{"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.