Research & Reviews: Journal of Engineering and Technology
MenuISSN: 2319-9873
ISSN: 2319-9873
+44 7456 035580 Sasanka Katreddi
West Virginia University, USA
ScientificTracks Abstracts: RRJET
Heavy-Duty/Freight Transportation is growing day-by-day resulting in many challenges for fleet operators such as increased cost of operation, fuel consumption, shortage of drivers and for manufacturing companies for emission control, driver safety, fuel efficiency etc. As Artificial Intelligence (AI) has seen impressive results in passenger cars with self-driving, driver assistance, vehicle-vehicle communication, extending artificial intelligence to trucks can help maintain operational efficiency for fleet. However, the lack of data, high computational cost, the complexity of heavy-duty vehicles are key restraints for implementing artificial intelligence techniques such as machine learning, deep learning, or computer vision in the trucking industry. The improved, sensors, telematics and IoT technology enabled the collection of data easier. The AI technologies on the data help original equipment manufacturers develop engines and systems that improves the fuel efficiency and emission control of trucks based on the factors that could be identified from pattern in data. The vehicle-to-vehicle (V2V) communication and vehicle-toinfrastructure (V2I) are stimulating the truck platooning which is a coordinated travel by two or more autonomous trucks. Truck Platooning and Autonomous Trucks are the future of AI transportation with ongoing trails. Route optimization, load monitoring, and fault diagnostic are few areas where artificial intelligence can be applied that helps companies in making efficient use of time and mileage. Companies are now looking at the edge AI technologies to overcome incorporating high computational devices on trucks. But the security and the speed of processing are still a challenge. Artificial Intelligence in trucking industry is still in the initial phase. There is lot of scope and feasibility where artificial intelligence along with Internet of Things and edge computing can be adopted to bring revolutionary changes to the trucking industry.
Sasanka Katreddi is currently pursuing Ph.D. degree with the Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, USA. Her research interests include artificial intelligence, machine learning, deep learning, autonomous vehicles, predictive maintenance, emissions, and fuel consumption in heavy-duty transportation. Sasanka has her expertise and passion in developing machine learning and deep learning models in improving the heavy-duty transportation using data-based analysis.