一种实时的自主CAN总线逆向工程方法
A Real-Time Approach to Autonomous CAN Bus Reverse Engineering
AI总结：
本文提出了一种实时方法，通过比较惯性测量和CAN数据，实现车辆CAN总线的逆向工程，提高了处理速度和计算效率，适用于自动驾驶套件和网络安全应用。
摘要：
本文介绍了一种实时方法，用于在不事先了解车辆及其CAN系统的情况下逆向工程车辆的CAN总线。通过在显著车辆事件中比较惯性测量数据和CAN数据，该方法准确识别了与加速踏板、刹车踏板和方向盘相关的CAN通道。利用IMU、CAN模块和事件驱动软件架构，系统通过先前研究中录制的序列数据进行了验证。这些数据在多次车辆行驶过程中收集，包括同步的IMU和CAN记录。通过使用这些一致的数据集，本工作中做出的改进是在与先前研究相同的条件下测试和验证的，从而能够直接与早期结果进行比较。与早期方法相比，本工作产生了更快的处理时间和更少的计算需求。本工作可能有潜在应用，用于制造后市场自动驾驶车辆套件以及网络安全应用。它是一种可扩展和适应性强的解决方案，适用于近实时的自主CAN逆向工程。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
