The invention discloses a driver road rage emotion detection method based on machine learning, and aims to acquire face images, sound and force states of a driver through various sensors so as to accurately detect the road rage emotion of the driver and improve the driving safety. According to the method, a sampling module, a face recognition module, a feature point positioning module, a road rage emotion detection module and a warning module are included, an AdaBoost algorithm, a Haar-Cascade algorithm, a constrained local neural domain model and a support vector machine model are adopted, and parameters are optimized in combination with a particle swarm optimization algorithm, so that the accuracy and generalization ability of road rage emotion detection are improved; the method can be applied to various vehicle types and drivers, and real-time detection and warning can be achieved.

    本发明公开了一种基于机器学习的驾驶员路怒情绪检测方法,旨在通过多种传感器采集驾驶员的人脸图像、声音和用力状态,以实现对驾驶员路怒情绪的准确检测,提高驾驶安全性。该方法包括采样模块、人脸识别模块、特征点定位模块、路怒情绪检测模块和警示模块,其中采用了AdaBoost算法、Haar‑Cascade算法、受约束的局部神经域模型和支持向量机模型,并结合粒子群优化算法对参数进行优化,以提高路怒情绪检测的准确性和泛化能力;本方法可应用于各类车型和驾驶员,可实现实时检测和警示。


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    Title :

    Driver road rage emotion detection method based on machine learning


    Additional title:

    一种基于机器学习的驾驶员路怒情绪检测方法


    Contributors:
    ZHENG HONGYU (author) / PAN ZHIYAO (author) / JIN LIQIANG (author) / LI JIANHUA (author) / XIAO FENG (author)

    Publication date :

    2023-05-30


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    Chinese


    Classification :

    IPC:    G06V / B60R Fahrzeuge, Fahrzeugausstattung oder Fahrzeugteile, soweit nicht anderweitig vorgesehen , VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR / B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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