Data Collection: Leveraged BeamNG.tech simulation, capturing images for processing through cameras that are virtually attached to the simulated vehicles.

Obstruction Detection: Used depth maps and OpenCV to detect obstructions and calculate minimum braking distance.

Lane Detection: Implemented lane detection using grayscaling, Gaussian Blur, Canny edge detector, and Hough Transform.

Speed Estimation: Employed the Lucas-Kanade method for optical flow estimation from image pairs.

Obstruction Detection: Successful in detecting objects in front of the vehicle and determining whether to brake, despite challenges with image noise and depth map inaccuracies.

Lane Detection: Accurately detected lanes on the road, though susceptible to false positives due to environmental variations.

Speed Estimation: Provided consistent results, albeit with some errors compared to actual vehicle speed, indicating the method's limited reliability for real-time applications.