As driver assistance systems (DAS) become more widely used in automobiles over the last few decades, the quality of driving has improved as a result. It is essential to customize modern DAS in order to enhance driver safety, security, and comfort. One of the most important areas of research is how to customize a vehicle to meet the needs and preferences of each individual driver. There are more complicated road safety applications because of technological improvements. Multiple improvements to road transportation are possible with these systems. Safety and efficiency are the focus of this paper’s integrated system, which integrates the two previously mentioned factors. An integrated advanced driver assistance system (ADAS) for rural and urban contexts is proposed to achieve this goal. Because of the greater complexity of single-carriageway roads compared to interstates and the higher frequency of serious and deadly accidents that occur on them, the system concentrates primarily on them. The proposed deep learning-based model for obstacle detection is more advantageous than the typical driver assistance system. The simulated low-cost driver system can be used to examine and prove the suitability in different scenarios before implementing it in a real-time environment. The proposed driver-assistance system has numerous benefits. Driver safety is monitored and reduced by using the proposed technology.