Incremental Lifelong Deep Learning for Autonomous Vehicles

Abstract

We investigate a deep learning methodology that can produce more accurate behaviors for autonomous vehicles with a much smaller amount of training data than by using supervised learning alone. In this paper, we develop a Correction-Based Incremental Learning (CBIL) algorithm that adds additional training examples strategically selected from cases where the autonomous vehicle has made mistakes, and is repeated over multiple iterations to dramatically improve mean time to failure. CBIL can be thought of as an online mistake bound learning model that reduces the number of training examples needed to define robust decision boundaries, and is trained offline to solve the problem of catastrophic forgetting. We quantitatively benchmark the performance of CBIL using several experiments related to autonomous platooning performed in truck driving simulations and in the laboratory with mobile robots.