Spline Trajectory Modeling for Noise-Robust Synthetic Scene Generation
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Solution Overview
Problem
Autonomous vehicles face challenges in generating synthetic scenes due to noise and artifacts in road data collected from various sensors, making it difficult to recreate real-world driving scenarios accurately.
Innovation Solution
The use of spline representations to process and compress trajectory data from sensor information, allowing for the creation of synthetic scenarios that accurately replicate real-world environments by eliminating noise and retaining only essential motion characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If raw sensor data is used to generate synthetic scenes, then the data contains complete information about real-world environments, but the noise and artifacts in the data reduce the accuracy of scenario recreation
Solution Approach 1:
The patent extracts only the essential motion characteristics from raw sensor data by fitting spline curves to entity trajectories. This extraction process separates useful trajectory information from noise and artifacts, retaining only the core motion patterns needed for accurate synthetic scene generation while discarding harmful data elements.
Solution Approach 2:
The patent creates simplified spline-based representations that copy the essential behavior patterns of entities in real-world scenes. Instead of directly using noisy raw sensor data, the system generates clean spline curve copies that replicate the fundamental motion characteristics, enabling accurate scenario recreation without transferring noise to the synthetic environment.
2Reliability
If spline representations are used to process trajectory data, then noise is eliminated and essential motion characteristics are retained, but the complexity of data processing increases
Solution Approach 1:
The patent transforms raw trajectory data into spline representations by changing the parameterization of motion data. Instead of working with discrete, noisy sensor readings, the system converts trajectories into continuous spline curves defined by control points and parameters, simplifying the data structure while improving reliability for synthetic scene generation.
3Loss of information
If complete sensor data is retained for synthetic scene generation, then all environmental details are preserved, but the data size and processing requirements increase significantly
Solution Approach 1:
The patent extracts only the essential trajectory information from complete sensor data by fitting spline curves to entity paths. This extraction removes redundant and noisy data while preserving the core motion characteristics, significantly reducing data size while maintaining the environmental details necessary for realistic synthetic scene generation.
Solution Approach 2:
Instead of reducing data by removing details, the patent inverts the approach by generating synthetic scenes from simplified spline representations that inherently contain only essential information. This inversion allows the system to work with compact data structures while still capturing the fundamental environmental characteristics needed for accurate simulation.
Data Source
AI summary
The disclosed technology provides solutions for generating synthetic scenes based on sensor data and in particular, for generating synthetic representations of entities using splines. A process of the disclosed technology can include steps for extracting trajectory data associated with movement of an entity in an environment, generating splines based on the trajectory data, and generating a synthetic scene based on the splines. Systems and machine-readable media are also provided.