  In this work, we aim to achieve efficient end-to-end learning of driving
policies in dynamic multi-agent environments. Predicting and anticipating
future events at the object level are critical for making informed driving
decisions. We propose an Instance-Aware Predictive Control (IPC) approach,
which forecasts interactions between agents as well as future scene structures.
We adopt a novel multi-instance event prediction module to estimate the
possible interaction among agents in the ego-centric view, conditioned on the
selected action sequence of the ego-vehicle. To decide the action at each step,
we seek the action sequence that can lead to safe future states based on the
prediction module outputs by repeatedly sampling likely action sequences. We
design a sequential action sampling strategy to better leverage predicted
states on both scene-level and instance-level. Our method establishes a new
state of the art in the challenging CARLA multi-agent driving simulation
environments without expert demonstration, giving better explainability and
sample efficiency.
