{"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/reactive-collision-avoidance-using","title":"Reactive Collision Avoidance using Evolutionary Neural Networks","arxiv_id":"1609.08414","date":"2016-09-27","proceeding":null,"authors":["Hesham Eraqi","Youssef EmadEldin","Mohamed Moustafa"],"abstract":"Collision avoidance systems can play a vital role in reducing the number of\naccidents and saving human lives. In this paper, we introduce and validate a\nnovel method for vehicles reactive collision avoidance using evolutionary\nneural networks (ENN). A single front-facing rangefinder sensor is the only\ninput required by our method. The training process and the proposed method\nanalysis and validation are carried out using simulation. Extensive experiments\nare conducted to analyse the proposed method and evaluate its performance.\nFirstly, we experiment the ability to learn collision avoidance in a static\nfree track. Secondly, we analyse the effect of the rangefinder sensor\nresolution on the learning process. Thirdly, we experiment the ability of a\nvehicle to individually and simultaneously learn collision avoidance. Finally,\nwe test the generality of the proposed method. We used a more realistic and\npowerful simulation environment (CarMaker), a camera as an alternative input\nsensor, and lane keeping as an extra feature to learn. The results are\nencouraging; the proposed method successfully allows vehicles to learn\ncollision avoidance in different scenarios that are unseen during training. It\nalso generalizes well if any of the input sensor, the simulator, or the task to\nbe learned is changed.","url_abs":"http://arxiv.org/abs/1609.08414v1","url_pdf":"http://arxiv.org/pdf/1609.08414v1.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":"reactive-collision-avoidance-using","repo_url":"https://github.com/heshameraqi/GA-NN-Car","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"collision-avoidance","task_name":"Collision Avoidance"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}