Graph-Based Autonomous Driving Simulation State Management
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Solution Overview
Problem
Autonomous driving simulations require testing with millions of kilometers of scenarios, leading to redundant simulation segments and excessive computational effort due to the need to replicate similar scenarios with varying parameters, which is inefficient and time-consuming.
Innovation Solution
A graph-based approach is implemented in autonomous driving simulations to store and link simulation states as nodes, allowing for the detection of branching points and execution of separate simulation sessions from stored states with different parameter variations, reducing redundant computations by transitioning between these states efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the same simulation scenario is executed multiple times with different parameter variations, then comprehensive testing coverage is achieved, but computational redundancy increases significantly
Solution Approach 1:
The simulation execution is segmented into distinct phases: common setup phase (executed once) and variation-specific phase (executed multiple times). The system identifies and separates the invariant portion of the simulation from the variant portion, allowing the common scene content and actor configurations to be loaded once while only re-executing the parameter-varying segments for each test case.
Solution Approach 2:
The system performs preliminary actions by pre-loading and preparing the common simulation environment, scene content, and actor configurations before the actual parameter variations are applied. This preliminary setup is cached and reused across multiple simulation runs, eliminating the need to repeatedly load and initialize identical simulation data.
2Reliability
If full simulation runs are executed for each parameter variation, then complete test scenarios are validated, but execution time increases exponentially
Solution Approach 1:
The simulation timeline is segmented at identified branching points, allowing the system to execute the common prefix of simulation runs once and then branch off to execute only the varying portions for each parameter set. This segmentation transforms exponential execution time into linear execution time relative to the number of parameter variations.
Solution Approach 2:
Instead of executing complete simulation runs for each parameter variation, the system performs partial executions by leveraging the common setup from previous runs and only re-executing the necessary varying segments. This partial action approach maintains validation reliability while dramatically reducing total execution time.
3Measurement precision
If scene content and actors are reloaded for each simulation variation, then simulation accuracy is maintained, but computational resources are wasted
Solution Approach 1:
The system creates and caches a copy of the common simulation environment, scene content, and actor configurations that can be reused across multiple parameter variations. Instead of reloading original data files for each run, the system references and utilizes this cached copy, maintaining simulation accuracy while eliminating redundant I/O operations and memory allocation.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on non-transitory computer storage medium(s), for efficiently executing autonomous driving (AD) simulations. The AD simulation can include segmented time events that include variations. The simulation including its variations can be implemented as a graph structure in which the simulation may be executed in a manner similar to traversing a graph.