Papers by M Sabbir Salek, Ph.D.
arXiv (Cornell University), Mar 8, 2024
Automated vehicle (AV) platooning has the potential to improve the safety, operational, and energ... more 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. The theoretical validity of the AV platooning strategies has been established and practical applications are being tested under real-world conditions. The emergence of sensors, communication, and control strategies has resulted in rapid and constant evolution of AV platooning strategies. In this paper, we review the state-of-the-art knowledge in AV longitudinal platoon formation using a five-component platooning framework, which includes vehicle model, information-receiving process, information flow topology, spacing policy, and controller and discuss the advantages and limitations of the components. Based on the discussion about existing strategies and associated limitations, potential future research directions are presented.
Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models
Environmental health insights., 2024
arXiv (Cornell University), Jan 29, 2024
In this study, we developed a real-time connected vehicle (CV) speed advisory application that us... more In this study, we developed a real-time connected vehicle (CV) speed advisory application that uses public cloud services and tested it on a simulated signalized corridor for different roadway traffic conditions. First, we developed a scalable serverless cloud computing architecture leveraging public cloud services offered by Amazon Web Services (AWS) to support the requirements of a real-time CV application. Second, we developed an optimization-based realtime CV speed advisory algorithm by taking a modular design approach, which makes the application automatically scalable and deployable in the cloud using the serverless architecture. Third, we developed a cloud-in-the-loop simulation testbed using AWS and an open-source microscopic roadway traffic simulator called Simulation of Urban Mobility (SUMO). Our analyses based on different roadway traffic conditions showed that the serverless CV speed advisory application meets the latency requirement of real-time CV mobility applications. Besides, our serverless CV speed advisory application reduced the average stopped delay (by 77%) and the aggregated risk of collision (by 21%) at signalized intersection of a corridor. These prove the feasibility as well as the efficacy of utilizing public cloud infrastructure to implement real-time roadway traffic management applications in a CV environment.
arXiv (Cornell University), 2024
This study developed a generative adversarial network (GAN)-based defense method for traffic sign... more This study developed a generative adversarial network (GAN)-based defense method for traffic sign classification in an autonomous vehicle (AV), referred to as the attack-resilient GAN (AR-GAN). 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. The AR-GAN classification system consists of a generator that denoises an image by reconstruction, and a classifier that classifies the reconstructed image. The authors have tested the AR-GAN under no-attack and under various adversarial attacks, such as Fast Gradient Sign Method (FGSM), DeepFool, Carlini and Wagner (C&W), and Projected Gradient Descent (PGD). The authors considered two forms of these attacks, i.e., (i) black-box attacks (assuming the attackers possess no prior knowledge of the classifier), and (ii) white-box attacks (assuming the attackers possess full knowledge of the classifier). The classification performance of the AR-GAN was compared with several benchmark adversarial defense methods. The results showed that both the AR-GAN and the benchmark defense methods are resilient against black-box attacks and could achieve similar classification performance to that of the unperturbed images. However, for all the white-box attacks considered in this study, the AR-GAN method outperformed the benchmark defense methods. In addition, the AR-GAN was able to maintain its high classification performance under varied white-box adversarial perturbation magnitudes, whereas the performance of the other defense methods dropped abruptly at increased perturbation magnitudes.
arXiv (Cornell University), Dec 16, 2023
The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed sign... more 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. Several data-driven machine learning (ML) models were tested to determine how well they identified fugitive CH4 and its related intensity in the affected areas. Various meteorological characteristics, including wind speed, temperature, pressure, relative humidity, water vapor, and heat flux, were included in the simulation. We used the ensemble learning method to determine the bestperforming weighted ensemble ML models built upon several weaker lower-layer ML models to (i) detect the presence of CH4 as a classification problem and (ii) predict the intensity of CH4 as a regression problem. The classification model performance for CH4 detection was evaluated using accuracy, F1 score, Matthew's Correlation Coefficient (MCC), and the area under the receiver operating characteristic curve (AUC ROC), with the output of the top-performing model being 97.2%, 0.972, 0.945 and 0.995, respectively. The R2 score was used to evaluate the regression model performance for CH4 intensity prediction, with the R2 score of the best-performing model being 0.858. The ML models developed in this study for fugitive CH4 detection and intensity prediction can be used with fixed environmental sensors deployed on the ground or with sensors mounted on unmanned aerial vehicles (UAVs) for mobile detection.
arXiv (Cornell University), Apr 8, 2024
Driven by the significant advantages offered by quantum computing, research in quantum machine le... more Driven by the significant advantages offered by quantum computing, research in quantum machine learning has increased in recent years. While quantum speed-up has been demonstrated in some applications of quantum machine learning, a comprehensive understanding of its underlying mechanisms for improved performance remains elusive. Our study address this problem by investigating the functional expressibility of quantum circuits integrated within a convolutional neural network (CNN). Through numerical experiments on the MNIST, Fashion MNIST, and Letter datasets, our hybrid quantum-classical CNN model demonstrates superior feature selection capabilities and substantially reduces the required training steps compared to classical CNNs. Notably, we observe similar performance improvements when incorporating three other quantum-inspired activation functions in classical neural networks, indicating the benefits of adopting quantum-inspired activation functions. Additionally, we developed a hybrid quantum Chebyshev-polynomial network (QCPN) based on the properties of quantum activation functions. We demonstrate that a three-layer QCPN can approximate any continuous function, a feat not achievable by a standard three-layer classical neural network. Our findings suggest that quantum-inspired activation functions can reduce model depth while maintaining high learning capability, making them a promising approach for optimizing large-scale machine-learning models. We also outline future research directions for leveraging quantum advantages in machine learning, aiming to unlock further potential in this rapidly evolving field.
The traditional build-and-expand approach is not a viable solution to keep roadway traffic rollin... more The traditional build-and-expand approach is not a viable solution to keep roadway traffic rolling safely, so technological solutions, such as Autonomous Vehicles (AVs), are favored. AVs have considerable potential to increase the carrying capacity of roads, ameliorate the chore of driving, improve safety, provide mobility for those who cannot drive, and help the environment. However, they also raise concerns over whether they are socially responsible, accounting for issues such as fairness, equity, and transparency. Regulatory bodies have focused on AV safety, cybersecurity, privacy, and legal liability issues, but have failed to adequately address social responsibility. Thus, existing AV developers do not have to embed social responsibility factors in their proprietary technology. Adverse bias may therefore occur in the development and deployment of AV technology. For instance, an artificial intelligencebased pedestrian detection application used in an AV may, in limited lighting conditions, be biased to detect pedestrians who belong to a particular racial demographic more efficiently compared to pedestrians from other racial demographics. Also, AV technologies tend to be costly, with a unique hardware and software setup which may be beyond the reach of lower-income people. In addition, data generated by AVs about their users may be misused by third parties such as corporations, criminals, or even foreign governments. AVs promise to dramatically impact labor markets, as many jobs that involve driving will be made redundant. We argue that the academic institutions, industry, and government agencies overseeing AV development and deployment must act proactively to ensure that AVs serve all and do not increase the digital divide in our society.
arXiv (Cornell University), Dec 29, 2021
arXiv (Cornell University), Jul 3, 2023
The traditional build-and-expand approach is not a viable solution to keep roadway traffic rollin... more The traditional build-and-expand approach is not a viable solution to keep roadway traffic rolling safely, so technological solutions, such as Autonomous Vehicles (AVs), are favored. AVs have considerable potential to increase the carrying capacity of roads, ameliorate the chore of driving, improve safety, provide mobility for those who cannot drive, and help the environment. However, they also raise concerns over whether they are socially responsible, accounting for issues such as fairness, equity, and transparency. Regulatory bodies have focused on AV safety, cybersecurity, privacy, and legal liability issues, but have failed to adequately address social responsibility. Thus, existing AV developers do not have to embed social responsibility factors in their proprietary technology. Adverse bias may therefore occur in the development and deployment of AV technology. For instance, an artificial intelligence-based pedestrian detection application used in an AV may, in limited lighting conditions, be biased to detect pedestrians who belong to a particular racial demographic more efficiently compared to pedestrians from other racial demographics. Also, AV technologies tend to be costly, with a unique hardware and software setup which may be beyond the reach of lower-income people. In addition, data generated by AVs about their users may be misused by third parties such as corporations, criminals, or even foreign governments. AVs promise to dramatically impact labor markets, as many jobs that involve driving will be made redundant. We argue that the academic institutions, industry, and government agencies overseeing AV development and deployment must act proactively to ensure that AVs serve all and do not increase the digital divide in our society.
Accident Analysis & Prevention, Feb 1, 2021
By handling conflicting traffic movements and establishing dynamic coordination between intersect... more By handling conflicting traffic movements and establishing dynamic coordination between intersections in real-time, the Adaptive Signal Control System (ASCS) can potentially improve the operation and safety of signalized intersections on a corridor. This study identifies the hierarchical effects of ASCS on the crash severity by exploring the heterogeneous effect of ASCS on the crash severity. Four different random-parameter ordered regression models (two ordered probit models, and two ordered logit models) are developed and compared. The analysis reveals that the random-parameter ordered probit and logit models (ROP and ROL) with observed heterogeneity perform better than the random-parameter ordered probit and logit models (RP and RL) without observed heterogeneity in terms of the Akaike information criteria and the goodness of fit of the model. The ROP model performs better than the ROL model in terms of classification model performance measures. The ROP model enables parameters (i.e., the coefficients of the explanatory variables) to vary as a function of explanatory variables as well as across observations, thus accounting for both observed (captured by available explanatory variables) and unobserved (not captured by available explanatory variables) heterogeneity. The analysis reveals that the presence of ASCS is associated with lower crash severity. In this study, observed heterogeneity of ASCS effects on the crash severity is captured by variables related to the intersection and corridor features. Other contributing factors besides ASCS, such as annual average daily traffic, speed limit, lighting, peak period, crash type (rearend, angle), and pedestrian involvements, are also associated with the probability of crash severity. Unobserved heterogeneity of the effect of angle crash type on the crash severity is found to exist across the observations. The findings of this research have practical implications 3 for establishing ASCS implementation guidelines in lowering the probability of higher crash severity.
Controller area network (CAN) is susceptible to various cyberattacks due to its broadcast-based c... more Controller area network (CAN) is susceptible to various cyberattacks due to its broadcast-based communication nature. In this study, we developed a hybrid approach for CAN intrusion detection using a classical convolutional neural network (CCNN) and a quantum restricted Boltzmann machine (quantum RBM). The CCNN is dedicated for feature extraction from CAN images generated from a vehicle's CAN bus data, while the quantum RBM is dedicated for CAN image reconstruction for a classification-based intrusion detection. To evaluate the performance of the hybrid approach, we used a real-world CAN fuzzy attack dataset to create three separate attack datasets, where each dataset represents a unique set of features related to the vehicle. We compared the performance of our hybrid approach to a similar but classical-only approach. Our analyses showed that the hybrid approach performs better in CAN intrusion detection compared to the classical-only approach. For the three datasets considered in this study, the best models in the hybrid approach achieved 97.5%, 97%, and 98.3% intrusion detection accuracies, and 94.7%, 93.9%, and 97.2% recall, respectively, whereas the best models in the classical-only approach achieved 86.7%, 95%, and 89.7% intrusion detection accuracies, and 70.7%, 89.8, and 80.6% recall, respectively.
Reproducing patient-specific 3D-model of pulmonary artery hemodynamics by means of <i>in vitro</i> benchtop simulation
Journal of 3D printing in medicine, Dec 1, 2022
Adaptive Signal System Safety Impacts
ACM Journal on Autonomous Transportation Systems
The traditional build-and-expand approach is not a viable solution to keep roadway traffic rollin... more The traditional build-and-expand approach is not a viable solution to keep roadway traffic rolling safely, so technological solutions, such as Autonomous Vehicles (AVs), are favored. AVs have considerable potential to increase the carrying capacity of roads, ameliorate the chore of driving, improve safety, provide mobility for those who cannot drive, and help the environment. However, they also raise concerns over whether they are socially responsible, accounting for issues such as fairness, equity, and transparency. Regulatory bodies have focused on AV safety, cybersecurity, privacy, and legal liability issues, but have failed to adequately address social responsibility. Thus, existing AV developers do not have to embed social responsibility factors in their proprietary technology. Adverse bias may therefore occur in the development and deployment of AV technology. For instance, an artificial intelligence-based pedestrian detection application used in an AV may, in limited lighting...
A Hybrid Approach for Intrusion Detection in an In-vehicle Controller Area Network using Classical Convolutional Neural Network and Quantum Restricted Boltzmann Machine
Reproducing patient-specific 3D-model of pulmonary artery hemodynamics by means of <i>in vitro</i> benchtop simulation
Journal of 3D printing in medicine, Sep 27, 2022
In this study, we develop a real-time connected vehicle (CV) speed advisory application, which we... more In this study, we develop a real-time connected vehicle (CV) speed advisory application, which we refer to as “Serverless CloSA”, using commercial cloud services and present case studies for a signalized corridor for different roadway traffic conditions. First, we develop a highly scalable serverless cloud computing architecture using Amazon Web Services (AWS) to support the requirements of a real-time CV application. Second, we develop an optimization-based real-time CV speed advisory algorithm that is deployable in the cloud. Third, we develop a cloud-in-the-loop simulation testbed using AWS and an open-source microscopic roadway traffic simulator called Simulation of Urban Mobility (SUMO). Then, we conduct three case studies for three different roadway traffic conditions, i.e., low, medium, and high-density traffic. Our analyses show that Serverless CloSA can reduce the average stopped delays at signalized intersections in a corridor by 77% while reducing the aggregated risk of c...
ArXiv, 2021
Glenn Department of Civil Engineering, Clemson University, Clemson, SC 29634, USA; Email: msalek@... more Glenn Department of Civil Engineering, Clemson University, Clemson, SC 29634, USA; Email: [email protected] Ph.D., P.E., F.ASCE, Eugene Douglas Mays Endowed Professor of Transportation, Glenn Department of Civil Engineering, Clemson University, 216 Lowry Hall, Clemson, SC 29634, USA; Email: [email protected] Ph.D., Assistant Professor, Department of Civil, Construction & Environmental Engineering, The University of Alabama, Tuscaloosa, AL 35487; Email: [email protected] Ph.D., Assistant Professor, Department of Civil and Environmental Engineering, West Virginia University, Morgantown, WV 26505; Email: [email protected] Ph.D., Postdoctoral Research Associate, Department of Mathematics, Iowa State University, Ames, IA 50011; Email: [email protected]
Investigating hierarchical effects of adaptive signal control system on crash severity using random-parameter ordered regression models incorporating observed heterogeneity
Accident Analysis & Prevention
By handling conflicting traffic movements and establishing dynamic coordination between intersect... more By handling conflicting traffic movements and establishing dynamic coordination between intersections in real-time, the Adaptive Signal Control System (ASCS) can potentially improve the operation and safety of signalized intersections on a corridor. This study identifies the hierarchical effects of ASCS on the crash severity by exploring the heterogeneous effect of ASCS on the crash severity. Four different random-parameter ordered regression models (two ordered probit models, and two ordered logit models) are developed and compared. The analysis reveals that the random-parameter ordered probit and logit models (ROP and ROL) with observed heterogeneity perform better than the random-parameter ordered probit and logit models (RP and RL) without observed heterogeneity in terms of the Akaike information criteria and the goodness of fit of the model. The ROP model performs better than the ROL model in terms of classification model performance measures. The ROP model enables parameters (i.e., the coefficients of the explanatory variables) to vary as a function of explanatory variables as well as across observations, thus accounting for both observed (captured by available explanatory variables) and unobserved (not captured by available explanatory variables) heterogeneity. The analysis reveals that the presence of ASCS is associated with lower crash severity. In this study, observed heterogeneity of ASCS effects on the crash severity is captured by variables related to the intersection and corridor features. Other contributing factors besides ASCS, such as annual average daily traffic, speed limit, lighting, peak period, crash type (rear-end, angle), and pedestrian involvements, are also associated with the probability of crash severity. Unobserved heterogeneity of the effect of angle crash type on the crash severity is found to exist across the observations. The findings of this research have practical implications for establishing ASCS implementation guidelines in lowering the probability of higher crash severity.
Uploads
Papers by M Sabbir Salek, Ph.D.