Driving Behavior Recognition Using Sensor Data Filtering
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Solution Overview
Problem
Current systems for recognizing driving behavior in on-demand transportation services lack efficient methods to collect and analyze movement data from mobile devices, leading to incomplete and inaccurate assessments of driver behavior.
Innovation Solution
An electronic device equipped with sensors such as a gyroscope, acceleration sensor, and GPS, which collects movement data and filters out unwanted information using a machine learning trained model, then sends relevant data to a remote server for analysis, enabling real-time monitoring and evaluation of driving behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If movement data is collected continuously from mobile devices, then the quantity of driving behavior data is improved, but the loss of time for data processing and transmission increases
Solution Approach 1:
The system performs preliminary actions by pre-defining behavior patterns and criteria before data collection. The server prepares analysis models and thresholds in advance, so when movement data is received, the matching and analysis can be performed rapidly without extensive real-time processing, thus reducing time loss while maintaining comprehensive data collection.
Solution Approach 2:
The system extracts only the essential and relevant movement data features that indicate specific driving behaviors, rather than processing all raw sensor data in full detail. By identifying and extracting key behavioral indicators, the system reduces the effective data volume requiring intensive processing while preserving the quantity of observed behaviors.
2Measurement precision
If all movement data is transmitted to the server, then the measurement precision of driving behavior is improved, but the loss of time for data transmission increases
Solution Approach 1:
The system extracts and transmits only the essential movement data and pre-identified behavior indicators to the server, rather than transmitting all raw sensor data. This selective extraction maintains measurement precision for critical driving behaviors while significantly reducing transmission time and data volume.
Solution Approach 2:
The system performs preliminary local processing and identification of driving behaviors before transmission. By pre-analyzing data on the device to identify specific behaviors of interest, the system prepares refined data packets for transmission, reducing the time required for both transmission and subsequent server processing while maintaining assessment accuracy.
3Reliability
If comprehensive movement data is collected, then the reliability of driving behavior recognition is improved, but the device complexity increases
Solution Approach 1:
The system uses universal mobile device sensors (accelerometers, gyroscopes, GPS) that are already present in smartphones and tablets, making the data collection apparatus multi-functional and applicable to various driving scenarios without requiring specialized equipment. This approach maintains reliability through comprehensive data collection while avoiding increased device complexity by leveraging existing hardware.
Solution Approach 2:
The server acts as an intermediary that receives, processes, and analyzes movement data from multiple devices. By offloading the complex analysis and pattern recognition tasks to the server, the mobile devices themselves remain relatively simple data collection points, thus maintaining system reliability through centralized processing without increasing the complexity of individual collecting devices.
4Productivity
If real-time data transmission is implemented, then the productivity of driving behavior monitoring is improved, but the loss of energy for data transmission increases
Solution Approach 1:
The system implements periodic data transmission based on detected driving behaviors rather than continuous real-time transmission. When specific driving behaviors are identified, data is transmitted promptly; otherwise, transmission occurs at scheduled intervals or is deferred. This periodic approach maintains monitoring productivity by ensuring timely data delivery for critical events while significantly reducing overall energy consumption compared to continuous transmission.
Solution Approach 2:
The mobile device performs self-service by conducting preliminary analysis of its own movement data to identify significant driving behaviors before transmission. This local intelligence allows the device to autonomously determine when transmission is necessary, improving productivity by ensuring relevant data is transmitted while reducing energy waste from transmitting routine or insignificant data.
Data Source
AI summary
Electronic devices and methods for recognizing driving behavior are provided. The electronic devices may perform the methods to obtain first electronic signals encoding movement data associated with the electronic device from the at least one sensor at a target time point; operate logic circuits to determine whether the first electronic signals encoding movement data meets a precondition; and upon the first electronic signals encoding movement data meeting the precondition, send second electronic signals encoding movement data within a predetermined time period associated with the target time point to a remote server.