  Environmental perception obtained via object detectors have no predictable
safety layer encoded into their model schema, which creates the question of
trustworthiness about the system's prediction. As can be seen from recent
adversarial attacks, most of the current object detection networks are
vulnerable to input tampering, which in the real world could compromise the
safety of autonomous vehicles. The problem would be amplified even more when
uncertainty errors could not propagate into the submodules, if these are not a
part of the end-to-end system design. To address these concerns, a parallel
module which verifies the predictions of the object proposals coming out of
Deep Neural Networks are required. This work aims to verify 3D object proposals
from MonoRUn model by proposing a plausibility framework that leverages cross
sensor streams to reduce false positives. The verification metric being
proposed uses prior knowledge in the form of four different energy functions,
each utilizing a certain prior to output an energy value leading to a
plausibility justification for the hypothesis under consideration. We also
employ a novel two-step schema to improve the optimization of the composite
energy function representing the energy model.
