  We study how state-of-the-art neural networks for 3D object detection using a
single-stage pipeline can be made safety aware. We start with the safety
specification (reflecting the capability of other components) that partitions
the 3D input space by criticality, where the critical area employs a separate
criterion on robustness under perturbation, quality of bounding boxes, and the
tolerance over false negatives demonstrated on the training set. In the
architecture design, we consider symbolic error propagation to allow
feature-level perturbation. Subsequently, we introduce a specialized loss
function reflecting (1) the safety specification, (2) the use of single-stage
detection architecture, and finally, (3) the characterization of robustness
under perturbation. We also replace the commonly seen non-max-suppression
post-processing algorithm by a safety-aware non-max-inclusion algorithm, in
order to maintain the safety claim created by the neural network. The concept
is detailed by extending the state-of-the-art PIXOR detector which creates
object bounding boxes in bird's eye view with inputs from point clouds.
