deep learning for line road detection in smart cars

In recent years, smart cars have advanced rapidly and use artificial intelligence technology to predict behavior, make decisions, and control goals. this technology significantly determines the knowledge level of vehicles. in the complex and dynamic environment of road traffic, negligence, and inattention can lead to irreparable damage. real-time identification and positioning of road lines are key to improving the safety of driving cars. to improve the performance of safe driving assistance, this paper shows how to detect road lines using the yolov8 algorithm and then make decisions to continue driving straight, turn right, and turn left. simulation results and accuracy comparison show that this approach can be used as a reliable source for creating driving assistance scenarios in natural road traffic environments. the use of artificial intelligence and the precise architecture of yolov8 promise high speed and accuracy in smart cars.