End-to-End Xception Model Implementation on Carla Self Driving Car in Moderate Dense Environment
Willy Dharmawan, Hidetaka Nambo · 2019
Recently, with hardware limitation, many autonomous car developers using a simulator to test their network model or solve some self-driving car issues. With that in mind, Carla simulator provides an open platform with many different varieties of maps and real environment parameter, which indicate multiple challenges to be accomplished. There are many approaches to solve these problems, ranging from a complex model such as imitation learning followed by inverse reinforcement learning to a simple model adopting spatial or time-based network with performance-based oriented putting computational time aside. Pertaining this matter, we look into a light-weight model for spatial classification which can reduce computational time with a slight trade back.