Feature/carla0914

.gitignore

@@ -136,7 +136,7 @@ dmypy.json
 # Cython debug symbols
 cython_debug/
 .idea
-*.json
+#*.json
 *.csv
 output/
 outputs

opencda.py

@@ -30,9 +30,9 @@ def arg_parse():
                         action='store_true',
                         help='whether ml/dl framework such as sklearn/pytorch is needed in the testing. '
                              'Set it to true only when you have installed the pytorch/sklearn package.')
-    parser.add_argument('-v', "--version", type=str, default='0.9.11',
+    parser.add_argument('-v', "--version", type=str, default='0.9.14',
                         help='Specify the CARLA simulator version, default'
-                             'is 0.9.11, 0.9.12 is also supported.')
+                             'is 0.9.14')
     # parse the arguments and return the result
     opt = parser.parse_args()
     return opt

opencda/assets/blueprint_meta/bbx_stats_0914.json

@@ -0,0 +1,89 @@
+{
+    "vehicle.audi.a2": {
+        "class": "car"
+    },
+    "vehicle.audi.tt": {
+        "class": "car"
+    },
+    "vehicle.ford.ambulance": {
+        "class": "truck"
+    },
+    "vehicle.ford.crown": {
+        "class": "car"
+    },
+    "vehicle.mini.cooper_s_2021": {
+        "class": "car"
+    },
+    "vehicle.nissan.micra": {
+        "class": "car"
+    },
+    "vehicle.nissan.patrol": {
+        "class": "suv"
+    },
+    "vehicle.nissan.patrol_2021": {
+        "class": "suv"
+    },
+    "vehicle.tesla.cybertruck": {
+        "class": "truck"
+    },
+    "vehicle.volkswagen.t2": {
+        "class": "bus"
+    },
+    "vehicle.volkswagen.t2_2021": {
+        "class": "bus"
+    },
+    "vehicle.micro.microlino": {
+        "class": "car"
+    },
+    "vehicle.dodge.charger_police": {
+        "class": "car"
+    },
+    "vehicle.dodge.charger_police_2020": {
+        "class": "car"
+    },
+    "vehicle.dodge.charger_2020": {
+        "class": "car"
+    },
+    "vehicle.jeep.wrangler_rubicon": {
+        "class": "suv"
+    },
+    "vehicle.chevrolet.impala": {
+        "class": "car"
+    },
+    "vehicle.mini.cooper_s": {
+        "class": "car"
+    },
+    "vehicle.audi.etron": {
+        "class": "suv"
+    },
+    "vehicle.mercedes.coupe": {
+        "class": "car"
+    },
+    "vehicle.mercedes.coupe_2020": {
+        "class": "car"
+    },
+    "vehicle.bmw.grandtourer": {
+        "class": "car"
+    },
+    "vehicle.toyota.prius": {
+        "class": "car"
+    },
+    "vehicle.citroen.c3": {
+        "class": "car"
+    },
+    "vehicle.ford.mustang": {
+        "class": "car"
+    },
+    "vehicle.tesla.model3": {
+        "class": "car"
+    },
+    "vehicle.lincoln.mkz_2017": {
+        "class": "car"
+    },
+    "vehicle.lincoln.mkz_2020": {
+        "class": "car"
+    },
+    "vehicle.seat.leon": {
+        "class": "car"
+    }
+}

opencda/core/map/map_manager.py

@@ -289,7 +289,11 @@ def generate_lane_cross_info(self):
             while nxt.road_id == waypoint.road_id \
                     and nxt.lane_id == waypoint.lane_id:
                 waypoints.append(nxt)
-                nxt = nxt.next(self.lane_sample_resolution)[0]
+                nxt = nxt.next(self.lane_sample_resolution)
+                if len(nxt) > 0:
+                    nxt = nxt[0]
+                else:
+                    break
 
             # waypoint is the centerline, we need to calculate left lane mark
             left_marking = [lateral_shift(w.transform, -w.lane_width * 0.5) for

opencda/core/sensing/perception/perception_manager.py

@@ -613,14 +613,14 @@ def deactivate_mode(self, objects):
         objects.update({'vehicles': vehicle_list})
 
         if self.camera_visualize:
-            while self.rgb_camera[0].image is None:
-                continue
 
             names = ['front', 'right', 'left', 'back']
 
             for (i, rgb_camera) in enumerate(self.rgb_camera):
                 if i > self.camera_num - 1 or i > self.camera_visualize - 1:
                     break
+                while rgb_camera.image is None:
+                    continue
                 # we only visualiz the frontal camera
                 rgb_image = np.array(rgb_camera.image)
                 # draw the ground truth bbx on the camera image
@@ -675,6 +675,9 @@ def filter_vehicle_out_sensor(self, vehicle_list):
         semantic_idx = self.semantic_lidar.obj_idx
         semantic_tag = self.semantic_lidar.obj_tag
 
+        if semantic_tag is None or semantic_idx is None:
+            print('none')
+            return vehicle_list
         # label 10 is the vehicle
         vehicle_idx = semantic_idx[semantic_tag == 10]
         # each individual instance id

opencda/scenario_testing/config_yaml/default.yaml

@@ -10,7 +10,7 @@ world:
   fixed_delta_seconds: 0.05
   seed: 11 # seed for numpy and random
   weather:
-    sun_altitude_angle: 15 # 90 is the midday and -90 is the midnight
+    sun_altitude_angle: 50 # 90 is the midday and -90 is the midnight
     cloudiness: 0 # 0 is the clean sky and 100 is the thickest cloud
     precipitation: 0 # rain, 100 is the heaviest rain
     precipitation_deposits: 0 # Determines the creation of puddles. Values range from 0 to 100, being 0 none at all and 100 a road completely capped with water.
@@ -105,7 +105,7 @@ vehicle_base:
       history_size: 30
       col_thresh: 1
     stuck_dector:
-      len_thresh: 500
+      len_thresh: 1000
       speed_thresh: 0.5
     offroad_dector: [ ]
     traffic_light_detector: # whether the vehicle violate the traffic light
@@ -164,6 +164,11 @@ carla_traffic_manager:
   set_osm_mode: true # Enables or disables the OSM mode.
   auto_lane_change: false
   ignore_lights_percentage: 0 # whether set the traffic ignore traffic lights
+  ignore_signs_percentage: 0 # whether the cars will ignore stop signs
+  ignore_vehicles_percentage: 0 # whether the cars will ignore other cars and cause collisions
+  ignore_walkers_percentage: 0 # whether the cars will ingore walkers
+  random_left_lanechange_percentage: 0 # Adjust probability that in each timestep the actor will perform a left lane change, dependent on lane change availability.
+  random_right_lanechange_percentage: 0 # Adjust probability that in each timestep the actor will perform a right lane change, dependent on lane change availability.
   random: false # whether to random select vehicles' color and model
   vehicle_list: []  # define in each scenario. If set to ~, then the vehicles be spawned in a certain range
   # Used only when vehicle_list is ~

opencda/scenario_testing/config_yaml/platoon_joining_2lanefree_carla.yaml

@@ -84,7 +84,7 @@ scenario:
       spawn_position: [-380, 4.8, 0.3, 0, 0, 0]
       # when this is defined, the above parameter will be ignored, and a special map function will
       # be used to define the spawn position based on the argument
-      spawn_special: [0.625]
+      spawn_special: [0.700]
       destination: [300, 12.0, 0]
       sensing:
         perception:

opencda/scenario_testing/config_yaml/platoon_joining_town06_carla.yaml

@@ -47,7 +47,6 @@ carla_traffic_manager:
   #  Carla default speed is 30 km/h, so -100 represents 60 km/h,
   # and 20 represents 24 km/h
   global_speed_perc: -50
-  ignore_lights_percentage: 0 # whether set the traffic ignore traffic lights
   vehicle_list:
     - spawn_position: [101.7194, 136.51, 0.3, 0, 0, 0]
       vehicle_speed_perc: -200

opencda/scenario_testing/config_yaml/single_2lanefree_cosim.yaml

@@ -12,6 +12,9 @@ world:
 
 # First define the basic parameters of the vehicles
 vehicle_base:
+  behavior:
+    max_speed: 101 # maximum speed, km/h
+
   map_manager:
     visualize: false
     activate: false

opencda/scenario_testing/config_yaml/single_intersection_town06_carla.yaml

@@ -21,6 +21,7 @@ vehicle_base:
 # define the background traffic control by carla
 carla_traffic_manager:
   random: true # whether to random select vehicles' color and model
+  global_speed_perc: 0
   vehicle_list: ~  # a number or a list
   # Used only when vehicle_list is a number.
   # x_min, x_max, y_min, y_max, x_step, y_step, veh_num

opencda/scenario_testing/config_yaml/single_town06_carla.yaml

@@ -31,7 +31,6 @@ vehicle_base:
 # define the background traffic control by carla
 carla_traffic_manager:
   random: true # whether to random select vehicles' color and model
-  ignore_lights_percentage: 0 # whether set the traffic ignore traffic lights
   vehicle_list: ~  # a number or a list
   # Used only when vehicle_list is a number.
   # x_min, x_max, y_min, y_max, x_step, y_step

opencda/scenario_testing/config_yaml/v2xp_datadump_town06_carla.yaml

@@ -7,14 +7,13 @@ description: |-
 # Comment out this chunk of code or set use_multi_class_bp to be False if you don't want to spawn multi-class actors
 blueprint:
   use_multi_class_bp: true
-  bp_meta_path: "opencda/assets/blueprint_meta/bbx_stats_0912.json" # Define the path for loading the blueprint metadata for defining the class of each blueprint
+  bp_meta_path: "opencda/assets/blueprint_meta/bbx_stats_0914.json" # Define the path for loading the blueprint metadata for defining the class of each blueprint
   # Define blueprint type sample probabilities
   bp_class_sample_prob:
     car: 0.5
     truck: 0.1
     bus: 0.1
-    bicycle: 0.1
-    motorcycle: 0.1
+    suv: 0.2
 
 
 # Define the basic parameters of the vehicles
@@ -46,7 +45,7 @@ vehicle_base:
     visualize: false
     activate: false
   behavior:
-    max_speed: 30 # maximum speed, km/h
+    max_speed: 25 # maximum speed, km/h
     tailgate_speed: 34 # when a vehicles needs to be close to another vehicle asap
     overtake_allowed: false # whether overtake allowed, typically false for platoon leader
 
@@ -80,12 +79,3 @@ scenario:
         local_planner:
           debug_trajectory: false
           debug: false
-    - name: cav2
-      spawn_position: [-5.51, 110.24, 0.3, 0, 90, 0]
-      destination: [-2.43, 263, 0.3]
-      v2x:
-        communication_range: 45
-      behavior:
-        local_planner:
-          debug_trajectory: false
-          debug: false

opencda/scenario_testing/utils/sim_api.py

@@ -47,32 +47,21 @@ def car_blueprint_filter(blueprint_library, carla_version='0.9.11'):
         The list of suitable blueprints for vehicles.
     """
 
-    if carla_version == '0.9.11':
-        print('old version')
+    if carla_version == '0.9.14':
+        print('CARLA 0914 version is selected')
         blueprints = [
             blueprint_library.find('vehicle.audi.a2'),
             blueprint_library.find('vehicle.audi.tt'),
-            blueprint_library.find('vehicle.dodge_charger.police'),
-            blueprint_library.find('vehicle.jeep.wrangler_rubicon'),
-            blueprint_library.find('vehicle.chevrolet.impala'),
-            blueprint_library.find('vehicle.mini.cooperst'),
-            blueprint_library.find('vehicle.audi.etron'),
-            blueprint_library.find('vehicle.mercedes-benz.coupe'),
-            blueprint_library.find('vehicle.bmw.grandtourer'),
-            blueprint_library.find('vehicle.toyota.prius'),
-            blueprint_library.find('vehicle.citroen.c3'),
-            blueprint_library.find('vehicle.mustang.mustang'),
-            blueprint_library.find('vehicle.tesla.model3'),
-            blueprint_library.find('vehicle.lincoln.mkz2017'),
-            blueprint_library.find('vehicle.seat.leon'),
-            blueprint_library.find('vehicle.nissan.patrol'),
+            blueprint_library.find('vehicle.ford.ambulance'),
+            blueprint_library.find('vehicle.ford.crown'),
+            blueprint_library.find('vehicle.mini.cooper_s_2021'),
             blueprint_library.find('vehicle.nissan.micra'),
-        ]
-
-    else:
-        blueprints = [
-            blueprint_library.find('vehicle.audi.a2'),
-            blueprint_library.find('vehicle.audi.tt'),
+            blueprint_library.find('vehicle.nissan.patrol'),
+            blueprint_library.find('vehicle.nissan.patrol_2021'),
+            blueprint_library.find('vehicle.tesla.cybertruck'),
+            blueprint_library.find('vehicle.volkswagen.t2'),
+            blueprint_library.find('vehicle.volkswagen.t2_2021'),
+            blueprint_library.find('vehicle.micro.microlino'),
             blueprint_library.find('vehicle.dodge.charger_police'),
             blueprint_library.find('vehicle.dodge.charger_police_2020'),
             blueprint_library.find('vehicle.dodge.charger_2020'),
@@ -91,8 +80,12 @@ def car_blueprint_filter(blueprint_library, carla_version='0.9.11'):
             blueprint_library.find('vehicle.lincoln.mkz_2020'),
             blueprint_library.find('vehicle.seat.leon'),
             blueprint_library.find('vehicle.nissan.patrol'),
-            blueprint_library.find('vehicle.nissan.micra'),
+            blueprint_library.find('vehicle.nissan.micra')
         ]
+    else:
+        sys.exit("Since v0.1.4, we do not support version earlier than "
+                 "CARLA v0.9.14. If you want to use early CARLA version including"
+                 "0.9.11 and 0.9.12, please use OpenCDA v0.1.3.")
 
     return blueprints
 
@@ -290,9 +283,7 @@ def create_vehicle_manager(self, application,
         """
         print('Creating single CAVs.')
         # By default, we use lincoln as our cav model.
-        default_model = 'vehicle.lincoln.mkz2017' \
-            if self.carla_version == '0.9.11' else 'vehicle.lincoln.mkz_2017'
-
+        default_model = 'vehicle.lincoln.mkz_2017'
         cav_vehicle_bp = \
             self.world.get_blueprint_library().find(default_model)
         single_cav_list = []
@@ -372,8 +363,7 @@ def create_platoon_manager(self, map_helper=None, data_dump=False):
 
         # we use lincoln as default choice since our UCLA mobility lab use the
         # same car
-        default_model = 'vehicle.lincoln.mkz2017' \
-            if self.carla_version == '0.9.11' else 'vehicle.lincoln.mkz_2017'
+        default_model = 'vehicle.lincoln.mkz_2017'
 
         cav_vehicle_bp = \
             self.world.get_blueprint_library().find(default_model)
@@ -493,8 +483,7 @@ def spawn_vehicles_by_list(self, tm, traffic_config, bg_list):
 
         # if not random select, we always choose lincoln.mkz with green color
         color = '0, 255, 0'
-        default_model = 'vehicle.lincoln.mkz2017' \
-            if self.carla_version == '0.9.11' else 'vehicle.lincoln.mkz_2017'
+        default_model = 'vehicle.lincoln.mkz_2020'
         ego_vehicle_bp = blueprint_library.find(default_model)
 
         for i, vehicle_config in enumerate(traffic_config['vehicle_list']):
@@ -568,8 +557,7 @@ def spawn_vehicle_by_range(self, tm, traffic_config, bg_list):
 
         # if not random select, we always choose lincoln.mkz with green color
         color = '0, 255, 0'
-        default_model = 'vehicle.lincoln.mkz2017' \
-            if self.carla_version == '0.9.11' else 'vehicle.lincoln.mkz_2017'
+        default_model = 'vehicle.lincoln.mkz_2020'
         ego_vehicle_bp = blueprint_library.find(default_model)
 
         spawn_ranges = traffic_config['range']
@@ -638,10 +626,23 @@ def spawn_vehicle_by_range(self, tm, traffic_config, bg_list):
             vehicle.set_autopilot(True, 8000)
             tm.auto_lane_change(vehicle, traffic_config['auto_lane_change'])
 
-            if 'ignore_lights_percentage' in traffic_config:
-                tm.ignore_lights_percentage(vehicle,
-                                            traffic_config[
+            # hazard behavior for traffic
+            tm.ignore_lights_percentage(vehicle, traffic_config[
                                                 'ignore_lights_percentage'])
+            tm.ignore_signs_percentage(vehicle, traffic_config[
+                                                'ignore_signs_percentage'])
+            tm.ignore_vehicles_percentage(vehicle, traffic_config[
+                                                'ignore_vehicles_percentage'])
+            tm.ignore_walkers_percentage(vehicle, traffic_config[
+                                                'ignore_walkers_percentage'])
+
+            # left/right lane change
+            if traffic_config['random_left_lanechange_percentage'] != 0:
+                tm.random_left_lanechange_percentage(vehicle,
+                                                     traffic_config['random_left_lanechange_percentage'])
+            if traffic_config['random_right_lanechange_percentage'] != 0:
+                tm.random_right_lanechange_percentage(vehicle,
+                                                     traffic_config['random_right_lanechange_percentage'])
 
             # each vehicle have slight different speed
             tm.vehicle_percentage_speed_difference(

opencda/version.py

@@ -1,3 +1,3 @@
 """Specifies the current version number of OpenCDA."""
 
-__version__ = "0.1.2"
+__version__ = "0.1.4"