Online Monitoring Framework for Automotive Time Series Data using JEPA Embeddings

• URL: http://arxiv.org/abs/2602.09985v1
• Date: Tue, 10 Feb 2026 17:10:29 GMT
• Title: Online Monitoring Framework for Automotive Time Series Data using JEPA Embeddings
• Authors: Alexander Fertig, Karthikeyan Chandra Sekaran, Lakshman Balasubramanian, Michael Botsch,
• Abstract summary: This work proposes an online monitoring framework to detect anomalies in object state representations.<n>A JEPA-based self-supervised prediction task is constructed, allowing training without anomaly labels.<n>The resulting expressive JEPA embeddings serve as input for established anomaly detection methods.
• Score: 42.21870009971486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As autonomous vehicles are rolled out, measures must be taken to ensure their safe operation. In order to supervise a system that is already in operation, monitoring frameworks are frequently employed. These run continuously online in the background, supervising the system status and recording anomalies. This work proposes an online monitoring framework to detect anomalies in object state representations. Thereby, a key challenge is creating a framework for anomaly detection without anomaly labels, which are usually unavailable for unknown anomalies. To address this issue, this work applies a self-supervised embedding method to translate object data into a latent representation space. For this, a JEPA-based self-supervised prediction task is constructed, allowing training without anomaly labels and the creation of rich object embeddings. The resulting expressive JEPA embeddings serve as input for established anomaly detection methods, in order to identify anomalies within object state representations. This framework is particularly useful for applications in real-world environments, where new or unknown anomalies may occur during operation for which there are no labels available. Experiments performed on the publicly available, real-world nuScenes dataset illustrate the framework's capabilities.

Related papers

• Verification of Visual Controllers via Compositional Geometric Transformations [49.81690518952909]
  We introduce a novel verification framework for perception-based controllers that can generate outer-approximations of reachable sets.<n>We provide theoretical guarantees on the soundness of our method and demonstrate its effectiveness across benchmark control environments.
  arXiv  Detail & Related papers  (2025-07-06T20:22:58Z)
• Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline [63.96226274616927]
  A new framework called Track Any Anomalous Object (TAO) introduces a granular video anomaly detection pipeline.<n>Unlike methods that assign anomaly scores to every pixel, our approach transforms the problem into pixel-level tracking of anomalous objects.<n>Experiments demonstrate that TAO sets new benchmarks in accuracy and robustness.
  arXiv  Detail & Related papers  (2025-06-05T15:49:39Z)
• Vision Foundation Model Embedding-Based Semantic Anomaly Detection [12.940376547110509]
  This work explores semantic anomaly detection by leveraging the semantic priors of state-of-the-art vision foundation models.<n>We propose a framework that compares local vision embeddings from runtime images to a database of nominal scenarios in which the autonomous system is deemed safe and performant.
  arXiv  Detail & Related papers  (2025-05-12T19:00:29Z)
• Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection [73.85890512959861]
  We propose a task-agnostic framework to unify Salient Object Detection (SOD) and Camouflaged Object Detection (COD)<n>We design a simple yet effective contextual decoder involving the interval-layer and global context, which achieves an inference speed of 67 fps.<n> Experiments on public SOD and COD datasets demonstrate the superiority of our proposed framework in both supervised and unsupervised settings.
  arXiv  Detail & Related papers  (2024-12-22T03:25:43Z)
• Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection [56.66677293607114]
  We propose Code-as-Monitor (CaM) for both open-set reactive and proactive failure detection.<n>To enhance the accuracy and efficiency of monitoring, we introduce constraint elements that abstract constraint-related entities.<n>Experiments show that CaM achieves a 28.7% higher success rate and reduces execution time by 31.8% under severe disturbances.
  arXiv  Detail & Related papers  (2024-12-05T18:58:27Z)
• GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features [68.14842693208465]
  GeneralAD is an anomaly detection framework designed to operate in semantic, near-distribution, and industrial settings. We propose a novel self-supervised anomaly generation module that employs straightforward operations like noise addition and shuffling to patch features. We extensively evaluated our approach on ten datasets, achieving state-of-the-art results in six and on-par performance in the remaining.
  arXiv  Detail & Related papers  (2024-07-17T09:27:41Z)
• Finding Dino: A Plug-and-Play Framework for Zero-Shot Detection of Out-of-Distribution Objects Using Prototypes [12.82756672393553]
  PRototype-based OOD detection Without Labels (PROWL)<n>We present a plug-and-play framework - PRototype-based OOD detection Without Labels (PROWL)<n>It is an inference-based method that does not require training on the domain dataset and relies on extracting relevant features from self-supervised pre-trained models.<n>It achieves state-of-the-art results on the RoadAnomaly and RoadObstacle datasets provided in road driving benchmarks.
  arXiv  Detail & Related papers  (2024-04-11T11:55:42Z)
• Real-Time Outlier Detection with Dynamic Process Limits [0.609170287691728]
  This paper proposes an online anomaly detection algorithm for existing real-time infrastructures. Online inverse cumulative distribution-based approach is introduced to eliminate common problems of offline anomaly detectors. The benefit of the proposed method is the ease of use, fast computation, and deployability as shown in two case studies of real microgrid operation data.
  arXiv  Detail & Related papers  (2023-01-31T10:23:02Z)
• Label-Efficient Interactive Time-Series Anomaly Detection [17.799924009674694]
  We propose a Label-Efficient Interactive Time-Series Anomaly Detection (LEIAD) system. To achieve this goal, the system integrates weak supervision and active learning collaboratively. We conduct experiments on three time-series anomaly detection datasets, demonstrating that the proposed system is superior to existing solutions.
  arXiv  Detail & Related papers  (2022-12-30T10:16:15Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• Anomaly Detection by One Class Latent Regularized Networks [36.67420338535258]
  Semi-supervised Generative Adversarial Networks (GAN)-based methods have been gaining popularity in anomaly detection task recently. A novel adversarial dual autoencoder network is proposed, in which the underlying structure of training data is captured in latent feature space. Experiments show that our model achieves the state-of-the-art results on MNIST and CIFAR10 datasets as well as GTSRB stop signs dataset.
  arXiv  Detail & Related papers  (2020-02-05T02:21:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.