Multi-Modal Sensor Fusion for Real-Time ADAS Failure Diagnosis
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Solution Overview
Problem
Advanced driver assistance systems (ADAS) face challenges in real-time sensor failure detection and verification due to the reliance on single sensor channels, especially in complex environments, and require efficient methods for mutual verification between sensors to ensure system reliability and safety.
Innovation Solution
An apparatus and method utilizing multi-modal deep learning to diagnose sensor failures by extracting shared representations between sensors, reconstructing normal outputs for abnormal sensors, and enhancing detection efficiency through inter-sensor mutual verification using deep learning algorithms like autoencoders and generative adversarial networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single sensor channels are used for ADAS, then system simplicity is maintained, but sensor failure detection capability deteriorates
Solution Approach 1:
The patent combines multiple sensor channels (camera, radar, ultrasonic sensors) into a unified sensor network that performs mutual verification. The sensor fusion architecture integrates data from different modalities to detect failures, where the combined system's reliability exceeds that of individual sensors while maintaining manageable complexity through centralized processing.
Solution Approach 2:
The patent implements a feedback mechanism where sensor outputs are continuously monitored and compared against expected values derived from other sensors. When a sensor's output deviates from the feedback provided by the sensor network, the system detects potential failures and adjusts processing to maintain system reliability.
2Measurement precision
If multiple sensors are deployed for comprehensive environmental recognition, then recognition accuracy is improved, but mutual verification complexity increases
Solution Approach 1:
The patent segments the verification process into modular components: individual sensor validation modules, pairwise comparison modules, and aggregate decision modules. Each sensor type has dedicated processing pathways that feed into a centralized fusion module, reducing verification complexity while maintaining comprehensive environmental recognition accuracy.
Solution Approach 2:
The patent introduces a sensor fusion module as an intermediary that receives data from multiple sensors and performs coordinated verification. This intermediary consolidates the complex mutual verification logic into a single processing unit, managing the complexity of cross-sensor validation while preserving the benefits of multi-sensor environmental recognition.
3Reliability
If sensor failure correction is implemented in real-time, then system reliability is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by continuously pre-processing sensor data and establishing baseline relationships between sensors during normal operation. When a failure is detected, the system has already prepared alternative processing pathways and reconstructed data models, enabling immediate correction without significant time loss.
Solution Approach 2:
The patent implements skipping mechanisms that allow the system to bypass detailed verification steps for sensors identified as failed. When a sensor failure is detected, the system rushes through the correction process by directly applying pre-computed reconstruction algorithms from the sensor network, minimizing the time penalty for real-time failure correction.
Data Source
AI summary
An apparatus for processing multi-type sensor signals on the basis of multi-modal deep learning, the apparatus including: an individual sensor failure diagnosis unit configured to measure a normality of a sensor output of a single modal sensor at each sampling period, and sense an abnormal operation of the sensor on the basis of the measured normality; an inter-sensor mutual failure diagnosis unit including a multi-modal deep auto encoder, and configured to learn a correlation existing between multi-modalities, extract shared representation between modalities from multi-modal inputs on the basis of the learned correlation, and perform an inter-sensor mutual failure diagnosis; and a reconstruction target sensor output value reconstructing unit configured to, when an output value of a specific sensor is missing, predict and reconstruct the output value of the sensor using other sensor information using the shared representation extracted from other modal sensors.