Vehicle Accident Detection via Sensor and Camera Fusion
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Solution Overview
Problem
Current safety systems in vehicles, while effective, do not comprehensively detect potential accident situations involving driver behavior and environmental factors in real-time, particularly for abnormal conditions that may lead to accidents.
Innovation Solution
A method that collects vehicle behavior data and environmental data from sensors and cameras, transmitting this information to a control center for analysis, where abnormal conditions are detected and verified, triggering alerts and warnings to authorities and the driver if a high-risk situation is identified.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors and cameras are used to collect comprehensive data, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The system divides the detection function into multiple independent components: internal sensors (accelerometer, gyroscope, microphone, camera) and external traffic monitoring cameras. Each component independently collects specific types of data, and the control center integrates these segmented data streams to achieve comprehensive detection with high accuracy while managing system complexity through modular architecture.
2Loss of time
If real-time data transmission and analysis is implemented, then response time is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic data transmission and analysis cycles rather than continuous real-time processing. Sensors collect data continuously, but transmission to the control center and subsequent analysis occur at periodic intervals or when threshold values are exceeded. This approach maintains timely response capability while significantly reducing energy consumption compared to continuous real-time processing.
3Reliability
If comprehensive behavior data and environmental data are collected, then detection reliability is improved, but data processing complexity increases
Solution Approach 1:
The control center serves as an intermediary that receives, integrates, and processes data from multiple sources (internal sensors and external cameras). It combines behavior data from accelerometers, gyroscopes, microphones, and cameras with environmental data from traffic monitoring cameras, using pattern recognition and machine learning algorithms to detect abnormal conditions. This intermediary approach improves detection reliability through comprehensive data analysis while managing processing complexity through centralized intelligent processing.
Data Source
AI summary
A method is disclosed for detecting potential accident situations with a vehicle driven by a driver. In an embodiment, the method includes collecting behavior data of the vehicle by sensors located in the vehicle; obtaining a position of the vehicle; transmitting the behavior data and the position of the vehicle to a control center; selecting at least one traffic monitoring camera based on the position of the vehicle; acquiring images by the traffic monitoring camera, the images including the vehicle; transmitting the images to the control center; analyzing the behavior data for detecting a driver's abnormal condition; analyzing the images for detecting an abnormal condition of the vehicle; and if the two analyses detect an abnormal condition, registering the vehicle in a probable accident list.