AV Simulation Scenario Generation with Dynamic Actor Behavior
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Solution Overview
Problem
Current simulation environments for testing autonomous vehicles lack the ability to effectively replicate real-world scenarios, particularly in terms of actor behavior, making it difficult to ensure that simulation outcomes accurately represent potential real-world vehicle behavior.
Innovation Solution
A computer-implemented method and system for generating scenarios in simulation environments, allowing users to create and edit paths and behavioral parameters for agent vehicles, which includes rendering road layouts, marking paths, and defining behavioral parameters, enabling the simulation of various scenarios, including those with unpredictable actor behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical world testing is used to evaluate sensor processing and control systems, then testing accuracy and reliability are improved, but testing cost and time consumption increase
Solution Approach 1:
The patent creates virtual copies of real-world driving scenarios, environments, and actor behaviors through detailed 3D modeling and scenario generation systems. These digital replicas allow comprehensive testing of autonomous vehicle systems without physical deployment, maintaining test validity while eliminating time and resource constraints of physical testing.
Solution Approach 2:
The system pre-generates diverse test scenarios, environmental conditions, and actor behavior patterns before actual testing begins. By preparing extensive scenario libraries in advance, the system enables immediate execution of comprehensive test suites without requiring time-consuming setup during physical testing campaigns.
2Productivity
If simulation environments are used to test autonomous vehicle behavior, then testing efficiency is improved, but scenario realism and accuracy deteriorate
Solution Approach 1:
The system dynamically adjusts simulation parameters including weather conditions, road surface properties, lighting scenarios, and actor behavior characteristics to match real-world variations. By systematically varying these parameters across thousands of test scenarios, the system achieves both computational efficiency and realistic representation of diverse driving conditions.
Solution Approach 2:
The simulation environment implements dynamic actor behavior models that adapt to changing scenario conditions, making actors respond realistically to ego vehicle actions and environmental factors. This dynamic behavior generation ensures scenarios remain realistic while allowing efficient automated execution of diverse test cases.
3Adaptability or versatility
If diverse real-world scenarios are simulated to test all possible driving situations, then testing completeness is improved, but system complexity increases
Solution Approach 1:
The comprehensive test scenario system is segmented into independent modular components: environment models, actor behavior modules, scenario templates, and evaluation criteria. This segmentation allows the complex testing framework to be constructed from reusable building blocks, managing system complexity while achieving complete scenario coverage through systematic combination of modules.
Solution Approach 2:
The simulation platform implements universal infrastructure components that serve multiple testing functions simultaneously. A single scenario generation system handles diverse scenario types, multiple actor behaviors, various environmental conditions, and different test objectives, reducing overall system complexity through shared resources and standardized interfaces.
4Measurement precision
If manual creation of test scenarios is performed to ensure scenario quality, then scenario accuracy is improved, but scenario generation speed deteriorates
Solution Approach 1:
The scenario generation system incorporates automated validation, verification, and optimization routines that self-correct scenario parameters and behaviors without manual intervention. This self-service capability maintains high scenario accuracy through systematic checking while enabling rapid automated generation of comprehensive test suites.
Solution Approach 2:
The system implements feedback loops where generated scenarios are automatically evaluated against quality criteria, and results feed back into the generation process to refine and improve scenario accuracy. This automated feedback mechanism ensures high-quality scenarios are produced rapidly without requiring manual review of each individual scenario.
Data Source
AI summary
A computer implemented method of generating a scenario to be run in a simulation environment for testing the behaviour of an autonomous vehicle is described. An image is rendered on a display. A user can mark multiple locations to create at least one path for an agent vehicle in the rendered image. A path is generated which passes through the locations and rendered on the display. A user can define at least one behavioural parameter for controlling behaviour of the agent vehicle associated with the at least one path when the scenario is run in a simulation environment. The scenario is recorded for future use.