Autonomous vehicles (AVs) are self-contained vehicles equipped with control systems to execute various tasks. The Lane Departure Warning (LDW) system is widely employed to prevent the most common cause of vehicle collisions. An autonomous lane-departure system will aid and reduce such collisions. When the vehicle is at risk of drifting or departing its lane, the LDW system monitors its relative position to the lane edge and sends an alarming warning signal to the driver. This work uses an ML-based technique to detect lane markers in an Indian context using a high-resolution camera mounted on the car. Considering that, the LDW system requires three primary operations. The camera geometry information is used to divide the acquired image into two parts: a road part and a non-road component. Then, to circumvent the obstacles caused by the perspective effect, inverse perspective mapping is applied. Then, using a sliding window technique, lane markers are filtered, and Canny edge detection is performed. In addition, the Hough transform method is employed to detect lane markers. If the system detects the vehicle is too close to the right, or left lane markings, a warning light, vibration, or sound will be activated, according to the euro NCAP standard for a lateral offset of 0.76 m. TiHAN IITH test track evaluates ADAS functionality for the Indian market as per euro NCAP standard. In addition, a TiHAN IITH test track was built and validated using real-time testing with ADAS capabilities using the SIL-based software environment. Many sensors in the vehicle dynamics of the mid-size M1 automobile have been adapted to communicate with the SIL environment. For experimental validation, SIL provides iterations of repeated actions for test scenarios that pass or fail based on the NCAP test score.