Anomaly Detection Model for Real-Time Accident Risk Prediction
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Solution Overview
Problem
Conventional accident prediction technologies require rich data such as geographic information, video images, and radar data, leading to increased instrumentation and computational costs, and can only detect accidents after they occur, failing to predict risks in real time.
Innovation Solution
A computer-implemented method using sensor data to compute anomaly scores and calculate an accident risk score through an anomaly detection model, which processes sensor data from vehicles to predict the risk of traffic accidents in real time without the need for extensive data processing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional accident detection techniques use rich information such as geographic data, video images, and radar data, then detection accuracy is improved, but instrumentation cost and computational cost increase
Solution Approach 1:
The patent extracts only the essential features needed for accident risk prediction from sensor data, rather than processing all available rich information. The anomaly detection model identifies specific patterns in sensor data that precede accidents, eliminating the need for expensive geographic data, video images, and radar data while maintaining prediction effectiveness
Solution Approach 2:
The patent uses standard, inexpensive vehicle sensors that are already available in most vehicles rather than expensive specialized instrumentation. The system processes sensor data through a computational model that requires minimal processing power, making the solution cost-effective and suitable for widespread deployment
2Measurement precision
If conventional accident detection techniques use rich information such as geographic data, video images, and radar data, then detection accuracy is improved, but computational cost increases
Solution Approach 1:
The patent extracts only the essential features needed for accident risk prediction from sensor data, rather than processing all available rich information. The anomaly detection model identifies specific patterns in sensor data that precede accidents, eliminating the need for expensive geographic data, video images, and radar data while maintaining prediction effectiveness
Solution Approach 2:
The patent replaces complex computational processing of rich information with a streamlined anomaly detection model that processes standard sensor data. This substitution reduces computational requirements while maintaining the ability to predict accidents, making the system more energy-efficient
3Device complexity
If conventional techniques detect accidents after they occur, then simple instrumentation is used, but the ability to prevent accidents is lost
Solution Approach 1:
The patent performs preliminary detection of anomaly patterns in sensor data that precede accidents, enabling prediction before the actual accident occurs. The system identifies temporal patterns and anomalies in the time series sensor data that indicate elevated risk, allowing drivers to take preventive action before the accident happens
Solution Approach 2:
The patent implements a feedback mechanism where the anomaly detection model continuously monitors sensor data and provides real-time risk assessment. The system processes sensor data sequentially through time, updating the accident risk score as new data becomes available, enabling dynamic adjustment of driver behavior based on current risk levels
Data Source
AI summary
A computer-implemented method of predicting a risk of an accident is disclosed. The method includes computing an anomaly score based on sensor data to obtain a series of anomaly scores. The method also includes processing the anomaly score to limit a processed anomaly score below a predetermined value. The method further includes calculating an accident risk score at time of prediction by using a series of processed anomaly scores up to the time of the prediction. The method includes further outputting a prediction result based on the accident risk score.