BerkeleyLearnVerify · a-luque · Jul 22, 2026
diff --git a/examples/modd/followCarBehaviorMODD.scenic b/examples/modd/followCarBehaviorMODD.scenic
@@ -11,27 +11,30 @@ import scenic.domains.driving.model as _model
 from scenic.domains.driving.roads import ManeuverType
 from scenic.domains.driving.behaviors import concatenateCenterlines
 
-LEADER_SPEED = TimeSeries(VerifaiRange(6,8))
+target_speed_noise = TimeSeries(VerifaiRange(-1,1))
 EGO_BRAKING_THRESHOLD = 6
 
 def run_MODD(car, monitor_model, obstacle, leader, monitor_type="sklearn"):
-    current_lane = car.lane
-    nearby_intersection = current_lane.maneuvers[0].intersection
-    distIntersection = distance from car to nearby_intersection
-    distObstacle = distance from car to obstacle
-    visibleObstacle = int(car can see obstacle)
-    visibleLeader = int(car can see leader)
-
-    input_features = np.concatenate((car.weather, np.array([car.r,car.g,car.b, distIntersection, distObstacle, visibleObstacle, visibleLeader])))
-    input_features = np.expand_dims(input_features, axis=0)
-    if monitor_type == "sklearn":
-        car.isSafe = monitor_model.predict(input_features)[0]
+    if car._lane is None:
+        car.isSafe = 0
     else:
-        x = torch.Tensor(input_features).unsqueeze(0).cuda() 
-        car.isSafe = (torch.nn.Sigmoid()(monitor_model(x)).cpu().detach().numpy()[0][0]  > 0.65).astype(int)
+        current_lane = car.lane
+        nearby_intersection = current_lane.maneuvers[0].intersection
+        distIntersection = distance from car to nearby_intersection
+        distObstacle = distance from car to obstacle
+        visibleObstacle = int(car can see obstacle)
+        visibleLeader = int(car can see leader)
+
+        input_features = np.concatenate((car.weather, np.array([car.r,car.g,car.b, distIntersection, distObstacle, visibleObstacle, visibleLeader])))
+        input_features = np.expand_dims(input_features, axis=0)
+        if monitor_type == "sklearn":
+            car.isSafe = monitor_model.predict(input_features)[0]
+        else:
+            x = torch.Tensor(input_features).unsqueeze(0).cuda() 
+            car.isSafe = (torch.nn.Sigmoid()(monitor_model(x)).cpu().detach().numpy()[0][0]  > 0.65).astype(int)
 
 
-behavior FollowCarBehaviorMODD(target_speed = 10, laneToFollow=None, is_oppositeTraffic=False, leaderCar=None, addNoise=False, monitor_model=None):
+behavior FollowCarBehaviorMODD(target_speed = 10, laneToFollow=None, is_oppositeTraffic=False, leaderCar=None, addNoise=False, monitor_model=None, obstacleCar=None):
     """ 
     This implementation is heavily inspired by the FollowLaneBehavior.
     The main difference is that this behavior is used to follow a leaderCar with the addition of an ODD monitor.
@@ -44,6 +47,8 @@ behavior FollowCarBehaviorMODD(target_speed = 10, laneToFollow=None, is_opposite
     :param addNoise: When true, random noise is added to the steering and the speed. 
     :param monitor_model: ODD monitor to be used by the ego car. If existing, it is used to identify check if it is safe enough for the system to stop using the safe controller.
     """
+    noise = target_speed_noise.getSample()
+
 
     past_steer_angle = 0
     past_speed = 0 # making an assumption here that the agent starts from zero speed
@@ -60,9 +65,7 @@ behavior FollowCarBehaviorMODD(target_speed = 10, laneToFollow=None, is_opposite
     intersection_passed = False
     entering_intersection = False # assumption that the agent is not instantiated within an intersection
     end_lane = None
-    if isinstance(target_speed, TimeSeriesParameter):
-        target_speed = target_speed.getSample()
-    original_target_speed = target_speed
+    original_target_speed = target_speed + noise
     TARGET_SPEED_FOR_TURNING = 3 # KM/H
     TRIGGER_DISTANCE_TO_SLOWDOWN = 20 # FOR TURNING AT INTERSECTIONS
 
@@ -82,7 +85,8 @@ behavior FollowCarBehaviorMODD(target_speed = 10, laneToFollow=None, is_opposite
         steps_running = 0
 
     while True:
-
+        if self._lane is None:
+            continue
         if self.speed is not None:
             current_speed = self.speed
         else:
@@ -140,7 +144,6 @@ behavior FollowCarBehaviorMODD(target_speed = 10, laneToFollow=None, is_opposite
                 target_speed = TARGET_SPEED_FOR_TURNING
 
                 trajectory = current_centerline
-                target_speed = target_speed
                 if isinstance(trajectory, PolylineRegion):
                     trajectory_centerline = trajectory
                 else:
@@ -175,16 +178,16 @@ behavior FollowCarBehaviorMODD(target_speed = 10, laneToFollow=None, is_opposite
                         if leaderCar is None:
                             self.steps_speed += 1
                             if self.steps_speed > 20:
-                                original_target_speed = LEADER_SPEED.getSample()
+                                original_target_speed = target_speed + target_speed_noise.getSample()
                                 self.steps_speed = 0
                         else:
                             steps_running += 1
 
                         if not leaderCar is None and not monitor_model is None and steps_running > 80:
                             # Input format: (weather, np.array([r,g,b, distIntersection, distObstacle, visibleObstacle, visibleLeader]))
                             distIntersection = distance from self to nearby_intersection
-                            distObstacle = distance from self to obstacle
-                            visibleObstacle = int(ego can see obstacle)
+                            distObstacle = distance from self to obstacleCar
+                            visibleObstacle = int(ego can see obstacleCar)
                             visibleLeader = int(ego can see leader)
 
                             input_features = np.concatenate((self.weather, np.array([self.r,self.g,self.b, distIntersection, distObstacle, visibleObstacle, visibleLeader])))
@@ -238,13 +241,13 @@ behavior FollowCarBehaviorMODD(target_speed = 10, laneToFollow=None, is_opposite
             if leaderCar is None:
                 self.steps_speed += 1
                 if self.steps_speed > 20:
-                    original_target_speed = LEADER_SPEED.getSample()
+                    original_target_speed = target_speed + target_speed_noise.getSample()
                     self.steps_speed = 0
 
             else:
                 steps_running += 1
 
             if not leaderCar is None and monitor_model and steps_running > 80:
-                run_MODD(self, monitor_model, obstacle, leader)
+                run_MODD(self, monitor_model, obstacleCar, leaderCar)
 
 
diff --git a/examples/modd/followLeader_extracar.scenic b/examples/modd/followLeader_extracar.scenic
@@ -36,7 +36,7 @@ from followCarBehaviorMODD import (
 
 #CONSTANTS
 EGO_MODEL = "vehicle.tesla.model3"
-LEADER_SPEED = TimeSeries(VerifaiRange(6,8))
+LEADER_SPEED = 7
 EGO_SPEED = 3
 THROTTLE_ACTION = 0.5
 EGO_TO_LEADER = Range(-15, -10)
@@ -58,21 +58,29 @@ if globalParameters.monitor != "":
 
 behavior EgoBehavior(target_speed = 10, controller_path = None, leaderCar=None, obstacleCar=None, monitor_model=None):
     if monitor_model is None:
-        do ControllerBehavior(target_speed, controller_path, leaderCar)
+        try:
+            do ControllerBehavior(target_speed, controller_path, leaderCar)
+        interrupt when self._lane is None:
+            take SetBrakeAction(1.0)
     else:
         try:
             do ControllerBehavior(target_speed=target_speed, controller_path=controller_path, leaderCar=leaderCar, obstacleCar=obstacleCar)
-        interrupt when not self.isSafe:
-            print("-- Monitor: Not safe! Switching to SafeBehavior.")
-            take SetBrakeAction(1.0)
-            do EgoBehavior2(target_speed = target_speed, controller_path = controller_path, leaderCar=leaderCar, obstacleCar=obstacleCar, monitor_model=monitor_model)
+        interrupt when not self.isSafe or self._lane is None:
+            if self._lane is None:
+                take SetBrakeAction(1.0)
+            else:
+                print("-- Monitor: Not safe! Switching to SafeBehavior.")
+                do EgoBehavior2(target_speed = target_speed, controller_path = controller_path, leaderCar=leaderCar, obstacleCar=obstacleCar, monitor_model=monitor_model)
 
 behavior EgoBehavior2(target_speed = 10, controller_path = None, leaderCar=None, obstacleCar=None, monitor_model=None):
     try:
-        do FollowCarBehaviorMODD(target_speed=target_speed, leaderCar=leader, monitor_model=monitor_model)
-    interrupt when self.isSafe:
-        print("-- Monitor: Safe! Switching back to CNN Controller.")
-        do EgoBehavior(target_speed=target_speed, controller_path=controller_path, leaderCar=leaderCar, obstacleCar=obstacleCar, monitor_model=monitor_model)
+        do FollowCarBehaviorMODD(target_speed=target_speed, leaderCar=leader, monitor_model=monitor_model, obstacleCar=obstacleCar)
+    interrupt when self.isSafe or self._lane is None:
+        if self._lane is None:
+            take SetBrakeAction(1.0)
+        else:
+            print("-- Monitor: Safe! Switching back to CNN Controller.")
+            do EgoBehavior(target_speed=target_speed, controller_path=controller_path, leaderCar=leaderCar, obstacleCar=obstacleCar, monitor_model=monitor_model)
 
 
 
@@ -83,7 +91,7 @@ behavior ControllerBehavior(target_speed = 10, controller_path = None, leaderCar
     past_speed = 0 # making an assumption here that the agent starts from zero speed
 
     original_target_speed = target_speed
-
+    
     current_lane = self.lane
     nearby_intersection = current_lane.centerline[-1]
 
@@ -138,7 +146,6 @@ behavior ControllerBehavior(target_speed = 10, controller_path = None, leaderCar
 
         speed_error = target_speed - current_speed
 
-
         # compute throttle : Longitudinal Control
         throttle = _lon_controller.run_step(speed_error)
 

diff --git a/examples/modd/modd_learner_main.py b/examples/modd/modd_learner_main.py
@@ -72,17 +72,21 @@ def labeler(train_data_samples):
 
 if MODEL == "DT":
     base_model = DecisionTreeClassifier(max_depth=10)
+    monitor_type = "sklearn"
 
 if MODEL == "NN":
     nn = MLPClassifier(solver='adam', alpha=1e-5,
                     hidden_layer_sizes=(100,100), max_iter=100, random_state=42, verbose=1)
     base_model = Pipeline([('scaler', StandardScaler()), ('nn', nn)])
+    monitor_type = "sklearn"
 
 if MODEL == "LR":   
     base_model = LogisticRegression(verbose=1)
+    monitor_type = "sklearn"
 
 if MODEL == "NN_TORCH":
     base_model = MLP()
+    monitor_type = "torch"
 
 trainer_params = DotMap(
     model=base_model,
@@ -111,6 +115,7 @@ def specification(trace):
     scenes_save_dir=os.path.join(os.path.dirname(__file__), "out/scene_timeseries"),
     datagen_nomon_save_dir=os.path.join(os.path.dirname(__file__), "out/eval_samples_timeseries_nomonitor"),
     save_model_path=trainer_params.save_model_path,
+    monitor_type=monitor_type,
     verbosity=VERBOSITY,
 )
 
@@ -142,7 +147,9 @@ def spec(simulation):
                                       "verbosity": 3, 
                                       "timeBound": 300, 
                                       "controller": os.path.join(os.path.dirname(__file__), 'models/controller_cte_dist_130.pth')},
-                        eval_params={"seed": 42, 
+                        eval_params={"monitor" : trainer_params.save_model_path,
+                                     "monitor_type" : monitor_type,
+                                     "seed": 42, 
                                      "render" : 0, 
                                      "verbosity": 3, 
                                      "timeBound": 300, 
@@ -201,6 +208,3 @@ def spec(simulation):
 print(modd.training_results)
 print('Evaluation results:')
 print(modd.evaluation_results)
-
-
-
diff --git a/src/verifai/modd/odd_data_generator.py b/src/verifai/modd/odd_data_generator.py
@@ -67,9 +67,9 @@ def sample_simulations(self, num_simulations, num_steps, save_path):
                         print(f"Saving in {save_path}training_{i}.pkl")
                         with open(os.path.join(save_path + os.sep, f"training_{i}.pkl"), 'wb') as filehandler:
                             pickle.dump(self.samples, filehandler)
-                except TerminationException:
-                    if self.datagen_params.verbosity >= 1:
-                        print("Sampler has generated all possible samples")
+                except Exception as e:
+                    if self.eval_params.verbosity >= 1:
+                        print(f'  Failed to create simulation: {e}')
                     break
                 except KeyboardInterrupt:
                     break

diff --git a/src/verifai/modd/odd_evaluator.py b/src/verifai/modd/odd_evaluator.py
@@ -30,8 +30,6 @@ def __init__(self, eval_params, sampling_params, global_params):
 
     def sample_simulations(self, num_simulations, num_steps, save_datagen_path, save_scenes_path):
         i = 0
-        print(save_datagen_path)
-        print(save_scenes_path)
         self.total_sample_time = 0
         self.total_simulate_time = 0
         self.sampling_params.server.maxSteps = num_steps
@@ -84,9 +82,9 @@ def sample_simulations(self, num_simulations, num_steps, save_datagen_path, save
                         with open(filename, 'wb') as filehandler: 
                             pickle.dump(self.samples, filehandler)
 
-                except TerminationException:
+                except Exception as e:
                     if self.eval_params.verbosity >= 1:
-                        print("Sampler has generated all possible samples")
+                        print(f'  Failed to create simulation: {e}')
                     break
                 except KeyboardInterrupt:
                     break

diff --git a/src/verifai/modd/odd_learner.py b/src/verifai/modd/odd_learner.py
@@ -18,7 +18,7 @@ def __init__(self, datagen_params=DotMap(), trainer_params=DotMap(),
         self.sampling_params = sampling_params
         self.global_params = global_params      
 
-    def run(self):
+    def generate_monitor(self):
         pass