SimNet: Learning Reactive Self-driving Simulations from Real-world Observations
Luca Bergamini, Yawei Ye, Oliver Scheel, Long Chen, Chih Hu, Luca Del Pero, Błazej Osinski, Hugo Grimmett and Peter Ondruska
Road testing is:
How it works
At each frame, SimNet predicts the next position of each agent independently and the next frame is updated
Examples of agents being controlled by SimNet. SimNet agents exhibit realistic behaviours across different scenes.
Compared to log-replay agents, SimNet agents can react properly to the SDV behaviours.
SimNet error decreases when more data is available for training.
Evaluating planning system
We implemented and tested an existing ML planner based on  using both log-replay and SimNet agents. SimNet decreases false positives and exposes false negatives errors of the planning system.
Reducing false positives
The car behind the SDV runs over it
The same car keeps a safe distance when SimNet controls it
Discovering false negatives
The SDV looks at the car behind to sprint back
The car behind is now waiting for the SDV to start