Sensor Data Stream Analysis Using Dynamic Time Warping for Traffic Scenario Recognition
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Solution Overview
Problem
Existing driver assistance systems face challenges in reliably recognizing and analyzing traffic scenarios from sensor data streams, particularly in identifying known scenarios amidst variations, which affects their performance and accuracy in autonomous interventions.
Innovation Solution
A method utilizing dynamic time warping to map sections of sensor data streams onto templates, calculating a similarity measure to assign known traffic scenarios, and dynamically adapting templates to improve recognition and classification efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data streams are analyzed using traditional methods to identify traffic scenarios, then the system can recognize known scenarios, but the computational complexity increases and processing time extends when dealing with variations in the scenarios
Solution Approach 1:
The patent pre-generates template data streams representing various traffic scenarios and their variations before actual analysis is needed. These templates are stored in a database for quick comparison during real-time operation, eliminating the need to process entire raw sensor streams from scratch and significantly reducing processing time while maintaining recognition reliability
Solution Approach 2:
The patent segments the sensor data stream into smaller sections and compares each section against corresponding segments of template data streams. This segmentation allows for more efficient localized matching rather than processing entire long sequences, reducing computational complexity while preserving scenario recognition accuracy
2Measurement precision
If the system processes entire sensor data streams to ensure accurate traffic scenario identification, then measurement precision improves, but computational complexity increases
Solution Approach 1:
The patent divides both the sensor data stream and template data streams into comparable segments, allowing for efficient section-by-section matching. This segmentation reduces the computational burden of comparing entire long sequences while maintaining overall scenario identification accuracy through cumulative segment matching
Solution Approach 2:
The patent creates template copies of typical traffic scenario data streams and stores them in a database. During analysis, the system compares incoming sensor data against these pre-generated templates rather than processing raw data from scratch, significantly reducing computational complexity while maintaining identification precision
Data Source
AI summary
The invention relates to a method and to a device for analyzing a sensor data stream, which characterizes a vehicle environment, with respect to a presence of traffic scenarios. The invention further relates to a method for guiding a vehicle. A similarity measure, which indicates the degree of correspondence between a section of the sensor data stream and at least one template stored in a database, is determined by mapping the section of the sensor data stream to the at least one template, preferably by means of dynamic time normalization. The template thereby characterizes a known traffic scenario. The known traffic scenario is assigned to the section of the sensor data stream, if the similarity measure satisfies a predetermined similarity criterion.