Simulated AV Scene Diversity via Attachment System
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Solution Overview
Problem
The diversity of simulated Autonomous Vehicle (AV) environment scenes used for training machine learning models is limited by the need for actual data from AV encounters, which restricts the variety of scenarios available for training, impacting the models' performance and efficiency.
Innovation Solution
An attachment system is introduced to modify simulated AV environment scenes by adding attachments to objects based on defined probabilities and a database of possible attachments, exponentially increasing the diversity of scenarios without requiring actual data from AV encounters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If simulated AV environment scenes are based on real-world data from AV encounters, then the training data reflects actual driving conditions, but the diversity of scenarios is limited
Solution Approach 1:
The patent creates copies of real-world AV environment scenes through simulation, where each scene is replicated and modified with various attachments (pedestrians, vehicles, obstacles) added to objects. These synthetic copies preserve the core characteristics of real encounters while expanding scenario diversity through systematic variations.
Solution Approach 2:
The patent systematically changes parameters of simulated scenes by adding attachments to objects with defined probabilities. Different attachment types, positions, and configurations are applied to base scenes, transforming a limited set of real-world encounters into a diverse training dataset covering numerous scenario variations.
2Adaptability or versatility
If more diverse simulated scenes are generated without real-world data, then scenario variety increases, but the training data may lack authenticity
Solution Approach 1:
The patent performs preliminary actions by first collecting real-world AV encounter data and creating accurate base scene representations. These authenticated base scenes serve as the foundation for subsequent simulation and attachment operations, ensuring that all diverse variations originate from verified real-world conditions.
Solution Approach 2:
The simulation system acts as an intermediary between real-world data and training requirements. It takes authentic real-world encounters as input and produces diverse training scenarios as output, mediating the transformation while preserving the authenticity of the source data through controlled attachment and modification processes.
3Measurement precision
If manual annotation of geospatial data is performed to improve accuracy, then data quality increases, but the time and resources required increase significantly
Solution Approach 1:
The patent implements self-service through automated attachment systems that programmatically add objects and features to simulated scenes based on defined rules and probabilities. The system automatically generates annotations for geospatial data without requiring manual human intervention, while still maintaining high accuracy through structured attachment protocols and validation processes.
Solution Approach 2:
The patent performs preliminary automated annotation during the simulation scene generation process itself. Rather than requiring separate manual annotation steps after data collection, the attachment system pre-annotates all simulated scenes with appropriate objects, attributes, and geospatial information during the creation phase, eliminating time-consuming post-processing.
Data Source
AI summary
The present technology pertains to increasing diversity of simulated autonomous vehicle (AV) environment scenes to be used for training machine learning (ML) models. Such an increase in diversity may be achieved by selecting objects from simulated AV environemnt scenes, and determining whether to add attachments to attachment points of the objects based on probabilities associated with the attachment points. When an attachment is to be added to an attachment point, the particular attachment is selected from among a set of compatible attachments.