arXiv subjects

Md Ruman Islam

Publications and source records attributed to Md Ruman Islam.

3 recordsLinked to original sources

arXiv2025

A Practical-Driven Framework for Transitioning Drive-by-Wire to Autonomous Driving Systems: A Case Study with a Chrysler Pacifica Hybrid Vehicle

Transitioning from a Drive-by-Wire (DBW) system to a fully autonomous driving system (ADS) involves multiple stages of development and demands robust positioning and sensing capabilities. This paper presents a practice-driven framework for facilitating the DBW-to-ADS transition using a 2022 Chrysler Pacifica Hybrid Minivan equipped with cameras, LiDAR, GNSS, and onboard computing hardware configured with the Robot Operating System (ROS) and Autoware.AI. The implementation showcases offline autonomous operations utilizing pre-recorded LiDAR and camera data, point clouds, and vector maps, enabling effective localization and path planning within a structured test environment. The study addresses key challenges encountered during the transition, particularly those related to wireless-network-assisted sensing and positioning. It offers practical solutions for overcoming software incompatibility constraints, sensor synchronization issues, and limitations in real-time perception. Furthermore, the integration of sensing, data fusion, and automation is emphasized as a critical factor in supporting autonomous driving systems in map generation, simulation, and training. Overall, the transition process outlined in this work aims to provide actionable strategies for researchers pursuing DBW-to-ADS conversion. It offers direction for incorporating real-time perception, GNSS-LiDAR-camera integration, and fully ADS-equipped autonomous vehicle operations, thus contributing to the advancement of robust autonomous vehicle technologies.

cs.RO↗

arXiv2024

Characterizing Encrypted Application Traffic through Cellular Radio Interface Protocol

Modern applications are end-to-end encrypted to prevent data from being read or secretly modified. 5G tech nology provides ubiquitous access to these applications without compromising the application-specific performance and latency goals. In this paper, we empirically demonstrate that 5G radio communication becomes the side channel to precisely infer the user's applications in real-time. The key idea lies in observing the 5G physical and MAC layer interactions over time that reveal the application's behavior. The MAC layer receives the data from the application and requests the network to assign the radio resource blocks. The network assigns the radio resources as per application requirements, such as priority, Quality of Service (QoS) needs, amount of data to be transmitted, and buffer size. The adversary can passively observe the radio resources to fingerprint the applications. We empirically demonstrate this attack by considering four different categories of applications: online shopping, voice/video conferencing, video streaming, and Over-The-Top (OTT) media platforms. Finally, we have also demonstrated that an attacker can differentiate various types of applications in real-time within each category.

cs.NI↗

arXiv2024

Image-based Deep Learning for Smart Digital Twins: a Review

Smart Digital twins (SDTs) are being increasingly used to virtually replicate and predict the behaviors of complex physical systems through continual data assimilation enabling the optimization of the performance of these systems by controlling the actions of systems. Recently, deep learning (DL) models have significantly enhanced the capabilities of SDTs, particularly for tasks such as predictive maintenance, anomaly detection, and optimization. In many domains, including medicine, engineering, and education, SDTs use image data (image-based SDTs) to observe and learn system behaviors and control their behaviors. This paper focuses on various approaches and associated challenges in developing image-based SDTs by continually assimilating image data from physical systems. The paper also discusses the challenges involved in designing and implementing DL models for SDTs, including data acquisition, processing, and interpretation. In addition, insights into the future directions and opportunities for developing new image-based DL approaches to develop robust SDTs are provided. This includes the potential for using generative models for data augmentation, developing multi-modal DL models, and exploring the integration of DL with other technologies, including 5G, edge computing, and IoT. In this paper, we describe the image-based SDTs, which enable broader adoption of the digital twin DT paradigms across a broad spectrum of areas and the development of new methods to improve the abilities of SDTs in replicating, predicting, and optimizing the behavior of complex systems.

cs.CV↗