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This project forked from udacity/carnd-capstone
we downloaded a lot of udacity and LISA data, we need to convert it into a different format to train the tensorflow models. If we get it working, we will then post the format we want the data in from the simulator
Now that these files are stable, we can add them to the repository so that we can submit the project.
Use /traffic_waypoint
to change the waypoint target velocities before publishing to /final_waypoints
. Your car should now stop at red traffic lights and move when they are green.
Complete a partial waypoint updater which subscribes to /base_waypoints
and /current_pose
and publishes to /final_waypoints
.
Once you have correctly identified the traffic light and determined its position, you can convert it to a waypoint index and publish it.
Reported by @alanswx on Slack:
FYI - right now we have a crash bug when we play back the rosbag - i forget whether it is in the waypoint updater or dbw
Detect the traffic light and its color from the /image_color. Udacity has provided a bag file to test with.
Detect the traffic light and its color from the /image_color
. The topic /vehicle/traffic_lights
contains the exact location and status of all traffic lights in simulator, so you can test your output.
Once your waypoint updater is publishing /final_waypoints
, the waypoint_follower
node will start publishing messages to the /twist_cmd
topic. At this point, you have everything needed to build the dbw_node
. After completing this step, the car should drive in the simulator, ignoring the traffic lights.
Test the complete self-driving car stack prior to submission.
Does this work?
Stop and restart PID controllers depending on the state of /vehicle/dbw_enabled
In the tl_detector we need to load two different models based on the situation. Can someone figure out if there is a variable to read, or how to tell which model we should load?
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