End to End Learning for Self-Driving Cars
Mariusz Bojarski Affiliation: NVIDIA Corporation Affiliation: Holmdel, NJ 07735 Davide Del Testa Affiliation: NVIDIA Corporation Affiliation: Holmdel, NJ 07735 Daniel Dworakowski Affiliation: NVIDIA Corporation Affiliation: Holmdel, NJ 07735 Bernhard Firner Affiliation: NVIDIA Corporation Affiliation: Holmdel, NJ 07735 Beat Flepp Affiliation: NVIDIA Corporation Affiliation: Holmdel, NJ 07735 Prasoon Goyal Affiliation: NVIDIA Corporation Affiliation: Holmdel, NJ 07735 Lawrence D. Jackel Affiliation: NVIDIA Corporation Affiliation: Holmdel, NJ 07735 Mathew Monfort Affiliation: NVIDIA Corporation Affiliation: Holmdel, NJ 07735 Urs Muller Affiliation: NVIDIA Corporation Affiliation: Holmdel, NJ 07735 Jiakai Zhang Affiliation: NVIDIA Corporation Affiliation: Holmdel, NJ 07735 Xin Zhang Affiliation: NVIDIA Corporation Affiliation: Holmdel, NJ 07735 Jake Zhao Affiliation: NVIDIA Corporation Affiliation: Holmdel, NJ 07735 Karol Zieba Affiliation: NVIDIA Corporation Affiliation: Holmdel, NJ 07735
Abstract
We trained a convolutional neural network (CNN) to map raw pixels from a single front-facing camera directly to steering commands. This end-to-end approach proved surprisingly powerful. With minimum training data from humans the system learns to drive in traffic on local roads with or without lane markings and on highways. It also operates in areas with unclear visual guidance such as in parking lots and on unpaved roads.
原文 arXiv:1604.07316;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1604.07316v1