Vehicle Sensor Neural Network Interference Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The quality of sensor data for vehicles is often unstable due to environmental influences like fog, snow, and heavy rain, leading to potential misinterpretations by neural networks, especially in autonomous driving or driver assistance systems, which can compromise safety.
Innovation Solution
A method that involves monitoring the analysis behavior of a neural network to detect sensor interferences by identifying key elements, evaluating sensor data for interference, and implementing reaction measures such as notifications or adjustments to ensure accurate data interpretation, using a combination of sensor units, computing units, and trained neural networks to classify and validate data in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If sensor data is processed by a neural network for autonomous driving or driver assistance, then automation capability is improved, but reliability deteriorates due to sensor interferences from environmental factors
Solution Approach 1:
The patent implements a feedback mechanism where the neural network's analysis behavior is continuously monitored and evaluated. When sensor interferences are detected through this feedback loop, the system adjusts its operation by reducing automation capability or alerting the driver, thus maintaining reliability while preserving automation benefits during normal conditions
Solution Approach 2:
The patent introduces an intermediary evaluation layer between the sensor data input and the neural network analysis. This intermediary component monitors the analysis behavior and detects sensor interferences, acting as a mediator that prevents corrupted data from compromising the automated driving decisions, thereby resolving the contradiction between automation and reliability
2Measurement precision
If sensor data quality is compromised by environmental interferences, then measurement precision deteriorates, but detecting these interferences requires additional computing resources
Solution Approach 1:
The patent applies partial action by monitoring only specific aspects of the neural network's analysis behavior that are most indicative of sensor interferences, rather than comprehensively analyzing all possible parameters. This selective monitoring approach detects sensor data quality issues while minimizing the additional computing resources required
Solution Approach 2:
The system performs self-service by using the neural network's own analysis behavior as the monitoring target. The neural network's intermediate results are evaluated to detect interferences, allowing the system to self-diagnose data quality issues without requiring separate complex detection systems, thus maintaining measurement precision with minimal extra computing effort
Data Source
AI summary
The invention relates to a method (100) for supporting the operation of a vehicle (2) with a sensor unit (4) for acquiring sensor data (200) for an evaluation in a trained, artificial neural network (10) with a plurality of network elements (11) for intermediate evaluations (210) of the sensor data (200), the method comprising the following steps: providing (102) the sensor data (200) for the neural network (10), evaluating (103) the sensor data (200) by means of the neural network (10) in view of a result. The invention also relates to a computer program product and to a system (1).