{"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/reinforcement-learning-and-deep-learning","title":"Reinforcement Learning and Deep Learning based Lateral Control for Autonomous Driving","arxiv_id":"1810.12778","date":"2018-10-30","proceeding":null,"authors":["Dong Li","Dongbin Zhao","Qichao Zhang","Yaran Chen"],"abstract":"This paper investigates the vision-based autonomous driving with deep\nlearning and reinforcement learning methods. Different from the end-to-end\nlearning method, our method breaks the vision-based lateral control system down\ninto a perception module and a control module. The perception module which is\nbased on a multi-task learning neural network first takes a driver-view image\nas its input and predicts the track features. The control module which is based\non reinforcement learning then makes a control decision based on these\nfeatures. In order to improve the data efficiency, we propose visual TORCS\n(VTORCS), a deep reinforcement learning environment which is based on the open\nracing car simulator (TORCS). By means of the provided functions, one can train\nan agent with the input of an image or various physical sensor measurement, or\nevaluate the perception algorithm on this simulator. The trained reinforcement\nlearning controller outperforms the linear quadratic regulator (LQR) controller\nand model predictive control (MPC) controller on different tracks. The\nexperiments demonstrate that the perception module shows promising performance\nand the controller is capable of controlling the vehicle drive well along the\ntrack center with visual input.","url_abs":"http://arxiv.org/abs/1810.12778v1","url_pdf":"http://arxiv.org/pdf/1810.12778v1.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":"reinforcement-learning-and-deep-learning","repo_url":"https://github.com/hbzhang/AwesomeSelfDriving","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":"deep-learning","task_name":"Deep Learning"},{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"model-predictive-control","task_name":"Model Predictive Control"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.12778","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}