Design and Evaluation Image Recognition Sub-tasks to Improve End-to-End Learning Model for Self Driving Cars

Abstract

Sub-task training for a deep neural network can improve main task performance, for example, for self-driving cars and other tasks. However there is no theoretical design principle that how to make sub-tasks suitable for a main task. In order to improve the self-driving task, searching the optimal sub-tasks design is necessary. In this research, we compared multiple combination of sub-tasks sharing a network to generate driving command. In the research of Li et al.2018, a multi-task learning method used two modules, a perception module (extracting semantic segmentation and depth map) for recognition of surrounding circumstances and a driving module for driving operation. Their multi-task method scored higher generalization performance in unknown environment than previous end-to-end self-driving method. In this research, we conducted experiments to improve the generalization ability of their model by modifying sub-task design. As a result, generating semantic segmentation map as sub-task got the best performance for self-driving cars.