SimNet: Learning Reactive Self-driving Simulations from Real-world Observations
Luca Bergamini Yawei Ye Oliver Scheel Long Chen Chih Hu Luca Del Pero Błażej Osiński Hugo Grimmett Peter Ondruska Luca Bergamini Yawei Ye Oliver Scheel Long Chen Chih Hu Luca Del Pero Błażej Osiński Hugo Grimmett Peter Ondruska
In this work we present a simple end-to-end trainable machine learning system capable of realistically simulating driving experiences. This can be used for verification of self-driving system performance without relying on expensive and time-consuming road testing. In particular, we frame the simulation problem as a Markov Process, leveraging deep neural networks to model both state distribution a...