{"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-braking-system-via-deep","title":"Autonomous Braking System via Deep Reinforcement Learning","arxiv_id":"1702.02302","date":"2017-02-08","proceeding":null,"authors":["Hyunmin Chae","Chang Mook Kang","ByeoungDo Kim","Jaekyum Kim","Chung Choo Chung","Jun Won Choi"],"abstract":"In this paper, we propose a new autonomous braking system based on deep\nreinforcement learning. The proposed autonomous braking system automatically\ndecides whether to apply the brake at each time step when confronting the risk\nof collision using the information on the obstacle obtained by the sensors. The\nproblem of designing brake control is formulated as searching for the optimal\npolicy in Markov decision process (MDP) model where the state is given by the\nrelative position of the obstacle and the vehicle's speed, and the action space\nis defined as whether brake is stepped or not. The policy used for brake\ncontrol is learned through computer simulations using the deep reinforcement\nlearning method called deep Q-network (DQN). In order to derive desirable\nbraking policy, we propose the reward function which balances the damage\nimposed to the obstacle in case of accident and the reward achieved when the\nvehicle runs out of risk as soon as possible. DQN is trained for the scenario\nwhere a vehicle is encountered with a pedestrian crossing the urban road.\nExperiments show that the control agent exhibits desirable control behavior and\navoids collision without any mistake in various uncertain environments.","url_abs":"http://arxiv.org/abs/1702.02302v2","url_pdf":"http://arxiv.org/pdf/1702.02302v2.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-braking-system-via-deep","repo_url":"https://github.com/clarky104/carla_aebs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"autonomous-braking-system-via-deep","repo_url":"https://github.com/ethanmclark1/carla_aebs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement 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":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dqn","method_name":"DQN"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"q-learning","method_name":"Q-Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}