# Driver Identification and Detection of Drowsiness while Driving
## 7 Conclusions  
This work reflects the culmination of a research project that has produced significant technological advancements with practical applications in vehicle security and driver safety, leveraging cutting-edge technologies, including 5G/6G, edge computing, and real-time data processing. The project's demonstration at Nokia's facilities showcased the effectiveness of the client-server architecture. Importantly, the demonstration was conducted in a simulated environment to ensure the safety of individuals, infrastructure, and other road users. The setup involved a simulated car with all the essential driving controls, including a camera optimally positioned to record the driver's face, and two 5G modems, as discussed in the architecture section, enabling high-speed data transmission and low latency communication. One notable achievement was the successful implementation of facial recognition to verify the driver's authorization. The tests demonstrated that unauthorized individuals could not start the vehicle, effectively addressing vehicle security concerns through the use of advanced 5G connectivity and edge computing capabilities. Additionally, attaining a significantly low execution time for the facial recognition component enhances effectiveness and efficiency without compromising driver comfort or time expenditure. Furthermore, the real-time monitoring of driver drowsiness, as discussed in previous sections, was also validated during the demonstration. This capability holds immense promise for preventing not only material damage but also accidents and physical harm caused by drowsy driving, made possible by the real-time data processing capabilities of the system. Finally, the system underwent a comprehensive assessment encompassing speed, latency, and performance. The outcomes substantiate the effective visibility of the individual's face for person recognition and authorization to operate the vehicle. Moreover, the algorithm adeptly assesses the driver's drowsiness in real time, achieved through the adjustment of frames per second parameters and image resolution during data transmission from the client to the server. Leveraging the capabilities of 5G networks ensures a more streamlined and efficient real-time data transmission, eliminating latency issues while sending frames promptly. In order to facilitate real-time execution on the Jetson Nano platform and achieve acceptable inference times, future work should focus on either enhancing the data training process or optimizing the system architecture. Additionally, a comprehensive investigation into server infrastructure requirements, including server quantity and processing capacity, is necessary for large-scale deployment within vehicles. This deployment scenario necessitates a robust server infrastructure capable of handling the periodic transmission and processing of images for driver monitoring tasks, such as fatigue detection. Conceptualization, E.G.-S., P.C.-G. and C.C.-G.; Methodology, S.D.-S., O.C.-A., E.G.-S., P.C.-G. and C.C.-G.; Software, S.D.-S., O.C.-A. and E.G.-S.; Investigation, S.D.-S., O.C.-A. and E.G.-S.; Resources, S.D.-S. and E.G.-S.; Writing --original draft, S.D.-S. and O.C.-A. ; Writing--review & editing, P.C.-G. and C.C.-G.; Supervision, P.C.-G. and C.C.-G.; Project administration, P.C.-G. and C.C.-G.; Funding acquisition, P.C.-G. and C.C.-G.. All authors have read and agreed to the published version of the manuscript. This Research was supported by the CDTI (Centre for the Development of Industrial Technology), the Ministry of Economy Industry and Competitiveness, Celtic-Plus EUREKA, and the European Regional Development Fund, under Project IMMINENCE C2020/2-2 and the European Union (Next Generation) under the strategic project C064/23 SCITALA. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The raw data supporting the conclusions of this article will be made available by the authors on request. Author [PERSON] was employed by the company eXtended Reality Lab.