This document defines the in-vehicle devices include nomadic device supported in-vehicle passengers monitoring and care services and specifies general information and use-cases for providing this service based on deep learning model. This system comprises a nomadic device, an in-vehicle autonomous device, a cloud-based server, and an emergency response center that leverages advanced deep-learning algorithms. This document contains algorithm for monitoring and supporting services, segregating them into urgent aid and convenience assistance categories. In addition, this document also delivers the detection and management of health-related emergencies and the observation of passenger well-being through automated recognition and communication of critical situations. Additionally, it facilitates the transmission of physiological distress and discomfort incidents to the emergency response center, followed by the coordinated deployment of safety personnel. The advocated technology encompasses a synergistic network of ITS-Stations, featuring an in-vehicle device, cloud infrastructure, and an emergency response center. This service performs deep learning through in-vehicle devices equipped with vision sensors, depth cameras, infrared thermal cameras, identifies and responds to critical passenger events, forwards real-time video and/or image data to cloud servers, and performs emergency response through communications networks. Moreover, it regularly updates on passenger conditions to the emergency response center. The cloud server performs uploading and deciphering the contextual imagery and categorizing it through deep learning techniques. Subsequently, the emergency response center performs an emergency protocol initiation. This document defines the in-vehicle devices include nomadic device supported in-vehicle passengers monitoring and care services and specifies general information and use-cases for providing this service based on deep learning model. This system comprises a nomadic device, an in-vehicle autonomous device, a cloud-based server, and an emergency response center that leverages advanced deep-learning algorithms. This document contains algorithm for monitoring and supporting services, segregating them into urgent aid and convenience assistance categories. In addition, this document also delivers the detection and management of health-related emergencies and the observation of passenger well-being through automated recognition and communication of critical situations. Additionally, it facilitates the transmission of physiological distress and discomfort incidents to the emergency response center, followed by the coordinated deployment of safety personnel. The advocated technology encompasses a synergistic network of ITS-Stations, featuring an in-vehicle device, cloud infrastructure, and an emergency response center. This service performs deep learning through in-vehicle devices equipped with vision sensors, depth cameras, infrared thermal cameras, identifies and responds to critical passenger events, forwards real-time video and/or image data to cloud servers, and performs emergency response through communications networks. Moreover, it regularly updates on passenger conditions to the emergency response center. The cloud server performs uploading and deciphering the contextual imagery and categorizing it through deep learning techniques. Subsequently, the emergency response center performs an emergency protocol initiation