class_docstr: Class that store and process information about an actor at certain moment.
class_docstr_tok: ["Class","that","store","and","process","information","about","an","actor","at","certain","moment","."]
code: def process(self):
      # Filter lidar points that correspond to my actor id
      data_actor = self._lidar_data[
          self._lidar_data["ObjIdx"] == self._actor_id
      ]
  
      # Take the xyz point cloud data and transform it to actor's frame
      points = np.array(
          [data_actor["x"], data_actor["y"], data_actor["z"]]
      ).T
      points = np.append(points, np.ones((points.shape[0], 1)), axis=1)
      points = np.dot(
          self._lidar_transf.get_matrix(), points.T
      ).T  # sensor -> world
      points = np.dot(
          self._actor_transf.get_inverse_matrix(), points.T
      ).T  # world -> actor
      points = points[:, :-1]
  
      # Saving the points in 'local' coordinates
      self._lidar_pc_local = points
  
      # We compute the limits in the local frame of reference using the
      # vertices of the bounding box
      vertices = self._actor_bb.get_local_vertices()
      ver_py = []
      for v in vertices:
          ver_py.append([v.x, v.y, v.z])
      ver_np = np.array(ver_py)
  
      self._bb_vertices = ver_np
  
      self._bb_minlimits = ver_np.min(axis=0) - 0.001
      self._bb_maxlimits = ver_np.max(axis=0) + 0.001
  
imports: import glob
  import os
  import sys
  import numpy
  from queue import Queue
  from queue import Empty
  import carla
label_desc: Use image node_id 4 for calling the ActorTrace obj's underlying member method code with example usage: obj.process() without return types