  We present a learning-based planner that aims to robustly drive a vehicle by
mimicking human drivers' driving behavior. We leverage a mid-to-mid approach
that allows us to manipulate the input to our imitation learning network
freely. With that in mind, we propose a novel feedback synthesizer for data
augmentation. It allows our agent to gain more driving experience in various
previously unseen environments that are likely to encounter, thus improving
overall performance. This is in contrast to prior works that rely purely on
random synthesizers. Furthermore, rather than completely commit to imitating,
we introduce task losses that penalize undesirable behaviors, such as
collision, off-road, and so on. Unlike prior works, this is done by introducing
a differentiable vehicle rasterizer that directly converts the waypoints output
by the network into images. This effectively avoids the usage of heavyweight
ConvLSTM networks, therefore, yields a faster model inference time. About the
network architecture, we exploit an attention mechanism that allows the network
to reason critical objects in the scene and produce better interpretable
attention heatmaps. To further enhance the safety and robustness of the
network, we add an optional optimization-based post-processing planner
improving the driving comfort. We comprehensively validate our method's
effectiveness in different scenarios that are specifically created for
evaluating self-driving vehicles. Results demonstrate that our learning-based
planner achieves high intelligence and can handle complex situations. Detailed
ablation and visualization analysis are included to further demonstrate each of
our proposed modules' effectiveness in our method.
