Log-Based Driving Simulation Agent Conversion
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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due to noisy, inconsistent, or incomplete data, and are resource-intensive, often resulting in short-lived and outdated tests when vehicle controllers change.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 accurate real-world data to represent real scenarios, then simulation realism is improved, but data quality issues (noisy, inconsistent, or incomplete data) make creation and execution difficult and expensive
Solution Approach 1:
The patent uses playback agents that copy and replay recorded real-world driving log data in the simulation environment. This allows the simulation to accurately represent real scenarios by directly copying historical driving data, avoiding the need to manually create complex simulation scenarios while maintaining high realism.
Solution Approach 2:
The system performs preliminary data collection in the form of driving logs from real vehicles before simulation. These logs are pre-processed and stored, allowing the simulation to reuse this prepared data multiple times without re-collecting, thus reducing the complexity of simulation creation while maintaining realism.
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 resource and computational cost increases
Solution Approach 1:
The patent dynamically converts playback agents to smart agents based on interaction detection. This dynamic adaptation allows the simulation to maintain comprehensiveness by adding intelligent behavior only when interactions occur, rather than having all agents be computationally expensive smart agents throughout, thus reducing overall computational resource consumption.
Solution Approach 2:
The simulation system segments agents into different types (playback agents and smart agents) with different computational requirements. By dividing the agent population into these segments based on their role and interaction needs, the system reduces overall computational resource consumption while maintaining simulation comprehensiveness.
3Duration of action of stationary object
If playback agents are converted to smart agents during simulation interactions, then simulation durability is improved, but conversion overhead and system complexity increases
Solution Approach 1:
The simulation system uses feedback from detected interactions to trigger the conversion of playback agents to smart agents. This feedback mechanism allows the system to maintain durability by converting agents only when necessary (when interactions occur), rather than converting all agents upfront, thus managing conversion complexity more effectively.
Solution Approach 2:
The system implements self-service by automatically detecting interactions and triggering conversions without manual intervention. This automated self-service approach reduces the operational complexity of agent conversion while maintaining simulation durability, as the system manages its own agent population dynamically based on simulation needs.
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.