Synthetic LiDAR Data Generation via Physics and Machine Learning
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Solution Overview
Problem
Existing simulation systems for autonomous vehicles primarily focus on simulating physics and behaviors rather than sensory input, which limits the realism and effectiveness of testing perception systems in safety-critical situations.
Innovation Solution
The proposed system combines physics-based rendering with machine-learned models to generate synthetic LiDAR data that accurately mimics real-world data, including geometry and intensity, by processing initial point clouds to predict dropout probabilities and adjust the point cloud accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physics-based rendering is used to generate synthetic LiDAR data, then the simulation capability is improved, but the realism of sensory input is insufficient
Solution Approach 1:
The patent combines physics-based rendering with machine learning models to generate synthetic LiDAR data. The physics-based system provides the structural framework while the machine learning component adds realistic sensory characteristics, merging the advantages of both approaches to resolve the contradiction between simulation capability and sensory realism.
Solution Approach 2:
The machine learning model acts as an intermediary between the physics-based rendering system and the final synthetic LiDAR data. It processes the physics-generated data and transforms it into more realistic sensory input, mediating the transition from physically accurate but less realistic data to both physically accurate and realistic data.
2Measurement precision
If machine learning models are used to predict dropout probabilities, then the quality of synthetic data is improved, but the computational complexity increases
Solution Approach 1:
The machine learning model is trained in advance on real LiDAR data to learn dropout patterns and characteristics. This preliminary training allows the model to quickly predict dropout probabilities during synthetic data generation without requiring complex real-time computations, thus improving data quality while managing computational complexity.
Solution Approach 2:
The machine learning model learns to copy the dropout characteristics from real LiDAR data by training on actual sensor measurements. Instead of modeling the complex physical processes that cause dropouts, the system copies the observed dropout patterns from real data, achieving high quality synthetic data with reduced computational complexity.
Data Source
AI summary
The present disclosure provides systems and methods that combine physics-based systems with machine learning to generate synthetic LiDAR data that accurately mimics a real-world LiDAR sensor system. In particular, aspects of the present disclosure combine physics-based rendering with machine-learned models such as deep neural networks to simulate both the geometry and intensity of the LiDAR sensor. As one example, a physics-based ray casting approach can be used on a three-dimensional map of an environment to generate an initial three-dimensional point cloud that mimics LiDAR data. According to an aspect of the present disclosure, a machine-learned model can predict one or more dropout probabilities for one or more of the points in the initial three-dimensional point cloud, thereby generating an adjusted three-dimensional point cloud which more realistically simulates real-world LiDAR data.