Introspective False Negative Prediction for Black-Box Object Detectors in Autonomous Driving.

ABSTRACT: Object detection plays a critical role in autonomous driving, but current state-of-the-art object detectors will inevitably fail in many driving scenes, which is unacceptable for safety-critical automated vehicles. Given the complexity of the real traffic scenarios, it is impractical to guarantee zero detection failure; thus, online failure prediction is of crucial importance to mitigate the risk of traffic accidents. Of all the failure cases, False Negative (FN) objects are most likely to cause catastrophic consequences, but little attention has been paid to the online FN prediction. In this paper, we propose a general introspection framework that can make online prediction of FN objects for black-box object detectors. In contrast to existing methods which rely on empirical assumptions or

SUBMITTER: Yang Q 

PROVIDER: S-EPMC8073889 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature