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Training of Neural Networks with Automated Labeling of Simulated Sensor Data
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 02, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
While convolutional neural networks (CNNs) have revolutionized ground-vehicle autonomy in the last decade, this class of algorithms requires large, truth-labeled data sets to be trained. The process of collecting and labeling training data is tedious, time-consuming, expensive, and error-prone. In order to automate this process, an automated method for training CNNs with simulated data was developed. This method utilizes physics-based simulation of sensors, along with automated truth labeling, to improve the speed and accuracy of training data acquisition for both camera and LIDAR sensors. This framework is enabled by the MSU Autonomous Vehicle Simulator (MAVS), a physics-based sensor simulator for ground vehicle robotics that includes high-fidelity simulations of LIDAR, cameras, and other sensors.
- Chris Goodin - Center for Advanced Vehicular Systems
- Suvash Sharma - Center for Advanced Vehicular Systems
- Matthew Doude - Center for Advanced Vehicular Systems
- Daniel Carruth - Center for Advanced Vehicular Systems
- Lalitha Dabbiru - Center for Advanced Vehicular Systems
- Christopher Hudson - Center for Advanced Vehicular Systems
CitationGoodin, C., Sharma, S., Doude, M., Carruth, D. et al., "Training of Neural Networks with Automated Labeling of Simulated Sensor Data," SAE Technical Paper 2019-01-0120, 2019, https://doi.org/10.4271/2019-01-0120.
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