Multi-Phase Simulation Initialization for Autonomous Vehicles
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Solution Overview
Problem
Current simulation initialization processes for autonomous vehicles lack efficiency in accurately representing real-world behavior, leading to inaccuracies in simulation outputs due to drift from actual road geometry and performance differences.
Innovation Solution
A multi-phase simulation initialization process involving a warmup phase for decoupling control and vehicle dynamics models, a blending phase for gradual input integration, a settlement phase for oscillation reduction, and an optional finalization phase for transient settling, all utilizing real-world data for precise adjustments and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional simulation initialization processes are used, then the simulation can start quickly, but the simulation outputs drift from actual road geometry and real-world behavior
Solution Approach 1:
The patent applies preliminary action by performing a warmup phase before the actual simulation evaluation. During this warmup phase, the control stack and vehicle dynamics models are initialized using real-world data from recorded trips, allowing the system to settle into realistic operating conditions before measurements begin. This preliminary initialization ensures that when the evaluation period starts, the simulation is already aligned with real-world behavior, eliminating drift without requiring excessive initialization time.
Solution Approach 2:
The simulation initialization process is segmented into distinct phases: a warmup phase for initialization and an evaluation phase for measurement. This segmentation allows the system to separate the time-consuming initialization activities (loading real-world data, warming up control stacks, initializing vehicle dynamics) from the actual evaluation period. By dividing the process this way, the patent ensures that initialization time does not contaminate the evaluation results while still achieving accurate simulation outputs.
2Measurement precision
If real-world data is used for initialization adjustments, then simulation accuracy improves, but processing complexity increases
Solution Approach 1:
The patent applies self-service by using real-world data that has already been collected from actual vehicle trips to automatically initialize and adjust the simulation parameters. The system serves itself by leveraging its own operational data to calibrate the virtual environment, eliminating the need for manual configuration or external calibration processes. This approach improves simulation accuracy while keeping the process manageable, as the data comes from the system's own operations rather than requiring complex external datasets.
3Reliability
If multiple initialization phases are implemented, then drift and oscillations are minimized, but the initialization process becomes more complex
Solution Approach 1:
The patent applies dynamics by implementing a multi-phase initialization process that transitions the simulation through different states: first a warmup phase where parameters are dynamically adjusted using real-world data, then an evaluation phase where the system operates in a stable measured state. This dynamic approach allows the system to adapt during initialization, minimizing drift and oscillations by progressively settling into realistic operating conditions rather than attempting static preconfiguration.
Data Source
AI summary
Architectures and techniques for initializing a simulation architecture are described. A platform model and an environment model are run on recorded data during a first period of time. The platform model and the environment model provide information to control modules. The platform model is run on a blended combination of recorded data and data from the set of control modules and the environment model is run on processed output data from the platform model during a second period of time. The platform model is run on data from the set of control modules and the environment model is run on output from the platform model from a previous period of time during a third period of time. Output signals from the platform model can be adjusted based on a subset of the recorded data during the first, second and/or third period of time.