Scenario Description Language for Autonomous Vehicle Simulation Coverage
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Solution Overview
Problem
Complex scenarios in simulation environments for autonomous vehicles are difficult to test and validate due to the impracticality of physical testing, leading to inefficiencies in scenario enumeration and computational resource utilization.
Innovation Solution
A domain-specific language (SDL) is developed to concisely describe scenarios, allowing for the instantiation of relevant scenarios in a simulation environment, reducing computational complexity and memory requirements by filtering out non-physical or marginally informative scenarios through combinatorial combination of primitives and linear temporal logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical testing is used to validate scenarios, then testing accuracy is improved, but safety risks and repeatability increase
Solution Approach 1:
The patent creates virtual copies of the physical environment, vehicles, and scenarios through simulation. Instead of physically testing autonomous vehicles on real roads, the system generates digital replicas of roads, vehicles, pedestrians, and traffic conditions. This allows accurate scenario validation without the safety risks and repeatability issues of physical testing, as the same virtual scenarios can be endlessly reproduced without endangering human lives.
Solution Approach 2:
The patent replaces the mechanical physical testing system with a computational simulation system. Rather than physically maneuvering vehicles and recreating real-world conditions, the system uses software to model and simulate scenario outcomes. This substitution eliminates the need for physical risk while maintaining testing accuracy through sophisticated mathematical models of vehicle dynamics, sensor behavior, and environmental conditions.
2Reliability
If comprehensive scenario enumeration is performed, then validation coverage is improved, but computational complexity and resource utilization worsen
Solution Approach 1:
The patent segments the vast scenario space into manageable components and parameters. Instead of attempting to enumerate every possible scenario simultaneously, the system breaks down scenarios into discrete elements such as vehicle states, environmental conditions, traffic patterns, and sensor readings. This segmentation allows for systematic exploration of the parameter space while maintaining computational tractability through structured organization of the validation process.
Solution Approach 2:
The patent employs partial action by focusing computational resources on the most critical and informative scenarios rather than attempting exhaustive enumeration of all possible scenarios. The system identifies and prioritizes scenarios that provide the highest validation value, such as edge cases and safety-critical situations, while using probabilistic methods to sample less critical scenarios. This approach achieves sufficient validation coverage without the prohibitive computational cost of complete enumeration.
3Measurement precision
If physical testing is performed to validate autonomous vehicle software, then scenario realism is improved, but time and resource efficiency worsen
Solution Approach 1:
The patent creates virtual replicas of physical scenarios that maintain realism while enabling efficient testing. The simulation system generates digital copies of roads, vehicles, pedestrians, and environmental conditions with high fidelity to real-world physics and behavior. These virtual copies can be instantiated and modified rapidly without the time constraints of physical setup, allowing numerous scenarios to be tested in parallel and significantly improving testing throughput while maintaining scenario authenticity.
Solution Approach 2:
The patent performs preliminary setup and configuration in the virtual environment before actual validation begins. Scenarios are pre-programmed with all necessary parameters, initial conditions, and expected outcomes. This preliminary action eliminates the time-consuming setup procedures required for physical testing, such as manually positioning vehicles, configuring test tracks, and coordinating human participants. The pre-configured virtual scenarios can be executed immediately, dramatically improving testing efficiency.
Data Source
AI summary
A domain specific language, or Scenario Description Language (SDL), can be used for quickly enumerating scenarios in a simulation for testing and validating interaction of an object (e.g., an autonomous vehicle) within an environment. Scenarios in a simulation are defined using one or more primitives. Primitives are used to define objects to be instantiated (such as body size, position, orientation, velocities, etc.) and/or actions to be performed by the objects in the simulation (such as wait for a period of time, goal positions, follow a particular object, etc.). The SDL enables simple creation of multiple scenarios by combining primitives combinatorially and in some examples, limiting which scenarios are created to those that correspond to combinations that provide meaningful information. Additionally, the SDL allows for instantiation to be agnostic of map features so that a particular scenario can be instantiated automatically over all possible positions within a map.


