Synthetic Sensor Data for Autonomous Model Training Under Occlusion
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Solution Overview
Problem
Autonomous control systems for vehicles face challenges in training accurate detection and control algorithms due to the high cost and time-consuming nature of gathering data, especially for simulating various environmental conditions and scenarios not covered in existing training data.
Innovation Solution
The system generates synthetic sensor data by simulating modifications to sensor data, allowing autonomous control systems to augment training data and improve model performance by mimicking scenarios such as precipitation, occlusions, and sensor malfunctions, thereby enhancing the robustness of detection and control algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real sensor data is collected for training computer models, then model performance improves, but data collection cost and time increase significantly
Solution Approach 1:
The patent creates synthetic sensor data that copies the essential characteristics of real sensor data through simulation. Virtual environments replicate physical scenarios including sensor perspectives, environmental conditions, and object interactions, generating training data without physical data collection. This copying approach maintains model training effectiveness while eliminating time-consuming field data gathering.
Solution Approach 2:
The system performs preliminary data preparation by pre-simulating diverse environmental conditions and scenarios before actual deployment. Virtual environments pre-generate training data covering edge cases, rare events, and varied conditions that would be difficult or time-consuming to capture in the physical world, allowing models to be trained in advance on comprehensive datasets.
2Adaptability or versatility
If diverse environmental scenarios are simulated for training, then model robustness improves, but simulation complexity increases
Solution Approach 1:
The virtual environment simulation system serves multiple functions simultaneously: it generates synthetic sensor data, models various environmental conditions (weather, lighting, terrain), simulates different sensor types and perspectives, and creates diverse scenario variations. This multi-functionality allows comprehensive model training without requiring separate systems for each simulation aspect, managing complexity through integrated design.
3Measurement precision
If high-capacity sensors are used for data collection, then data quality improves, but system cost increases
Solution Approach 1:
The system copies the data quality characteristics of high-capacity sensors through accurate simulation models rather than requiring physical high-capacity sensors for data collection. Virtual sensors in the simulation environment replicate the measurement precision and data characteristics of expensive real sensors, allowing training on high-quality data without the associated hardware costs.
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.