{"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/autonomous-vehicle-control-end-to-end","title":"Autonomous Vehicle Control: End-to-end Learning in Simulated Urban Environments","arxiv_id":"1905.06712","date":"2019-05-16","proceeding":null,"authors":["Hege Haavaldsen","Max Aasboe","Frank Lindseth"],"abstract":"In recent years, considerable progress has been made towards a vehicle's ability to operate autonomously. An end-to-end approach attempts to achieve autonomous driving using a single, comprehensive software component. Recent breakthroughs in deep learning have significantly increased end-to-end systems' capabilities, and such systems are now considered a possible alternative to the current state-of-the-art solutions. This paper examines end-to-end learning for autonomous vehicles in simulated urban environments containing other vehicles, traffic lights, and speed limits. Furthermore, the paper explores end-to-end systems' ability to execute navigational commands and examines whether improved performance can be achieved by utilizing temporal dependencies between subsequent visual cues. Two end-to-end architectures are proposed: a traditional Convolutional Neural Network and an extended design combining a Convolutional Neural Network with a recurrent layer. The models are trained using expert driving data from a simulated urban setting, and are evaluated by their driving performance in an unseen simulated environment. The results of this paper indicate that end-to-end systems can operate autonomously in simple urban environments. Moreover, it is found that the exploitation of temporal information in subsequent images enhances a system's ability to judge movement and distance.","url_abs":"https://arxiv.org/abs/1905.06712v1","url_pdf":"https://arxiv.org/pdf/1905.06712v1.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":"autonomous-vehicle-control-end-to-end","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"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}