Out-of-Distribution Detection for Automotive Perception

TL;DR

This paper introduces a computationally efficient method for detecting out-of-distribution inputs in automotive perception neural networks, enhancing safety by reliably identifying unfamiliar data without additional training data.

Contribution

The proposed method detects OOD samples without requiring OOD data during training and maintains real-time inference performance, outperforming existing techniques in automotive scenarios.

Findings

Outperforms state-of-the-art OOD detection methods on automotive datasets

Does not require OOD data during training

Maintains low computational cost for real-time applications

Abstract

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.