  End-to-end autonomous driving has made impressive progress in recent years.
Existing methods usually adopt the decoupled encoder-decoder paradigm, where
the encoder extracts hidden features from raw sensor data, and the decoder
outputs the ego-vehicle's future trajectories or actions. Under such a
paradigm, the encoder does not have access to the intended behavior of the ego
agent, leaving the burden of finding out safety-critical regions from the
massive receptive field and inferring about future situations to the decoder.
Even worse, the decoder is usually composed of several simple multi-layer
perceptrons (MLP) or GRUs while the encoder is delicately designed (e.g., a
combination of heavy ResNets or Transformer). Such an imbalanced resource-task
division hampers the learning process.
  In this work, we aim to alleviate the aforementioned problem by two
principles: (1) fully utilizing the capacity of the encoder; (2) increasing the
capacity of the decoder. Concretely, we first predict a coarse-grained future
position and action based on the encoder features. Then, conditioned on the
position and action, the future scene is imagined to check the ramification if
we drive accordingly. We also retrieve the encoder features around the
predicted coordinate to obtain fine-grained information about the
safety-critical region. Finally, based on the predicted future and the
retrieved salient feature, we refine the coarse-grained position and action by
predicting its offset from ground-truth. The above refinement module could be
stacked in a cascaded fashion, which extends the capacity of the decoder with
spatial-temporal prior knowledge about the conditioned future. We conduct
experiments on the CARLA simulator and achieve state-of-the-art performance in
closed-loop benchmarks. Extensive ablation studies demonstrate the
effectiveness of each proposed module.
