Autonomous Vehicle Perception Scenarios from Validated Simulation Data
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Solution Overview
Problem
Existing methods for training autonomous vehicle perception systems face challenges in acquiring sufficient and high-quality data to accurately represent a wide variety of driving conditions, as real-world data collection is insufficient, and simulation data from video games lacks realism.
Innovation Solution
A method and system for generating perception scenarios using simulation data, including logged data, to create realistic training scenarios that are validated to meet constraints, and updating perception models based on the difference between predicted and simulated results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If simulation data from video games is used for training, then data quantity is increased, but data quality and realism deteriorate
Solution Approach 1:
The patent uses real-world logged sensor data as a template to create synthetic simulation data. By copying the structure, characteristics, and patterns from real data, the system generates large volumes of training data that maintain the quality and realism of actual driving conditions while expanding the dataset size.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms real logged data into enhanced simulation data. This intermediary process involves extracting features from real data, augmenting them with simulation parameters, and validating the output against quality metrics, thereby bridging the gap between real-world data scarcity and simulation data volume.
2Reliability
If more training data is collected from real autonomous vehicle operation, then data quality improves, but data quantity remains insufficient
Solution Approach 1:
The patent performs preliminary analysis of real logged data to identify key patterns, scenarios, and edge cases before generating simulation data. By pre-processing and understanding the characteristics of real data, the system can create synthetic data that preserves these critical features while multiplying the overall dataset volume through controlled generation.
Solution Approach 2:
The patent applies parameter changes by taking real logged data and systematically varying simulation parameters (such as environmental conditions, sensor noise levels, and scenario frequencies) to generate diverse training scenarios. This allows multiplication of data quantity while maintaining quality through controlled parameter manipulation.
3Productivity
If simulation scenarios are generated without validation, then data generation speed increases, but scenario accuracy deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where generated simulation scenarios are automatically validated against predefined quality criteria and constraints. The validation process provides feedback that can be used to refine and correct scenarios, ensuring accuracy while maintaining efficient generation through automated checking rather than manual review.
Solution Approach 2:
The patent applies partial validation by focusing quality checks on the most critical aspects of perception scenarios (such as object detection accuracy, constraint satisfaction, and physical realism) rather than exhaustive validation of all parameters. This selective approach maintains high productivity while ensuring sufficient accuracy for training purposes.
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.