  On-road obstacle detection is an important field of research that falls in
the scope of intelligent transportation infrastructure systems. The use of
vision-based approaches results in an accurate and cost-effective solution to
such systems. In this research paper, we propose a threat detection mechanism
for autonomous self-driving cars using dashcam videos to ensure the presence of
any unwanted obstacle on the road that falls within its visual range. This
information can assist the vehicle's program to en route safely. There are four
major components, namely, YOLO to identify the objects, advanced lane detection
algorithm, multi regression model to measure the distance of the object from
the camera, the two-second rule for measuring the safety, and limiting speed.
In addition, we have used the Car Crash Dataset(CCD) for calculating the
accuracy of the model. The YOLO algorithm gives an accuracy of around 93%. The
final accuracy of our proposed Threat Detection Model (TDM) is 82.65%.
