Driver Urgency Scoring With Context-Aware Anomaly Detection
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Solution Overview
Problem
Existing methods for determining driver urgency in vehicles lack accuracy and reliability due to insufficient consideration of contextual information, leading to suboptimal classification of driving behavior.
Innovation Solution
A computer-implemented method that fetches ego vehicle dynamic data and contextual information, generates a feature vector, classifies it using supervised or unsupervised algorithms, and determines an urgency score by tracking anomalies, incorporating factors like road congestion, lane changes, and weather data to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If contextual information is not considered in urgency detection, then the system complexity is reduced, but the measurement precision of driver urgency deteriorates
Solution Approach 1:
The system segments the feature vector into multiple components: vehicle dynamic data (speed, acceleration, jerk), contextual information data (congestion, weather, road conditions), and behavioral patterns. This segmentation allows the system to process and evaluate each component separately while maintaining overall accuracy, resolving the contradiction between measurement precision and system complexity.
Solution Approach 2:
The system transitions from analyzing single-dimensional driving parameters to multi-dimensional analysis by incorporating contextual information across different dimensions (environmental, vehicular, behavioral). This dimensional expansion enables more accurate urgency detection while the modular architecture manages the resulting complexity through structured data organization and processing pipelines.
2Reliability
If multiple data sources are integrated for urgency detection, then the reliability of classification improves, but the loss of information processing increases
Solution Approach 1:
The system extracts only the most relevant features from multiple data sources through the feature vector construction process. By selecting and extracting key parameters (speed, acceleration, jerk, congestion level, weather conditions) rather than processing all raw data, the system maintains classification reliability while reducing information processing load and preventing information loss.
Solution Approach 2:
The system transforms raw data from multiple sources into standardized parameters within a unified feature vector. This parameter transformation allows different data sources to be integrated effectively while managing processing complexity, maintaining reliability through consistent parameter representation across all input sources.
3Speed
If real-time contextual data is processed, then the detection speed is improved, but the use of energy increases
Solution Approach 1:
The system processes data at different levels of detail based on situational requirements. For normal driving conditions, it uses partial processing with lower computational demand, while activating full real-time processing only when urgency indicators are detected or contextual conditions warrant heightened monitoring. This approach maintains detection speed when needed while reducing energy consumption during routine operation.
Data Source
AI summary
The invention proposes a computer-implemented method for determining an urgency score of a driver (12) of a ego vehicle (10) based on contextual information data (26), the method comprising: a) fetching vehicle dynamic data (24) that are indicative of at least one quantity related to the motion of the ego vehicle (10); b) fetching contextual information data (26) that are indicative of at least one property external to the ego vehicle (10); c) generating a feature vector from the vehicle dynamic data (24) and the contextual information data (26); d) classifying the feature vector into an urgency class or a non-urgency class and tracking the amount of occurrences of the urgency class with an anomaly counter; and e) determining an urgency score based on the value of the anomaly counter.