Smart Vehicle Assistant

Driver safety is a prime challenge in present day transportation systems, in which factors inclusive of sleep, sur- roundings, and vehicle dynamics considerably have an impact on site visitors accidents. This paper provides an incorporated machine, with AI and includes a proposal that integrates superior pc imaginative and prescient, system studying algorithms and real-time for statistics processing In addition to dates detected through landmark evaluation, vehicles that detection using latest object detection models inclusive of YOLO and lane departure warnings the usage of convolutional neural networks (CNNs), the system includes photo enhancement to overcome challenges which includes light a address ground conditions and environmental modifications. Extensive checking out and evaluation demon- strates the robustness, scalability and flexibility of the system to actual-international situations, massively improving accuracy and reaction time This painting lays the foundation for sensible, secure using solutions and contributes to the development of self sustaining automobile technology. Keywords- Vehicle Detection, Lane Departure Warning, Object Detection Algorithms, YOLO (You Only Look Once), Convolutional Neural Networks (CNNs), Facial Landmark Analysis, Real-Time Image Processing, Autonomous Vehi- cles, Low-Light Image Enhancement, Machine Learning for Transportation, AI in Driver Assistance Systems, Deep Learning in Automotive Applications, Traffic Safety Tech- nologies.