Safety-aware Active Learning with Perceptual Ambiguity and Severity Assessment

Deep Neural Networks (DNN) used in self-driving cars need a large data coverage and labelling to manage all potential hazards in safety-critical scenarios. Active learning approaches make use of automated data selection and labelling that can build diverse datasets, with less human costs and more accuracy. Traditional active learning methods consider uncertainty of the model predictions and diversity of the data points for query selection. However, they are not optimal in capturing many critical data points, which are potentially risky with respect to safety considerations. In this position paper, we propose a novel approach that uses human feedback related to perceptual data ambiguity and a criticality score, linked to system-level safety assessment. This approach includes a continual learning model that learns to identify corner cases and blindspots with high impact in potential risk, and combines them with uncertainty-sampling and diversity-sampling models to create a safety-aware acquisition function for active learning.