Customized Vehicle Feature Control through Driver Recognition Technology

<div class="section abstract"><div class="htmlview paragraph">This paper presents a novel approach for customizing vehicle features through driver recognition technology. The system combines Cultural Adaptive Face Recognition (CAFR) using FaceNet and Contrastive Language-Image Pretraining (CLIP) models, along with OpenCV, to recognize drivers and customize vehicle feature control. To identify a driver, the system compares their features against a pre-existing database using FaceNet, which generates efficient face embeddings. The driver image and contextual information collected is processed by OpenAI’s CLIP to generate CLIP embeddings which leverages multimodal learning. FaceNet and CLIP embeddings’ fusion is done and are stored in the Qdrant search database for efficient retrieval and similarity searches. Once the driver is recognized, the system adjusts vehicle features such as temperature settings, music selections, and seat adjustments according to the driver's preferences. Additionally, the system implements optical character recognition (OCR) using OpenCV to extract information real time information from ID cards and other documents, further customizing features to the driver’s needs. The system's novelty lies in its ability to integrate multiple technologies to provide a seamless and personalized driving experience, enhancing driver assistance. The paper's findings demonstrate the system's effectiveness in recognizing and tracking driver behavior, as well as setting up customized features. This technology has the potential to improve road safety, reduce driver fatigue, and enhance the overall driving experience.</div></div>