Search Results for author: Mashrur Chowdhury

Found 24 papers, 1 papers with code

An Overview of Automated Vehicle Platooning Strategies

no code implementations8 Mar 2024 M Sabbir Salek, Mugdha Basu Thakur, Pardha Sai Krishna Ala, Mashrur Chowdhury, Matthias Schmid, Pamela Murray-Tuite, Sakib Mahmud Khan, Venkat Krovi

Automated vehicle (AV) platooning has the potential to improve the safety, operational, and energy efficiency of surface transportation systems by limiting or eliminating human involvement in the driving tasks.

AR-GAN: Generative Adversarial Network-Based Defense Method Against Adversarial Attacks on the Traffic Sign Classification System of Autonomous Vehicles

no code implementations31 Dec 2023 M Sabbir Salek, Abdullah Al Mamun, Mashrur Chowdhury

The novelty of the AR-GAN lies in (i) assuming zero knowledge of adversarial attack models and samples and (ii) providing consistently high traffic sign classification performance under various adversarial attack types.

Adversarial Attack Adversarial Defense +3

Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models

no code implementations18 Dec 2023 Reek Majumder, Jacquan Pollard, M Sabbir Salek, David Werth, Gurcan Comert, Adrian Gale, Sakib Mahmud Khan, Samuel Darko, Mashrur Chowdhury

The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4.

Ensemble Learning

A Hybrid Defense Method against Adversarial Attacks on Traffic Sign Classifiers in Autonomous Vehicles

no code implementations25 Apr 2022 Zadid Khan, Mashrur Chowdhury, Sakib Mahmud Khan

Moreover, the hybrid defense method, presented in this study, improves the accuracy for traffic sign classification compared to the traditional defense methods (i. e., JPEG filtering, feature squeezing, binary filtering, and random filtering) up to 6%, 50%, and 55% for FGSM, MIM, and PGD attacks, respectively.

Autonomous Vehicles Navigate +3

Theoretical Development and Numerical Validation of an Asymmetric Linear Bilateral Control Model- Case Study for an Automated Truck Platoon

no code implementations29 Dec 2021 M Sabbir Salek, Mashrur Chowdhury, Mizanur Rahman, Kakan Dey, Md Rafiul Islam

The novelty of the asymmetric LBCM is that using this model all the follower vehicles in a platoon can adjust their acceleration and deceleration to closely follow a constant desired time gap to improve platoon operational efficiency while maintaining local and string stability.

Hybrid Quantum-Classical Neural Network for Cloud-supported In-Vehicle Cyberattack Detection

no code implementations14 Oct 2021 Mhafuzul Islam, Mashrur Chowdhury, Zadid Khan, Sakib Mahmud Khan

A classical computer works with ones and zeros, whereas a quantum computer uses ones, zeros, and superpositions of ones and zeros, which enables quantum computers to perform a vast number of calculations simultaneously compared to classical computers.

A Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles

no code implementations19 Aug 2021 Sagar Dasgupta, Mizanur Rahman, Mhafuzul Islam, Mashrur Chowdhury

Data from multiple low-cost in-vehicle sensors (i. e., accelerometer, steering angle sensor, speed sensor, and GNSS) are fused and fed into a recurrent neural network model, which is a long short-term memory (LSTM) network for predicting the location shift, i. e., the distance that an AV travels between two consecutive timestamps.

Autonomous Vehicles Dynamic Time Warping +1

Sensor Fusion-based GNSS Spoofing Attack Detection Framework for Autonomous Vehicles

no code implementations5 Jun 2021 Sagar Dasgupta, Mizanur Rahman, Mhafuzul Islam, Mashrur Chowdhury

In this study, a sensor fusion based GNSS spoofing attack detection framework is presented that consists of three concurrent strategies for an autonomous vehicle (AV): (i) prediction of location shift, (ii) detection of turns (left or right), and (iii) recognition of motion state (including standstill state).

Autonomous Vehicles Dynamic Time Warping +1

Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks

no code implementations18 Nov 2020 Gurcan Comert, Mashrur Chowdhury, David M. Nicol

This study presents a methodology to quantify vulnerability of cyber attacks and their impacts based on probabilistic graphical models for intelligent transportation systems under connected and autonomous vehicles framework.

Autonomous Vehicles

Prediction-Based GNSS Spoofing Attack Detection for Autonomous Vehicles

no code implementations16 Oct 2020 Sagar Dasgupta, Mizanur Rahman, Mhafuzul Islam, Mashrur Chowdhury

A spoofed attack is difficult to detect as a spoofer (attacker who performs spoofing attack) can mimic the GNSS signal and transmit inaccurate location coordinates to an AV.

Autonomous Vehicles

Change Point Models for Real-time Cyber Attack Detection in Connected Vehicle Environment

no code implementations5 Mar 2020 Gurcan Comert, Mizanur Rahman, Mhafuzul Islam, Mashrur Chowdhury

Connected vehicle (CV) systems are cognizant of potential cyber attacks because of increasing connectivity between its different components such as vehicles, roadside infrastructure, and traffic management centers.

Cyber Attack Detection Management

Dynamic Error-bounded Lossy Compression (EBLC) to Reduce the Bandwidth Requirement for Real-time Vision-based Pedestrian Safety Applications

no code implementations29 Jan 2020 Mizanur Rahman, Mhafuzul Islam, Jon C. Calhoun, Mashrur Chowdhury

The objective of this study is to develop a real-time error-bounded lossy compression (EBLC) strategy to dynamically change the video compression level depending on different environmental conditions in order to maintain a high pedestrian detection accuracy.

Pedestrian Detection Video Compression

Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control

no code implementations29 Dec 2019 Gurcan Comert, Zadid Khan, Mizanur Rahman, Mashrur Chowdhury

Thus, the objective of this study is to develop queue length prediction models for signalized intersections that can be leveraged by ASCS using four variations of Grey systems: (i) the first order single variable Grey model (GM(1, 1)); (ii) GM(1, 1) with Fourier error corrections; (iii) the Grey Verhulst model (GVM), and (iv) GVM with Fourier error corrections.

Time Series Time Series Analysis

Vision-based Pedestrian Alert Safety System (PASS) for Signalized Intersections

no code implementations2 Jul 2019 Mhafuzul Islam, Mizanur Rahman, Mashrur Chowdhury, Gurcan Comert, Eshaa Deepak Sood, Amy Apon

The contribution of this paper lies in the development of a system using a vision-based deep learning model that is able to generate personal safety messages (PSMs) in real-time (every 100 milliseconds).

Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network

no code implementations24 Jun 2019 Zadid Khan, Mashrur Chowdhury, Mhafuzul Islam, Chin-Ya Huang, Mizanur Rahman

This attack detection model can detect false information with an accuracy, precision and recall of 95%, 95% and 87%, respectively, while satisfying the real-time communication and computational requirements.

Anomaly Detection Time Series +2

Connected Vehicle Application Development Platform (CVDeP) for Edge-centric Cyber-Physical Systems

1 code implementation2 Dec 2018 Mhafuzul Islam, Mizanur Rahman, Sakib Mahmud Khan, Mashrur Chowdhury, Lipika Deka

Connected vehicle (CV) application developers need a development platform to build, test and debug CV applications, such as safety, mobility, and environmental applications, in an edge-centric Cyber-Physical Systems.

Networking and Internet Architecture

Change Point Models for Real-time V2I Cyber Attack Detection in a Connected Vehicle Environment

no code implementations30 Nov 2018 Gurcan Comert, Mizanur Rahman, Mhafuzul Islam, Mashrur Chowdhury

Connected vehicle (CV) systems are cognizant of potential cyber attacks because of increasing connectivity between its different components such as vehicles, roadside infrastructure and traffic management centers.

Cryptography and Security

Real time Traffic Flow Parameters Prediction with Basic Safety Messages at Low Penetration of Connected Vehicles

no code implementations8 Nov 2018 Mizanur Rahman, Mashrur Chowdhury, Jerome McClendon

This estimated traffic flow parameters from low penetration of connected vehicles become noisy compared to 100 percent penetration of CVs, and such noise reduces the real time prediction accuracy of a machine learning model, such as the accuracy of long short term memory (LSTM) model in terms of predicting traffic flow parameters.

Real-time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategy

no code implementations27 Aug 2018 Mizanur Rahman, Mhafuzul Islam, Jon Calhoun, Mashrur Chowdhury

We utilize a lossy compression technique on traffic camera data to determine the tradeoff between the reduction of the communication bandwidth requirements and a defined object detection accuracy.

Edge-computing object-detection +2

Development and Evaluation of Recurrent Neural Network based Models for Hourly Traffic Volume and AADT Prediction

no code implementations15 Aug 2018 MD Zadid Khan, Sakib Mahmud Khan, Mashrur Chowdhury, Kakan Dey

The analysis indicates that the LSTM model performs better than simple RNN and GRU models, and imputation performs better than masking to predict future traffic volume.

Imputation Time Series +1

Development of Statewide AADT Estimation Model from Short-Term Counts: A Comparative Study for South Carolina

no code implementations30 Nov 2017 Sakib Mahmud Khan, Sababa Islam, MD Zadid Khan, Kakan Dey, Mashrur Chowdhury, Nathan Huynh

SVR models are validated for each roadway functional class using the 2016 ATR data and selected short-term count data collected by the South Carolina Department of Transportation (SCDOT).


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