Runtime Virtual Lane Prediction Based on Inverse Perspective Transformation and Machine Learning for Lane Departure Warning in Low-Power Embedded Systems

This paper proposes a virtual lane prediction algorithm based on inverse perspective transformation and machine learning for lane departure warning in low-power embedded systems. The virtual lane prediction method helps in more accurate lane detection by predicting the opposite lane when only one lane is detected or checking whether the distance between lanes is correct when both lanes are detected. The inverse perspective transformation is used for obtaining a bird's-eye view of the scene from a perspective image to remove perspective effects for lane detection and virtual lane prediction. This method requires only the internal and external parameters of the camera without a homography matrix with 8 degrees of freedom (DoF) that maps the points in one image to the corresponding points in the other image. To improve the accuracy and speed of lane detection in complex road environments, we use a machine learning algorithm to accurately detect lanes in the region that passed the first classifier that roughly detects lanes. The system has been tested through the driving video of the vehicle in embedded system. The experimental results show that the proposed virtual lane prediction method works well in various road environments and meet the real-time requirements for low-power embedded systems.