{"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/baidu-apollo-auto-calibration-system-an","title":"Baidu Apollo Auto-Calibration System - An Industry-Level Data-Driven and Learning based Vehicle Longitude Dynamic Calibrating Algorithm","arxiv_id":"1808.10134","date":"2018-08-30","proceeding":null,"authors":["Fan Zhu","Lin Ma","Xin Xu","Dingfeng Guo","Xiao Cui","Qi Kong"],"abstract":"For any autonomous driving vehicle, control module determines its road\nperformance and safety, i.e. its precision and stability should stay within a\ncarefully-designed range. Nonetheless, control algorithms require vehicle\ndynamics (such as longitudinal dynamics) as inputs, which, unfortunately, are\nobscure to calibrate in real time. As a result, to achieve reasonable\nperformance, most, if not all, research-oriented autonomous vehicles do manual\ncalibrations in a one-by-one fashion. Since manual calibration is not\nsustainable once entering into mass production stage for industrial purposes,\nwe here introduce a machine-learning based auto-calibration system for\nautonomous driving vehicles. In this paper, we will show how we build a\ndata-driven longitudinal calibration procedure using machine learning\ntechniques. We first generated offline calibration tables from human driving\ndata. The offline table serves as an initial guess for later uses and it only\nneeds twenty-minutes data collection and process. We then used an\nonline-learning algorithm to appropriately update the initial table (the\noffline table) based on real-time performance analysis. This longitudinal\nauto-calibration system has been deployed to more than one hundred Baidu Apollo\nself-driving vehicles (including hybrid family vehicles and electronic\ndelivery-only vehicles) since April 2018. By August 27, 2018, it had been\ntested for more than two thousands hours, ten thousands kilometers (6,213\nmiles) and yet proven to be effective.","url_abs":"http://arxiv.org/abs/1808.10134v1","url_pdf":"http://arxiv.org/pdf/1808.10134v1.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":"baidu-apollo-auto-calibration-system-an","repo_url":"https://github.com/purewater0901/carCalibration","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}