GPS operator

The GPS Operator take a map a current position and wished waypoints and compute the best route to go to the wished waypoint.

It does by using carla.GlobalRoutePlanner. This modules takes an opendrive map as an input and can compute most efficient route between two points.

• The caching of result such that we don't compute it every single time. The computation of route is really costly.

# Used cached waypoints but start at closest point.
if len(self.waypoints) != 0:
    (index, _) = closest_vertex(
        self.waypoints,
        np.array([self.position[:2]]),
    )

    self.waypoints = self.waypoints[
        index : index + NUM_WAYPOINTS_AHEAD
    ]
    self.target_speeds = self.target_speeds[
        index : index + NUM_WAYPOINTS_AHEAD
    ]

• The computation of the gps waypoints happens here:

# Compute the waypoints
waypoints = self.hd_map.compute_waypoints(
    [
        x,
        y,
        self._goal_location[2],
    ],
    self._goal_location,

Sometimes the computation might put the position on the wrong side of the road. I have put a failsafe mecanism, but that might have other consequences. You can try to revisit this part. To increase the precision of the map, you can change the parameters in _hd_map.py:

       self._grp = GlobalRoutePlanner(
           self._map, 1.0
       )  # Distance between waypoints

Graph Description

  - id: carla_gps_op
    operator:
      python: ../../carla/carla_gps_op.py
      outputs:
        - gps_waypoints
      inputs:
        opendrive: oasis_agent/opendrive
        objective_waypoints: oasis_agent/objective_waypoints
        position: oasis_agent/position

Graph Viz

        flowchart TB
  oasis_agent
subgraph fot_op
  fot_op/op[op]
end
subgraph carla_gps_op
  carla_gps_op/op[op]
end
  oasis_agent -- objective_waypoints --> carla_gps_op/op
  oasis_agent -- opendrive --> carla_gps_op/op
  oasis_agent -- position --> carla_gps_op/op
  carla_gps_op/op -- gps_waypoints --> fot_op/op