Caption-Triggered Driving Scenario Retrieval With GAN Matching
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Solution Overview
Problem
Current vehicle control systems for autonomous vehicles, particularly in complex interactions, are ineffective due to reliance on machine learning that lacks reliable data indexing and text representation methods, leading to safety concerns in dynamic environments.
Innovation Solution
A system and method utilizing a discriminator network within a generative adversarial network (GAN) to automatically learn scenario representations from text descriptions, matching vehicle log data with text/sentence queries to generate a dataset of high-matching scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning techniques are used for vehicle control, then automation is improved, but reliability deteriorates in complex interaction situations
Solution Approach 1:
The system performs preliminary actions by collecting and storing diverse driving scenario data in advance, including complex interaction situations. The discriminator network is trained beforehand with labeled data to recognize and categorize various scenario types. This preliminary preparation ensures that when complex interactions occur during autonomous operation, the system has pre-processed reference data and trained models ready to handle them reliably.
Solution Approach 2:
The system creates copies of real driving scenarios by generating synthetic training data that replicates complex interaction situations. The discriminator network learns from multiple copies of scenario variations, including edge cases and rare events that may not be frequently observed in real data. This copying approach allows the system to improve reliability through extensive training on diverse scenario representations without requiring every possible real-world situation to be physically encountered.
2Ease of operation
If text/sentence descriptions are used to query driving scenarios, then ease of operation is improved, but measurement precision deteriorates in matching accuracy
Solution Approach 1:
The system introduces an intermediary discriminator network that acts as a bridge between natural language queries and driving scenario data. The network contains embedded text representations that link linguistic descriptions to scenario features. When a user submits a text query, the intermediary network translates it into structured scenario parameters, enabling accurate matching without requiring users to understand complex data structures or query languages.
Solution Approach 2:
The system transforms the query parameter from raw text to structured scenario representations through the discriminator network. The network dynamically adjusts parameter mappings based on the input text, converting linguistic variations into consistent scenario feature vectors. This parameter transformation maintains measurement precision by ensuring that different text descriptions of the same scenario type map to identical or equivalent scenario parameters.
3Adaptability or versatility
If diverse driving scenario data is collected and stored, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system segments the large volume of driving scenario data into distinct categories and types using the discriminator network. Data is organized by scenario characteristics such as interaction type, environment, weather conditions, and traffic patterns. This segmentation allows the system to manage diverse data collections through modular, organized structures rather than monolithic storage, reducing the complexity of data retrieval and processing while maintaining comprehensive adaptability.
Data Source
AI summary
A method for triggering capture of diverse driving data from captions is described. The method includes training a discriminator network to identify similarities between a received text description and a received scene description. The method also includes feeding a trained discriminator network with real scene information along with text/sentence descriptions to verify whether the real scene information matches the text/sentence description. The method further includes generating a dataset of diverse driving scenarios retrieved from a dataset of vehicle driving log data in response to a text/sentence query.