AV Simulation Scenario Generation via Static Topology Matching
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Solution Overview
Problem
Current simulation environments for testing autonomous vehicles lack the ability to efficiently generate diverse and realistic scenarios that accurately reflect real-world conditions, making it difficult to thoroughly test the behavior of autonomous vehicles in all possible driving scenarios.
Innovation Solution
A computer-implemented method for generating simulation environments that includes creating scenarios with dynamic interactions between an ego object and challenger objects, using a static scene topology and accessing maps to match the scenario topology, allowing for the generation of multiple concrete scenarios based on an abstract scenario, which can be authored using editing tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical world testing is used to test autonomous vehicle behavior, then testing accuracy and realism are improved, but testing cost and time consumption increase
Solution Approach 1:
The patent creates virtual copies of real-world driving scenarios by capturing actual sensor data from physical environments and reconstructing them as simulated scenes. These virtual scene replicas preserve the authenticity of real-world conditions while enabling unlimited replay and variation without additional physical testing costs, thus maintaining testing accuracy while reducing time consumption.
Solution Approach 2:
The system modifies scenario parameters such as weather conditions, lighting, road types, and actor behaviors to generate diverse test variations from a single captured scene. This allows comprehensive testing of different driving conditions without requiring physical testing in each scenario, thereby maintaining reliability while significantly reducing testing time and cost.
2Productivity
If simulation environments are used to test autonomous vehicle behavior, then testing efficiency is improved, but scenario realism and diversity decrease
Solution Approach 1:
The patent directly copies real sensor data and environmental characteristics into the simulation environment, creating virtual scenes that are faithful reproductions of real-world conditions. This approach ensures that simulated scenarios maintain high realism and diversity while enabling efficient automated testing, thus resolving the trade-off between testing efficiency and scenario realism.
Solution Approach 2:
The system dynamically generates and modifies scenario parameters during simulation, allowing for adaptive adjustment of weather, lighting, road conditions, and actor behaviors. This dynamic capability enables the simulation to cover a wide range of realistic scenarios efficiently, maintaining both realism and testing productivity.
3Adaptability or versatility
If comprehensive scenario coverage is attempted through physical testing, then scenario diversity is improved, but resource consumption and cost increase
Solution Approach 1:
The patent captures diverse real-world driving scenarios once and creates multiple virtual copies with varying parameters. This single capture-multiple reproduction approach achieves comprehensive scenario diversity without requiring proportional physical testing resources, thus resolving the contradiction between scenario diversity and resource consumption.
Solution Approach 2:
The simulated environment serves multiple testing functions simultaneously - it can replay the same scene under different conditions, test various autonomous vehicle responses, and evaluate different sensor configurations using a single captured scenario. This multi-functionality achieves comprehensive scenario coverage with minimal resource investment.
Data Source
AI summary
A computer implemented method for generating a simulation environment for testing an autonomous vehicle, the method comprising generating a scenario comprising a dynamic interaction between an ego object and challenger object, the interaction being defined relative to a static scene topology. The method comprises providing a dynamic layer comprising parameters of the dynamic interaction and a static layer comprising the static scene topology to a simulator, searching a store of maps to access a map having a matching scene topology to the static scene topology, and generating a simulated version of the dynamic interaction using the matching scene topology.