NEURAL NETWORK TRAINING USING GROUND TRUTH DATA AUGMENTED WITH MAP INFORMATION FOR AUTONOMOUS MACHINE APPLICATIONS

摘要

In various examples, training sensor data generated by one or more sensors of autonomous machines may be localized to high definition (HD) map data to augment and/or generate ground truth data – e.g., automatically, in embodiments. The ground truth data may be associated with the training sensor data for training one or more deep neural networks (DNNs) to compute outputs corresponding to autonomous machine operations – such as object or feature detection, road feature detection and classification, wait condition identification and classification, etc. As a result, the HD map data may be leveraged during training such that the DNNs – in deployment – may aid autonomous machines in navigating environments safely without relying on HD map data to do so.