Out-of-Distribution Detection for Automotive Perception

Neural networks (NNs) are widely used for object recognition tasks 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 in safety-critical applications, such as automotive perception, in order 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 simple but effective method for determining whether inputs are OOD. We propose an OOD detection approach that combines auxiliary training techniques with post hoc statistics. Unlike other approaches, our proposed method does not require OOD data during training, and it does not increase the computational cost during inference. The latter property is especially important in automotive applications with limited computational resources and real-time constraints. Our proposed method outperforms state-of-the-art methods on real world automotive datasets.