Abstract
The project proposes a Raspberry Pi-based system for real-time detection of lanes, obstacles, and traffic signals to improve road safety and assist drivers. The system uses computer vision algorithms and sensor data to provide situational awareness for vehicles. The lane detection module uses image processing to identify lane markings and estimate vehicle position, while the obstacle detection module uses visual and distance sensors to identify obstacles in the vehicle’s path. The traffic signal detection module uses image processing to identify and interpret traffic signals, helping drivers adhere to traffic regulations. The integration of these modules on a Raspberry Pi platform allows for compact deployment within vehicles, ensuring timely response to dynamic road conditions. This project offers a promising solution for intelligent transportation systems aimed at reducing accidents and improving traffic flow in urban environments. Key Words: Spotting,hough transform,obstascle detection,rasp cam,traffic signal detection,lane.
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