Simulated LiDAR Point Clouds With Motion Distortion Modeling
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Solution Overview
Problem
Autonomous vehicle systems face challenges in accurately simulating LiDAR data to replicate real-world scenarios, as existing methods fail to account for distortions caused by relative movement between sensors and objects, limiting the effectiveness of simulation-based testing and validation.
Innovation Solution
The generation of simulated LiDAR data that includes distortions, using techniques such as depth offset generators and intensity value generators, to create point clouds that accurately represent a moving platform's sensor data, allowing for precise simulation of LiDAR returns in a controlled environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If physical testing is used to validate autonomous vehicle systems, then reliability of validation is improved, but safety risks and inability to repeat scenarios worsen
Solution Approach 1:
The patent creates virtual copies of physical testing scenarios through simulation environments. Instead of repeatedly conducting physical tests with real vehicles, the system generates synthetic LiDAR data that replicates real-world sensor outputs, allowing unlimited repetition of test scenarios without physical risks while maintaining validation reliability
Solution Approach 2:
The system performs preliminary generation of synthetic training data before actual validation needs occur. By pre-generating diverse scenario data including edge cases in a virtual environment, the system prepares comprehensive test datasets that can be directly used for validating autonomous vehicle systems without needing to recreate physical conditions
2Reliability
If simulation environments are used to test autonomous vehicles, then safety and repeatability are improved, but accuracy of sensor data representation worsens
Solution Approach 1:
The patent replaces physical sensor systems with computational models that generate synthetic LiDAR data. Instead of using actual LiDAR sensors in physical environments, the system uses algorithms to generate point cloud data that mimics real sensor outputs, achieving both safety and measurement precision through mathematical modeling of sensor behavior
Solution Approach 2:
The system dynamically adjusts parameters of the synthetic data generation process to match real-world conditions. By modifying parameters such as point density, intensity distributions, noise characteristics, and distortion patterns in the generated LiDAR data, the system ensures that simulated sensor data accurately represents physical sensor behavior while maintaining testing safety
3Ease of manufacture
If synthetic LiDAR data is generated without distortion, then simplicity of generation is improved, but realism of sensor data worsens
Solution Approach 1:
The patent applies different quality characteristics to different regions of the generated LiDAR data. Instead of uniformly simplifying or complicating the entire dataset, the system selectively introduces distortions and variations in specific areas where they most closely match real sensor behavior, maintaining simplicity in data generation while enhancing realism where it matters most
Data Source
AI summary
A system for generating simulated LiDAR data may include a depth offset generator and an intensity value generator. The depth offset generator may be configured to receive environment data including depth information, e.g., a depth map, the environment. The depth offset generator may determine an optical flow field from the depth information and estimate depths for positions to simulate LiDAR sensor data. field. The depth offset generator can also generate timestamp information based on attributes of the simulated LiDAR sensor, and determine the estimated depths using the timestamp information. The intensity value generator may be configured to determine an intensity for pixels based on physical attributes associated with those pixels. The simulated LiDAR data may be used in simulations run on autonomous vehicle control systems.