Driving Event Classification System Using Sensor Fusion
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Solution Overview
Problem
The vast amount of data from vehicle sensors is challenging to interpret effectively for applications like usage-based insurance, preventative maintenance, and driving behavior improvement, as existing systems struggle to derive meaningful insights from this data.
Innovation Solution
A system that utilizes a combination of in-vehicle sensors such as GPS, accelerometers, and OBD data to classify driving events with precision, incorporating both internal and external data sources for accurate and up-to-date information, and employs parametric representation and dynamic data compression to optimize data transmission and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive sensor data is collected from multiple sources, then measurement precision and information completeness improve, but data complexity and processing difficulty increase
Solution Approach 1:
The system segments the complex data processing task into distinct modules: data collection from multiple sensors, data preprocessing/fusion, driving event classification, and result output. Each module handles specific aspects of the data flow, making the overall system more manageable despite the comprehensive data collection from GPS, accelerometers, and OBD sources
Solution Approach 2:
The patent introduces intermediate processing layers between raw sensor data and final classification results. These intermediaries include data fusion algorithms that combine information from multiple sensors, and feature extraction processes that transform raw data into meaningful patterns for classification, thereby managing the complexity of processing comprehensive sensor data
2Loss of information
If raw sensor data is transmitted and stored in full detail, then information completeness improves, but data transmission bandwidth and storage requirements increase
Solution Approach 1:
The system extracts only the essential features and patterns from comprehensive sensor data that are relevant for driving event classification. Instead of transmitting or storing all raw sensor readings, the system identifies and retains key characteristics such as acceleration patterns, speed changes, and location sequences that are sufficient for accurate event detection, thereby reducing data volume while maintaining information completeness
Solution Approach 2:
Data preprocessing and feature extraction are performed before transmission and storage operations. By conducting preliminary processing to consolidate and summarize sensor data into meaningful driving events and patterns, the system reduces the quantity of data that needs to be transmitted and stored while preserving the essential information needed for analysis
Data Source
AI summary
This vehicle monitoring system provides a plurality of sensors in the vehicle recording performance of the vehicle. A processor (remote or on-board) receives data from the sensors. The processor classifies the data from the at least one sensor as an event in one of a plurality of classifications. The processor associates at least one parameter with the classification.