diff --git a/example/example_opendd_scenario_usage.py b/example/example_opendd_scenario_usage.py
deleted file mode 100644
index 214b27b..0000000
--- a/example/example_opendd_scenario_usage.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/bin/env python3
-import carla
-from carla_real_traffic_scenarios.opendd.scenario import OpenDDScenario
-
-
-def main():
-    carla_client = carla.Client('localhost', 2000)
-    carla_client.set_timeout(60)
-
-    # Download from http://bit.ly/PPUU-data
-    data_dir = '/home/pawel/sandbox/opendd'
-
-    print("Trying to connect to CARLA server. Make sure its up and running.")
-    world = carla_client.load_world('rdb1')
-    print("Connected!")
-
-    car_blueprint = world.get_blueprint_library().find('vehicle.audi.a2')
-    # spawn points doesnt matter - scenario sets up position in reset
-    dummy_spawn_point = carla.Transform(carla.Location(0, 0, 500), carla.Rotation())
-    ego_car = world.spawn_actor(car_blueprint, dummy_spawn_point)
-    # Setup car sensors. Later use to it make predictions
-
-    scenario = OpenDDScenario(carla_client, data_dir)
-    scenario.reset(ego_car)
-
-    spectator = world.get_spectator()
-
-    # OPEN-AI gym like loop:
-    EPISODES_N = 100
-    for ep_ix in range(EPISODES_N):
-        print(f"Running episode {ep_ix}")
-
-        # NGSim scenario places ego_agent in a place of one of real-world vehicles and asks it to replicate
-        # its either LANECHANGE_LEFT or LANECHANGE_RIGHT manuveur.
-        scenario.reset(ego_car)
-        done = False
-        while not done:
-            # Read sensors, use policy to generate action and apply it as vehicle control to ego_car
-            # ego_car.apply_control(carla.VehicleControl(throttle=1.0, steer=-1.0))
-            chauffeur_cmd, reward, done, info = scenario.step(ego_car)
-            world.tick()
-
-            birds_eye_view = carla.Transform(
-                ego_car.get_transform().location + carla.Vector3D(x=0, y=0, z=20),
-                carla.Rotation(pitch=-90),
-            )
-            spectator.set_transform(birds_eye_view)
-
-    print("Scenario finished!")
-    scenario.close()
-
-
-if __name__ == '__main__':
-    main()
diff --git a/example/example_scenario_usage.py b/example/example_scenario_usage.py
deleted file mode 100644
index f0cd575..0000000
--- a/example/example_scenario_usage.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import carla
-
-from carla_real_traffic_scenarios.ngsim import NGSimDatasets, DatasetMode
-from carla_real_traffic_scenarios.ngsim.ngsim_lanechange_scenario import NGSimLaneChangeScenario
-
-if __name__ == '__main__':
-    carla_client = carla.Client('localhost', 2000)
-    carla_client.set_timeout(60)
-
-    # Download from http://bit.ly/PPUU-data
-    data_dir = '/directory/with/ngsim/data/xy-trajectories'
-
-    ngsim_dataset = NGSimDatasets.I80
-    print("Trying to connect to CARLA server. Make sure its up and running.")
-    world = carla_client.load_world(ngsim_dataset.carla_map.level_path)
-    print("Connected!")
-
-    car_blueprint = world.get_blueprint_library().find('vehicle.audi.a2')
-    # spawn points doesnt matter - scenario sets up position in reset
-    dummy_spawn_point = carla.Transform(carla.Location(0, 0, 500), carla.Rotation())
-    ego_car = world.spawn_actor(car_blueprint, dummy_spawn_point)
-    # Setup car sensors. Later use to it make predictions
-
-    scenario = NGSimLaneChangeScenario(
-        ngsim_dataset, DatasetMode.TRAIN,
-        data_dir=data_dir, client=carla_client
-    )
-    scenario.reset(ego_car)
-
-    spectator = world.get_spectator()
-
-    # OPEN-AI gym like loop:
-    EPISODES_N = 10
-    for ep_ix in range(EPISODES_N):
-        print(f"Running episode {ep_ix}")
-
-        # NGSim scenario places ego_agent in a place of one of real-world vehicles and asks it to replicate
-        # its either LANECHANGE_LEFT or LANECHANGE_RIGHT manuveur.
-        scenario.reset(ego_car)
-        done = False
-        while not done:
-            # Read sensors, use policy to generate action and apply it as vehicle control to ego_car
-            # ego_car.apply_control(carla.VehicleControl(throttle=1.0, steer=-1.0))
-            chauffeur_cmd, reward, done, info = scenario.step(ego_car)
-            world.tick()
-
-            birds_eye_view = carla.Transform(
-                ego_car.get_transform().location + carla.Vector3D(x=0, y=0, z=20),
-                carla.Rotation(pitch=-90),
-            )
-            spectator.set_transform(birds_eye_view)
-
-    print("Scenario finished!")
-    scenario.close()