Parameterized Agent Controllers for Realistic Driving Simulation
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Solution Overview
Problem
Existing driving simulations struggle to accurately simulate realistic agent behaviors due to insufficient or incomplete log data, leading to inefficiencies in testing vehicle control systems, which are resource and computationally expensive.
Innovation Solution
Implement parameterized object controllers to control smart agents in simulations, using log data to determine operational parameters that reflect real-world behaviors, and evaluate their performance by comparing with non-simulated agents, adjusting parameters for improved realism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If driving simulations are created to accurately reflect real world scenarios, then the validation accuracy of vehicle control systems is improved, but the computational resources and execution cost increase significantly
Solution Approach 1:
The patent creates simplified copies of real-world driving scenarios by extracting essential behavior patterns from log data and representing them as parameterized simulations. Instead of replicating complete complex scenarios, the system copies only the critical behavioral characteristics needed for validation, significantly reducing computational requirements while preserving validation accuracy.
Solution Approach 2:
The patent transforms complex real-world driving behaviors into parameterized representations by identifying key parameters that capture essential agent behaviors. These parameters are derived from log data and used to control simulated agents, allowing the system to maintain high validation accuracy through parameter optimization rather than full scenario replication.
2Adaptability or versatility
If multiple different interacting systems and components are executed in driving simulations, then the comprehensiveness of vehicle control system testing is improved, but the execution time and computational complexity increase
Solution Approach 1:
The patent segments the complex simulation execution into distinct components: log data processing, parameter extraction, parameterized agent creation, and simulation execution. This segmentation allows each component to be optimized independently and enables parallel processing of multiple simulation scenarios, reducing overall execution time while maintaining comprehensive testing coverage.
Solution Approach 2:
The patent performs preliminary processing of log data to extract behavior parameters and create parameterized agent definitions before actual simulation execution. This preliminary action prepares the simulation environment in advance, allowing the main simulation loop to run more efficiently by using pre-processed parameters rather than processing raw data during execution.
3Reliability
If log data is used to create driving simulations, then the realism of agent behaviors is improved, but the data quality issues (noise, inconsistency, incompleteness) affect simulation accuracy
Solution Approach 1:
The patent implements feedback mechanisms where simulation results are compared against expected outcomes and log data patterns. This feedback is used to iteratively refine the extracted parameters and adjust the parameterized agent behaviors, improving simulation accuracy while maintaining realism. The system continuously validates that parameterized agents reproduce characteristic behaviors from the original log data.
Solution Approach 2:
The patent addresses data quality issues by transforming noisy, inconsistent, or incomplete log data into refined behavior parameters through statistical processing and pattern recognition. The parameter extraction process filters out noise and fills gaps by inferring missing information from available data patterns, converting low-quality raw data into high-quality simulation parameters.
Data Source
AI summary
Techniques are discussed herein for executing simulations with parameterized object controllers to control smart agents, to evaluate the agent realism of the smart agents and to validate the efficacy of the simulations. A simulation system may analyze log data captured by a vehicle operating in a physical environment, to determine observable and non-observable behavior characteristics of the agents in the environment. The simulation system may execute simulations using object controllers to control simulated objects (e.g., “smart agents”) based on parameters associated with scenarios, object types, and/or scenario locations. Log data associated with smart agent behaviors may be aggregated and compared to the behavior characteristics of agents in physical environments, to determine metrics for agent realism and simulation efficacy. Based on such metrics, simulation results may be validated and/or object controller parameters may be modified.