test

26262

SOTIF

robustness against weather conditions

[3] camera-based object detection robustness against simulated "faults" of camera noise, delay, etc. (evaluated only against one fault)

inputs / outputs (black-box)

[4] STPA guided fault injection (fault type - simulated faults - communication, unavailable, corruption)

[4] STPA guided fault injection (fault type - simulated effects of weather conditions on sensors)

ML component (white-box)

SOTIF: scenario space coverage, finding critical scenarios / test cases

system level

object detection level

[15] synthesize realistic driving scenes, different weather conditions & test inconsistencies using metamorphic testing

[16] automatically identify missed detections for camera-based object detection

[18] test case generation for system / environment critical aspects (at system level)

[19] automatic generation of test cases - different environmental conditions (weather mainly), maximize neuron coverage in test cases

[14] camera-based lane detection robustness against simulated rain

[17] falsification of formal requirements, combinatorial testing (at system level)

[11] combinatorial testing based on a domain model (at object detection level)

[1] and [2], tensorflow related but goal is to do fault injection at ML operator level + identify safety critical bits for a specific application like object detection

[5] bayesian network representation of architecture + safety constraints to guide fault injection