Anomaly Detection in Autonomous Vehicle Telemetry Data using Deep Learning
Abstract
[1] Anomaly detection in connected and autonomous vehicles has garnered significant attention in recent years. The automotive industry is undergoing radical architectural changes, shaped by development and deployment of fully autonomous vehicles. This revolution requires highly interconnected embedded control systems (ECS) capable of coordinating with each other, resulting in a decoupling of hardware and software. When coupled with the continuous Environmental Perception (e.g. lane lines, traffic signs, pedestrians, etc.) and Decision Making (e.g. trajectory planning, motion control, etc.), the complexity of Automotive ECS increases significantly. This coupling results in large and diverse data sets, whose inner statistical relationships guide conventional anomaly detection algorithms to their limit. Thus, dedicated Anomaly Detection (AD) frameworks are required for Autonomous Vehicle.[2]
Downloads
References
Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.
Shahane, Vishal. "Harnessing Serverless Computing for Efficient and Scalable Big Data Analytics Workloads." Journal of Artificial Intelligence Research 1.1 (2021): 40-65.
Abouelyazid, Mahmoud. "YOLOv4-based Deep Learning Approach for Personal Protective Equipment Detection." Journal of Sustainable Urban Futures 12.3 (2022): 1-12.
Prabhod, Kummaragunta Joel. "Utilizing Foundation Models and Reinforcement Learning for Intelligent Robotics: Enhancing Autonomous Task Performance in Dynamic Environments." Journal of Artificial Intelligence Research 2.2 (2022): 1-20.
Tatineni, Sumanth, and Anirudh Mustyala. "AI-Powered Automation in DevOps for Intelligent Release Management: Techniques for Reducing Deployment Failures and Improving Software Quality." Advances in Deep Learning Techniques 1.1 (2021): 74-110.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of research papers submitted to Distributed Learning and Broad Applications in Scientific Research retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agree to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. Scientific Research Canada disclaims any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
If you have any questions or concerns regarding these license terms, please contact us at editor@dlabi.org.
