Similar Scenario Detection Using Reduced Sensor Feature Embeddings
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Solution Overview
Problem
Existing methods for determining similar scenarios from sensor data in driver assistance systems and automated driving are computationally intensive and require significant resources, necessitating a more efficient approach to identify relevant scenarios.
Innovation Solution
A method utilizing machine learning algorithms with encoders and optimization techniques to generate dimensionally reduced feature representations from sensor data augmentations, clustering similar interaction maneuvers by minimizing similarity loss, and applying LSTM layers to process trajectory data effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing methods for determining similar scenarios from sensor data are used, then comprehensive scenario analysis is achieved, but computational resources and processing time are excessively consumed
Solution Approach 1:
The patent extracts and processes only the most relevant features from sensor data using selective augmentation and feature extraction techniques. By taking out only the critical scenario-determining features rather than processing all raw sensor data, the system achieves comprehensive scenario analysis with reduced computational resource consumption.
Solution Approach 2:
The patent segments the scenario determination process into distinct stages: data augmentation, feature extraction, similarity calculation, and scenario classification. This segmentation allows each stage to be optimized independently, improving overall productivity while managing computational resources efficiently at each step.
2Reliability
If comprehensive sensor data processing is performed to identify all relevant scenarios, then scenario coverage is improved, but processing time increases significantly
Solution Approach 1:
The patent performs preliminary data augmentation and feature extraction before actual scenario matching. By preparing augmented datasets and extracting key features in advance, the system ensures comprehensive scenario coverage during execution while minimizing real-time processing time.
Solution Approach 2:
The patent applies selective data augmentation techniques that generate additional training samples only for underrepresented scenario types. This partial action approach ensures comprehensive scenario coverage for critical cases while avoiding unnecessary processing of already-well-represented scenarios, thus reducing overall processing time.
Data Source
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AI summary
The invention relates to a computer-implemented method for providing a machine learning algorithm for determining similar scenarios based on scenario data from a dataset (D) of sensor data, wherein an optimization algorithm (A3) is applied to the feature representation (18) of the first augmentation (14) of the dataset (D) of sensor data output by the first machine learning algorithm (A1), and wherein the optimization algorithm (A3) approximates the feature representation (20) of the second augmentation (16) of the dataset (D) of sensor data output by the second machine learning algorithm (A2). The invention further relates to a method for determining similar scenarios based on scenario data from a dataset (D) of sensor data and a training controller.