Collision Avoidance System for Self-Driving Vehicles by Delta Autonomy, Robotics Institute, CMU. This stack was developed for my MRSD capstone project. Our use-case involves an oncoming vehicle encroaching into the ego-vehicle's (heavy-duty truck) lane, on a two-lane countryside highway. The perception algorithms perform the detection and tracking of vehicles, and lane marking detection, using a sensor fusion of a monocular camera and RADAR. The prediction algorithms predict the trajectories of all vehicles in the environment including the ego-vehicle. Based on the predicted trajectories, the probability of collision, position and time-to-impact is computed. An evasive maneuver, such as steering or braking, is planned and executed to avoid or mitigate the crash. The project was developed in Carla simulator and ROS.