  Safe autonomous driving requires robust detection of other traffic
participants. However, robust does not mean perfect, and safe systems typically
minimize missed detections at the expense of a higher false positive rate. This
results in conservative and yet potentially dangerous behavior such as avoiding
imaginary obstacles. In the context of behavioral cloning, perceptual errors at
training time can lead to learning difficulties or wrong policies, as expert
demonstrations might be inconsistent with the perceived world state. In this
work, we propose a behavioral cloning approach that can safely leverage
imperfect perception without being conservative. Our core contribution is a
novel representation of perceptual uncertainty for learning to plan. We propose
a new probabilistic birds-eye-view semantic grid to encode the noisy output of
object perception systems. We then leverage expert demonstrations to learn an
imitative driving policy using this probabilistic representation. Using the
CARLA simulator, we show that our approach can safely overcome critical false
positives that would otherwise lead to catastrophic failures or conservative
behavior.
