diff --git a/behavior_metrics/brains/CARLA/brain_carla_meiqi_no_segmentation_based_imitation_learning.py b/behavior_metrics/brains/CARLA/brain_carla_meiqi_no_segmentation_based_imitation_learning.py
new file mode 100644
index 00000000..c30e4dbc
--- /dev/null
+++ b/behavior_metrics/brains/CARLA/brain_carla_meiqi_no_segmentation_based_imitation_learning.py
@@ -0,0 +1,195 @@
+from brains.CARLA.utils.pilotnet_onehot import PilotNetOneHot
+from brains.CARLA.utils.test_utils import traffic_light_to_int, model_control, calculate_delta_yaw
+from utils.constants import PRETRAINED_MODELS_DIR, ROOT_PATH
+from brains.CARLA.utils.high_level_command import HighLevelCommandLoader
+from os import path
+
+import numpy as np
+
+import torch
+import time
+import math
+import carla
+
+PRETRAINED_MODELS = ROOT_PATH + '/' + PRETRAINED_MODELS_DIR + 'CARLA/'
+
+class Brain:
+
+    def __init__(self, sensors, actuators, model=None, handler=None, config=None):
+        self.motors = actuators.get_motor('motors_0')
+        self.camera_rgb = sensors.get_camera('camera_0') # rgb front view camera
+        self.camera_seg = sensors.get_camera('camera_2') # segmentation camera
+        self.handler = handler
+        self.inference_times = []
+        self.gpu_inference = config['GPU']
+        self.device = torch.device('cuda' if (torch.cuda.is_available() and self.gpu_inference) else 'cpu')
+        self.red_light_counter = 0
+        self.running_light = False
+
+        client = carla.Client('localhost', 2000)
+        client.set_timeout(100.0)
+        world = client.get_world()
+        self.map = world.get_map()
+        
+        weather = carla.WeatherParameters.ClearNoon
+        world.set_weather(weather)
+
+        self.vehicle = None
+        while self.vehicle is None:
+            for vehicle in world.get_actors().filter('vehicle.*'):
+                if vehicle.attributes.get('role_name') == 'ego_vehicle':
+                    self.vehicle = vehicle
+                    break
+            if self.vehicle is None:
+                print("Waiting for vehicle with role_name 'ego_vehicle'")
+                time.sleep(1)  # sleep for 1 second before checking again
+        
+        if model:
+            if not path.exists(PRETRAINED_MODELS + model):
+                print("File " + model + " cannot be found in " + PRETRAINED_MODELS)
+            
+            if config['UseOptimized']:
+                self.net = torch.jit.load(PRETRAINED_MODELS + model).to(self.device)
+            else:
+                #self.net = PilotNetOneHot((288, 200, 6), 3, 4, 4).to(self.device)
+                #self.net = PilotNetOneHot((288, 200, 3), 3, 4, 4).to(self.device)
+                self.net = PilotNetOneHot((288, 200, 3), 2, 4, 4).to(self.device) # combined control
+                self.net.load_state_dict(torch.load(PRETRAINED_MODELS + model,map_location=self.device))
+                self.net.eval()
+        
+        if 'Route' in config:
+            route = config['Route']
+            print('route: ', route)
+        else: 
+            route = None
+            
+        self.hlc_loader = HighLevelCommandLoader(self.vehicle, self.map, route=route)
+        self.prev_hlc = 0
+        self.prev_yaw = None
+        self.delta_yaw = 0
+
+        self.target_point = None
+        self.termination_code = 0 # 0: not terminated; 1: arrived at target; 2: wrong turn
+    
+    def update_frame(self, frame_id, data):
+        """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)
+        """
+        if data.shape[0] != data.shape[1]:
+            if data.shape[0] > data.shape[1]:
+                difference = data.shape[0] - data.shape[1]
+                extra_left, extra_right = int(difference/2), int(difference/2)
+                extra_top, extra_bottom = 0, 0
+            else:
+                difference = data.shape[1] - data.shape[0]
+                extra_left, extra_right = 0, 0
+                extra_top, extra_bottom = int(difference/2), int(difference/2)
+                
+
+            data = np.pad(data, ((extra_top, extra_bottom), (extra_left, extra_right), (0, 0)), mode='constant', constant_values=0)
+
+        self.handler.update_frame(frame_id, data)
+    
+    def execute(self):
+        """Main loop of the brain. This will be called iteratively each TIME_CYCLE (see pilot.py)"""
+         
+        rgb_image = self.camera_rgb.getImage().data
+        seg_image = self.camera_seg.getImage().data
+        
+        self.update_frame('frame_0', rgb_image)
+        self.update_frame('frame_1', seg_image)
+        
+        start_time = time.time()
+        try:
+            # calculate speed
+            velocity = self.vehicle.get_velocity()
+            speed_m_s = np.sqrt(velocity.x**2 + velocity.y**2 + velocity.z**2)
+            speed = 3.6 * speed_m_s #m/s to km/h 
+            
+            # read next high-level command or choose a random direction
+            hlc = self.hlc_loader.get_next_hlc()
+            if hlc != 0:
+                if self.prev_hlc == 0:
+                    self.prev_yaw = self.vehicle.get_transform().rotation.yaw
+                else:
+                    cur_yaw = self.vehicle.get_transform().rotation.yaw
+                    self.delta_yaw += calculate_delta_yaw(self.prev_yaw, cur_yaw)
+                    self.prev_yaw = cur_yaw
+            
+            # detect whether the vehicle made the correct turn
+            turning_infraction = False
+            if self.prev_hlc != 0 and hlc == 0:
+                print(f'turned {self.delta_yaw} degrees')
+                if 45 < np.abs(self.delta_yaw) < 180:
+                    if self.delta_yaw < 0 and self.prev_hlc != 1:
+                        turning_infraction = True
+                    elif self.delta_yaw > 0 and self.prev_hlc != 2:
+                        turning_infraction = True
+                elif self.prev_hlc != 3:
+                    turning_infraction = True
+                if turning_infraction:
+                    print('Wrong Turn!!!')
+                    self.termination_code = 2
+                self.delta_yaw = 0
+            
+            self.prev_hlc = hlc
+
+            # get traffic light status
+            light_status = -1
+            vehicle_location = self.vehicle.get_transform().location
+            if self.vehicle.is_at_traffic_light():
+                traffic_light = self.vehicle.get_traffic_light()
+                light_status = traffic_light.get_state()
+                traffic_light_location = traffic_light.get_transform().location
+                distance_to_traffic_light = np.sqrt((vehicle_location.x - traffic_light_location.x)**2 + (vehicle_location.y - traffic_light_location.y)**2)
+                if light_status == carla.libcarla.TrafficLightState.Red and distance_to_traffic_light < 6 and speed_m_s > 5:
+                    if not self.running_light:
+                        self.running_light = True
+                        self.red_light_counter += 1
+                else:
+                    self.running_light = False
+            hlc = 0
+            print(f'high-level command: {hlc}')
+            print(f'light: {light_status}')
+            frame_data = {
+                'hlc': hlc,
+                'measurements': speed,
+                'rgb': np.copy(rgb_image),
+                'segmentation': np.copy(seg_image),
+                'light': np.array([traffic_light_to_int(light_status)])
+            }
+
+
+            throttle, steer, brake = model_control(self.net, 
+                                    frame_data, 
+                                    ignore_traffic_light=False, 
+                                    device=self.device, 
+                                    combined_control=True)
+                                    #combined_control=False)
+            
+            
+            self.inference_times.append(time.time() - start_time)
+
+            print(throttle, steer, brake)
+
+            self.motors.sendThrottle(throttle)
+            #self.motors.sendThrottle(0.25)
+            self.motors.sendSteer(steer)
+            self.motors.sendBrake(brake)
+            #sself.motors.sendBrake(0.0)
+
+            # calculate distance to target point
+            # print(f'vehicle location: ({vehicle_location.x}, {-vehicle_location.y})')
+            # print(f'target point: ({self.target_point[0]}, {self.target_point[1]})')
+            if self.target_point != None:
+                distance_to_target = np.sqrt((self.target_point[0] - vehicle_location.x)**2 + (self.target_point[1] - (-vehicle_location.y))**2)
+                print(f'Euclidean distance to target: {distance_to_target}')
+                if distance_to_target < 1.5:
+                    self.termination_code = 1
+
+
+        except Exception as err:
+            print(err)
\ No newline at end of file
diff --git a/behavior_metrics/brains/CARLA/brain_carla_segmentation_based_imitation_learning.py b/behavior_metrics/brains/CARLA/brain_carla_segmentation_based_imitation_learning.py
index d45c06d5..dcd60bee 100644
--- a/behavior_metrics/brains/CARLA/brain_carla_segmentation_based_imitation_learning.py
+++ b/behavior_metrics/brains/CARLA/brain_carla_segmentation_based_imitation_learning.py
@@ -52,6 +52,8 @@ def __init__(self, sensors, actuators, model=None, handler=None, config=None):
                 self.net = torch.jit.load(PRETRAINED_MODELS + model).to(self.device)
             else:
                 self.net = PilotNetOneHot((288, 200, 6), 3, 4, 4).to(self.device)
+                #self.net = PilotNetOneHot((288, 200, 3), 3, 4, 4).to(self.device)
+                #self.net = PilotNetOneHot((288, 200, 3), 3, 4, 4).to(self.device)
                 self.net.load_state_dict(torch.load(PRETRAINED_MODELS + model,map_location=self.device))
                 self.net.eval()
         
diff --git a/behavior_metrics/brains/CARLA/tensorflow/brain_carla_bird_eye_deep_learning_previous_v_generate_dataset.py b/behavior_metrics/brains/CARLA/tensorflow/brain_carla_bird_eye_deep_learning_previous_v_generate_dataset.py
new file mode 100644
index 00000000..e03a568f
--- /dev/null
+++ b/behavior_metrics/brains/CARLA/tensorflow/brain_carla_bird_eye_deep_learning_previous_v_generate_dataset.py
@@ -0,0 +1,234 @@
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+import csv
+import cv2
+import math
+import numpy as np
+import threading
+import time
+import carla
+from os import path
+from albumentations import (
+    Compose, Normalize, RandomRain, RandomBrightness, RandomShadow, RandomSnow, RandomFog, RandomSunFlare
+)
+from utils.constants import PRETRAINED_MODELS_DIR, ROOT_PATH
+from utils.logger import logger
+from traceback import print_exc
+
+PRETRAINED_MODELS = ROOT_PATH + '/' + PRETRAINED_MODELS_DIR + 'CARLA/'
+
+from tensorflow.python.framework.errors_impl import NotFoundError
+from tensorflow.python.framework.errors_impl import UnimplementedError
+import tensorflow as tf
+import pandas as pd
+
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+
+#gpus = tf.config.experimental.list_physical_devices('GPU')
+#for gpu in gpus:
+#    tf.config.experimental.set_memory_growth(gpu, True)
+
+
+class Brain:
+
+    def __init__(self, sensors, actuators, handler, model, config=None):
+        self.camera_0 = sensors.get_camera('camera_0')
+        self.camera_1 = sensors.get_camera('camera_1')
+        self.camera_2 = sensors.get_camera('camera_2')
+        self.camera_3 = sensors.get_camera('camera_3')
+        
+        self.cameras_first_images = []
+
+        self.pose = sensors.get_pose3d('pose3d_0')
+
+        self.bird_eye_view = sensors.get_bird_eye_view('bird_eye_view_0')
+
+        self.motors = actuators.get_motor('motors_0')
+        self.handler = handler
+        self.config = config
+        self.inference_times = []
+        self.gpu_inference = True if tf.test.gpu_device_name() else False
+
+        self.threshold_image = np.zeros((640, 360, 3), np.uint8)
+        self.color_image = np.zeros((640, 360, 3), np.uint8)
+
+        client = carla.Client('localhost', 2000)
+        client.set_timeout(10.0) # seconds
+        world = client.get_world()
+        
+        time.sleep(5)
+        self.vehicle = world.get_actors().filter('vehicle.*')[0]
+
+        if model:
+            if not path.exists(PRETRAINED_MODELS + model):
+                logger.info("File " + model + " cannot be found in " + PRETRAINED_MODELS)
+            logger.info("** Load TF model **")
+            self.net = tf.keras.models.load_model(PRETRAINED_MODELS + model)
+            logger.info("** Loaded TF model **")
+        else:
+            logger.info("** Brain not loaded **")
+            logger.info("- Models path: " + PRETRAINED_MODELS)
+            logger.info("- Model: " + str(model))
+
+        self.previous_speed = 0
+        self.previous_bird_eye_view_image = 0
+        self.bird_eye_view_images = 0
+        self.bird_eye_view_unique_images = 0
+
+        self.folder_name = '../../carla_dataset_ATLAS/18_03_anticlockwise_town_02/'
+
+        # open the file in the write mode
+        self.f = open(self.folder_name + 'dataset.csv', 'a')
+        # create the csv writer
+        self.writer = csv.writer(self.f)
+
+        try: 
+            df = pd.read_csv(self.folder_name + 'dataset.csv')
+            self.batch_number = int(df.iloc[-1]['batch']) + 1
+        except Exception as ex:
+            #fkjfbdjkhfsd
+            self.batch_number = 0
+            header = ['batch', 'image_id', 'timestamp', 'throttle', 'steer', 'brake', 'location_x', 'location_y', 'previous_velocity', 'current_velocity']
+            self.writer.writerow(header)
+
+        print(self.batch_number)
+        self.frame_number = 0
+
+
+    def update_frame(self, frame_id, data):
+        """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)
+        """
+        if data.shape[0] != data.shape[1]:
+            if data.shape[0] > data.shape[1]:
+                difference = data.shape[0] - data.shape[1]
+                extra_left, extra_right = int(difference/2), int(difference/2)
+                extra_top, extra_bottom = 0, 0
+            else:
+                difference = data.shape[1] - data.shape[0]
+                extra_left, extra_right = 0, 0
+                extra_top, extra_bottom = int(difference/2), int(difference/2)
+                
+
+            data = np.pad(data, ((extra_top, extra_bottom), (extra_left, extra_right), (0, 0)), mode='constant', constant_values=0)
+            
+        self.handler.update_frame(frame_id, data)
+
+    def update_pose(self, pose_data):
+        self.handler.update_pose3d(pose_data)
+
+    def execute(self):
+        image = self.camera_0.getImage().data
+        image_1 = self.camera_1.getImage().data
+        image_2 = self.camera_2.getImage().data
+        image_3 = self.camera_3.getImage().data
+
+        base_img_copy = image.copy()
+
+        bird_eye_view_1 = self.bird_eye_view.getImage(self.vehicle)
+        bird_eye_view_1 = cv2.cvtColor(bird_eye_view_1, cv2.COLOR_BGR2RGB)
+
+        if self.cameras_first_images == []:
+            self.cameras_first_images.append(image)
+            self.cameras_first_images.append(image_1)
+            self.cameras_first_images.append(image_2)
+            self.cameras_first_images.append(image_3)
+            self.cameras_first_images.append(bird_eye_view_1)
+
+        self.cameras_last_images = [
+            image,
+            image_1,
+            image_2,
+            image_3,
+            bird_eye_view_1
+        ]
+
+        self.update_frame('frame_1', image_1)
+        self.update_frame('frame_2', image_2)
+        self.update_frame('frame_3', image_3)
+
+        self.update_frame('frame_0', bird_eye_view_1)
+        
+        self.update_pose(self.pose.getPose3d())
+
+        image_shape=(66, 200)
+        img_base = cv2.resize(bird_eye_view_1, image_shape)
+
+        AUGMENTATIONS_TEST = Compose([
+            Normalize()
+        ])
+        image = AUGMENTATIONS_TEST(image=img_base)
+        img = image["image"]
+
+        self.bird_eye_view_images += 1
+        if (self.previous_bird_eye_view_image==img).all() == False:
+            self.bird_eye_view_unique_images += 1
+        self.previous_bird_eye_view_image = img
+
+        velocity_dim = np.full((200, 66), self.previous_speed/30)
+        new_img_vel = np.dstack((img, velocity_dim))
+        img = new_img_vel
+
+        img = np.expand_dims(img, axis=0)
+        start_time = time.time()
+        try:
+            prediction = self.net.predict(img, verbose=0)
+            self.inference_times.append(time.time() - start_time)
+            throttle = prediction[0][0]
+            steer = prediction[0][1] * (1 - (-1)) + (-1)
+            break_command = prediction[0][2]
+            speed = self.vehicle.get_velocity()
+            vehicle_speed = 3.6 * math.sqrt(speed.x**2 + speed.y**2 + speed.z**2)
+
+            '''
+                Save dataset        
+            '''
+            from PIL import Image
+            pil_image = Image.fromarray(base_img_copy)
+            pil_image.save(self.folder_name + str(self.batch_number) + '_' +  str(self.frame_number) + '.png')
+            #cv2.imwrite('../../carla_dataset_ATLAS/18_03_anticlockwise_town_02/' + str(self.batch_number) + '_' +  str(self.frame_number) + '.png', img)
+            vehicle_location = self.vehicle.get_location()
+            row = [self.batch_number, str(self.batch_number) + '_' + str(self.frame_number) + '.png', time.time(), throttle, steer, break_command, vehicle_location.x, vehicle_location.y, self.previous_speed, vehicle_speed]
+            
+            print(str(self.batch_number) + '_' + str(self.frame_number) + '.png')
+            print(row)
+            
+            self.writer.writerow(row)
+            self.frame_number += 1
+            self.previous_speed = vehicle_speed
+
+            if vehicle_speed < 5:
+                self.motors.sendThrottle(1.0)
+                self.motors.sendSteer(0.0)
+                self.motors.sendBrake(0)
+            else:
+                self.motors.sendThrottle(throttle)
+                self.motors.sendSteer(steer)
+                self.motors.sendBrake(break_command)
+        
+            
+
+        except NotFoundError as ex:
+            logger.info('Error inside brain: NotFoundError!')
+            logger.warning(type(ex).__name__)
+            print_exc()
+            raise Exception(ex)
+        except UnimplementedError as ex:
+            logger.info('Error inside brain: UnimplementedError!')
+            logger.warning(type(ex).__name__)
+            print_exc()
+            raise Exception(ex)
+        except Exception as ex:
+            logger.info('Error inside brain: Exception!')
+            logger.warning(type(ex).__name__)
+            print_exc()
+            raise Exception(ex)
+            
+        
+            
+
+
diff --git a/behavior_metrics/brains/CARLA/tensorflow/brain_carla_bird_eye_deep_lerning_atlas_uc3m.py b/behavior_metrics/brains/CARLA/tensorflow/brain_carla_bird_eye_deep_lerning_atlas_uc3m.py
new file mode 100644
index 00000000..7cacca82
--- /dev/null
+++ b/behavior_metrics/brains/CARLA/tensorflow/brain_carla_bird_eye_deep_lerning_atlas_uc3m.py
@@ -0,0 +1,203 @@
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+import csv
+import cv2
+import math
+import numpy as np
+import threading
+import time
+import carla
+from os import path
+from albumentations import (
+    Compose, Normalize, RandomRain, RandomBrightness, RandomShadow, RandomSnow, RandomFog, RandomSunFlare, GridDropout
+)
+from utils.constants import PRETRAINED_MODELS_DIR, ROOT_PATH
+from utils.logger import logger
+from traceback import print_exc
+
+PRETRAINED_MODELS = ROOT_PATH + '/' + PRETRAINED_MODELS_DIR + 'CARLA/'
+
+from tensorflow.python.framework.errors_impl import NotFoundError
+from tensorflow.python.framework.errors_impl import UnimplementedError
+import tensorflow as tf
+
+
+#import os
+#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+
+gpus = tf.config.experimental.list_physical_devices('GPU')
+for gpu in gpus:
+    tf.config.experimental.set_memory_growth(gpu, True)
+
+class Brain:
+
+    def __init__(self, sensors, actuators, handler, model, config=None):
+        self.camera_0 = sensors.get_camera('camera_0')
+        self.camera_1 = sensors.get_camera('camera_1')
+        self.camera_2 = sensors.get_camera('camera_2')
+        self.camera_3 = sensors.get_camera('camera_3')
+        
+        self.cameras_first_images = []
+
+        self.pose = sensors.get_pose3d('pose3d_0')
+
+        self.bird_eye_view = sensors.get_bird_eye_view('bird_eye_view_0')
+
+        self.motors = actuators.get_motor('motors_0')
+        self.handler = handler
+        self.config = config
+        self.inference_times = []
+        self.gpu_inference = True if tf.test.gpu_device_name() else False
+
+        self.threshold_image = np.zeros((640, 360, 3), np.uint8)
+        self.color_image = np.zeros((640, 360, 3), np.uint8)
+
+        client = carla.Client('localhost', 2000)
+        client.set_timeout(30.0) # seconds
+        world = client.get_world()
+        
+        time.sleep(5)
+        for vehicle in world.get_actors().filter('vehicle.*'):
+            if vehicle.attributes.get('role_name') == 'ego_vehicle':
+                self.vehicle = vehicle
+                break
+
+        if model:
+            if not path.exists(PRETRAINED_MODELS + model):
+                logger.info("File " + model + " cannot be found in " + PRETRAINED_MODELS)
+            logger.info("** Load TF model **")
+            self.net = tf.keras.models.load_model(PRETRAINED_MODELS + model, compile=False)
+            logger.info("** Loaded TF model **")
+        else:
+            logger.info("** Brain not loaded **")
+            logger.info("- Models path: " + PRETRAINED_MODELS)
+            logger.info("- Model: " + str(model))
+
+        self.previous_bird_eye_view_image = 0
+        self.bird_eye_view_images = 0
+        self.bird_eye_view_unique_images = 0
+
+
+    def update_frame(self, frame_id, data):
+        """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)
+        """
+        if data.shape[0] != data.shape[1]:
+            if data.shape[0] > data.shape[1]:
+                difference = data.shape[0] - data.shape[1]
+                extra_left, extra_right = int(difference/2), int(difference/2)
+                extra_top, extra_bottom = 0, 0
+            else:
+                difference = data.shape[1] - data.shape[0]
+                extra_left, extra_right = 0, 0
+                extra_top, extra_bottom = int(difference/2), int(difference/2)
+                
+
+            data = np.pad(data, ((extra_top, extra_bottom), (extra_left, extra_right), (0, 0)), mode='constant', constant_values=0)
+
+        self.handler.update_frame(frame_id, data)
+
+    def update_pose(self, pose_data):
+        self.handler.update_pose3d(pose_data)
+
+    def execute(self):
+        image = self.camera_0.getImage().data
+        image_1 = self.camera_1.getImage().data
+        image_2 = self.camera_2.getImage().data
+        image_3 = self.camera_3.getImage().data
+
+        bird_eye_view_1 = self.bird_eye_view.getImage(self.vehicle)
+        bird_eye_view_1 = cv2.cvtColor(bird_eye_view_1, cv2.COLOR_BGR2RGB)
+
+        if self.cameras_first_images == []:
+            self.cameras_first_images.append(image)
+            self.cameras_first_images.append(image_1)
+            self.cameras_first_images.append(image_2)
+            self.cameras_first_images.append(image_3)
+            self.cameras_first_images.append(bird_eye_view_1)
+
+        self.cameras_last_images = [
+            image,
+            image_1,
+            image_2,
+            image_3,
+            bird_eye_view_1
+        ]
+
+        self.update_frame('frame_0', image)
+        self.update_frame('frame_1', image_1)
+        self.update_frame('frame_2', image_2)
+        self.update_frame('frame_3', image_3)
+
+        '''
+        nombre_archivo = "imagen_guardada.png"
+        imagen_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        cv2.imwrite(nombre_archivo, imagen_rgb)
+        nombre_archivo = "image_1_guardada.png"
+        imagen_rgb = cv2.cvtColor(image_1, cv2.COLOR_BGR2RGB)
+        cv2.imwrite(nombre_archivo, imagen_rgb)
+        nombre_archivo = "image_2_guardada.png"
+        imagen_rgb = cv2.cvtColor(image_2, cv2.COLOR_BGR2RGB)
+        cv2.imwrite(nombre_archivo, imagen_rgb)
+        nombre_archivo = "image_3_guardada.png"
+        imagen_rgb = cv2.cvtColor(image_3, cv2.COLOR_BGR2RGB)
+        cv2.imwrite(nombre_archivo, imagen_rgb)
+        '''
+        #self.update_frame('frame_0', bird_eye_view_1)
+        
+        self.update_pose(self.pose.getPose3d())
+
+        image_shape=(66, 200)
+        #image_shape=(50, 150)
+        img_base = cv2.resize(bird_eye_view_1, image_shape)
+
+        AUGMENTATIONS_TEST = Compose([
+            Normalize()
+        ])
+        image = AUGMENTATIONS_TEST(image=img_base)
+        img = image["image"]
+
+        self.bird_eye_view_images += 1
+        if (self.previous_bird_eye_view_image==img).all() == False:
+            self.bird_eye_view_unique_images += 1
+        self.previous_bird_eye_view_image = img
+
+        img = np.expand_dims(img, axis=0)
+        start_time = time.time()
+        try:
+            prediction = self.net.predict(img, verbose=0)
+            self.inference_times.append(time.time() - start_time)
+            throttle = prediction[0][0]
+            steer = prediction[0][1] * (1 - (-1)) + (-1)
+            break_command = prediction[0][2]
+
+            speed = self.vehicle.get_velocity()
+            vehicle_speed = 3.6 * math.sqrt(speed.x**2 + speed.y**2 + speed.z**2)
+
+            if vehicle_speed < 5:
+                self.motors.sendThrottle(1.0)
+                self.motors.sendSteer(0.0)
+                self.motors.sendBrake(0)
+            else:
+                self.motors.sendThrottle(throttle)
+                self.motors.sendSteer(steer)
+                self.motors.sendBrake(break_command)
+        except NotFoundError as ex:
+            logger.info('Error inside brain: NotFoundError!')
+            logger.warning(type(ex).__name__)
+            print_exc()
+            raise Exception(ex)
+        except UnimplementedError as ex:
+            logger.info('Error inside brain: UnimplementedError!')
+            logger.warning(type(ex).__name__)
+            print_exc()
+            raise Exception(ex)
+        except Exception as ex:
+            logger.info('Error inside brain: Exception!')
+            logger.warning(type(ex).__name__)
+            print_exc()
+            raise Exception(ex)
+            
\ No newline at end of file
diff --git a/behavior_metrics/configs/CARLA/CARLA_launch_files/carla_new_ada_bridge.launch b/behavior_metrics/configs/CARLA/CARLA_launch_files/carla_new_ada_bridge.launch
new file mode 100644
index 00000000..5cf5b766
--- /dev/null
+++ b/behavior_metrics/configs/CARLA/CARLA_launch_files/carla_new_ada_bridge.launch
@@ -0,0 +1,100 @@
+<?xml version="1.0"?>
+<launch>
+
+  <!--Arguments for carla-->
+  <arg name='host' default='localhost'/>
+  <arg name='port' default='2000'/>
+  <arg name='role_name' default='ego_vehicle'/>
+  <!-- first generation ADA's twin -->
+  <!--arg name="vehicle_filter" default='vehicle.nissan.micra'/-->
+
+  <!-- second generation ADA's twin  (Real ATLAS model-->
+  <arg name="vehicle_filter" default='vehicle.atlas.atlas'/>
+
+  <!-- use comma separated format "x,y,z,roll,pitch,yaw" (by default the spawn point is 2m above the ground)-->
+  <!-- "" (empty) for a random spawn point-->
+  <!--arg name="spawn_point" default=""/-->
+ 
+  <!-- standard spawn (map 03, carla's roundabout demo) --> 
+  <!--arg name="spawn_point" default="2.1,120.3,-1.5,0,0,90"/-->
+ 
+  <!-- Avila's demo spawn (avila_demo_env.xodr/.osm) -->
+  <!--arg name="spawn_point" default="52.5,-99.8,0,0,0,40"/-->
+
+  <!-- Avila's demo spawn (avila_demo_env_big.xodr/.osm) -->
+  <!--arg name="spawn_point" default="447.1,-271.2,0,0,0,39"/-->
+
+  <!-- Avila's demo spawn (avila_world.xodr/.osm) -->
+  <!--arg name="spawn_point" default="80.7,-103.8,0,0,0,90"/-->
+
+  <!-- Avila's lane control spawn point -->
+  <arg name="spawn_point" default="89.8,-163.2,0,0,0,70"/>
+
+  <!-- Avila's demo spawn (avila_world.xodr/.osm) (middle of the road) -->
+  <!--arg name="spawn_point" default="89.9,-168.7,0,0,0,68"/-->
+
+  <!-- INTA's results -->
+  <!--arg name="spawn_point" default="-70.398,-1.144189766,0,0,0,-166.89"/-->
+  <!-- INTA 5 complete -->
+  <!--arg name="spawn_point" default="-38.764125824,-6.94816493988,0,0,0,-167.4579198"/-->
+  <!-- INTA 6 complete -->
+  <!--arg name="spawn_point" default="-50.3,231.2,0,0,0,-9"/-->
+
+  <!--arg name="spawn_point" default="0,0,-1.5,0,0,0"/-->
+  
+  <arg name="town" default=''/>
+
+  <!-- Synchronous mode-->
+  <arg name='synchronous_mode' default='false'/>
+  <arg name='synchronous_mode_wait_for_vehicle_control_command' default='false'/>
+  <arg name='fixed_delta_seconds' default=''/>
+  
+  <!--Arguments for Ada-->
+  <arg name="vehicle" default="ada"/>
+  <arg name="use_machine" default="false"/>
+  <arg name="machine" default="localhost"/>
+  <machine name="localhost" address="localhost" unless="$(arg use_machine)"/>
+
+  <!--carla_ros_bridge-->
+  <include file="$(find carla_ros_bridge)/launch/carla_ros_bridge.launch">
+    <arg name='host' value='$(arg host)'/>
+    <arg name='port' value='$(arg port)'/>
+    <arg name='town' value='$(arg town)'/>
+    <!--remap from=/tf to=/ttt-->
+    <arg name='synchronous_mode' value='$(arg synchronous_mode)'/>
+    <arg name='synchronous_mode_wait_for_vehicle_control_command' value='$(arg synchronous_mode_wait_for_vehicle_control_command)'/>
+    <arg name='fixed_delta_seconds' value='$(arg fixed_delta_seconds)'/>
+  </include>
+
+  <!--ego_vehicle in carla_ros_bridge-->
+  <include file="$(find carla_ego_vehicle)/launch/carla_example_ego_vehicle.launch">
+    <arg name='host' value='$(arg host)'/>
+    <arg name='port' value='$(arg port)'/>
+    <arg name="vehicle_filter" value='$(arg vehicle_filter)'/>
+    <arg name='role_name' value='$(arg role_name)'/>
+    <arg name="spawn_point" value="$(arg spawn_point)"/>
+  </include>
+
+  <!--manual control for carla_ros_bridge-->
+  <include file="$(find carla_manual_control)/launch/carla_manual_control.launch">
+    <arg name='role_name' value='$(arg role_name)'/>
+  </include>
+  
+  <!-- ADA'S DIGITAL TWIN TF TREE -->
+  <!--include file="$(find carla_ada_ros_bridge)/launch/static_transf_blender.launch"/-->
+
+  <node pkg="carla_ada_ros_bridge" type="image_transform.py" name="image_transform" output="log" respawn="true"/>
+
+  <arg name="respawn" default="false" />
+  <arg name="debug" default="false" />
+  <arg name="node_name" default="rgb_front" />
+
+  <group ns="ada">
+    <group ns="$(arg node_name)">
+          <node name="camera_proc" pkg="image_proc" type="image_proc" output="screen"
+                respawn="$(arg respawn)">
+          </node>
+    </group>
+  </group>
+
+</launch>
\ No newline at end of file
diff --git a/behavior_metrics/configs/CARLA/CARLA_launch_files/carla_new_ada_bridge_updated.launch b/behavior_metrics/configs/CARLA/CARLA_launch_files/carla_new_ada_bridge_updated.launch
new file mode 100644
index 00000000..67c8b5f0
--- /dev/null
+++ b/behavior_metrics/configs/CARLA/CARLA_launch_files/carla_new_ada_bridge_updated.launch
@@ -0,0 +1,85 @@
+<!-- -->
+<launch>
+  <!-- CARLA connection -->
+  <arg name='host' default='localhost'/>
+  <arg name='port' default='2000'/>
+  <arg name='timeout' default='20'/>
+  
+  <!-- Map to load on startup (either a predefined CARLA town (e.g. 'Town01'), or a OpenDRIVE map file) -->
+  <arg name="town" default='Town02'/>
+
+  <!-- Enable/disable passive mode -->
+  <arg name='passive' default=''/>
+
+  <!-- Synchronous mode-->
+  <arg name='synchronous_mode' default='True'/>
+  <arg name='synchronous_mode_wait_for_vehicle_control_command' default='False'/>
+  <arg name='fixed_delta_seconds' default='0.05'/>
+
+  <!-- the role names of vehicles that are controllable from within ROS-->
+  <arg name='ego_vehicles' default='["ego_vehicle"]'/>
+
+  <!-- launch carla ros bridge node -->
+  <node pkg="carla_ros_bridge" name="carla_ros_bridge" type="bridge.py" output="screen" required="true">
+    <param name="host" value="$(arg host)" unless="$(eval host == '')"/>
+    <param name="port" value="$(arg port)" unless="$(eval port == '')"/>
+    <param name="timeout" value="$(arg timeout)" unless="$(eval timeout == '')"/>
+    <param name="passive" value="$(arg passive)"/>
+    <param name="synchronous_mode" value="$(arg synchronous_mode)"/>
+    <param name="synchronous_mode_wait_for_vehicle_control_command" value="$(arg synchronous_mode_wait_for_vehicle_control_command)"/>
+    <param name="fixed_delta_seconds" value="$(arg fixed_delta_seconds)"/>
+    <param name="register_all_sensors" value="true"/>
+    <param name="town" value="$(arg town)"/>
+    <param name="ego_vehicle_role_name" value="$(arg ego_vehicles)"/>
+  </node>
+
+  <!-- the object json file that configures objects to spawn -->
+  <arg name="objects_definition_file" default='$(env OBJECT_PATH)/sensors_atlas_updated_copy.json'/>
+
+  <!-- Ego Vehicle -->
+  <arg name='ego_vehicle_role_name' default='ego_vehicle'/>
+  <!--<arg name="spawn_point" default="300.0, -2.0, 1.37, 0.0, 0.0, 0.0"/>-->
+  <!--<arg name="spawn_point" default="40.0, 2.0, 1.37, 0.0, 0.0, 180.0"/>-->
+  <!--<arg name="spawn_point" default='50.3, -105.6, 1.37, 0.0, 0.0, -180.0'/>-->
+
+  
+
+  <!-- TOWN 02 -->
+  <!--<arg name="spawn_point" default="70.3, -105.6, 1.37, 0.0, 0.0, 180.0"/>-->
+  <arg name="spawn_point" default="10.0, -307.0, 1.37, 0.0, 0.0, 0.0"/>
+  <!-- PARQUE 01 -->
+  <!--<arg name="spawn_point" default="114.9, -266.8, 1.37, 0.0, 0.0, -15.0"/>-->
+  
+  <!-- spawn the objects -->
+  <node pkg="carla_spawn_objects" type="carla_spawn_objects.py" name="$(anon carla_spawn_objects)" output="screen">
+    <param name="objects_definition_file" value="$(arg objects_definition_file)"/>
+    <param name="spawn_point_$(arg ego_vehicle_role_name)" value="$(arg spawn_point)"/>
+    <param name="spawn_sensors_only" value="false"/>
+  </node>
+
+  <include file="$(find carla_manual_control)/launch/carla_manual_control.launch">
+    <arg name='role_name' value='$(arg ego_vehicle_role_name)'/>
+  </include>
+
+  <!--<node pkg="carla_ada_ros_bridge" type="image_transform.py" name="image_transform" output="log" respawn="true"/>-->
+  <node pkg="carla_ros_bridge" type="image_transform.py" name="image_transform" output="log" respawn="true"/>
+
+  <arg name="vehicle" default="ada"/>
+  <arg name="use_machine" default="false"/>
+  <arg name="machine" default="localhost"/>
+  <machine name="localhost" address="localhost" unless="$(arg use_machine)"/>
+
+  <arg name="respawn" default="false" />
+  <arg name="debug" default="false" />
+  <arg name="node_name" default="rgb_front" />
+
+  <group ns="ada">
+    <group ns="$(arg node_name)">
+          <node name="camera_proc" pkg="image_proc" type="image_proc" output="screen"
+                respawn="$(arg respawn)">
+          </node>
+    </group>
+  </group>
+
+
+</launch>
\ No newline at end of file
diff --git a/behavior_metrics/configs/CARLA/CARLA_launch_files/carla_new_ada_bridge_updated_autopilot.launch b/behavior_metrics/configs/CARLA/CARLA_launch_files/carla_new_ada_bridge_updated_autopilot.launch
new file mode 100644
index 00000000..15c389d2
--- /dev/null
+++ b/behavior_metrics/configs/CARLA/CARLA_launch_files/carla_new_ada_bridge_updated_autopilot.launch
@@ -0,0 +1,83 @@
+<!-- -->
+<launch>
+  <!-- CARLA connection -->
+  <arg name='host' default='localhost'/>
+  <arg name='port' default='2000'/>
+  <arg name='timeout' default='10'/>
+
+  <!-- Ego vehicle -->
+  <arg name='role_name' default='ego_vehicle'/>
+  <arg name="vehicle_filter" default='vehicle.*'/>
+  <!--<arg name="spawn_point" default=""/>--><!-- use comma separated format "x,y,z,roll,pitch,yaw" -->
+
+  <!--ANTICLOCKWISE-->
+  <!--<arg name="spawn_point" default="55.3, -105.6, 1.37, 0.0, 0.0, 180.0"/>-->
+  <!--<arg name="spawn_point" default="10.3, -105.6, 1.37, 0.0, 0.0, 180.0"/>-->
+  <!--<arg name="spawn_point" default="100.3, -105.6, 1.37, 0.0, 0.0, 180.0"/>-->
+
+  <!--<arg name="spawn_point" default="-7.0, -120.6, 1.37, 0.0, 0.0, -90.0"/>-->
+  <!--<arg name="spawn_point" default="-7.0, -270.6, 1.37, 0.0, 0.0, -90.0"/>-->
+
+  <!--<arg name="spawn_point" default="100.0, -307.0, 1.37, 0.0, 0.0, 0.0"/>-->
+  <!--<arg name="spawn_point" default="100.0, -307.0, 1.37, 0.0, 0.0, 0.0"/>-->
+  <!--<arg name="spawn_point" default="150.0, -307.0, 1.37, 0.0, 0.0, 0.0"/>-->
+
+  <!--<arg name="spawn_point" default="193.0, -290.0, 1.37, 0.0, 0.0, 90.0"/>-->
+  <!--<arg name="spawn_point" default="193.0, -150.6, 1.37, 0.0, 0.0, 90.0"/>-->
+
+  <!--CLOCKWISE-->
+  <!--<arg name="spawn_point" default="55.3, -110, 1.37, 0.0, 0.0, 0.0"/>-->
+  <!--<arg name="spawn_point" default="10.3, -110, 1.37, 0.0, 0.0, 0.0"/>-->
+  <!--<arg name="spawn_point" default="100.3, -110, 1.37, 0.0, 0.0, 0.0"/>-->
+
+  <!--<arg name="spawn_point" default="-3.0, -120.6, 1.37, 0.0, 0.0, 90.0"/>-->
+  <!--<arg name="spawn_point" default="-3.0, -270.6, 1.37, 0.0, 0.0, 90.0"/>-->
+
+  <!--<arg name="spawn_point" default="10.0, -303.0, 1.37, 0.0, 0.0, 180.0"/>-->
+  <!--<arg name="spawn_point" default="100.0, -303.0, 1.37, 0.0, 0.0, 180.0"/>-->
+  <!--<arg name="spawn_point" default="150.0, -303.0, 1.37, 0.0, 0.0, 180.0"/>-->
+
+  <!--<arg name="spawn_point" default="190.0, -290.0, 1.37, 0.0, 0.0, -90.0"/>-->
+  <!--<arg name="spawn_point" default="190.0, -150.0, 1.37, 0.0, 0.0, -90.0"/>-->
+
+  <!--TOWN02-->
+  <arg name="spawn_point" default="40.0, 2.0, 1.37, 0.0, 0.0, 180.0"/>
+
+
+  <!-- Map to load on startup (either a predefined CARLA town (e.g. 'Town01'), or a OpenDRIVE map file) -->
+  <arg name="town" default='Parque01'/>
+
+  <!-- Enable/disable passive mode -->
+  <arg name='passive' default=''/>
+
+  <!-- Synchronous mode-->
+  <arg name='synchronous_mode_wait_for_vehicle_control_command' default='False'/>
+  <arg name='fixed_delta_seconds' default='0.05'/>
+
+
+  <include file="$(find carla_ros_bridge)/launch/carla_ros_bridge.launch">
+    <arg name='host' value='$(arg host)'/>
+    <arg name='port' value='$(arg port)'/>
+    <arg name='town' value='$(arg town)_Opt'/>
+    <arg name='timeout' value='$(arg timeout)'/>
+    <arg name='passive' value='$(arg passive)'/>
+    <arg name='synchronous_mode_wait_for_vehicle_control_command' value='$(arg synchronous_mode_wait_for_vehicle_control_command)'/>
+    <arg name='fixed_delta_seconds' value='$(arg fixed_delta_seconds)'/>
+  </include>
+
+  <!-- the object json file that configures objects to spawn -->
+  <arg name="objects_definition_file" default='$(env OBJECT_PATH)/sensors_atlas_updated_autopilot.json'/>
+
+  <!-- the ego vehicle, that will be controlled by an agent (e.g. carla_ad_agent) -->
+  <include file="$(find carla_spawn_objects)/launch/carla_example_ego_vehicle.launch">
+    <arg name="objects_definition_file" value="$(arg objects_definition_file)"/>
+    <arg name='role_name' value='$(arg role_name)'/>
+    <arg name="spawn_point_ego_vehicle" value="$(arg spawn_point)"/>
+    <arg name="spawn_sensors_only" value="false"/>
+  </include>
+
+  <include file="$(find carla_manual_control)/launch/carla_manual_control.launch">
+    <arg name='role_name' value='$(arg role_name)'/>
+  </include>
+
+</launch>
\ No newline at end of file
diff --git a/behavior_metrics/configs/CARLA/CARLA_launch_files/carla_new_ada_bridge_updated_parque01.launch b/behavior_metrics/configs/CARLA/CARLA_launch_files/carla_new_ada_bridge_updated_parque01.launch
new file mode 100644
index 00000000..1125d783
--- /dev/null
+++ b/behavior_metrics/configs/CARLA/CARLA_launch_files/carla_new_ada_bridge_updated_parque01.launch
@@ -0,0 +1,90 @@
+<!-- -->
+<launch>
+  <!-- CARLA connection -->
+  <arg name='host' default='localhost'/>
+  <arg name='port' default='2000'/>
+  <arg name='timeout' default='20'/>
+  
+  <!-- Map to load on startup (either a predefined CARLA town (e.g. 'Town01'), or a OpenDRIVE map file) -->
+  <arg name="town" default='Parque01'/>
+
+  <!-- Enable/disable passive mode -->
+  <arg name='passive' default=''/>
+
+  <!-- Synchronous mode-->
+  <arg name='synchronous_mode' default='True'/>
+  <arg name='synchronous_mode_wait_for_vehicle_control_command' default='False'/>
+  <arg name='fixed_delta_seconds' default='0.05'/>
+
+  <!-- the role names of vehicles that are controllable from within ROS-->
+  <arg name='ego_vehicles' default='["ego_vehicle"]'/>
+
+  <!-- launch carla ros bridge node -->
+  <node pkg="carla_ros_bridge" name="carla_ros_bridge" type="bridge.py" output="screen" required="true">
+    <param name="host" value="$(arg host)" unless="$(eval host == '')"/>
+    <param name="port" value="$(arg port)" unless="$(eval port == '')"/>
+    <param name="timeout" value="$(arg timeout)" unless="$(eval timeout == '')"/>
+    <param name="passive" value="$(arg passive)"/>
+    <param name="synchronous_mode" value="$(arg synchronous_mode)"/>
+    <param name="synchronous_mode_wait_for_vehicle_control_command" value="$(arg synchronous_mode_wait_for_vehicle_control_command)"/>
+    <param name="fixed_delta_seconds" value="$(arg fixed_delta_seconds)"/>
+    <param name="register_all_sensors" value="true"/>
+    <param name="town" value="$(arg town)"/>
+    <param name="ego_vehicle_role_name" value="$(arg ego_vehicles)"/>
+  </node>
+
+  <!-- the object json file that configures objects to spawn -->
+  <arg name="objects_definition_file" default='$(env OBJECT_PATH)/sensors_atlas_updated_copy.json'/>
+
+  <!-- Ego Vehicle -->
+  <arg name='ego_vehicle_role_name' default='ego_vehicle'/>
+
+  <!--<arg name="spawn_point" default="114.9, -266.8, 1.37, 0.0, 0.0, -15.0"/>-->
+
+  <!--<arg name="spawn_point" default="142.9, -239.8, 1.37, 0.0, 0.0, -170.0"/>-->
+
+  <arg name="spawn_point" default="42.0, 79.6, 1.37, 0.0, 0.0, 10.0"/>
+
+  <!--<arg name="spawn_point" default="-234.6, -220.2, 1.37, 0.0, 0.0, 0.0"/>-->
+
+  <!--<arg name="spawn_point" default="-98.9, 278.9, 1.37, 0.0, 0.0, 10.0"/>-->
+  
+  
+  <!-- spawn the objects -->
+  <node pkg="carla_spawn_objects" type="carla_spawn_objects.py" name="$(anon carla_spawn_objects)" output="screen">
+    <param name="objects_definition_file" value="$(arg objects_definition_file)"/>
+    <param name="spawn_point_$(arg ego_vehicle_role_name)" value="$(arg spawn_point)"/>
+    <param name="spawn_sensors_only" value="false"/>
+  </node>
+
+  <include file="$(find carla_manual_control)/launch/carla_manual_control.launch">
+    <arg name='role_name' value='$(arg ego_vehicle_role_name)'/>
+  </include>
+
+  <!--<node pkg="carla_ada_ros_bridge" type="image_transform.py" name="image_transform" output="log" respawn="true"/>-->
+  <node pkg="carla_ros_bridge" type="image_transform.py" name="image_transform" output="log" respawn="true"/>
+
+  <arg name="vehicle" default="ada"/>
+  <arg name="use_machine" default="false"/>
+  <arg name="machine" default="localhost"/>
+  <machine name="localhost" address="localhost" unless="$(arg use_machine)"/>
+
+  <arg name="respawn" default="false" />
+  <arg name="debug" default="false" />
+  <arg name="node_name" default="rgb_front" />
+
+  <group ns="ada">
+    <group ns="$(arg node_name)">
+          <node name="camera_proc" pkg="image_proc" type="image_proc" output="screen"
+                respawn="$(arg respawn)">
+          </node>
+    </group>
+  </group>
+
+  <!-- Weather control -->
+  <!--<node pkg="rostopic" type="rostopic" name="weather_control" args="pub /carla/weather_control/set_weather carla_msgs/CarlaWeatherParameters '{cloudiness: 80.0, precipitation: 30.0, sun_altitude_angle: 70.0}'" />-->
+  <!-- Weather control using predefined weather settings -->
+  <!--<node pkg="rostopic" type="rostopic" name="weather_control" args="pub /carla/weather_control/set_weather carla_msgs/CarlaWeatherParameters '{weather_type: HardRainSunset}'" />-->
+
+
+</launch>
\ No newline at end of file
diff --git a/behavior_metrics/configs/CARLA/CARLA_launch_files/image_transform.py b/behavior_metrics/configs/CARLA/CARLA_launch_files/image_transform.py
new file mode 100644
index 00000000..293f1348
--- /dev/null
+++ b/behavior_metrics/configs/CARLA/CARLA_launch_files/image_transform.py
@@ -0,0 +1,60 @@
+#!/usr/bin/env python
+# license removed for brevity
+
+import rospy
+import numpy as np
+import roslib
+import tf
+from nav_msgs.msg import Odometry
+from std_msgs.msg import Float64
+from sensor_msgs.msg import PointCloud2
+from geometry_msgs.msg import PoseWithCovarianceStamped
+
+from sensor_msgs.msg import Imu
+
+from sensor_msgs.msg import Image
+from sensor_msgs.msg import CameraInfo
+
+import sys
+import cv2
+from std_msgs.msg import String
+from cv_bridge import CvBridge, CvBridgeError
+
+pub = []
+
+def image_update(msg):
+    bridge = CvBridge()
+    new_img = Image()
+    new_img_cv = bridge.imgmsg_to_cv2(msg,"bgr8")
+    new_img = bridge.cv2_to_imgmsg(new_img_cv,"bgr8")
+    new_img.header = msg.header
+    new_img.header.frame_id = "pylon_camera"
+    pub.publish(new_img)
+
+def image_info_update(msg):
+    new_img_info = CameraInfo()
+    new_img_info = msg
+    new_img_info.header.frame_id = "pylon_camera" 
+    new_img_info.distortion_model = "plumb_bob"
+
+    # copy the camera info of atlas here
+    new_img_info.D = [-0.288902, 0.085422, 0.0013, -6.3e-05, 0.0]
+    new_img_info.K = [664.9933754342509, 0.0, 1024.0, 0.0, 664.9933754342509, 768.0, 0.0, 0.0, 1.0]
+    new_img_info.R = [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]
+    new_img_info.P = [664.9933754342509, 0.0, 1024.0, 0.0, 0.0, 664.9933754342509, 768.0, 0.0, 0.0, 0.0, 1.0, 0.0]
+
+    pub_2.publish(new_img_info)
+
+if __name__ == '__main__':
+    rospy.init_node('camera_rename')
+    # pub = rospy.Publisher('/ada/Lidar32/point_cloud', PointCloud2, queue_size=1)
+    pub = rospy.Publisher('/ada/rgb_front/image_color', Image, queue_size=1)
+    pub_2 = rospy.Publisher('/ada/rgb_front/camera_info',CameraInfo, queue_size=1)
+
+    #img_sub = rospy.Subscriber('/carla/ego_vehicle/camera/rgb/rgb_front/image_color', Image, image_update)
+    img_sub = rospy.Subscriber('/carla/ego_vehicle/rgb_front/image', Image, image_update)
+    #img_inf_sub = rospy.Subscriber('/carla/ego_vehicle/camera/rgb/rgb_front/camera_info', CameraInfo, image_info_update)
+    img_inf_sub = rospy.Subscriber('/carla/ego_vehicle/rgb_front/camera_info', CameraInfo, image_info_update)
+
+
+    rospy.spin()
diff --git a/behavior_metrics/configs/CARLA/CARLA_launch_files/town_02_anticlockwise_meiqi.launch b/behavior_metrics/configs/CARLA/CARLA_launch_files/town_02_anticlockwise_meiqi.launch
new file mode 100644
index 00000000..cc7405d8
--- /dev/null
+++ b/behavior_metrics/configs/CARLA/CARLA_launch_files/town_02_anticlockwise_meiqi.launch
@@ -0,0 +1,78 @@
+<!-- -->
+<launch>
+  <!-- CARLA connection -->
+  <arg name='host' default='localhost'/>
+  <arg name='port' default='2000'/>
+  <arg name='timeout' default='10'/>
+
+  <!-- Ego vehicle -->
+  <arg name='role_name' default='ego_vehicle'/>
+  <arg name="vehicle_filter" default='vehicle.*'/>
+  <!--<arg name="spawn_point" default=""/>--><!-- use comma separated format "x,y,z,roll,pitch,yaw" -->
+
+  <!--ANTICLOCKWISE-->
+  <!--<arg name="spawn_point" default="55.3, -105.6, 1.37, 0.0, 0.0, 180.0"/>-->
+  <!--<arg name="spawn_point" default="10.3, -105.6, 1.37, 0.0, 0.0, 180.0"/>-->
+  <!--<arg name="spawn_point" default="100.3, -105.6, 1.37, 0.0, 0.0, 180.0"/>-->
+
+  <!--<arg name="spawn_point" default="-7.0, -120.6, 1.37, 0.0, 0.0, -90.0"/>-->
+  <!--<arg name="spawn_point" default="-7.0, -270.6, 1.37, 0.0, 0.0, -90.0"/>-->
+
+  <!--<arg name="spawn_point" default="10.0, -307.0, 1.37, 0.0, 0.0, 0.0"/>-->
+  <!--<arg name="spawn_point" default="100.0, -307.0, 1.37, 0.0, 0.0, 0.0"/>-->
+  <!--<arg name="spawn_point" default="150.0, -307.0, 1.37, 0.0, 0.0, 0.0"/>-->
+
+  <!--<arg name="spawn_point" default="193.0, -290.0, 1.37, 0.0, 0.0, 90.0"/>-->
+  <!--<arg name="spawn_point" default="193.0, -150.6, 1.37, 0.0, 0.0, 90.0"/>-->
+
+  <!--CLOCKWISE-->
+  <!--<arg name="spawn_point" default="55.3, -110, 1.37, 0.0, 0.0, 0.0"/>-->
+  <!--<arg name="spawn_point" default="10.3, -110, 1.37, 0.0, 0.0, 0.0"/>-->
+  <!--<arg name="spawn_point" default="100.3, -110, 1.37, 0.0, 0.0, 0.0"/>-->
+
+  <!--<arg name="spawn_point" default="-3.0, -120.6, 1.37, 0.0, 0.0, 90.0"/>-->
+  <!--<arg name="spawn_point" default="-3.0, -270.6, 1.37, 0.0, 0.0, 90.0"/>-->
+
+  <!--<arg name="spawn_point" default="10.0, -303.0, 1.37, 0.0, 0.0, 180.0"/>-->
+  <!--<arg name="spawn_point" default="100.0, -303.0, 1.37, 0.0, 0.0, 180.0"/>-->
+  <!--<arg name="spawn_point" default="150.0, -303.0, 1.37, 0.0, 0.0, 180.0"/>-->
+
+  <!--<arg name="spawn_point" default="190.0, -290.0, 1.37, 0.0, 0.0, -90.0"/>-->
+  <arg name="spawn_point" default="190.0, -150.0, 1.37, 0.0, 0.0, -90.0"/>
+
+
+  <!-- Map to load on startup (either a predefined CARLA town (e.g. 'Town01'), or a OpenDRIVE map file) -->
+  <arg name="town" default='Town02'/>
+
+  <!-- Enable/disable passive mode -->
+  <arg name='passive' default=''/>
+
+  <!-- Synchronous mode-->
+  <arg name='synchronous_mode_wait_for_vehicle_control_command' default='False'/>
+  <arg name='fixed_delta_seconds' default='0.05'/>
+
+
+  <include file="$(find carla_ros_bridge)/launch/carla_ros_bridge.launch">
+    <arg name='host' value='$(arg host)'/>
+    <arg name='port' value='$(arg port)'/>
+    <arg name='town' value='$(arg town)_Opt'/>
+    <arg name='timeout' value='$(arg timeout)'/>
+    <arg name='passive' value='$(arg passive)'/>
+    <arg name='synchronous_mode_wait_for_vehicle_control_command' value='$(arg synchronous_mode_wait_for_vehicle_control_command)'/>
+    <arg name='fixed_delta_seconds' value='$(arg fixed_delta_seconds)'/>
+  </include>
+
+  <!-- the ego vehicle, that will be controlled by an agent (e.g. carla_ad_agent) -->
+  <include file="$(find carla_spawn_objects)/launch/carla_example_ego_vehicle.launch">
+    <!-- the object json file that configures objects to spawn -->
+    <arg name="objects_definition_file" default='$(env OBJECT_PATH)/main_car_custom_camera.json'/>
+    <arg name='role_name' value='$(arg role_name)'/>
+    <arg name="spawn_point_ego_vehicle" value="$(arg spawn_point)"/>
+    <arg name="spawn_sensors_only" value="false"/>
+  </include>
+
+  <include file="$(find carla_manual_control)/launch/carla_manual_control.launch">
+    <arg name='role_name' value='$(arg role_name)'/>
+  </include>
+
+</launch>
\ No newline at end of file
diff --git a/behavior_metrics/configs/CARLA/default_carla.yml b/behavior_metrics/configs/CARLA/default_carla.yml
index ddc3427c..c4466576 100644
--- a/behavior_metrics/configs/CARLA/default_carla.yml
+++ b/behavior_metrics/configs/CARLA/default_carla.yml
@@ -33,9 +33,9 @@ Behaviors:
                     Topic: '/carla/ego_vehicle/vehicle_control_cmd'
                     MaxV: 3
                     MaxW: 0.3
-        BrainPath: 'brains/CARLA/brain_carla_slow_and_turn.py'
+        BrainPath: 'brains/CARLA/brain_carla_autopilot.py'
         PilotTimeCycle: 50 # Turn up to reduce number of control decisions
-        AsyncMode: True # Set to False to control simulator time
+        AsyncMode: False # Set to False to control simulator time
         Parameters:
             Model: ''
             ImageCropped: True
@@ -47,7 +47,7 @@ Behaviors:
             ImageTranform: ''
         Type: 'CARLA'
     Simulation:
-        World: configs/CARLA/CARLA_launch_files/town_02_anticlockwise.launch
+        World: configs/CARLA/CARLA_launch_files/carla_new_ada_bridge_updated_autopilot.launch
         RandomSpawnPoint: False
     Dataset:
         In: '/tmp/my_bag.bag'
diff --git a/behavior_metrics/configs/CARLA/default_carla_autopilot.yml b/behavior_metrics/configs/CARLA/default_carla_autopilot.yml
index 2eeeb996..374e8036 100644
--- a/behavior_metrics/configs/CARLA/default_carla_autopilot.yml
+++ b/behavior_metrics/configs/CARLA/default_carla_autopilot.yml
@@ -36,6 +36,7 @@ Behaviors:
                     
         BrainPath: 'brains/CARLA/brain_carla_autopilot.py'
         PilotTimeCycle: 300
+        AsyncMode: False # Set to False to control simulator time
         Parameters:
             ImageCropped: True
             ImageSize: [ 100,50 ]
@@ -47,7 +48,8 @@ Behaviors:
         Type: 'CARLA'
         RandomSpawnPoint: False
     Simulation:
-        World: configs/CARLA/CARLA_launch_files/town_01_anticlockwise.launch
+        World: configs/CARLA/CARLA_launch_files/carla_new_ada_bridge_updated_autopilot.launch
+        RandomSpawnPoint: False
     Dataset:
         In: '/tmp/my_bag.bag'
         Out: ''
diff --git a/behavior_metrics/configs/CARLA/default_carla_tensorflow.yml b/behavior_metrics/configs/CARLA/default_carla_tensorflow.yml
index 46fae066..bcd84b52 100644
--- a/behavior_metrics/configs/CARLA/default_carla_tensorflow.yml
+++ b/behavior_metrics/configs/CARLA/default_carla_tensorflow.yml
@@ -33,11 +33,11 @@ Behaviors:
                     Topic: '/carla/ego_vehicle/vehicle_control_cmd'
                     MaxV: 3
                     MaxW: 0.3
-        BrainPath: 'brains/CARLA/tensorflow/brain_carla_bird_eye_deep_learning.py'
+        BrainPath: 'brains/CARLA/brain_carla_subjective_vision_deep_learning_previous_v.py'
         PilotTimeCycle: 50 # Turn up to reduce number of control decisions
         AsyncMode: True # Set to False to control simulator time
         Parameters:
-            Model: 'pilotnet.h5'
+            Model: 'subjective_vision_pilotnet/pilotnet_starstar_traffic_6.h5'
             ImageCropped: True
             ImageSize: [ 200,66 ]
             ImageNormalized: True
diff --git a/behavior_metrics/configs/CARLA/default_carla_tensorflow_updated.yml b/behavior_metrics/configs/CARLA/default_carla_tensorflow_updated.yml
new file mode 100644
index 00000000..07944ed0
--- /dev/null
+++ b/behavior_metrics/configs/CARLA/default_carla_tensorflow_updated.yml
@@ -0,0 +1,80 @@
+Behaviors:
+    Robot:
+        Sensors:
+            Cameras:
+                Camera_0:
+                    Name: 'camera_0'
+                    Topic: '/carla/ego_vehicle/rgb_front/image'
+                Camera_1:
+                    Name: 'camera_1'
+                    Topic: '/carla/ego_vehicle/rgb_view/image'
+                Camera_2:
+                    Name: 'camera_2'
+                    Topic: '/ada/rgb_front/image_color'
+                Camera_3:
+                    Name: 'camera_3'
+                    Topic: '/ada/rgb_front/image_rect_color'
+                #Camera_2:
+                #    Name: 'camera_2'
+                #    Topic: '/carla/ego_vehicle/semantic_segmentation_front/image'
+                #Camera_3:
+                #    Name: 'camera_3'
+                #    Topic: '/carla/ego_vehicle/dvs_front/image'
+            Pose3D:
+                Pose3D_0:
+                    Name: 'pose3d_0'
+                    Topic: '/carla/ego_vehicle/odometry'
+            BirdEyeView:
+                BirdEyeView_0:
+                    Name: 'bird_eye_view_0'
+                    Topic: ''
+            Speedometer:
+                Speedometer_0:
+                    Name: 'speedometer_0'
+                    Topic: '/carla/ego_vehicle/speedometer'
+        Actuators:
+            CARLA_Motors:
+                Motors_0:
+                    Name: 'motors_0'
+                    Topic: '/carla/ego_vehicle/vehicle_control_cmd'
+                    MaxV: 3
+                    MaxW: 0.3
+        BrainPath: 'brains/CARLA/tensorflow/brain_carla_bird_eye_deep_learning_previous_v_generate_dataset.py'
+        PilotTimeCycle: 50 # Turn up to reduce number of control decisions
+        AsyncMode: True # Set to False to control simulator time
+        Parameters:
+            Model: 'pilotnet_no_visual_kinematic.h5'
+            ImageCropped: True
+            ImageSize: [ 200,66 ]
+            ImageNormalized: True
+            PredictionsNormalized: True
+            GPU: True
+            UseOptimized: False
+            ImageTranform: ''
+        Type: 'CARLA'
+    Simulation:
+        World: configs/CARLA/CARLA_launch_files/carla_new_ada_bridge_updated.launch
+        RandomSpawnPoint: False
+    Dataset:
+        In: '/tmp/my_bag.bag'
+        Out: ''
+    Stats:
+        Out: './'
+        PerfectLap: './perfect_bags/lap-simple-circuit.bag'
+    Layout:
+        Frame_0:
+            Name: frame_0
+            Geometry: [1, 1, 1, 1]
+            Data: rgbimage
+        Frame_1:
+            Name: frame_1
+            Geometry: [0, 1, 1, 1]
+            Data: rgbimage
+        Frame_2:
+            Name: frame_2
+            Geometry: [0, 2, 1, 1]
+            Data: rgbimage
+        Frame_3:
+            Name: frame_3
+            Geometry: [1, 2, 1, 1]
+            Data: rgbimage
diff --git a/behavior_metrics/robot/interfaces/speedometer.py b/behavior_metrics/robot/interfaces/speedometer.py
index 4a75a079..f0723659 100644
--- a/behavior_metrics/robot/interfaces/speedometer.py
+++ b/behavior_metrics/robot/interfaces/speedometer.py
@@ -22,7 +22,7 @@ def __init__(self):
         self.timeStamp = 0  # Time stamp [s]
 
     def __str__(self):
-        s = "Speedometer: {\n   x: " + str(self.x) + "\n   timeStamp: " + str(self.timeStamp) + "\n}"
+        s = "Speedometer: {\n   data: " + str(self.data) + "\n   timeStamp: " + str(self.timeStamp) + "\n}"
         return s
 
 
diff --git a/behavior_metrics/utils/environment.py b/behavior_metrics/utils/environment.py
index 8225c697..e400b708 100644
--- a/behavior_metrics/utils/environment.py
+++ b/behavior_metrics/utils/environment.py
@@ -73,9 +73,13 @@ def launch_env(launch_file, random_spawn_point=False, carla_simulator=False, con
                 tree = ET.parse(ROOT_PATH + '/' + launch_file)
                 root = tree.getroot()
                 quality = root.find(".//*[@name=\"quality\"]")
+                
                 if quality == None:
-                    subprocess.Popen([os.environ["CARLA_ROOT"] + "CarlaUE4.sh", "-RenderOffScreen"], stdout=out, stderr=err)
+                    print('>>>>>>>> HIGH QUALITY RENDERING!')
+                    subprocess.Popen([os.environ["CARLA_ROOT"] + "CarlaUE4.sh", "-RenderOffScreen"], stdout=out, stderr=err)                
+                    #subprocess.Popen([os.environ["CARLA_ROOT"] + "CarlaUE4.sh", "-RenderOffScreen", "-quality-level=Low"], stdout=out, stderr=err)
                 elif quality.attrib['default'] == 'Low':
+                    print('>>>>>>>> LOW QUALITY RENDERING!')
                     subprocess.Popen([os.environ["CARLA_ROOT"] + "CarlaUE4.sh", "-RenderOffScreen", "-quality-level=Low"], stdout=out, stderr=err)                
                 #subprocess.Popen(["/home/jderobot/Documents/Projects/carla_simulator_0_9_13/CarlaUE4.sh", "-RenderOffScreen", "-quality-level=Low"], stdout=out, stderr=err)
             logger.info("SimulatorEnv: launching simulator server.")