Mobile Sensor Driving Behavior Detection via Energy Domain Transformation
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Solution Overview
Problem
Existing systems for identifying risky driving behaviors in online taxi services face challenges due to variations in sensor accuracy across different mobile devices, leading to inconsistent detection of driving behaviors, which compromises traffic safety.
Innovation Solution
A system that utilizes sensors installed on mobile devices to obtain driving data, filters out irrelevant information using machine learning models, and identifies risky driving behaviors by determining target time periods and extracting relevant features from acceleration data, enabling timely and accurate detection of risky driving events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If sensor data from mobile devices is used to detect driving behaviors, then real-time detection capability is improved, but measurement precision deteriorates due to sensor accuracy variations across different devices
Solution Approach 1:
The patent transforms raw acceleration data into energy measurements by changing the parameter representation from time-domain to energy-domain. This transformation normalizes the data across different devices, compensating for sensor accuracy variations. The energy measurement parameter is calculated by integrating the squared acceleration values over time, creating a device-independent metric for detecting driving behaviors.
2Reliability
If all sensor data is processed to ensure comprehensive detection, then detection coverage is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential energy measurement feature from the raw acceleration data, discarding redundant information. By focusing on the energy measurement parameter that directly correlates with driving behavior intensity, the system achieves comprehensive detection coverage while significantly reducing computational complexity compared to processing all raw sensor data.
Solution Approach 2:
The patent performs preliminary processing of acceleration data by calculating energy measurements before detailed driving behavior analysis. This preliminary energy-based filtering identifies potential risky events, allowing the system to focus computational resources only on relevant time segments, thereby reducing overall computational complexity while maintaining reliable detection coverage.
Data Source
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AI summary
The present disclosure relates to systems and methods for identifying a risky driving behavior of a driver. The systems may obtain driving data from sensors associated with a vehicle driven by a driver; determine, based on the driving data, a target time period; obtain, based on the driving data, target data within the target time period; and identify, based on the target data, a presence of a risky driving behavior of the driver.