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The evolution of electric vehicles (EVs) is reshaping the automotive industry, advocating for more sustainable transportation practices. Accurately predicting EV charging behavior is essential for effective infrastructure planning and…
Given the increasing popularity and demand for connected and autonomous vehicles (CAVs), Eco-driving and platooning in highways and urban areas to increase the efficiency of the traffic system is becoming a possibility. This paper presents…
The rising energy footprint of artificial intelligence has become a measurable component of US data center emissions, yet cybersecurity research seldom considers its environmental cost. This study introduces an eco aware anomaly detection…
Mutual understanding between driver and vehicle is critically important to the design of intelligent vehicles and customized interaction interface. In this study, a unified driver behavior reasoning system toward multi-scale and multi-tasks…
Environmental sustainability is crucial for Integrated Circuits (ICs) across their lifecycle, particularly in manufacturing and use. Meanwhile, ICs using 3D/2.5D integration technologies have emerged as promising solutions to meet the…
In the design of traffic monitoring solutions for optimizing the urban mobility infrastructure, acoustic vehicle counting models have received attention due to their cost effectiveness and energy efficiency. Although deep learning has…
Machine learning continues to emerge as an important tool to be utilised within structural engineering and structural health monitoring, due to its ability to accurately and quickly perform both regression and classification tasks. However,…
In the contemporary world with degrading natural resources, the urgency of energy efficiency has become imperative due to the conservation and environmental safeguarding. Therefore, it's crucial to look for advanced technology to minimize…
Deep learning (DL) can achieve impressive results across a wide variety of tasks, but this often comes at the cost of training models for extensive periods on specialized hardware accelerators. This energy-intensive workload has seen…
Connected and Autonomous Electrified Vehicles (CAEV) is the solution to the future smart mobility having benefits of efficient traffic flow and cleaner environmental impact. Although CAEV has advantages they are still susceptible to…
Electric vehicles (EVs) have been gaining popularity due to their environmental friendliness and efficiency. EV charging station networks are scalable solutions for supporting increasing numbers of EVs within modern electric grid…
Emerging mobility systems, e.g., connected and automated vehicles (CAVs), shared mobility, and electric vehicles, provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better…
Urban driving with connected and automated vehicles (CAVs) offers potential for energy savings, yet most eco-driving strategies focus solely on longitudinal speed control within a single lane. This neglects the significant impact of lateral…
To maintain high perception performance among connected and autonomous vehicles (CAVs), in this paper, we propose an accuracy-aware and resource-efficient raw-level cooperative sensing and computing scheme among CAVs and road-side…
Automotive manufacturing assembly tasks are built upon visual inspections such as scratch identification on machined surfaces, part identification and selection, etc, which guarantee product and process quality. These tasks can be related…
This paper addresses the problem of predicting the energy consumption for the drivers of Battery electric vehicles (BEVs). Several external factors (e.g., weather) are shown to have huge impacts on the energy consumption of a vehicle…
Driving information and data under potential vehicle crashes create opportunities for extensive real-world observations of driver behaviors and relevant factors that significantly influence the driving safety in emergency scenarios.…
Deep Learning methods have been proven to be flexible to model complex phenomena. This has also been the case of Intelligent Transportation Systems (ITS), in which several areas such as vehicular perception and traffic analysis have widely…
Eco-driving strategies have demonstrated substantial potential for improving energy efficiency and reducing emissions, especially at signalized intersections. However, evaluations of eco-driving methods typically rely on simplified…
Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. While performance seems to be improving on benchmark datasets,…