Development of an Autonomous Reverse Engineering Capability for Controller Area Network Messages to Support Autonomous Control Retrofits

Kevin Setterstrom, Jeremy Straub
{"title":"Development of an Autonomous Reverse Engineering Capability for Controller Area Network Messages to Support Autonomous Control Retrofits","authors":"Kevin Setterstrom, Jeremy Straub","doi":"arxiv-2307.11781","DOIUrl":null,"url":null,"abstract":"As the autonomous vehicle industry continues to grow, various companies are\nexploring the use of aftermarket kits to retrofit existing vehicles with\nsemi-autonomous capabilities. However, differences in implementation of the\ncontroller area network (CAN) used by each vehicle manufacturer poses a\nsignificant challenge to achieving large-scale implementation of retrofits. To\naddress this challenge, this research proposes a method for reverse engineering\nthe CAN channels associated with a vehicle's accelerator and brake pedals,\nwithout any prior knowledge of the vehicle. By simultaneously recording\ninertial measurement unit (IMU) and CAN data during vehicle operation, the\nproposed algorithms can identify the CAN channels that correspond to each\ncontrol. During testing of six vehicles from three manufacturers, the proposed\nmethod was shown to successfully identify the CAN channels for the accelerator\npedal and brake pedal for each vehicle tested. These promising results\ndemonstrate the potential for using this approach for developing aftermarket\nautonomous vehicle kits - potentially with additional research to facilitate\nreal-time use. Notably, the proposed system has the potential to maintain its\neffectiveness despite changes in vehicle CAN standards, and it could\npotentially be adapted to function with any vehicle communications medium.","PeriodicalId":501310,"journal":{"name":"arXiv - CS - Other Computer Science","volume":"44 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Other Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2307.11781","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

As the autonomous vehicle industry continues to grow, various companies are exploring the use of aftermarket kits to retrofit existing vehicles with semi-autonomous capabilities. However, differences in implementation of the controller area network (CAN) used by each vehicle manufacturer poses a significant challenge to achieving large-scale implementation of retrofits. To address this challenge, this research proposes a method for reverse engineering the CAN channels associated with a vehicle's accelerator and brake pedals, without any prior knowledge of the vehicle. By simultaneously recording inertial measurement unit (IMU) and CAN data during vehicle operation, the proposed algorithms can identify the CAN channels that correspond to each control. During testing of six vehicles from three manufacturers, the proposed method was shown to successfully identify the CAN channels for the accelerator pedal and brake pedal for each vehicle tested. These promising results demonstrate the potential for using this approach for developing aftermarket autonomous vehicle kits - potentially with additional research to facilitate real-time use. Notably, the proposed system has the potential to maintain its effectiveness despite changes in vehicle CAN standards, and it could potentially be adapted to function with any vehicle communications medium.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
支持自主控制改造的控制器局域网信息自主逆向工程能力的开发
随着自动驾驶汽车行业的不断发展,许多公司都在探索使用售后套件来改造具有半自动驾驶功能的现有车辆。然而,每个汽车制造商使用的控制器区域网络(CAN)的实现差异对实现大规模实施改造构成了重大挑战。为了应对这一挑战,本研究提出了一种方法,在不事先了解车辆的情况下,对与车辆加速器和刹车踏板相关的CAN通道进行逆向工程。通过在车辆运行过程中同时记录惯性测量单元(IMU)和CAN数据,该算法可以识别对应于每个控制的CAN通道。在对来自三家制造商的六辆汽车的测试中,所提出的方法被证明可以成功地识别每辆被测试车辆的加速踏板和制动踏板的CAN通道。这些有希望的结果表明,使用这种方法开发售后自动驾驶汽车套件的潜力——可能需要额外的研究来促进实时使用。值得注意的是,尽管车辆CAN标准发生了变化,但所提出的系统仍有可能保持其有效性,并且它可以潜在地适应任何车辆通信介质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Artificial Intelligence-based Smart Port Logistics Metaverse for Enhancing Productivity, Environment, and Safety in Port Logistics: A Case Study of Busan Port Evaluating the Usability of Qualified Electronic Signatures: Systematized Use Cases and Design Paradigms A Brief Discussion on the Philosophical Principles and Development Directions of Data Circulation Predicting Star Scientists in the Field of Artificial Intelligence: A Machine Learning Approach A Match Made in Semantics: Physics-infused Digital Twins for Smart Building Automation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1