The future of road safety integrating computational intelligence with network paradigms and AI innovations

This chapter explores how AI significantly enhances road safety by integrating computational intelligence with network paradigms and AI innovations. Key applications include predictive analytics to identify accident hotspots, advanced driver-assistance systems (ADAS) like adaptive cruise control and automated braking, and traffic flow optimization to ease congestion and prevent accidents. The future of road safety is being transformed by computational intelligence (CI) and machine learning (ML). These technologies enable real-time traffic monitoring, predictive analytics, and autonomous driving systems to reduce accidents. ML algorithms analyze vast datasets from sensors, cameras, and GPS to detect risky behaviors, optimize traffic flow, and enhance driver assistance systems. Deep learning enhances pedestrian detection, road hazard prediction, and vehicle-to-everything (V2X) communication. As AI evolves, road safety will become more proactive, reducing fatalities and making transportation smarter and safer. Autonomous vehicles with AI promise safer driving by eliminating human error. The research also presents a real-time driver drowsiness detection system using Raspberry Pi, computer vision, and ML, which monitors driver behavior and issues alerts to prevent accidents. This chapter highlights AI’s transformative power in creating safer roads.