Abstract
This paper develops a novel virtual navigation route generation scheme for an augmented reality (AR) car navigation system based on the generative adversarial network-long short-term memory network (GAN-LSTM) framework with an integrated camera and GPS module. Unlike the present AR car navigation systems, the virtual navigation route is "autonomously" generated in captured images rather than superimposed on the image utilizing the pre-rendered 3D content, such as an arrow or trajectory, which not only provide a more authentic and correct AR effect to the user but also correctly guide the driver earlier when driving in complex road traffic environments. First, an evolved fully convolutional network architecture which uses a top-view image through an inverse perspective mapping scheme as input is utilized to obtain a more accurate semantic segmentation result for the lane markings in the traffic scene. Next, according to the above segmentation result and known location information from path planning, an AR Navigation-Nets based on an LSTM framework is proposed to predict the global relationship codes of the virtual navigation route. Simultaneously, the discriminator is utilized to evaluate the generated virtual navigation route that can approximate the real-world vehicle trajectory. Finally, the virtual navigation route can be superimposed on the original image with the correct ratio and position through an IPM process.