Traffic Situation Classification Using Directed Graph Segmentation
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Solution Overview
Problem
Current methods for analyzing and categorizing driving scenario data records are inadequate for effectively identifying and classifying critical traffic situations, particularly in autonomous driving systems, as they rely on expert knowledge and struggle with generalizing to different scenarios.
Innovation Solution
A computer-implemented method using a directed graph to segment and classify traffic situations by applying nodes and edges to sensor data, allowing for the identification of relevant segments and their temporal relationships, enabling the classification of predefined traffic scenarios without requiring extensive coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If expert knowledge-based methods are used to define test scenarios, then scenario coverage for specific test purposes is improved, but adaptability to different and similar traffic situations deteriorates
Solution Approach 1:
The patent creates abstract templates that copy the essential structure and characteristics of expert-defined scenarios, allowing these templates to be automatically matched against real-world sensor data. This enables the system to generalize across different scenarios while maintaining the reliability of expert-knowledge-based scenario design.
Solution Approach 2:
The patent develops a universal template-based classification system that can handle multiple types of traffic scenarios through a single framework. The templates are designed to be adaptable to different scenario types while maintaining consistent classification logic, thus achieving both reliability and versatility.
2Adaptability or versatility
If machine learning algorithms are used for real-world data analysis, then adaptability to different traffic situations is improved, but ease of operation and interpretability deteriorates
Solution Approach 1:
The patent introduces templates as an intermediary layer between raw sensor data and classification results. These templates serve as interpretable rules that bridge the gap between complex data patterns and meaningful scenario classifications, maintaining both adaptability and ease of operation.
Solution Approach 2:
The patent segments the classification process into distinct steps: data segmentation into relevant portions, template matching against segmented data, and classification based on matched templates. This segmentation makes the process more interpretable and easier to operate while maintaining adaptability.
3Measurement precision
If comprehensive sensor data analysis is performed to identify critical situations, then measurement precision is improved, but device complexity and processing time increases
Solution Approach 1:
The patent extracts only the relevant portions of sensor data that are necessary for scenario classification, rather than analyzing all available data. This extraction process maintains measurement precision for critical situations while reducing device complexity and processing requirements.
Solution Approach 2:
The patent applies partial action by focusing analysis only on data segments that are relevant to specific scenario templates. Instead of processing all sensor data comprehensively, the system selectively analyzes only the portions necessary for accurate classification, reducing complexity while maintaining precision.
Data Source
AI summary
A computer-implemented method and system for classifying a predefined traffic situation comprised by a data record of environment data of a motor vehicle, with an application of a directed graph to the first data record. Nodes of the directed graph segment the first data record in each case into at least one segment of a movement behavior of the ego vehicle and/or the fellow vehicle relative to a vehicle environment according to a first condition satisfied in a time interval comprising edges of the directed graph symbolizing links between the respective nodes. The predefined traffic situation is classified if all of the specified segments meet a second condition of the predefined traffic situation. A class is outputted representing the predefined traffic situation and/or a respective start and end time of the second data record comprising a segment representing the predefined traffic situation.