Automotive CAN-Based Intelligent Collision Avoidance System using Machine Learning and Cloud Computing

New automobiles must use Controller Area Network (CAN) architecture to meet automation needs and simplify point-to-point wire harnesses. This study offers a complete system that uses a CAN bus-based network to promote effective data transmission inside the automotive environment. Essential data, including speed, distance from other cars, in-car alcohol concentration, and unintentional lane changes, are all monitored by the system's integrated sensors. The technology may successfully avoid accidents by constantly monitoring these factors and immediately alerting the driver if any move beyond the safe range. The system's use goes much beyond simple warnings to drivers. The device uses a bump sensor to detect crashes and immediately sends an SMS message using GSM technology to seek fast help in distant places where accidents may occur. Changing lanes when another car is in the blind area is a prime example of a situation where this technology might be useful. The technology identifies the other vehicle's presence using built-in sensors and alerts the driver before the lane change is initiated, reducing the likelihood of a collision. The suggested system's reliance on CAN bus-based communication increases its adaptability and scalability, making it better able to accommodate the inevitable technological advances of the future. The combination of sensors and immediate warning mechanisms shows the potential of cutting-edge in-vehicle technology to improve road safety and accident avoidance.