Ensemble Uncertainty Guided Road Scene Anomaly Detection: A Simple Meta-Learning Approach

A simple method is presented for road scene anomaly detection, which is particularly important in autonomous driving. Without the prior knowledge about the appearance and size, anomaly detection is formulated as an OoD (Out-of-Distribution) detection task. Epistemic uncertainty indicates that when a model is trained with finite training data, it will fail to yield reliable prediction if the testing data is far from the training data distribution (OoD). Deviating from previous works, instead of modelling the uncertainty of a single model explicitly, this work casts the individual uncertainty into an ensemble uncertainty metric by measuring the discrepancy between the different models. The benefits are: 1) Following a meta-learning framework, outputs from the off-the-shelf models can be used directly, which results in a very small training cost; 2) With the ensemble approach, the model is especially robust to dataset shift. Experimental results show that the performance of the proposed method is competitive: 1) Its APs (Average precisions) are 3.5%, 3.1%, and 5.2% higher than the best SOTAs (State-of-the-arts) on LaF (Lost-and-Found), RA (Road Anomaly), and RO (Road Obstacles) respectively; 2) For all datasets with different road conditions, it obtains consistent performance.