arXiv subjects

M Sabbir Salek

Publications and source records attributed to M Sabbir Salek.

22 records · Page 2Linked to original sources

arXiv2023

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

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 best-performing 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.

cs.LG↗

arXiv2023

Autonomous Vehicles for All?

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.

cs.CY↗

arXiv2023

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

In this paper, we theoretically develop and numerically validate an asymmetric linear bilateral control model (LBCM). 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. We theoretically analyze the local stability of the asymmetric LBCM using the condition for asymptotic stability of a linear time-invariant system and prove the string stability of the asymmetric LBCM using a space gap error attenuation approach. Then, we evaluate the efficacy of the asymmetric LBCM by simulating a closely coupled cooperative adaptive cruise control (CACC) platoon of fully automated trucks in various non-linear acceleration and deceleration states. We choose automated truck platooning as a case study since heavy-duty trucks experience higher delays and lags in the powertrain system, and limited acceleration and deceleration capabilities than passenger cars. To evaluate the platoon operational efficiency of the asymmetric LBCM, we compare the performance of the asymmetric LBCM to a baseline model, i.e., the symmetric LBCM, for different powertrain delays and lags. Our analyses found that the asymmetric LBCM can handle any combined powertrain delays and lags up to 0.6 sec while maintaining a constant desired time gap during a stable platoon operation, whereas the symmetric LBCM fails to ensure stable platoon operation as well as maintain a constant desired time gap for any combined powertrain delays and lags over 0.2 sec. These findings demonstrate the potential of the asymmetric LBCM in improving platoon operational efficiency and stability of an automated truck platoon.

eess.SY↗

arXiv2020

Commercial Cloud Computing for Connected Vehicle Applications in Transportation Cyber-Physical Systems

This study focuses on the feasibility of commercial cloud services for connected vehicle (CV) applications in a Transportation Cyber-Physical Systems (TCPS) environment. TCPS implies that CVs, in addition to being connected with each other, communicates with the transportation and computing infrastructure to fulfill application requirements. The motivation of this study is to accelerate commercial cloud-based CV application development by presenting the lessons learned by implementing a CV mobility application using Amazon Web Services (AWS). The feasibility of the cloud-based CV application is assessed at three levels: (i) the development of a cloud-based TCPS architecture, (ii) the deployment of a cloud-based CV application using AWS, and (iii) the evaluation of the cloud-based CV application. We implemented this CV mobility application using a serverless cloud architecture and found that such a cloud-based TCPS environment could meet the permissible delay limits of CV mobility applications. Commercial cloud services, as an integral part of TCPS, could reduce costs associated with establishing and maintaining vast computing infrastructure for supporting CV applications. As the CV penetration levels on the surface transportation systems increase significantly over the next several years, scaling the backend infrastructure to support such applications is a critical issue. This study shows how commercial cloud services could automatically scale the backend infrastructure to meet the rapidly changing demands of real-world CV applications. Through real-world experiments, we demonstrate how commercial cloud services along with serverless cloud architecture could advance the transportation digital infrastructure for supporting connected mobility applications in a TCPS environment.

cs.DC↗