Speed Bump Segmentation an Application of Conditional Generative Adversarial Network for Self-driving Vehicles

Abstract

The intervention of AI technology and self-driving vehicles changed the transportation systems. The current self-driving vehicles demand reliable and accurate information from various functional modules. One of the major modules accommodated in vehicles is object detection and classification. In this paper a speed bump detection approach is developed for slow moving electric vehicle platform. The developed system uses monocular images as input and segment the speed bump using GAN network. The results obtained by new approach show that the GAN network is capable of segmenting various types of speed bumps with good accuracy. This new alternative approach shows the ability of GANs for speed bump detection application in self-driving vehicles.