重型商用载货汽车驾驶环境复杂，工况多变，驾驶员长时间作业易产生疲劳与分心等危险状态。本文提出一种基于多源感知融合的智能座舱驾驶状态识别与干预机制，通过融合车载摄像头、生理传感器及车辆总线等多源感知数据，采用基于 CNN-LSTM 的多源融合识别模型实时监测驾驶员注意力、疲劳度及危险驾驶行为，整体准确率达 92.3%。在此基础上，本文构建分级干预阈值动态调整机制，根据风险等级（0~1.0，划分为四级）实施声光提醒、座椅振动及主动制动等差异化干预措施，使系统误报率降低至 5%。研究表明，多源数据融合技术可克服单一传感器局限性，提升识别准确率与鲁棒性，为重型商用载货汽车智能座舱提供了技术支撑，对降低交通事故率、保障运输安全具有重要意义。

The driving environment of heavy commercial cargo vehicles is complex and the working conditions are variable. Drivers are prone to fatigue and distraction during long-term operation, which poses risks. This paper proposes an intelligent cockpit driving state recognition and intervention mechanism based on multi-source perception fusion. By integrating multi-source perception data such as vehicle cameras, physiological sensors, and vehicle buses, a multi-source fusion recognition model based on CNN-LSTM is used to monitor the driver's attention, fatigue level, and dangerous driving behaviors in real time. The overall accuracy rate reaches 92.3%. On this basis, a dynamic adjustment mechanism for the intervention threshold at different levels is designed. According to the risk level (divided into four grades: 0-1.0), differentiated intervention measures such as audio-visual alerts, seat vibration, and active braking are taken. The false alarm rate is reduced to 5%. The research shows that multi-source data fusion technology can overcome the limitations of a single sensor, improve the recognition accuracy and robustness, provide technical support for the intelligent cockpit of heavy commercial cargo vehicles, and is of great significance for reducing the accident rate and ensuring transportation safety.