The disclosure provides automotive CAN decoding using supervised machine learning. Techniques for identifying certain signals sent over the CAN bus between components of a vehicle are provided herein. Specifically, certain testing maneuvers designed to engage the component of interest are provided to a technician for performing on the vehicle. The messages can be captured from the CAN bus and analyzed, using supervised machine learning algorithms, to isolate the message ids and the byte numbers so that the values of the component of interest may be observed for determining performance metrics. Once identified, these performance metrics may be used to compare with other vehicles or improve the design and performance of the vehicle.

    本公开提供“使用监督机器学习的汽车CAN解码”。本文提供了用于识别在车辆部件之间通过CAN总线发送的某些信号的技术。具体地,被设计为接合感兴趣部件的某些测试操纵被提供给技术人员以在车辆上执行。可以从所述CAN总线捕获消息并使用监督机器学习算法分析消息,以隔离消息id和字节号,使得可以观察所述感兴趣部件的值以确定性能度量。一旦被识别,这些性能度量即可以用于与其他车辆进行比较或改进所述车辆的设计和性能。


    Access

    Download


    Export, share and cite



    Title :

    AUTOMOTIVE CAN DECODING USING SUPERVISED MACHINE LEARNING


    Additional title:

    使用监督机器学习的汽车CAN解码


    Contributors:

    Publication date :

    2021-08-03


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    B60R Fahrzeuge, Fahrzeugausstattung oder Fahrzeugteile, soweit nicht anderweitig vorgesehen , VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



    Deep Self-Supervised Learning Models for Automotive Systems

    Kurumbudel, Prashanth Ram | British Library Conference Proceedings | 2021


    Deep Self-Supervised Learning Models for Automotive Systems

    Kurumbudel, Prashanth Ram | SAE Technical Papers | 2021


    ARC: Automotive Radar Consistency Regularization for Semi-Supervised Learning

    Lee, Wei-Yu / Jovanov, Ljubomir / Kumcu, Asli et al. | IEEE | 2024


    Detecting Missing Flow Separation using Supervised Machine Learning

    Hedayat, Amirpasha / Ollivier Gooch, Carl F. | AIAA | 2023


    Driver Distraction Detection Using Semi-Supervised Machine Learning

    Liu, Tianchi / Yang, Yan / Huang, Guang-Bin et al. | IEEE | 2016