Multi-Sensor Collision Detection for Heavy Vehicle False Alarms
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Solution Overview
Problem
Existing vehicle event recorders face challenges in accurately detecting collisions due to false positives from mundane events in heavy vehicles and missed events from collisions with smaller vehicles, as simple threshold adjustments are insufficient to address these issues.
Innovation Solution
A system for sensor fusion that preprocesses data from multiple sensors, including accelerometers, gyroscopes, GPS, and audio, using filtering, normalization, and alignment, and processes it through neural network models to create a collision score, which determines collision occurrence, severity, and triggers appropriate actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a simple accelerometer threshold is used for collision detection, then the system is simple to operate, but it produces false positives from mundane events and false negatives from collisions with smaller vehicles
Solution Approach 1:
The patent combines data from multiple sensors (accelerometers, gyroscopes, GPS, audio sensors, video cameras) into a unified sensor fusion system. This merging of multiple data sources allows the system to cross-validate signals and distinguish true collision events from false positives caused by mundane events, thereby improving measurement precision while maintaining operational simplicity through automated processing.
Solution Approach 2:
The sensor fusion system performs multiple functions simultaneously: it detects collisions, distinguishes them from false events, determines collision severity, and triggers appropriate responses. By making the system multi-functional, it can handle both simple threshold-based detection and complex pattern recognition without requiring separate systems, thus improving precision without sacrificing ease of operation.
2Measurement precision
If multiple sensors and complex processing are used to improve collision detection accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The sensor fusion system incorporates automated processing algorithms that self-manage the complex task of fusing multiple sensor data streams. The system automatically correlates data from accelerometers, gyroscopes, GPS, and other sensors without requiring manual configuration or intervention, thereby reducing the operational complexity burden despite the sophisticated multi-sensor architecture.
Solution Approach 2:
The patent divides the collision detection system into modular sensor components (accelerometers, gyroscopes, GPS, audio sensors, video cameras) that can be independently processed and then integrated. This segmentation allows each sensor to be optimized independently while the overall system benefits from their combined precision, managing complexity through modular design.
3Measurement precision
If manual review of potential collision events is performed, then measurement precision improves, but productivity decreases
Solution Approach 1:
The sensor fusion system incorporates feedback loops where the processed sensor data is continuously analyzed and refined. The system provides automated feedback on collision detection confidence levels, allowing it to self-correct and improve precision over time without requiring manual review of every event, thus maintaining high productivity while achieving accurate detection.
Data Source
AI summary
A system for collision detection includes an interface and a processor. The interface is configured to receive sensor data from a plurality of sensors. The processor is configured to preprocess the sensor data using a set of preprocessing steps to create preprocessed sensor data, wherein the set of preprocessing steps comprises filtering, normalization, and alignment; process the preprocessed sensor data using a compound model to create a collision score; and provide the collision score.