Details

Autor(en) / Beteiligte

• Nitsch, Julia
• Itkina, Masha
• Senanayake, Ransalu
• Nieto, Juan
• Schmidt, Max
• Siegwart, Roland
• Kochenderfer, Mykel J.
• Cadena, Cesar

Titel

Out-of-Distribution Detection for Automotive Perception

Ist Teil von

• 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021, p.2938-2943

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• 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.