End-to-End Learning with Memory Models for Complex Autonomous Driving Tasks in Indoor Environments

Abstract

AbstractThe interest in autonomous vehicles has increased exponentially in recent years. While Lidar is a proven autonomous driving technology, end-to-end learning approaches have become popular as computer performance has improved. A fully end-to-end method—NVIDIA’s PilotNet has shown its ability to predict speed and steering angle with only camera images. This method achieved the Lidar-based methods’ performance in simple driving tasks. However, a significant drawback was no past spatiotemporal information, imposing an error-sensitive performance, especially in complex driving tasks. Spurred by this deficiency, this paper introduces two novel models: CNN + LSTM and CNN3D, aiming for complex autonomous driving tasks in indoor environments.