Holistic Approach for Automated Reverse Engineering of Unified Diagnostics Service Data
Reverse engineering of internal vehicle communication is a crucial discipline in vehicle benchmarking. The process presents a time-consuming procedure associated with high manual effort. Car manufacturers use unique signal addresses and encodings for their internal data. Accessing this data requires...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/7/384 |
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| author | Nico Rosenberger Nikolai Hoffmann Alexander Mitscherlich Markus Lienkamp |
| author_facet | Nico Rosenberger Nikolai Hoffmann Alexander Mitscherlich Markus Lienkamp |
| author_sort | Nico Rosenberger |
| collection | DOAJ |
| description | Reverse engineering of internal vehicle communication is a crucial discipline in vehicle benchmarking. The process presents a time-consuming procedure associated with high manual effort. Car manufacturers use unique signal addresses and encodings for their internal data. Accessing this data requires either expensive tools suitable for the respective vehicles or experienced engineers who have developed individual approaches to identify specific signals. Access to the internal data enables reading the vehicle’s status, and thus, reducing the need for additional test equipment. This results in vehicles closer to their production status and does not require manipulating the vehicle under study, which prevents affecting future test results. The main focus of this approach is to reduce the cost of such analysis and design a more efficient benchmarking process. In this work, we present a methodology that identifies signals without physically manipulating the vehicle. Our equipment is connected to the vehicle via the On-Board Diagnostics (OBD)-II port and uses the Unified Diagnostics Service (UDS) protocol to communicate with the vehicle. We access, capture, and analyze the vehicle’s signals for future analysis. This is a holistic approach, which, in addition to decoding the signals, also grants access to the vehicle’s data, which allows researchers to utilize state-of-the-art methodologies to analyze their vehicles under study by greatly reducing necessary experience, time, and cost. |
| format | Article |
| id | doaj-art-52b10cc494d946a897f11a1b69b2c9f7 |
| institution | DOAJ |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-52b10cc494d946a897f11a1b69b2c9f72025-08-20T02:47:10ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-07-0116738410.3390/wevj16070384Holistic Approach for Automated Reverse Engineering of Unified Diagnostics Service DataNico Rosenberger0Nikolai Hoffmann1Alexander Mitscherlich2Markus Lienkamp3Department of Mobility Systems Engineering, Institute of Automotive Technology, TUM School of Engineering & Design, Technical University of Munich, Boltzmannstr. 15, 85748 Garching, GermanyDepartment of Mobility Systems Engineering, Institute of Automotive Technology, TUM School of Engineering & Design, Technical University of Munich, Boltzmannstr. 15, 85748 Garching, GermanyDepartment of Mobility Systems Engineering, Institute of Automotive Technology, TUM School of Engineering & Design, Technical University of Munich, Boltzmannstr. 15, 85748 Garching, GermanyDepartment of Mobility Systems Engineering, Institute of Automotive Technology, TUM School of Engineering & Design, Technical University of Munich, Boltzmannstr. 15, 85748 Garching, GermanyReverse engineering of internal vehicle communication is a crucial discipline in vehicle benchmarking. The process presents a time-consuming procedure associated with high manual effort. Car manufacturers use unique signal addresses and encodings for their internal data. Accessing this data requires either expensive tools suitable for the respective vehicles or experienced engineers who have developed individual approaches to identify specific signals. Access to the internal data enables reading the vehicle’s status, and thus, reducing the need for additional test equipment. This results in vehicles closer to their production status and does not require manipulating the vehicle under study, which prevents affecting future test results. The main focus of this approach is to reduce the cost of such analysis and design a more efficient benchmarking process. In this work, we present a methodology that identifies signals without physically manipulating the vehicle. Our equipment is connected to the vehicle via the On-Board Diagnostics (OBD)-II port and uses the Unified Diagnostics Service (UDS) protocol to communicate with the vehicle. We access, capture, and analyze the vehicle’s signals for future analysis. This is a holistic approach, which, in addition to decoding the signals, also grants access to the vehicle’s data, which allows researchers to utilize state-of-the-art methodologies to analyze their vehicles under study by greatly reducing necessary experience, time, and cost.https://www.mdpi.com/2032-6653/16/7/384reverse engineeringsignal identificationautomotive ethernetdiagnostics over internet protocolunified diagnostic servicemachine learning |
| spellingShingle | Nico Rosenberger Nikolai Hoffmann Alexander Mitscherlich Markus Lienkamp Holistic Approach for Automated Reverse Engineering of Unified Diagnostics Service Data World Electric Vehicle Journal reverse engineering signal identification automotive ethernet diagnostics over internet protocol unified diagnostic service machine learning |
| title | Holistic Approach for Automated Reverse Engineering of Unified Diagnostics Service Data |
| title_full | Holistic Approach for Automated Reverse Engineering of Unified Diagnostics Service Data |
| title_fullStr | Holistic Approach for Automated Reverse Engineering of Unified Diagnostics Service Data |
| title_full_unstemmed | Holistic Approach for Automated Reverse Engineering of Unified Diagnostics Service Data |
| title_short | Holistic Approach for Automated Reverse Engineering of Unified Diagnostics Service Data |
| title_sort | holistic approach for automated reverse engineering of unified diagnostics service data |
| topic | reverse engineering signal identification automotive ethernet diagnostics over internet protocol unified diagnostic service machine learning |
| url | https://www.mdpi.com/2032-6653/16/7/384 |
| work_keys_str_mv | AT nicorosenberger holisticapproachforautomatedreverseengineeringofunifieddiagnosticsservicedata AT nikolaihoffmann holisticapproachforautomatedreverseengineeringofunifieddiagnosticsservicedata AT alexandermitscherlich holisticapproachforautomatedreverseengineeringofunifieddiagnosticsservicedata AT markuslienkamp holisticapproachforautomatedreverseengineeringofunifieddiagnosticsservicedata |