Vehicle breakdowns are one of the major challenges faced in modern transportation systems, often causing delays, safety risks, and emergency situations for drivers. Traditional roadside assistance systems mainly rely on manual communication and delayed response mechanisms, which reduce operational efficiency and increase inconvenience. This research paper proposes an AI-powered vehicle breakdown prediction and emergency response system that utilizes Artificial Intelligence and Machine Learning techniques to identify potential vehicle failures, analyze vehicle conditions, and provide intelligent emergency assistance in real time. The proposed system integrates predictive analytics, location-based services, fault detection mechanisms, and emergency notification features to improve response time and enhance user safety. The study also discusses the reliability challenges, security concerns, and technical limitations associated with AI-driven transportation systems. Furthermore, future directions such as IoT integration, real-time cloud monitoring, and advanced predictive maintenance models are explored to improve system scalability and performance. The proposed approach aims to create a smarter, safer, and more efficient vehicle assistance ecosystem.
B et al. (Fri,) studied this question.
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