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Title	Design Of A Smart Road Accident Management Framework Using Federated Learning
ID_Doc	18978
Authors	Biswas M.; Banerjee S.; Das D.; Das S.; Dhibar S.; Kisku D.; Das P.; Biswas U.
Year	2025
Published	Smart Innovation, Systems and Technologies, 433
DOI	http://dx.doi.org/10.1007/978-981-96-1348-9_47
Abstract	The automobile industry is experiencing tremendous advancements due to the proliferation of Intra-Vehicular Connectivity, Intelligent Transportation System (ITS), Edge Computing and self-contained motor vehicles. Recent improvements in hardware and software have made this technology attainable. With these technologies, the driving experience can be made more effortless and safer. These technologies are inseparable part of ITS and will make our future Smart Cities. Being conscious of self and surroundings, these vehicles can make more effective decisions on the road, leading to a safer and more efficient traffic management system. This paper presents a smart road accident management framework utilizing IoV and Federated Learning. Being interconnected, the vehicles can share data within each other. Utilizing this data, the vehicles can judge the probability of collisions and can take necessary action of their own. Federated Learning is used to decentralize the process. Instead of perfecting a broader traffic system, our aim is to divide the system into a few zones and improve the traffic within, so that the system can adapt according to its needs, without affecting or depending on any other zone traffic. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Author Keywords	Edge computing; Federated learning; Intelligent transportation system; Internet of vehicle; Smart cities

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