AbstractSystems for High-Performance Computing (HPC) are essential for allowing real-time and data-intensive applications. For quick perception and decision-making in autonomous driving, HPC analyzes large amounts of sensor data (LiDAR, radar and cameras), guaranteeing efficiency and safety. HPC speeds up genomics, predictive analytics and diagnostics in healthcare, enhancing disease detection and individualized care. Real-time data analysis in recommendation systems is powered by HPC to provide tailored content and improve user engagement. All things considered, HPC makes intelligent automation, adaptive personalization and precision healthcare possible. Every year, road traffic accidents result in a large number of fatalities, serious injuries and financial losses, making them a major global public safety concern. Recent developments in artificial intelligence (AI) present viable ways to improve traffic safety and reduce collisions. With an emphasis on computer vision-based surveillance, intelligent traffic management systems, advanced driver assistance systems (ADAS) and predictive analytics, this study investigates the impact of AI-driven technologies in accident prevention. AI methods like machine learning, deep learning and real-time data processing make it possible to identify dangerous driving practices, dangerous road conditions and traffic jams early on. The study looks at how AI can enhance response time, accuracy and situational awareness to help drivers, traffic cops and legislators make better decisions. The paper also addresses important issues with system dependability, data privacy and ethical issues. The results demonstrate how successfully integrating AI into road safety systems can greatly lower accident rates and promote safer, more intelligent and environmentally friendly transportation networks.
Yadav et al. (Wed,) studied this question.
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