.
Solution Approach 2:
The system implements feedback mechanisms where the comparison module continuously compares predicted sensor data with actual sensor data, and predicted perception results with actual perception results. This feedback loop provides precise fault identification by quantifying deviations and feeding this information to the diagnostic module, which uses the deviation magnitude to determine fault conditions with high accuracy.
3Reliability
If real-time fault detection is implemented, then vehicle safety improves, but the computational requirements and system complexity increase
Solution Approach 1:
The system applies partial action by focusing computational resources on critical perception functions and key sensor data comparison. Rather than analyzing every possible parameter at maximum depth, the system identifies and monitors the most significant deviations between predicted and actual data, enabling real-time safety monitoring with reduced computational energy consumption by concentrating analysis on the most relevant fault indicators.
Data Source
AI summary
A perception system includes a perception module configured to capture first sensor data that includes data from at least one of an external sensor and a camera captured in a first period, a prediction module configured to receive the first sensor data, generate, based on the first sensor data, predicted sensor data for a second period subsequent to the first period, receive second sensor data for the second period, and output results of a comparison between the predicted sensor data and the second sensor data, and a diagnostic module configured to selectively identify a fault in the perception system based on the results of the comparison.