Perception Scenario Generation With Constraint Validation for AV Training
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Solution Overview
Problem
Autonomous vehicle technology faces challenges in acquiring sufficient and high-quality training data to accurately represent various driving conditions and scenarios, particularly for perception systems.
Innovation Solution
The method involves generating perception scenarios from simulation data or logged data, which includes receiving simulation data, executing a perception simulation, generating a perception scenario, and validating it by verifying constraints to produce a validated perception scenario.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If simulation data from video game-like simulators is used to train machine learning models, then the quantity of training data is increased, but the quality and accuracy of representing real-world driving conditions deteriorates
Solution Approach 1:
The patent uses logged sensor data from real autonomous vehicle operation as a high-quality copy of real-world driving conditions. This logged data serves as ground truth to train perception systems, accurately representing real driving scenarios while providing sufficient quantity for comprehensive training.
Solution Approach 2:
The system performs preliminary data collection and logging during real vehicle operation before simulation. By capturing actual sensor data from real-world driving in advance, the system prepares high-quality training data that can be used to train perception models without relying on lower-fidelity simulation data.
2Measurement precision
If only logged sensor data from real autonomous vehicle operation is used for training, then the quality of training data is high, but the quantity of available training data is insufficient
Solution Approach 1:
The patent merges logged sensor data from real operation with simulation data to create a comprehensive training dataset. By combining these two data sources, the system achieves both high quality (from logged data) and sufficient quantity (augmented by simulation), resolving the contradiction between data quality and quantity.
Solution Approach 2:
The trained perception system is designed to be universal, capable of handling both real-world scenarios (from logged data) and simulated scenarios (from simulation data). This multi-functionality allows the system to leverage diverse data sources for training while maintaining high performance across different driving conditions.
3Productivity
If perception scenarios are generated without constraint validation, then the generation process is faster and simpler, but the reliability and accuracy of perception scenarios deteriorates
Solution Approach 1:
The system implements feedback through constraint validation, where generated perception scenarios are automatically checked against predefined physical and logical constraints. This feedback mechanism ensures that only valid, realistic scenarios are used for training, maintaining high reliability while keeping the generation process efficient through automated validation.
Solution Approach 2:
The patent performs preliminary definition of constraints before scenario generation. By establishing validation rules and constraints in advance, the system can quickly validate generated scenarios without complex real-time analysis, maintaining both speed and reliability in the perception scenario generation process.
Data Source
AI summary
Simulation data of the autonomous vehicle is processed by executing a simulation based on the simulation data to generate a simulation result. Then a perception scenario is generated from the simulation result. The generated perception scenario is validated by verifying whether a constraint is satisfied to produce a validated perception scenario. The validated perception scenario can be used to create or refine a perception model used for controlling the operation of autonomous vehicles.