  Imitation learning is becoming more and more successful for autonomous
driving. End-to-end (raw signal to command) performs well on relatively simple
tasks (lane keeping and navigation). Mid-to-mid (environment abstraction to
mid-level trajectory representation) or direct perception (raw signal to
performance) approaches strive to handle more complex, real life environment
and tasks (e.g. complex intersection). In this work, we show that complex urban
situations can be handled with raw signal input and mid-level representation.
We build a hybrid end-to-mid approach predicting trajectories for neighbor
vehicles and for the ego vehicle with a conditional navigation goal. We propose
an original architecture inspired from social pooling LSTM taking low and mid
level data as input and producing trajectories as polynomials of time. We
introduce a label augmentation mechanism to get the level of generalization
that is required to control a vehicle. The performance is evaluated on CARLA
0.8 benchmark, showing significant improvements over previously published state
of the art.
