Collision Avoidance System for Self-Driving Vehicles by Delta Autonomy, Robotics Institute, CMU
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.
Find out more about our project on deltaautonomy.github.io.
This repository is only a collection of all the ROS packages developed by us. Feel free to raise issues and pull requests on the original repositories.