Road safety is crucial in today’s society, and effective detection of speed bumps, traffic signs and fatigue drivers is essential to averting accidents and guaranteeing the safety of both pedestrians and drivers. The need for contemporary technologies that can effectively monitor and inform drivers about their level of alertness has been underscored by the fact that driver fatigue and drowsiness have been identified as key contributors to traffic accidents. Additionally, by providing prompt caution and promoting adherence to traffic regulations, an effective recognition of speed bumps and traffic signs contributes to a safer driving environment. This article presents the previous research works which effectively used various methods and process in determining for spotting speed bumps, traffic signs, and sleepy drivers at different accuracy using computationally and experimentally. The article also shows the currently used method for detecting the different categorized scenarios. Also, a comparative study of repotted researches has been incorporated based on the method and its accuracy as well as concludes the advantages, disadvantages, and identified few future topics for research. Based on the outcomes, a hybrid method will be used to effectively and accurately determine the drowsiness of the driver, different roadside signs, symbol, bumps, etc.