sensor_agent.py

"""
Agent file that runs the evaluations for all models supported by this repo.
Run it by giving it as the agent option to the
leaderboard/leaderboard/leaderboard_evaluator.py file
"""

import os
from copy import deepcopy

import cv2
import carla
from collections import deque

import torch
import torch.nn.functional as F
import numpy as np
import math

from leaderboard.autoagents import autonomous_agent
from model import LidarCenterNet
from team_code.config import GlobalConfig
from data import CARLA_Data
from nav_planner import RoutePlanner
from nav_planner import extrapolate_waypoint_route

from filterpy.kalman import MerweScaledSigmaPoints
from filterpy.kalman import UnscentedKalmanFilter as UKF

from scenario_logger import ScenarioLogger
import transfuser_utils as t_u

import pathlib
import jsonpickle
import ujson  # Like json but faster
import gzip

# Configure pytorch for maximum performance
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.allow_tf32 = True


# Leaderboard function that selects the class used as agent.
def get_entry_point():
  return 'SensorAgent'


class SensorAgent(autonomous_agent.AutonomousAgent):
  """
    Main class that runs the agents with the run_step function
    """

  def setup(self, path_to_conf_file, route_index=None):
    """Sets up the agent. route_index is for logging purposes"""

    torch.cuda.empty_cache()
    self.track = autonomous_agent.Track.SENSORS
    self.config_path = path_to_conf_file
    self.step = -1
    self.initialized = False
    self.device = torch.device('cuda:0')

    # Load the config saved during training
    with open(os.path.join(path_to_conf_file, 'config.json'), 'r') as args_file:
      loaded_config = jsonpickle.decode(args_file.read())

    # Generate new config for the case that it has new variables.
    self.config = GlobalConfig()
    # Overwrite all properties that were set in the saved config.
    self.config.__dict__.update(loaded_config.__dict__)

    # For models supporting different output modalities we select which one to use here.
    # 0: Waypoints
    # 1: Path + Target Speed
    direct = os.environ.get('DIRECT', 1)
    self.uncertainty_weight = int(os.environ.get('UNCERTAINTY_WEIGHT', 1))
    print('Uncertainty weighting?: ', self.uncertainty_weight)
    if direct is not None:
      self.config.inference_direct_controller = int(direct)
      print('Direct control prediction?: ', direct)

    # If set to true, will generate visualizations at SAVE_PATH
    self.config.debug = int(os.environ.get('DEBUG_CHALLENGE', 0)) == 1

    self.config.brake_uncertainty_threshold = float(
        os.environ.get('UNCERTAINTY_THRESHOLD', self.config.brake_uncertainty_threshold))

    # Classification networks are known to be overconfident which leads to them braking a bit too late in our case.
    # Reducing the driving speed slightly counteracts that.
    if int(os.environ.get('SLOWER', 1)):
      print('Reduce target speed value by two m/s.')
      self.config.target_speeds[2] = self.config.target_speeds[2] - 2.0
      self.config.target_speeds[3] = self.config.target_speeds[3] - 2.0

    # Collects some statistics about the target point. Not needed usually.
    self.tp_stats = False
    self.tp_sign_agrees_with_angle = []
    if int(os.environ.get('TP_STATS', 0)):
      self.tp_stats = True

    if self.config.tp_attention:
      self.tp_attention_buffer = []

    # Stop signs can be occluded with our camera setup. This buffer remembers them until cleared.
    # Very useful on the LAV benchmark
    self.stop_sign_controller = int(os.environ.get('STOP_CONTROL', 0))
    print('Use stop sign controller:', self.stop_sign_controller)
    if self.stop_sign_controller:
      # There can be max 1 stop sign affecting the ego
      self.stop_sign_buffer = deque(maxlen=1)
      self.clear_stop_sign = 0  # Counter if we recently cleared a stop sign

    # Load model files
    self.nets = []
    self.model_count = 0  # Counts how many models are in our ensemble
    for file in os.listdir(path_to_conf_file):
      if file.endswith('.pth'):
        self.model_count += 1
        print(os.path.join(path_to_conf_file, file))
        net = LidarCenterNet(self.config)
        if self.config.sync_batch_norm:
          # Model was trained with Sync. Batch Norm.
          # Need to convert it otherwise parameters will load wrong.
          net = torch.nn.SyncBatchNorm.convert_sync_batchnorm(net)
        state_dict = torch.load(os.path.join(path_to_conf_file, file), map_location=self.device)

        net.load_state_dict(state_dict, strict=False)
        net.cuda(device=self.device)
        net.eval()
        self.nets.append(net)

    self.stuck_detector = 0
    self.force_move = 0

    self.bb_buffer = deque(maxlen=1)
    self.commands = deque(maxlen=2)
    self.commands.append(4)
    self.commands.append(4)
    self.target_point_prev = [1e5, 1e5]

    # Filtering
    self.points = MerweScaledSigmaPoints(n=4, alpha=0.00001, beta=2, kappa=0, subtract=residual_state_x)
    self.ukf = UKF(dim_x=4,
                   dim_z=4,
                   fx=bicycle_model_forward,
                   hx=measurement_function_hx,
                   dt=self.config.carla_frame_rate,
                   points=self.points,
                   x_mean_fn=state_mean,
                   z_mean_fn=measurement_mean,
                   residual_x=residual_state_x,
                   residual_z=residual_measurement_h)

    # State noise, same as measurement because we
    # initialize with the first measurement later
    self.ukf.P = np.diag([0.5, 0.5, 0.000001, 0.000001])
    # Measurement noise
    self.ukf.R = np.diag([0.5, 0.5, 0.000000000000001, 0.000000000000001])
    self.ukf.Q = np.diag([0.0001, 0.0001, 0.001, 0.001])  # Model noise
    # Used to set the filter state equal the first measurement
    self.filter_initialized = False
    # Stores the last filtered positions of the ego vehicle. Need at least 2 for LiDAR 10 Hz realignment
    self.state_log = deque(maxlen=max((self.config.lidar_seq_len * self.config.data_save_freq), 2))

    #Temporal LiDAR
    self.lidar_buffer = deque(maxlen=self.config.lidar_seq_len * self.config.data_save_freq)

    self.lidar_last = None

    self.data = CARLA_Data(root=[], config=self.config, shared_dict=None)

    # Path to where visualizations and other debug output gets stored
    self.save_path = os.environ.get('SAVE_PATH')

    # Logger that generates logs used for infraction replay in the results_parser.
    if self.save_path is not None and route_index is not None:
      self.save_path = pathlib.Path(self.save_path) / route_index
      pathlib.Path(self.save_path).mkdir(parents=True, exist_ok=True)

      self.lon_logger = ScenarioLogger(
          save_path=self.save_path,
          route_index=route_index,
          logging_freq=self.config.logging_freq,
          log_only=True,
          route_only=False,  # with vehicles
          roi=self.config.logger_region_of_interest,
      )
    else:
      self.save_path = None

  def _init(self):
    # During setup() not everything is available yet, so this _init is a second setup in run_step()
    # Privileged map access for logging and visualizations. Turned off during normal evaluation.
    if self.save_path is not None:
      from srunner.scenariomanager.carla_data_provider import CarlaDataProvider  # pylint: disable=locally-disabled, import-outside-toplevel
      from nav_planner import interpolate_trajectory  # pylint: disable=locally-disabled, import-outside-toplevel
      self.world_map = CarlaDataProvider.get_map()
      trajectory = [item[0].location for item in self._global_plan_world_coord]
      self.dense_route, _ = interpolate_trajectory(self.world_map, trajectory)  # privileged

      self._waypoint_planner = RoutePlanner(self.config.log_route_planner_min_distance,
                                            self.config.route_planner_max_distance)
      self._waypoint_planner.set_route(self.dense_route, True)

      vehicle = CarlaDataProvider.get_hero_actor()
      self.lon_logger.ego_vehicle = vehicle
      self.lon_logger.world = vehicle.get_world()

      self.nets[0].init_visualization()

    self._route_planner = RoutePlanner(self.config.route_planner_min_distance, self.config.route_planner_max_distance)
    self._route_planner.set_route(self._global_plan, True)
    self.initialized = True

  def sensors(self):
    sensors = [{
        'type': 'sensor.camera.rgb',
        'x': self.config.camera_pos[0],
        'y': self.config.camera_pos[1],
        'z': self.config.camera_pos[2],
        'roll': self.config.camera_rot_0[0],
        'pitch': self.config.camera_rot_0[1],
        'yaw': self.config.camera_rot_0[2],
        'width': self.config.camera_width,
        'height': self.config.camera_height,
        'fov': self.config.camera_fov,
        'id': 'rgb_front'
    }, {
        'type': 'sensor.other.imu',
        'x': 0.0,
        'y': 0.0,
        'z': 0.0,
        'roll': 0.0,
        'pitch': 0.0,
        'yaw': 0.0,
        'sensor_tick': self.config.carla_frame_rate,
        'id': 'imu'
    }, {
        'type': 'sensor.other.gnss',
        'x': 0.0,
        'y': 0.0,
        'z': 0.0,
        'roll': 0.0,
        'pitch': 0.0,
        'yaw': 0.0,
        'sensor_tick': 0.01,
        'id': 'gps'
    }, {
        'type': 'sensor.speedometer',
        'reading_frequency': self.config.carla_fps,
        'id': 'speed'
    }]
    # Don't set up LiDAR for camera only approaches
    if self.config.backbone not in ('aim'):
      sensors.append({
          'type': 'sensor.lidar.ray_cast',
          'x': self.config.lidar_pos[0],
          'y': self.config.lidar_pos[1],
          'z': self.config.lidar_pos[2],
          'roll': self.config.lidar_rot[0],
          'pitch': self.config.lidar_rot[1],
          'yaw': self.config.lidar_rot[2],
          'id': 'lidar'
      })

    return sensors

  @torch.inference_mode()  # Turns off gradient computation
  def tick(self, input_data):
    """Pre-processes sensor data and runs the Unscented Kalman Filter"""
    rgb = []
    for camera_pos in ['front']:
      rgb_cam = 'rgb_' + camera_pos
      camera = input_data[rgb_cam][1][:, :, :3]

      # Also add jpg artifacts at test time, because the training data was saved as jpg.
      _, compressed_image_i = cv2.imencode('.jpg', camera)
      camera = cv2.imdecode(compressed_image_i, cv2.IMREAD_UNCHANGED)

      rgb_pos = cv2.cvtColor(camera, cv2.COLOR_BGR2RGB)
      # Switch to pytorch channel first order
      rgb_pos = np.transpose(rgb_pos, (2, 0, 1))
      rgb.append(rgb_pos)
    rgb = np.concatenate(rgb, axis=1)
    rgb = torch.from_numpy(rgb).to(self.device, dtype=torch.float32).unsqueeze(0)

    gps_pos = self._route_planner.convert_gps_to_carla(input_data['gps'][1][:2])
    speed = input_data['speed'][1]['speed']
    compass = t_u.preprocess_compass(input_data['imu'][1][-1])

    result = {
        'rgb': rgb,
        'compass': compass,
    }

    if self.config.backbone not in ('aim'):
      result['lidar'] = t_u.lidar_to_ego_coordinate(self.config, input_data['lidar'])

    if not self.filter_initialized:
      self.ukf.x = np.array([gps_pos[0], gps_pos[1], t_u.normalize_angle(compass), speed])
      self.filter_initialized = True

    self.ukf.predict(steer=self.control.steer, throttle=self.control.throttle, brake=self.control.brake)
    self.ukf.update(np.array([gps_pos[0], gps_pos[1], t_u.normalize_angle(compass), speed]))
    filtered_state = self.ukf.x
    self.state_log.append(filtered_state)

    result['gps'] = filtered_state[0:2]

    waypoint_route = self._route_planner.run_step(filtered_state[0:2])

    if len(waypoint_route) > 2:
      target_point, far_command = waypoint_route[1]
    elif len(waypoint_route) > 1:
      target_point, far_command = waypoint_route[1]
    else:
      target_point, far_command = waypoint_route[0]

    if (target_point != self.target_point_prev).all():
      self.target_point_prev = target_point
      self.commands.append(far_command.value)

    one_hot_command = t_u.command_to_one_hot(self.commands[-2])
    result['command'] = torch.from_numpy(one_hot_command[np.newaxis]).to(self.device, dtype=torch.float32)

    ego_target_point = t_u.inverse_conversion_2d(target_point, result['gps'], result['compass'])
    ego_target_point = torch.from_numpy(ego_target_point[np.newaxis]).to(self.device, dtype=torch.float32)

    result['target_point'] = ego_target_point

    result['speed'] = torch.FloatTensor([speed]).to(self.device, dtype=torch.float32)

    if self.save_path is not None:
      waypoint_route = self._waypoint_planner.run_step(result['gps'])
      waypoint_route = extrapolate_waypoint_route(waypoint_route, self.config.route_points)
      route = np.array([[node[0][0], node[0][1]] for node in waypoint_route])[:self.config.route_points]
      self.lon_logger.log_step(route)

    return result

  @torch.inference_mode()  # Turns off gradient computation
  def run_step(self, input_data, timestamp, sensors=None):  # pylint: disable=locally-disabled, unused-argument
    self.step += 1

    if not self.initialized:
      self._init()
      control = carla.VehicleControl(steer=0.0, throttle=0.0, brake=1.0)
      self.control = control
      tick_data = self.tick(input_data)
      if self.config.backbone not in ('aim'):
        self.lidar_last = deepcopy(tick_data['lidar'])
      return control

    # Need to run this every step for GPS filtering
    tick_data = self.tick(input_data)

    lidar_indices = []
    for i in range(self.config.lidar_seq_len):
      lidar_indices.append(i * self.config.data_save_freq)

    #Current position of the car
    ego_x = self.state_log[-1][0]
    ego_y = self.state_log[-1][1]
    ego_theta = self.state_log[-1][2]

    ego_x_last = self.state_log[-2][0]
    ego_y_last = self.state_log[-2][1]
    ego_theta_last = self.state_log[-2][2]

    # We only get half a LiDAR at every time step. Aligns the last half into the current coordinate frame.
    if self.config.backbone not in ('aim'):
      lidar_last = self.align_lidar(self.lidar_last, ego_x_last, ego_y_last, ego_theta_last, ego_x, ego_y, ego_theta)

    # Updates stop boxes by vehicle movement converting past predictions into the current frame.
    if self.stop_sign_controller:
      self.update_stop_box(self.stop_sign_buffer, ego_x_last, ego_y_last, ego_theta_last, ego_x, ego_y, ego_theta)

    if self.config.backbone not in ('aim'):
      lidar_current = deepcopy(tick_data['lidar'])
      lidar_full = np.concatenate((lidar_current, lidar_last), axis=0)

      self.lidar_buffer.append(lidar_full)

    if self.config.backbone not in ('aim'):
      # We wait until we have sufficient LiDARs
      if len(self.lidar_buffer) < (self.config.lidar_seq_len * self.config.data_save_freq):
        self.lidar_last = deepcopy(tick_data['lidar'])
        tmp_control = carla.VehicleControl(0.0, 0.0, 1.0)
        self.control = tmp_control

        return tmp_control

    # Possible action repeat configuration
    if self.step % self.config.action_repeat == 1:
      self.lidar_last = deepcopy(tick_data['lidar'])

      return self.control

    if self.config.backbone in ('aim'):  # Image only method
      # Dummy data
      lidar_bev = torch.zeros((1, 1 + int(self.config.use_ground_plane), self.config.lidar_resolution_height,
                               self.config.lidar_resolution_width)).to(self.device, dtype=torch.float32)
    else:
      # Voxelize LiDAR and stack temporal frames
      lidar_bev = []
      # prepare LiDAR input
      for i in lidar_indices:
        lidar_point_cloud = deepcopy(self.lidar_buffer[i])

        # For single frame there is no point in realignment. The state_log index will also differ.
        if self.config.realign_lidar and self.config.lidar_seq_len > 1:
          # Position of the car when the LiDAR was collected
          curr_x = self.state_log[i][0]
          curr_y = self.state_log[i][1]
          curr_theta = self.state_log[i][2]

          # Voxelize to BEV for NN to process
          lidar_point_cloud = self.align_lidar(lidar_point_cloud, curr_x, curr_y, curr_theta, ego_x, ego_y, ego_theta)

        lidar_histogram = torch.from_numpy(
            self.data.lidar_to_histogram_features(lidar_point_cloud,
                                                  use_ground_plane=self.config.use_ground_plane)).unsqueeze(0)

        lidar_histogram = lidar_histogram.to(self.device, dtype=torch.float32)
        lidar_bev.append(lidar_histogram)

        lidar_bev = torch.cat(lidar_bev, dim=1)

    if self.config.backbone not in ('aim'):
      self.lidar_last = deepcopy(tick_data['lidar'])

    # prepare velocity input
    gt_velocity = tick_data['speed']
    velocity = gt_velocity.reshape(1, 1)  # used by transfuser

    compute_debug_output = self.config.debug and (not self.save_path is None)

    # forward pass
    pred_wps = []
    pred_target_speeds = []
    pred_checkpoints = []
    bounding_boxes = []
    wp_selected = None
    for i in range(self.model_count):
      if self.config.backbone in ('transFuser', 'aim', 'bev_encoder'):
        pred_wp, \
        pred_target_speed, \
        pred_checkpoint, \
        pred_semantic, \
        pred_bev_semantic, \
        pred_depth, \
        pred_bb_features,\
        attention_weights,\
        pred_wp_1,\
        selected_path = self.nets[i].forward(
          rgb=tick_data['rgb'],
          lidar_bev=lidar_bev,
          target_point=tick_data['target_point'],
          ego_vel=velocity,
          command=tick_data['command'])
        # Only convert bounding boxes when they are used.
        if self.config.detect_boxes and (compute_debug_output or self.config.backbone in ('aim') or
                                         self.stop_sign_controller):
          pred_bounding_box = self.nets[i].convert_features_to_bb_metric(pred_bb_features)
        else:
          pred_bounding_box = None
      else:
        raise ValueError('The chosen vision backbone does not exist. The options are: transFuser, aim, bev_encoder')

      if self.config.use_wp_gru:
        if self.config.multi_wp_output:
          wp_selected = 0
          if F.sigmoid(selected_path)[0].item() > 0.5:
            wp_selected = 1
            pred_wps.append(pred_wp_1)
          else:
            pred_wps.append(pred_wp)
        else:
          pred_wps.append(pred_wp)
      if self.config.use_controller_input_prediction:
        pred_target_speeds.append(F.softmax(pred_target_speed[0], dim=0))
        pred_checkpoints.append(pred_checkpoint[0][1])

      bounding_boxes.append(pred_bounding_box)

    # Average the predictions from ensembles
    if self.config.detect_boxes and (compute_debug_output or self.config.backbone in ('aim') or
                                     self.stop_sign_controller):
      # We average bounding boxes by using non-maximum suppression on the set of all detected boxes.
      bbs_vehicle_coordinate_system = t_u.non_maximum_suppression(bounding_boxes, self.config.iou_treshold_nms)

      self.bb_buffer.append(bbs_vehicle_coordinate_system)
    else:
      bbs_vehicle_coordinate_system = None

    if self.stop_sign_controller:
      stop_for_stop_sign = self.stop_sign_controller_step(gt_velocity.item())

    if self.config.tp_attention:
      self.tp_attention_buffer.append(attention_weights[2])

    # Visualize the output of the last model
    if compute_debug_output:
      if self.config.use_controller_input_prediction:
        prob_target_speed = F.softmax(pred_target_speed, dim=1)
      else:
        prob_target_speed = pred_target_speed

      self.nets[0].visualize_model(self.save_path,
                                   self.step,
                                   tick_data['rgb'],
                                   lidar_bev,
                                   tick_data['target_point'],
                                   pred_wp,
                                   pred_semantic=pred_semantic,
                                   pred_bev_semantic=pred_bev_semantic,
                                   pred_depth=pred_depth,
                                   pred_checkpoint=pred_checkpoint,
                                   pred_speed=prob_target_speed,
                                   pred_bb=bbs_vehicle_coordinate_system,
                                   gt_speed=gt_velocity,
                                   gt_wp=pred_wp_1,
                                   wp_selected=wp_selected)

    if self.config.use_wp_gru:
      self.pred_wp = torch.stack(pred_wps, dim=0).mean(dim=0)

    if self.config.use_controller_input_prediction:
      pred_target_speed = torch.stack(pred_target_speeds, dim=0).mean(dim=0)
      pred_aim_wp = torch.stack(pred_checkpoints, dim=0).mean(dim=0)

      pred_aim_wp = pred_aim_wp.detach().cpu().numpy()
      pred_angle = -math.degrees(math.atan2(-pred_aim_wp[1], pred_aim_wp[0])) / 90.0

      if self.tp_stats:
        loc_tp = tick_data['target_point'].detach().cpu().numpy()[0]
        deg_pred_angle = pred_angle * 90.0
        tp_angle = -math.degrees(math.atan2(-loc_tp[1], loc_tp[0]))
        if abs(tp_angle) > 1.0 and abs(deg_pred_angle) > 1.0:
          same_direction = float(tp_angle * deg_pred_angle >= 0.0)
          self.tp_sign_agrees_with_angle.append(same_direction)

      if self.uncertainty_weight:
        uncertainty = pred_target_speed.detach().cpu().numpy()
        if uncertainty[0] > self.config.brake_uncertainty_threshold:
          pred_target_speed = self.config.target_speeds[0]
        else:
          pred_target_speed = sum(uncertainty * self.config.target_speeds)
      else:
        pred_target_speed_index = torch.argmax(pred_target_speed)
        pred_target_speed = self.config.target_speeds[pred_target_speed_index]

    if self.config.inference_direct_controller and self.config.use_controller_input_prediction:
      steer, throttle, brake = self.nets[0].control_pid_direct(pred_target_speed, pred_angle, gt_velocity)
    elif self.config.use_wp_gru and not (hasattr(self.config, 'inference_direct_controller') and self.config.inference_direct_controller):
      steer, throttle, brake = self.nets[0].control_pid(self.pred_wp, gt_velocity)
    else:
      raise ValueError('An output representation was chosen that was not trained.')

    # 0.1 is just an arbitrary low number to threshold when the car is stopped
    if gt_velocity < 0.1:
      self.stuck_detector += 1
    else:
      self.stuck_detector = 0

    # Restart mechanism in case the car got stuck. Not used a lot anymore but doesn't hurt to keep it.
    if self.stuck_detector > self.config.stuck_threshold:
      self.force_move = self.config.creep_duration

    if self.force_move > 0:
      emergency_stop = False
      if self.config.backbone not in ('aim'):
        # safety check
        safety_box = deepcopy(self.lidar_buffer[-1])

        # z-axis
        safety_box = safety_box[safety_box[..., 2] > self.config.safety_box_z_min]
        safety_box = safety_box[safety_box[..., 2] < self.config.safety_box_z_max]

        # y-axis
        safety_box = safety_box[safety_box[..., 1] > self.config.safety_box_y_min]
        safety_box = safety_box[safety_box[..., 1] < self.config.safety_box_y_max]

        # x-axis
        safety_box = safety_box[safety_box[..., 0] > self.config.safety_box_x_min]
        safety_box = safety_box[safety_box[..., 0] < self.config.safety_box_x_max]
        emergency_stop = (len(safety_box) > 0)  # Checks if the List is empty

      if not emergency_stop:
        print('Detected agent being stuck. Step: ', self.step)
        throttle = max(self.config.creep_throttle, throttle)
        brake = False
        self.force_move -= 1
      else:
        print('Creeping stopped by safety box. Step: ', self.step)
        throttle = 0.0
        brake = True
        self.force_move = self.config.creep_duration

    if self.stop_sign_controller:
      if stop_for_stop_sign:
        throttle = 0.0
        brake = True

    control = carla.VehicleControl(steer=float(steer), throttle=float(throttle), brake=float(brake))

    # CARLA will not let the car drive in the initial frames.
    # We set the action to brake so that the filter does not get confused.
    if self.step < self.config.inital_frames_delay:
      self.control = carla.VehicleControl(0.0, 0.0, 1.0)
    else:
      self.control = control

    return control

  def stop_sign_controller_step(self, ego_speed):
    """Checks whether the car is intersecting with one of the detected stop signs"""
    if self.clear_stop_sign > 0:
      self.clear_stop_sign -= 1

    if len(self.bb_buffer) < 1:
      return False
    stop_sign_stop_predicted = False
    extent = carla.Vector3D(self.config.ego_extent_x, self.config.ego_extent_y, self.config.ego_extent_z)
    origin = carla.Location(x=0.0, y=0.0, z=0.0)

    car_box = carla.BoundingBox(origin, extent)

    for bb in self.bb_buffer[-1]:
      if bb[7] == 3:  # Stop sign detected
        self.stop_sign_buffer.append(bb)

    if len(self.stop_sign_buffer) > 0:
      # Check if we need to stop
      stop_box = self.stop_sign_buffer[0]
      stop_origin = carla.Location(x=stop_box[0], y=stop_box[1], z=0.0)
      stop_extent = carla.Vector3D(stop_box[2], stop_box[3], 1.0)
      stop_carla_box = carla.BoundingBox(stop_origin, stop_extent)
      stop_carla_box.rotation = carla.Rotation(0.0, np.rad2deg(stop_box[4]), 0.0)

      if t_u.check_obb_intersection(stop_carla_box, car_box) and self.clear_stop_sign <= 0:
        if ego_speed > 0.01:
          stop_sign_stop_predicted = True
        else:
          # We have cleared the stop sign
          stop_sign_stop_predicted = False
          self.stop_sign_buffer.pop()
          # Stop signs don't come in herds, so we know we don't need to clear one for a while.
          self.clear_stop_sign = 100

    if len(self.stop_sign_buffer) > 0:
      # Remove boxes that are too far away
      if np.linalg.norm(self.stop_sign_buffer[0][:2]) > abs(self.config.max_x):
        self.stop_sign_buffer.pop()

    return stop_sign_stop_predicted

  def bb_detected_in_front_of_vehicle(self, ego_speed):
    if len(self.bb_buffer) < 1:  # We only start after we have 4 time steps.
      return False

    collision_predicted = False

    extent = carla.Vector3D(self.config.ego_extent_x, self.config.ego_extent_y, self.config.ego_extent_z)

    # Safety box
    bremsweg = ((ego_speed.cpu().numpy().item() * 3.6) / 10.0)**2 / 2.0  # Bremsweg formula for emergency break
    safety_x = np.clip(bremsweg + 1.0, a_min=2.0, a_max=4.0)  # plus one meter is the car.

    center_safety_box = carla.Location(x=safety_x, y=0.0, z=1.0)

    safety_bounding_box = carla.BoundingBox(center_safety_box, extent)
    safety_bounding_box.rotation = carla.Rotation(0.0, 0.0, 0.0)

    for bb in self.bb_buffer[-1]:
      # We just give them some arbitrary height. Does not matter
      bb_extent_z = 1.0
      loc_local = carla.Location(bb[0], bb[1], 0.0)
      extent_det = carla.Vector3D(bb[2], bb[3], bb_extent_z)
      bb_local = carla.BoundingBox(loc_local, extent_det)
      bb_local.rotation = carla.Rotation(0.0, np.rad2deg(bb[4]).item(), 0.0)

      if t_u.check_obb_intersection(safety_bounding_box, bb_local):
        collision_predicted = True

    return collision_predicted

  def align_lidar(self, lidar, x, y, orientation, x_target, y_target, orientation_target):
    pos_diff = np.array([x_target, y_target, 0.0]) - np.array([x, y, 0.0])
    rot_diff = t_u.normalize_angle(orientation_target - orientation)

    # Rotate difference vector from global to local coordinate system.
    rotation_matrix = np.array([[np.cos(orientation_target), -np.sin(orientation_target), 0.0],
                                [np.sin(orientation_target),
                                 np.cos(orientation_target), 0.0], [0.0, 0.0, 1.0]])
    pos_diff = rotation_matrix.T @ pos_diff

    return t_u.algin_lidar(lidar, pos_diff, rot_diff)

  def update_stop_box(self, boxes, x, y, orientation, x_target, y_target, orientation_target):
    pos_diff = np.array([x_target, y_target]) - np.array([x, y])
    rot_diff = t_u.normalize_angle(orientation_target - orientation)

    # Rotate difference vector from global to local coordinate system.
    rotation_matrix = np.array([[np.cos(orientation_target), -np.sin(orientation_target)],
                                [np.sin(orientation_target), np.cos(orientation_target)]])
    pos_diff = rotation_matrix.T @ pos_diff

    # Rotation matrix in local coordinate system
    local_rot_matrix = np.array([[np.cos(rot_diff), -np.sin(rot_diff)], [np.sin(rot_diff), np.cos(rot_diff)]])

    for _, box_pred in enumerate(boxes):
      box_pred[:2] = (local_rot_matrix.T @ (box_pred[:2] - pos_diff).T).T
      box_pred[4] = t_u.normalize_angle(box_pred[4] - rot_diff)

  def destroy(self, results=None):  # pylint: disable=locally-disabled, unused-argument
    """
    Gets called after a route finished.
    The leaderboard client doesn't properly clear up the agent after the route finishes so we need to do it here.
    Also writes logging files to disk.
    """
    if self.save_path is not None:
      self.lon_logger.dump_to_json()
      if len(self.nets[0].speed_histogram) > 0:
        with gzip.open(self.save_path / 'target_speeds.json.gz', 'wt', encoding='utf-8') as f:
          ujson.dump(self.nets[0].speed_histogram, f, indent=4)

      if self.tp_stats:
        if len(self.tp_sign_agrees_with_angle) > 0:
          print('Agreement between TP and steering: ',
                sum(self.tp_sign_agrees_with_angle) / len(self.tp_sign_agrees_with_angle))
          with gzip.open(self.save_path / 'tp_agreements.json.gz', 'wt', encoding='utf-8') as f:
            ujson.dump(self.tp_sign_agrees_with_angle, f, indent=4)

      if self.config.tp_attention:
        if len(self.tp_attention_buffer) > 0:
          print('Average TP attention: ', sum(self.tp_attention_buffer) / len(self.tp_attention_buffer))
          with gzip.open(self.save_path / 'tp_attention.json.gz', 'wt', encoding='utf-8') as f:
            ujson.dump(self.tp_attention_buffer, f, indent=4)

        del self.tp_attention_buffer

    del self.tp_sign_agrees_with_angle
    del self.nets
    del self.config


# Filter Functions
def bicycle_model_forward(x, dt, steer, throttle, brake):
  # Kinematic bicycle model.
  # Numbers are the tuned parameters from World on Rails
  front_wb = -0.090769015
  rear_wb = 1.4178275

  steer_gain = 0.36848336
  brake_accel = -4.952399
  throt_accel = 0.5633837

  locs_0 = x[0]
  locs_1 = x[1]
  yaw = x[2]
  speed = x[3]

  if brake:
    accel = brake_accel
  else:
    accel = throt_accel * throttle

  wheel = steer_gain * steer

  beta = math.atan(rear_wb / (front_wb + rear_wb) * math.tan(wheel))
  next_locs_0 = locs_0.item() + speed * math.cos(yaw + beta) * dt
  next_locs_1 = locs_1.item() + speed * math.sin(yaw + beta) * dt
  next_yaws = yaw + speed / rear_wb * math.sin(beta) * dt
  next_speed = speed + accel * dt
  next_speed = next_speed * (next_speed > 0.0)  # Fast ReLU

  next_state_x = np.array([next_locs_0, next_locs_1, next_yaws, next_speed])

  return next_state_x


def measurement_function_hx(vehicle_state):
  '''
    For now we use the same internal state as the measurement state
    :param vehicle_state: VehicleState vehicle state variable containing
                          an internal state of the vehicle from the filter
    :return: np array: describes the vehicle state as numpy array.
                       0: pos_x, 1: pos_y, 2: rotatoion, 3: speed
    '''
  return vehicle_state


def state_mean(state, wm):
  '''
    We use the arctan of the average of sin and cos of the angle to calculate
    the average of orientations.
    :param state: array of states to be averaged. First index is the timestep.
    :param wm:
    :return:
    '''
  x = np.zeros(4)
  sum_sin = np.sum(np.dot(np.sin(state[:, 2]), wm))
  sum_cos = np.sum(np.dot(np.cos(state[:, 2]), wm))
  x[0] = np.sum(np.dot(state[:, 0], wm))
  x[1] = np.sum(np.dot(state[:, 1], wm))
  x[2] = math.atan2(sum_sin, sum_cos)
  x[3] = np.sum(np.dot(state[:, 3], wm))

  return x


def measurement_mean(state, wm):
  '''
  We use the arctan of the average of sin and cos of the angle to
  calculate the average of orientations.
  :param state: array of states to be averaged. First index is the
  timestep.
  '''
  x = np.zeros(4)
  sum_sin = np.sum(np.dot(np.sin(state[:, 2]), wm))
  sum_cos = np.sum(np.dot(np.cos(state[:, 2]), wm))
  x[0] = np.sum(np.dot(state[:, 0], wm))
  x[1] = np.sum(np.dot(state[:, 1], wm))
  x[2] = math.atan2(sum_sin, sum_cos)
  x[3] = np.sum(np.dot(state[:, 3], wm))

  return x


def residual_state_x(a, b):
  y = a - b
  y[2] = t_u.normalize_angle(y[2])
  return y


def residual_measurement_h(a, b):
  y = a - b
  y[2] = t_u.normalize_angle(y[2])
  return y