The Improved Automotive Network Optimization Scheme Through Time Series Analysis Using AI/ML

The car industry is pressured to lessen its environmental impact and the cost of proudly owning and working a vehicle. Researchers have evolved a stepped-forward car network Optimization (ANO) scheme combining time collection analysis and synthetic intelligence/system studying (AI/ML) algorithms to combat those demanding situations. This scheme systematically optimizes the car network by detecting anomalies inside the time collection statistics collected from an array of sensors and producing a fixed of moves based totally on the ML version's techniques. This device carries dynamic automobile fashions and real-time riding statistics, including pace, acceleration, and strength consumption, to identify community anomalies and enhance strength efficiency. Furthermore, it additionally analyzes and classifies one-of-a-kind driving scenarios and uses AI/ML-based total optimization strategies to similarly decorate the device's ability to respond to one-of-a-kind road conditions and using behaviors. Moreover, the scheme allows for higher verbal exchange between automobile and infrastructure additives, reducing electricity waste and mitigating road risks.