Out-of-Distribution Detection for Automotive Perception
This addresses safety-critical issues in automotive perception by enabling reliable failure detection without increasing inference costs, though it is incremental as it builds on existing OOD detection approaches.
The paper tackles the problem of neural networks failing on out-of-distribution (OOD) data in autonomous driving by proposing a method for OOD detection that does not require OOD data during training and maintains low computational cost, outperforming state-of-the-art methods on real-world automotive datasets.
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.