Published Paper Details:

In our rapidly urbanizing world, ensuring road safety is a pressing concern. The development of advanced driver assistance systems has become imperative to reduce accidents and enhance driver awareness. Maintaining full attention on the road is essential to prevent accidents. Distracted or inattentive driving is a leading cause of traffic accidents. When drivers are not fully engaged, their reaction times are slower, and they may fail to respond to sudden or unexpected events on the road. This study presents a comprehensive Lane and Speed Breaker Warning System designed to improve the safety of vehicles on the road. Fueled by cutting-edge machine learning techniques and trained on data containing videos of lanes and speed breakers, a system can be created that proficiently identifies lanes and speed breakers, offering timely alerts to drivers. This paper meticulously delves into the broader implications of our work in striving to prevent accidents on the road. As urbanization and vehicular traffic increase, road safety has never been more crucial. The Lane and Speed Breaker Warning system is a proactive step towards mitigating risks. By harnessing the power of machine learning, we empower the system to perceive and respond to its surroundings. This not only enhances driver awareness but also minimizes the potential for accidents and discomfort while driving. The Lane and Speed Breaker Warning System exemplifies a versatile solution for monitoring road conditions.