Free Space and Lane Boundary Fault Recognition and Prediction for Independent Vehicles Using Machine Learning

Autonomous Vehicles research proceeds acceleration as a result of the enormous benefit of an autonomous system. Along with the positivity of self-driving, there are various challenges such as the occurrence of faults, and bugs. Therefore, fault detection and prediction is a crucial step for the safety of autonomous vehicles, it can be achieved with the help of Machine Learning in artificial neural networks. This research proposes a machine learning solution to improve the accuracy and reliability of identifying free space and lane borders in autonomous vehicles. The use of sensor data from cameras and lidars trained machine learning algorithms to recognize and predict driving-related problems. The results showed increased accuracy and resilience compared to traditional computer vision methods, highlighting the potential of machine learning in enhancing the perceptual abilities of autonomous vehicles. The study contributes to the development of safe and reliable autonomous driving systems.