  Learned path planners have attracted research interest due to their ability
to model human driving behavior and rapid inference. Recent works on behavioral
cloning show that simple imitation of expert observations is not sufficient to
handle complex driving scenarios. Besides, predictions that land outside
drivable areas can lead to potentially dangerous situations. This paper
proposes a set of loss functions, namely Social loss and Road loss, which
account for modelling risky social interactions in path planning. These losses
act as a repulsive scalar field that surrounds non-drivable areas. Predictions
that land near these regions incur in a higher training cost, which is
minimized using backpropagation. This methodology provides additional
environment feedback to the traditional supervised learning set up. We
validated this approach on a large-scale urban driving dataset. The results
show the agent learns to imitate human driving while exhibiting better safety
metrics. Furthermore, the proposed methodology has positive effects on
inference without the need to artificially generate unsafe driving examples.
The explanability study suggests that the benefits obtained are associated with
a higher relevance of non-drivable areas in the agent's decisions compared to
classical behavioral cloning.
