Vehicle breakdowns are one of the most common, troublesome issues faced by individuals while driving on the road, especially during long journeys or in places where one is not familiar with the surroundings. At times like these, one needs immediate support and guidance from trusted sources. However, the conventional method of getting support from people is usually word of mouth, which increases the time of waiting for the right kind of assistance. In critical situations like this, time is of the essence, and wasting even a few more minutes could be dangerous for the people in the vehicle. In order to address the aforementioned issues, the study proposes an intelligent Smart On-Road Assistance and Emergency Vehicle Support System using Artificial Intelligence. This is a centralized web-based platform for providing immediate support to the users. This system uses the combination of location detection, communication technology, and artificial intelligence for immediate support to the users. Once the user requires support, the system will immediately locate the user’s position and scan the surrounding area for the availability of mechanics. A decision model based on AI evaluates different factors, like distance, the skill set of the mechanic, ratings, and the mechanic's availability, to determine the best mechanic for your service request. Once the mechanic has been determined, you are able to track your service request and get updates on the status of your car's repairs in real time. This increases the level of transparency and makes communication between the user and the service provider seamless. In addition, the system optimizes the use of resources by efficiently allocating the service requests to the available mechanics. The system combines the power of the web, geolocation, and AI to reduce response time, increase service efficiency, and provide a better user experience. It also increases the safety and convenience of traveling by providing instant support in the case of a car breakdown.
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VANISH M
Mr. M. Vijayakumar
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M et al. (Sun,) studied this question.
synapsesocial.com/papers/69b3abe702a1e69014ccd280 — DOI: https://doi.org/10.56975/jaafr.v4i3.504595