  We argue that object detectors in the safety critical domain should
prioritize detection of objects that are most likely to interfere with the
actions of the autonomous actor. Especially, this applies to objects that can
impact the actor's safety and reliability. To quantify the impact of object
(mis)detection on safety and reliability in the context of autonomous driving,
we propose new object detection measures that reward the correct identification
of objects that are most dangerous and most likely to affect driving decisions.
To achieve this, we build an object criticality model to reward the detection
of the objects based on proximity, orientation, and relative velocity with
respect to the subject vehicle. Then, we apply our model on the recent
autonomous driving dataset nuScenes, and we compare nine object detectors.
Results show that, in several settings, object detectors that perform best
according to the nuScenes ranking are not the preferable ones when the focus is
shifted on safety and reliability.
