Driving Simulation Agent Conversion for Durable Scenario Evaluation
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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scenarios due to noisy, inconsistent, or incomplete data, and are resource-intensive, often leading to short-lived and outdated tests when vehicle controllers change.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 execution, allowing for dynamic decision-making and path adjustments to prevent interactions, thereby creating a more robust and realistic simulation environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driving simulations use detailed real-world data to accurately represent scenarios, then simulation realism is improved, but data quality issues (noisy, inconsistent, incomplete) and computational resources increase
Solution Approach 1:
The patent introduces an intermediary data processing layer that mediates between raw real-world sensor data and the simulation system. This intermediary layer filters, validates, and standardizes data before it enters the simulation, reducing noise and inconsistency while preserving essential realism. The intermediary acts as a buffer that transforms complex raw data into usable simulation inputs without requiring complete data perfection.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting simulation parameters based on data quality metrics. When data quality deteriorates below thresholds, the system automatically modifies simulation parameters to maintain acceptable realism levels. This allows the simulation to adapt to varying data quality conditions, preserving reliability even when complete high-quality data is unavailable.
2Reliability
If driving simulations execute multiple interacting systems and components including vehicle control systems and agents, then evaluation comprehensiveness is improved, but computational resources and execution time increase
Solution Approach 1:
The patent segments the simulation execution into discrete, independently manageable components. Each vehicle control system, agent, and environmental element is divided into separate executable modules that can be initialized and terminated independently. This segmentation allows the system to execute only the necessary components for each specific test scenario, reducing overall computational resource consumption while maintaining evaluation comprehensiveness through selective component execution.
Solution Approach 2:
The patent implements periodic action by executing simulation components in scheduled intervals rather than continuously. The simulation system periodically updates agent behaviors, refreshes environmental conditions, and cycles through different test scenarios. This periodic execution pattern reduces computational resource consumption by allowing systems to enter low-power states between updates while maintaining evaluation comprehensiveness through regular assessment cycles.
3Reliability
If driving simulations are created with complete and accurate data, then simulation quality is improved, but data collection time and costs increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and validating data during the data collection phase itself. Rather than waiting until simulation creation to identify data quality issues, the system performs preliminary filtering, consistency checks, and completeness validation during data acquisition. This preliminary action reduces the time needed for later data processing and allows simulation creation to proceed more quickly with already-validated data subsets.
Solution Approach 2:
The patent implements partial action by using subsets of available data that meet minimum quality thresholds rather than requiring complete datasets. The system identifies and utilizes the essential portion of data needed for specific simulation objectives, accepting that not all collected data will be used. This approach reduces data collection time by focusing on critical data elements while maintaining sufficient simulation quality for evaluation purposes.
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.