Synthetic data generation — beating the data challenge of automated driving
Data is critical to neural network (NN) development. Automatic annotation is the most effective and cheapest way to generate training and validation data from real-world recordings. But what about hard-to-capture scenarios or corner cases that hardly ever occur in real life? Automated driving systems still need to be aware of such events.