{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/end-to-end-learning-for-self-driving-cars","title":"End to End Learning for Self-Driving Cars","arxiv_id":"1604.07316","date":"2016-04-25","proceeding":null,"authors":["Mariusz Bojarski","Davide Del Testa","Daniel Dworakowski","Bernhard Firner","Beat Flepp","Prasoon Goyal","Lawrence D. Jackel","Mathew Monfort","Urs Muller","Jiakai Zhang","Xin Zhang","Jake Zhao","Karol Zieba"],"abstract":"We trained a convolutional neural network (CNN) to map raw pixels from a\nsingle front-facing camera directly to steering commands. This end-to-end\napproach proved surprisingly powerful. With minimum training data from humans\nthe system learns to drive in traffic on local roads with or without lane\nmarkings and on highways. It also operates in areas with unclear visual\nguidance such as in parking lots and on unpaved roads.\n  The system automatically learns internal representations of the necessary\nprocessing steps such as detecting useful road features with only the human\nsteering angle as the training signal. We never explicitly trained it to\ndetect, for example, the outline of roads.\n  Compared to explicit decomposition of the problem, such as lane marking\ndetection, path planning, and control, our end-to-end system optimizes all\nprocessing steps simultaneously. We argue that this will eventually lead to\nbetter performance and smaller systems. Better performance will result because\nthe internal components self-optimize to maximize overall system performance,\ninstead of optimizing human-selected intermediate criteria, e.g., lane\ndetection. Such criteria understandably are selected for ease of human\ninterpretation which doesn't automatically guarantee maximum system\nperformance. Smaller networks are possible because the system learns to solve\nthe problem with the minimal number of processing steps.\n  We used an NVIDIA DevBox and Torch 7 for training and an NVIDIA DRIVE(TM) PX\nself-driving car computer also running Torch 7 for determining where to drive.\nThe system operates at 30 frames per second (FPS).","url_abs":"http://arxiv.org/abs/1604.07316v1","url_pdf":"http://arxiv.org/pdf/1604.07316v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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