Autonomous Vehicle Simulation Replay for Maneuver Validation
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Solution Overview
Problem
Existing autonomous vehicle control software requires thorough testing and validation before deployment in real-world scenarios to ensure safe interaction with other objects, but comprehensive real-world testing is limited by the availability of relevant data.
Innovation Solution
A simulation system that reconstructs real-world environments using log data to simulate various driving maneuvers, extracting metrics to evaluate and improve the software, allowing for predictive testing in new scenarios without actual deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive real-world testing is conducted to validate autonomous vehicle control software, then software reliability is improved, but the availability of relevant test data is limited
Solution Approach 1:
The patent creates virtual copies of real-world driving environments by reconstructing three-dimensional scenes from two-dimensional image data captured in actual driving scenarios. These synthesized virtual environments serve as testbeds for comprehensive software validation without requiring additional real-world data collection, thus resolving the contradiction between needing reliable validation and limited test data availability.
Solution Approach 2:
The system performs preliminary reconstruction of driving environments and pre-processing of test scenarios before actual software validation. By preparing virtual test environments in advance with diverse scenarios (including edge cases), the system enables comprehensive reliability testing without being constrained by the limited availability of real-world test data during the validation phase.
2Productivity
If extensive simulations are run to evaluate software in new scenarios, then testing completeness is improved, but computational resources and time are consumed
Solution Approach 1:
The system implements a hierarchical simulation approach where critical safety scenarios are simulated in full detail while less critical scenarios use simplified models. This partial action strategy achieves comprehensive testing coverage by focusing computational resources on the most important test cases, balancing testing completeness with acceptable computational time requirements.
Solution Approach 2:
The simulation process is divided into multiple independent stages: environment reconstruction, scenario generation, software execution, and result analysis. Each stage can be processed separately and parallelized, improving overall testing productivity while managing computational time through efficient resource allocation across different simulation segments.
Data Source
AI summary
Evaluating a simulation of an autonomous vehicle may be performed by using one or more processors to receive log data collected for a given area, generate environment data for the given area using the log data, run the set of simulations using an autonomous vehicle software, extract one or more metrics from the set of simulations, and evaluate the set of simulations using the one or more metrics. The set of simulations includes one or more of a selection simulation comprising a selection of a location related to the particular maneuver in the given area, a decision process simulation comprising a decision process playthrough for the particular maneuver in the given area, a maneuver simulation comprising a maneuver playthrough of the particular maneuver in the given area, and a replay simulation comprising a replay of the particular maneuver in a run from the log data.


