  In safety-critical domains like automated driving (AD), errors by the object
detector may endanger pedestrians and other vulnerable road users (VRU). As
common evaluation metrics are not an adequate safety indicator, recent works
employ approaches to identify safety-critical VRU and back-annotate the risk to
the object detector. However, those approaches do not consider the safety
factor in the deep neural network (DNN) training process. Thus,
state-of-the-art DNN penalizes all misdetections equally irrespective of their
criticality. Subsequently, to mitigate the occurrence of critical failure
cases, i.e., false negatives, a safety-aware training strategy might be
required to enhance the detection performance for critical pedestrians. In this
paper, we propose a novel safety-aware loss variation that leverages the
estimated per-pedestrian criticality scores during training. We exploit the
reachability set-based time-to-collision (TTC-RSB) metric from the motion
domain along with distance information to account for the worst-case threat
quantifying the criticality. Our evaluation results using RetinaNet and FCOS on
the nuScenes dataset demonstrate that training the models with our safety-aware
loss function mitigates the misdetection of critical pedestrians without
sacrificing performance for the general case, i.e., pedestrians outside the
safety-critical zone.
