{"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/learning-a-driving-simulator","title":"Learning a Driving Simulator","arxiv_id":"1608.01230","date":"2016-08-03","proceeding":null,"authors":["Eder Santana","George Hotz"],"abstract":"Comma.ai's approach to Artificial Intelligence for self-driving cars is based\non an agent that learns to clone driver behaviors and plans maneuvers by\nsimulating future events in the road. This paper illustrates one of our\nresearch approaches for driving simulation. One where we learn to simulate.\nHere we investigate variational autoencoders with classical and learned cost\nfunctions using generative adversarial networks for embedding road frames.\nAfterwards, we learn a transition model in the embedded space using action\nconditioned Recurrent Neural Networks. We show that our approach can keep\npredicting realistic looking video for several frames despite the transition\nmodel being optimized without a cost function in the pixel space.","url_abs":"http://arxiv.org/abs/1608.01230v1","url_pdf":"http://arxiv.org/pdf/1608.01230v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-a-driving-simulator","repo_url":"https://github.com/commaai/research","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"learning-a-driving-simulator","repo_url":"https://github.com/crearth/ai","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"learning-a-driving-simulator","repo_url":"https://github.com/mukesh40744/Behavioral-Cloning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"learning-a-driving-simulator","repo_url":"https://github.com/mukeshk05/Behavioral-Cloning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"self-driving-cars","task_name":"Self-Driving Cars"},{"task_slug":"video-prediction","task_name":"Video Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1608.01230","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1608.01230"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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