Multi-Sensor Event Detection With Neural Precursor Monitoring
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Solution Overview
Problem
Existing event data recorders (EDRs) lack real-time event detection and monitoring capabilities, as well as efficient interfaces for data management and viewing.
Innovation Solution
An event detection system comprising multiple sensor devices, including cameras and accelerometers, that generate and process data to detect events and precursors using neural networks and stereoscopic inference models, and stream data to a server system for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing EDRs continuously record data from multiple sensors, then data availability for event analysis is improved, but data management complexity and storage requirements increase
Solution Approach 1:
The system performs preliminary action by detecting event precursors before actual events occur. The neural network continuously monitors sensor data for patterns that indicate potential events (e.g., gradual acceleration, steering angle changes) and triggers enhanced recording only when precursors are detected, rather than continuously recording all data and filtering later.
Solution Approach 2:
The system extracts only the most relevant data streams and features for event detection. By using neural networks to identify significant patterns in sensor data, the system selectively extracts and records only the critical information (e.g., specific acceleration thresholds, GPS location changes) rather than managing all continuous sensor data equally.
2Productivity
If real-time event detection is implemented using neural networks, then event detection capability is improved, but processing requirements and system complexity increase
Solution Approach 1:
The system segments the event detection function into separate components: a neural network module for pattern recognition, a threshold-based detection module for immediate responses, and a data processing module for analysis. This segmentation allows each component to handle specific tasks independently, reducing overall system complexity while maintaining high detection capability.
Solution Approach 2:
The neural network acts as an intermediary between raw sensor data and event detection decisions. Rather than directly processing all sensor data through complex algorithms, the neural network intermediates by learning and recognizing event patterns, simplifying the detection process while improving accuracy.
3Adaptability or versatility
If multiple sensor devices are integrated for comprehensive monitoring, then monitoring coverage is improved, but device complexity and coordination difficulty increase
Solution Approach 1:
The system implements multi-functionality by using a unified neural network architecture that processes data from multiple sensor types (accelerometers, GPS, steering angle sensors, camera images). The same neural network model can detect different event types (crashes, near-misses, driver behavior) by learning from diverse data sources, eliminating the need for separate processing systems for each sensor.
4Reliability
If continuous data streaming to server is implemented, then real-time analysis capability is improved, but bandwidth consumption and data transmission complexity increase
Solution Approach 1:
The system performs preliminary data processing and feature extraction locally before transmission to the server. By pre-processing sensor data to identify and isolate only the critical event-related information, the system reduces the volume of data that needs to be transmitted while maintaining real-time analysis capability through edge computing.
Data Source
AI summary
Example embodiments described herein therefore relate to an event detection system that comprises a plurality of sensor devices, to perform operations that include: generating sensor data at the plurality of sensor devices; accessing the sensor data generated by the plurality of sensor devices; detecting an event, or precursor to an event, based on the sensor data, wherein the detected event corresponds to an event category; accessing an object model associated with the event type in response to detecting the event, wherein the object model defines a procedure to be applied by the event detection system to the sensor data; and streaming at least a portion of a plurality of data streams generated by the plurality of sensor devices to a server system based on the procedure, wherein the server system may perform further analysis or visualization based on the portion of the plurality of data streams.