Multimodal Defect Detection Engine for Low-Visibility Vehicle Sensing
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Solution Overview
Problem
Conventional autonomous driving systems face challenges in accurately determining object distances and performing well in low-visibility conditions, leading to low accuracy in data analysis and vehicle operation tasks.
Innovation Solution
A multi-modality data analysis engine that collects data from various sensors, uses a grid-based feature extractor to extract features, and applies cross-attention and time-series-based anomaly detection to identify defects, integrating residual scores for improved defect detection and vehicle control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera-based autonomous driving systems are used to construct a full 360-degree view, then the visual depiction accuracy is improved, but the ability to determine object distance and perform well in low-visibility conditions deteriorates
Solution Approach 1:
The patent combines multiple sensor modalities (cameras, LIDAR, radar, ultrasonic sensors) into a unified sensor fusion system. This merging allows the system to leverage the strengths of each sensor type - cameras provide high-resolution visual depiction, LIDAR provides accurate depth information, radar penetrates low-visibility conditions - thereby resolving the contradiction between visual accuracy and distance determination reliability
Solution Approach 2:
The patent implements a multi-functional sensor system where different sensor types serve multiple purposes. For example, the system can switch between camera-based visual analysis and LIDAR-based depth analysis depending on environmental conditions, making the system universally effective across diverse operating scenarios including low-visibility conditions
2Area of stationary object
If camera-based autonomous driving systems are used, then the visual field coverage is improved, but the performance in low-visibility conditions deteriorates
Solution Approach 1:
The patent merges camera systems with active sensing technologies like LIDAR and radar. While cameras provide broad visual field coverage, the addition of LIDAR and radar ensures reliable operation in low-visibility conditions by actively emitting signals that can penetrate fog, rain, and darkness, thereby maintaining reliability across all weather conditions
Solution Approach 2:
The patent changes the operational parameters of the sensing system based on environmental conditions. In low-visibility conditions, the system increases reliance on active sensing modalities (LIDAR, radar) that operate independently of ambient light, thereby maintaining performance reliability when passive optical sensing becomes insufficient
3Device complexity
If conventional camera-based systems are used for data analysis, then the system complexity is reduced, but the data analysis accuracy deteriorates
Solution Approach 1:
The patent combines data from multiple sensor modalities and processes them through a unified neural network architecture. This merging of multi-modal data streams enables comprehensive analysis that leverages complementary information from different sensors, significantly improving data analysis accuracy despite the increased complexity of handling multiple data types
4Measurement precision
If multi-modality sensor data is collected and analyzed, then the defect detection accuracy is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the complex computational task into distinct processing stages: individual sensor data preprocessing, feature extraction from each modality, and final integration through a neural network. This segmentation allows each stage to be optimized independently, managing computational complexity while maintaining high defect detection accuracy through comprehensive multi-modal analysis
Data Source
AI summary
Systems and methods for defect detection for vehicle operations, including collecting a multiple modality input data stream from a plurality of different types of vehicle sensors, extracting one or more features from the input data stream using a grid-based feature extractor, and retrieving spatial attributes of objects positioned in any of a plurality of cells of the grid-based feature extractor. One or more anomalies are detected based on residual scores generated by each of cross attention-based anomaly detection and time-series-based anomaly detection. One or more defects are identified based on a generated overall defect score determined by integrating the residual scores for the cross attention-based anomaly detection and the time-series based anomaly detection being above a predetermined defect score threshold. Operation of the vehicle is controlled based on the one or more defects identified.