Parametric Scenario Generation for Autonomous Driving Simulation
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Solution Overview
Problem
Current autonomous vehicle systems face challenges in effectively simulating and training for complex driving scenarios, which are resource-intensive and time-consuming in real-world settings, limiting their ability to handle diverse and unpredictable conditions.
Innovation Solution
A method and apparatus that utilize a simulator to generate and evaluate driving scenarios with parametric variations, assigning performance metrics to simulate and quantify driving complexity, allowing for rapid iteration and variation of scenarios, including weather, traffic density, and sensor malfunctions, to create a rich library of training data for cognitive models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world testing is used to evaluate autonomous driving systems in complex scenarios, then system reliability is improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The patent creates virtual copies of real-world driving scenarios through simulation environments. These synthetic scenarios replicate complex driving conditions (weather, traffic, road conditions) without requiring physical testing, thereby maintaining reliability assessment capability while dramatically reducing time and resource consumption.
Solution Approach 2:
The system performs preliminary evaluation of autonomous driving systems through simulation before real-world deployment. By pre-testing various scenarios and edge cases in virtual environments, the system identifies and addresses potential failures beforehand, improving reliability while avoiding time-consuming real-world trial-and-error testing.
2Adaptability or versatility
If diverse driving scenarios are simulated to improve system performance, then adaptability is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex simulation system into modular components: scenario generation modules, parameter variation modules, evaluation modules, and training modules. Each module handles specific aspects of scenario simulation independently, allowing diverse scenarios to be generated through combinatorial logic rather than monolithic complexity, thereby improving adaptability while managing system complexity.
Solution Approach 2:
The system dynamically adjusts scenario parameters and complexity levels based on training needs and system performance. Rather than maintaining fixed high complexity, the simulation adapts scenario difficulty and diversity to match current system capabilities, enabling progressive improvement in adaptability without proportional increases in system complexity.
3Productivity
If extensive parametric variations are applied to generate training data, then productivity is improved, but computational resources increase
Solution Approach 1:
The patent systematically varies key parameters (weather conditions, traffic density, road types, sensor failures) to generate diverse training scenarios. By focusing variations on the most critical parameters that impact autonomous driving performance, the system achieves high productivity in training data generation while avoiding computational waste on less significant parameter variations.
Solution Approach 2:
The system applies parametric variations selectively to the most critical scenario elements rather than exhaustively varying all possible parameters. This partial action approach generates sufficient training diversity for high productivity while constraining computational resource requirements by focusing variations where they provide maximum training value.
Data Source
AI summary
The present application generally relates to methods and apparatus for evaluating driving performance under a plurality of driving scenarios and conditions. More specifically, the application teaches a method and apparatus for testing a driving scenario repetitively while altering a parametric variation, such as fog level, in order to evaluate driving system performance under changing conditions.