Out-of-Distribution Detection for Automotive Perception

Autor:	Nitsch, Julia, Itkina, Masha, Senanayake, Ransalu, Nieto, Juan, Schmidt, Max, Siegwart, Roland, Kochenderfer, Mykel J., Cadena, Cesar
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Robotics I.2.10 I.2.9
Druh dokumentu:	Working Paper
Popis:	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. Comment: 6 pages, 4 figures, paper accepted at Intelligent Transportation Systems Conference (ITSC) 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.01413 Zobrazit plný text záznamu View this record from Arxiv