Abnormal Driving Detection via Normal Behavior Deviation
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Solution Overview
Problem
Existing systems face challenges in accurately detecting abnormal driving behaviors without pre-collected data for abnormal behavior models, due to variations in driver behavior and difficulty in collecting observed values during abnormal operations.
Innovation Solution
An abnormal driving behavior detection system that calculates mode probabilities for normal driving modes using previous observed values and predicts deviations from these modes to determine abnormal behavior without relying on abnormal behavior models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If abnormal behavior models are prepared by collecting observed values during abnormal driving operations, then detection accuracy may be improved, but data collection becomes extremely difficult and time-consuming
Solution Approach 1:
Instead of modeling abnormal behaviors directly, the patent inverts the approach by modeling only normal driving behaviors. The system determines abnormality by detecting deviations from the normal behavior model, eliminating the need to collect and model rare abnormal driving data.
Solution Approach 2:
The patent extracts and models only the normal behavior patterns from driving data, separating them from abnormal behaviors. By focusing exclusively on normal behavior modeling, the system avoids the practical difficulties of collecting sufficient abnormal driving data while still achieving accurate abnormal behavior detection.
2Measurement precision
If comprehensive abnormal behavior models covering all variations of abnormal driving behaviors are prepared, then detection accuracy is improved, but system complexity and data requirements increase significantly
Solution Approach 1:
The patent simplifies the modeling task by inverting the approach: instead of creating multiple complex models for different abnormal behaviors, it creates a single model for normal behavior. This dramatically reduces model complexity while maintaining detection capability across all types of abnormal behaviors.
Solution Approach 2:
The normal behavior model serves multiple functions: it represents all normal driving patterns and simultaneously enables detection of all types of abnormal behaviors through deviation analysis. This universal model replaces what would otherwise require multiple specialized abnormal behavior models.
3Reliability
If traditional abnormal behavior detection methods are used requiring pre-collected abnormal data, then theoretical detection capability is improved, but practical implementation becomes difficult
Solution Approach 1:
The system uses readily available normal driving data that is continuously generated during normal operation to build the detection model. This self-service approach eliminates the need for external data collection efforts during abnormal conditions, making the system practical to implement.
Solution Approach 2:
The normal behavior model is built in advance using normal driving data collected during regular operation. This preliminary modeling action prepares the system for future abnormal behavior detection without requiring any special data collection during actual abnormal events.
Data Source
AI summary
In an abnormal driving behavior detection system for a vehicle, an obtainer repeatedly obtains an observed value indicative of at least one of a running condition of the vehicle and a driver's driving operation of the vehicle. A mode-probability calculator calculates, each time an observed value is obtained at a given obtaining timing as a target obtained value, a mode probability for each of driving modes as a function of one or more previous observed values. A deviation calculator obtains a predicted observed value for each driving mode using a driver's normal behavior model defined therefor, and calculates a deviation of the target observed value from the predicted observed value for each driving mode. An abnormality determiner determines whether there is at least one driver's abnormal behavior based on the mode probability for each driving mode and the deviation calculated for each driving mode.