  Neural networks (NNs) are widely used for object classification in autonomous
driving. However, NNs can fail on input data not well represented by the
training dataset, known as out-of-distribution (OOD) data. A mechanism to
detect OOD samples is important for safety-critical applications, such as
automotive perception, to trigger a safe fallback mode. NNs often rely on
softmax normalization for confidence estimation, which can lead to high
confidences being assigned to OOD samples, thus hindering the detection of
failures. This paper presents a method for determining whether inputs are OOD,
which does not require OOD data during training and does not increase the
computational cost of inference. The latter property is especially important in
automotive applications with limited computational resources and real-time
constraints. Our proposed approach outperforms state-of-the-art methods on
real-world automotive datasets.
