Behavior Metrics tutorial using CARLA
In this tutorial, you will use Behavior Metrics to extract evaluation metrics using CARLA
Table of Contents
Prerequisites
First of all, make sure you have Behavior Metrics installed, following the installation section. You can try running the brain_f1_explicit that is already included on Behavior Metrics brains folder. This brain is capable of finishing every circuit available for the project.
Brain Class
There are three main functions that can be used to for performing I/O operations on the robot. You can also find a dummy file, with all the instructions, that you can use as a template for deployment.
Update Frame
Update the information to be shown in one of the GUI’s frames.
Arguments:
- frame_id {str} – Id of the frame that will represent the data
- data {*} – Data to be shown in the frame. Depending on the type of frame (rgbimage, laser, pose3d, etc)
def update_frame(self, frame_id, data):
Update Pose
Update the pose 3D information obtained from the robot.
Arguments:
- data {*} – Data to be updated, will be retrieved later by the UI.
def update_pose(self, pose_data):
Execute
Main loop of the brain. This will be called iteratively each TIME_CYCLE
def execute(self):