A Real-Time Approach to Autonomous CAN Bus Reverse Engineering

2026-02-14

AI Summary

This work proposes an event-driven, low-computation real-time method for reverse engineering CAN bus signals without prior knowledge, automatically identifying the communication channels corresponding to critical vehicle control signals such as throttle, brake, and steering. By synchronously collecting data from an inertial measurement unit (IMU) and the CAN bus, the approach establishes correlations between IMU-derived vehicle dynamics and CAN messages during salient driving events, enabling precise signal mapping. Implemented within a lightweight software architecture, the method requires only off-the-shelf IMU and CAN hardware, offering strong adaptability and scalability. Experimental results demonstrate that, under identical test conditions, the proposed technique significantly reduces computational overhead and improves processing speed compared to existing approaches, while accurately identifying key control channels—making it well-suited for post-market autonomous driving integration and in-vehicle security analysis.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: State Estimation
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper introduces a real-time method for reverse engineering a vehicle's CAN bus without prior knowledge of the vehicle or its CAN system. By comparing inertial measurement and CAN data during significant vehicle events, the method accurately identified the CAN channels associated with the accelerator pedal, brake pedal, and steering wheel. Utilizing an IMU, CAN module, and event-driven software architecture, the system was validated using prerecorded serialized data from previous studies. This data, collected during multiple vehicle drives, included synchronized IMU and CAN recordings. By using these consistent datasets, the improvements made in this work were tested and validated under the same conditions as in the previous studies, enabling direct comparison to earlier results. Faster processing times were produced and less computational power was needed, as compared to the earlier methods. This work could have potential application to making aftermarket autonomous vehicle kits and for cybersecurity applications. It is a scalable and adaptable solution for autonomous CAN reverse engineering in near real-time.

Problem

• CAN bus reverse engineering
• real-time
• autonomous vehicle
• inertial measurement unit
• vehicle cybersecurity

Innovation

• real-time reverse engineering
• CAN bus
• inertial measurement unit (IMU)
• event-driven architecture
• autonomous vehicle