# Out-of-Distribution Detection for Automotive Perception

arXiv: https://arxiv.org/abs/2011.01413 · HTML: https://mlsanity.com/paper/2011.01413/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 30 of 37)

- [SAFE: Multitask Failure Detection for Vision-Language-Action Models](https://mlsanity.com/paper/2506.09937/) (2025)
- [Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning](https://mlsanity.com/paper/2206.01558/) (2022)
- [Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction](https://mlsanity.com/paper/2211.08701/) (2022)
- [Segment Every Out-of-Distribution Object](https://mlsanity.com/paper/2311.16516/) (2023)
- [How Do We Fail? Stress Testing Perception in Autonomous Vehicles](https://mlsanity.com/paper/2203.14155/) (2022)
- [Out-of-Distribution Detection for LiDAR-based 3D Object Detection](https://mlsanity.com/paper/2209.14435/) (2022)
- [Correct-by-Construction Runtime Enforcement in AI -- A Survey](https://mlsanity.com/paper/2208.14426/) (2022)
- [The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning](https://mlsanity.com/paper/2405.03164/) (2024)
- [Failures Are Fated, But Can Be Faded: Characterizing and Mitigating Unwanted Behaviors in Large-Scale Vision and Language Models](https://mlsanity.com/paper/2406.07145/) (2024)
- [Out of Distribution Detection and Adversarial Attacks on Deep Neural Networks for Robust Medical Image Analysis](https://mlsanity.com/paper/2107.04882/) (2021)
- [Generating Out-Of-Distribution Scenarios Using Language Models](https://mlsanity.com/paper/2411.16554/) (2024)
- [Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications](https://mlsanity.com/paper/2107.11750/) (2021)
- [Projection Regret: Reducing Background Bias for Novelty Detection via Diffusion Models](https://mlsanity.com/paper/2312.02615/) (2023)
- [Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning](https://mlsanity.com/paper/2311.06481/) (2023)
- [A Unified Approach Towards Active Learning and Out-of-Distribution Detection](https://mlsanity.com/paper/2405.11337/) (2024)
- [RoboMD: Uncovering Robot Vulnerabilities through Semantic Potential Fields](https://mlsanity.com/paper/2412.02818/) (2024)
- [Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey](https://mlsanity.com/paper/2404.05219/) (2024)
- [A Comprehensive Review of Trends, Applications and Challenges In Out-of-Distribution Detection](https://mlsanity.com/paper/2209.12935/) (2022)
- [Multimodal Detection of Unknown Objects on Roads for Autonomous Driving](https://mlsanity.com/paper/2205.01414/) (2022)
- [Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection](https://mlsanity.com/paper/2209.07837/) (2022)
- [PLoc: A New Evaluation Criterion Based on Physical Location for Autonomous Driving Datasets](https://mlsanity.com/paper/2403.19893/) (2024)
- [RUNA: Object-level Out-of-Distribution Detection via Regional Uncertainty Alignment of Multimodal Representations](https://mlsanity.com/paper/2503.22285/) (2025)
- [Conquering Ghosts: Relation Learning for Information Reliability Representation and End-to-End Robust Navigation](https://mlsanity.com/paper/2203.09952/) (2022)
- [Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models](https://mlsanity.com/paper/2309.06655/) (2023)
- [Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers](https://mlsanity.com/paper/2207.06055/) (2022)
- [Conditioning Latent-Space Clusters for Real-World Anomaly Classification](https://mlsanity.com/paper/2309.09676/) (2023)
- [ExCeL : Combined Extreme and Collective Logit Information for Enhancing Out-of-Distribution Detection](https://mlsanity.com/paper/2311.14754/) (2023)
- [From COCO to COCO-FP: A Deep Dive into Background False Positives for COCO Detectors](https://mlsanity.com/paper/2409.07907/) (2024)
- [Multimodal-Enhanced Objectness Learner for Corner Case Detection in Autonomous Driving](https://mlsanity.com/paper/2402.02026/) (2024)
- [Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation](https://mlsanity.com/paper/2404.11266/) (2024)

## Abstract (arXiv)

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.