{"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-driving-via-conditional-imitation","title":"End-to-end Driving via Conditional Imitation Learning","arxiv_id":"1710.02410","date":"2017-10-06","proceeding":null,"authors":["Felipe Codevilla","Matthias Müller","Antonio López","Vladlen Koltun","Alexey Dosovitskiy"],"abstract":"Deep networks trained on demonstrations of human driving have learned to\nfollow roads and avoid obstacles. However, driving policies trained via\nimitation learning cannot be controlled at test time. A vehicle trained\nend-to-end to imitate an expert cannot be guided to take a specific turn at an\nupcoming intersection. This limits the utility of such systems. We propose to\ncondition imitation learning on high-level command input. At test time, the\nlearned driving policy functions as a chauffeur that handles sensorimotor\ncoordination but continues to respond to navigational commands. We evaluate\ndifferent architectures for conditional imitation learning in vision-based\ndriving. We conduct experiments in realistic three-dimensional simulations of\nurban driving and on a 1/5 scale robotic truck that is trained to drive in a\nresidential area. Both systems drive based on visual input yet remain\nresponsive to high-level navigational commands. The supplementary video can be\nviewed at https://youtu.be/cFtnflNe5fM","url_abs":"http://arxiv.org/abs/1710.02410v2","url_pdf":"http://arxiv.org/pdf/1710.02410v2.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":"end-to-end-driving-via-conditional-imitation","repo_url":"https://github.com/carla-simulator/imitation-learning","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"end-to-end-driving-via-conditional-imitation","repo_url":"https://github.com/Suryavf/SelfDrivingCar","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"end-to-end-driving-via-conditional-imitation","repo_url":"https://github.com/acm-uiuc/racerbot.rl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"end-to-end-driving-via-conditional-imitation","repo_url":"https://github.com/bitsauce/Carla-ppo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"end-to-end-driving-via-conditional-imitation","repo_url":"https://github.com/onlytailei/carla_cil_pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-2-Clause"}},{"paper_slug":"end-to-end-driving-via-conditional-imitation","repo_url":"https://github.com/shin-eunsu/GTA_Autodriving_LogitechG29","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"end-to-end-driving-via-conditional-imitation","repo_url":"https://github.com/shunchan0677/deepware","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"imitation-learning","task_name":"Imitation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1710.02410","atlas_url":"https://app.syntology.ai/?focus=1710.02410","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.02410"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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