Driving Simulation Agent Conversion for Realistic Interaction Testing
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Solution Overview
Problem
Existing driving simulations face challenges in accurately representing real-world scenarios due to noisy, inconsistent, or incomplete data, and are resource-intensive, often leading to short-lived and outdated tests when vehicle controllers change.
Innovation Solution
The implementation of log-based driving simulations that convert playback agents to smart agents during interactions, allowing for dynamic decision-making and path adjustments within the simulation, thereby enhancing realism and durability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driving simulations use complete and accurate real-world data to accurately represent real-world scenarios, then simulation realism is improved, but data processing complexity and resource requirements increase
Solution Approach 1:
The patent extracts only the essential elements needed for simulation from complete real-world data. It uses selective data extraction to obtain minimum necessary data for creating playback agents, filtering out noisy and redundant information while preserving critical driving scenario elements.
Solution Approach 2:
The patent creates simplified copies of real-world agents as playback agents that replicate essential behaviors without requiring complete original data. These playback agent copies enable realistic simulation scenarios while reducing data processing complexity through behavioral replication rather than full data representation.
2Adaptability or versatility
If driving simulations execute multiple different interacting systems and components including vehicle control systems and agents, then simulation comprehensiveness is improved, but computational resources and execution time increase
Solution Approach 1:
The patent implements dynamic conversion between playback agents and smart agents based on interaction requirements. Playback agents provide lightweight simulation for normal operations, while smart agents are dynamically activated only when interactions require intelligent decision-making, optimizing computational resource usage while maintaining simulation comprehensiveness.
Solution Approach 2:
The simulation system automatically manages agent conversions without external intervention. The system self-regulates computational resources by converting agents based on detected interaction requirements, eliminating the need for manual agent management while maintaining comprehensive simulation capabilities.
3Loss of time
If driving simulations use playback agents based on log data, then simulation setup time is reduced, but agent behavior realism deteriorates when interactions require dynamic decision-making
Solution Approach 1:
The patent performs preliminary creation of playback agents from log data to enable rapid simulation setup. These pre-configured playback agents provide immediate simulation capability while maintaining the option for dynamic conversion to smart agents when interaction realism requirements demand more sophisticated behavior.
Solution Approach 2:
The patent introduces smart agents as intermediaries between playback agents and complex interactions. When playback agents encounter scenarios requiring intelligent decision-making, the system introduces smart agents to mediate these interactions, preserving both rapid setup benefits and interaction realism.
4Reliability
If driving simulations convert all playback agents to smart agents to improve interaction realism, then agent behavior realism is improved, but computational resource consumption increases
Solution Approach 1:
The patent applies different agent qualities locally based on interaction requirements. Instead of uniformly converting all playback agents to smart agents, the system selectively converts only those agents involved in interactions requiring intelligent decision-making, optimizing the balance between realism and computational resources.
Solution Approach 2:
The patent dynamically changes agent parameters by converting between playback and smart agent states based on interaction context. This parameter transformation allows the system to adapt computational resource allocation to actual simulation needs, maintaining realism only where necessary.
Data Source
AI summary
Techniques are discussed herein for executing log-based driving simulations to evaluate the performance and functionalities of vehicle control systems. A simulation system may execute a log-based driving simulation including playback agents whose behavior is based on the log data captured by a vehicle operating in an environment. The simulation system may determine interactions associated with the playback agents, and may convert the playback agents to smart agents during the driving simulation. During a driving simulation, playback agents that have been converted to smart agents may interact with additional playback agents, causing a cascading effect of additional conversions. Converting playback agents to smart agents may include initiating a planning component to control the smart agent, which may be based on determinations of a destination and/or driving attributes based on the playback agent.