Abnormal Driving Identification Using Scenario-Aware Behavior Analysis
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Solution Overview
Problem
Conventional methods for identifying abnormal driving behavior lack accuracy due to the failure to consider driving scenarios, leading to misidentification of normal behaviors as abnormal.
Innovation Solution
A method that combines vehicle driving behavior data with current driving scenario data using algorithm logic tailored to specific scenarios, employing clustering analysis and neural networks to accurately distinguish between normal and abnormal driving behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods identify all abrupt braking as abnormal driving behavior, then the identification process is simple, but the identification accuracy deteriorates due to misclassification of normal braking scenarios
Solution Approach 1:
The patent segments the identification process into two distinct stages: first identifying suspicious abnormal driving behavior based on driving behavior data, then verifying whether such behavior is truly abnormal by analyzing driving scenario data. This segmentation allows the system to handle complex identification tasks through modular processing, improving accuracy without overwhelming system complexity
Solution Approach 2:
The patent introduces driving scenario data as an intermediary element that mediates between raw driving behavior data and final abnormal behavior identification. This intermediary layer provides contextual information (such as presence of front vehicles, road conditions) that helps distinguish normal braking from abnormal braking, resolving the accuracy-complexity contradiction
2Measurement precision
If driving scenario data is incorporated into the identification process, then identification accuracy improves, but the complexity of data processing increases
Solution Approach 1:
The patent performs preliminary classification of driving behavior data to identify suspicious abnormal behaviors before conducting detailed scenario analysis. This preliminary action filters out clearly normal behaviors early in the process, reducing the amount of data that requires complex scenario-based analysis and thereby managing processing difficulty while maintaining accuracy
Solution Approach 2:
The patent applies different analysis depths and methods to different types of driving behaviors based on their suspicious abnormality scores. High-suspicion cases receive comprehensive scenario analysis while low-suspicion cases undergo simpler verification, optimizing resource allocation and managing processing complexity through differentiated quality of analysis
Data Source
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AI summary
This application relates to the automated driving field, and discloses a method for identifying abnormal driving behavior, a system, and a vehicle including the system. The method for identifying abnormal driving behavior includes: obtaining vehicle driving behavior data, and determining, based on the vehicle driving behavior data, whether a vehicle is in a state of suspicious abnormal driving behavior; obtaining current vehicle driving scenario data if the vehicle is in the state of suspicious abnormal driving behavior; and determining, based on the vehicle driving behavior data and the current vehicle driving scenario data, whether the suspicious abnormal driving behavior is abnormal driving behavior. In the technical solutions of this application, current driving scenario information is introduced to an identification process of abnormal driving behavior of the vehicle, so that accuracy of identifying the abnormal driving behavior is improved.