Synthetic Sensor Data for Autonomous Control Under Occlusion
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Solution Overview
Problem
Autonomous vehicle control systems face challenges in training accurate detection and control algorithms due to the high cost and time required for gathering data, especially in simulating various environmental conditions and occlusions, which can affect sensor data quality.
Innovation Solution
A system generates synthetic sensor data to augment training data, simulating environments with modifications such as precipitation, occlusions, and sensor malfunctions, allowing computer models to improve performance and robustness in real-world conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real sensor data is collected for training computer models, then the training data reflects actual environmental conditions, but the data collection process is costly and time-consuming
Solution Approach 1:
The patent creates synthetic copies of sensor data through simulation rather than collecting real physical data. The system generates virtual sensor readings that replicate real-world environmental conditions, allowing training data to be produced quickly without physical data collection campaigns. This copying approach maintains training quality while eliminating time and cost constraints of real data gathering.
Solution Approach 2:
The system performs preliminary simulation of environmental conditions and sensor responses before actual deployment. By pre-generating synthetic training data that covers various scenarios including edge cases, the system prepares comprehensive training sets in advance, eliminating the need for time-consuming real-world data collection during model development.
2Adaptability or versatility
If diverse environmental conditions are simulated for training, then the computer model becomes more robust, but the complexity of data generation increases
Solution Approach 1:
The simulation system serves multiple functions simultaneously: it generates diverse environmental conditions, simulates sensor responses, creates occlusion scenarios, and produces labeled training data all through a single integrated platform. This multi-functionality achieves high model robustness without proportionally increasing complexity, as one system handles what would otherwise require multiple separate data collection and processing systems.
3Productivity
If synthetic data is generated to augment training data, then the training efficiency improves, but the accuracy of simulating real sensor data becomes challenging
Solution Approach 1:
The system varies multiple parameters in the synthetic data generation process including sensor positions, environmental conditions, occlusion types and positions, and sensor characteristics. By systematically changing these parameters to match real sensor behavior and environmental variability, the synthetic data maintains high fidelity to real sensor readings while enabling efficient training. The parameter variations ensure training efficiency without sacrificing measurement precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The use of synthetic data enhances the accuracy and robustness of autonomous vehicle control systems by enabling them to perform better in diverse weather conditions and occluded environments, reducing the reliance on high-capacity sensors and improving the efficiency of training processes.
Implementation Method 1
LIDAR sensor signals attenuate when absorbed by precipitation
Data Source
AI summary
An autonomous control system generates synthetic data that reflect simulated environments. Specifically, the synthetic data is a representation of sensor data of the simulated environment from the perspective of one or more sensors. The system generates synthetic data by introducing one or more simulated modifications to sensor data captured by the sensors or by simulating the sensor data for a virtual environment. The autonomous control system uses the synthetic data to train computer models for various detection and control algorithms. In general, this allows autonomous control systems to augment training data to improve performance of computer models, simulate scenarios that are not included in existing training data, and/or train computer models that remove unwanted effects or occlusions from sensor data of the environment.