sensor changes

sense.py

@@ -66,7 +66,10 @@ def sense(self, agent: BaseAgent, state_dict, lane_map):
                 x = cont['ego.x']
                 y = cont['ego.y']
                 position = np.array([x,y])
-                lateral = lane_map.get_lateral_distance("T0", position)
+                true_lateral = lane_map.get_lateral_distance("T0", position)
+
+                blowup = true_lateral + np.random.uniform(low=-0.25, high=0.25, size=5)
+                lateral = random.choice(blowup)
 
                 if(lateral < -1):
                     cont['ego.s'] = 0
@@ -77,6 +80,9 @@ def sense(self, agent: BaseAgent, state_dict, lane_map):
                 else:
                     cont['ego.s'] = 1
 
+
+                #perception contract: blow up lateral array and randomly sample
+
         else:
             if agent.id == 'car':
                 cont['ego.x'] = [state_dict['car'][0][0][1], state_dict['car'][0][1][1]]
@@ -86,14 +92,21 @@ def sense(self, agent: BaseAgent, state_dict, lane_map):
                 cont['ego.t'] = [state_dict['car'][0][0][5], state_dict['car'][0][1][5]]
                 disc['ego.agent_mode'] = state_dict['car'][1][0]
 
-                lateral = []
+                true_lateral = []
+                #print("new four")
                 for x in cont['ego.x']:
                     for y in cont['ego.y']:
                         position = np.array([x,y])
-                        print(position)
-                        lateral.append(lane_map.get_lateral_distance("T0", position))
+                        #print(position)
+                        true_lateral.append(lane_map.get_lateral_distance("T0", position))
 
-                min_lateral = min(lateral)
+                blowup = []
+                for l in true_lateral:
+                    blowup += list(l + np.random.uniform(low=-0.25, high=0.25, size=5))
+
+                min_lateral = min(blowup) #+ 0.1
+
+                #perception contract: blow up lateral array and choose minimum
 
                 if(min_lateral < -1):
                     cont['ego.s'] = [0,0]
@@ -113,7 +126,7 @@ def __init__(self):
         segment0 = CircularLane("Seg0", [14, 14], 12, np.pi * 3 / 2, np.pi, True, 4)
         segment1 = StraightLane("Seg1", [2, 14], [2, 24], 4)
         segment2 = CircularLane("Seg2", [14, 24], 12, np.pi, 0, True, 4)
-        segment3 = StraightLane("Seg3", [26, 24], [26, 14], 4)
+        #segment3 = StraightLane("Seg3", [26, 24], [26, 14], 4)
         lane0 = Lane("T0", [segment0, segment1, segment2])# segment3])
         self.add_lanes([lane0])
         self.h_dict = {("T0", "Left", "Right"): "T0", ("T0", "Right", "Left"): "T0",
@@ -192,7 +205,19 @@ def __init__(self, id, code=None, file_name=None):
 #traces_simu = scenario.simulate(60,0.01)
 traces_veri = scenario.verify(10, 0.01)
 
-traces_veri.dump("out.json")
+nodes = traces_veri._get_all_nodes(traces_veri.root)
+#print(len(nodes))
+# for n in nodes:
+#     print("new node")
+#     for l in n.trace['car']:
+#         print(l[1:3])
+
+#traces_veri.visualize()
+#print(nodes)
+#print("height")
+#print(traces_veri.height)
+
+#traces_veri.dump("out.json")
 
 # fig = go.Figure()
 # fig = simulation_tree(traces_simu, None, fig, 1, 2, [0, 1], "lines", "trace")

sensedl.py

@@ -53,12 +53,12 @@ def decisionLogic(ego: State, track_map):
         output.track_mode = TrackMode.T0
 
 
-    x = ego.x
-    y = ego.y
-    position = [x,y]
-    lateral = track_map.get_lateral_distance("T0", position)
+    # x = ego.x
+    # y = ego.y
+    # position = [x,y]
+    # lateral = track_map.get_lateral_distance("T0", position)
 
-    # assert -2 <= lateral and lateral <= 2
+    #assert -2 <= lateral and lateral <= 2
     #assert ((ego.x-14)**2 + (ego.y-14)**2 >= 10**2 and (ego.x-14)**2 + (ego.y-14)**2 <= 14**2 and ego.y <= 14) or (ego.x > 0 and ego.x < 4 and ego.y > 14 and ego.y <= 24) or ((ego.x-14)**2 + (ego.y-24)**2 >= 10**2 and (ego.x-14)**2 + (ego.y-24)**2 <= 14**2 and ego.y > 24)
     assert True 
 

verse/analysis/dryvr.py

@@ -285,6 +285,8 @@ def calc_bloated_tube(
     traces = [sim_func(mode_label, cur_center, time_horizon, time_step, lane_map)]
     # Simulate SIMTRACENUM times to learn the sensitivity
     for i in range(sim_trace_num):
+        # modified
+        random.seed(i)
         new_init_point = randomPoint(initial_set[0], initial_set[1])
         traces.append(sim_func(mode_label, new_init_point, time_horizon, time_step, lane_map))
 

verse/analysis/verifier.py

@@ -231,6 +231,7 @@ def compute_full_reachtube_step(
                 uncertain_param = node.uncertain_param[agent_id]
                 if consts.reachability_method == ReachabilityMethod.DRYVR:
                     # pp(('tube', agent_id, mode, inits))
+                    #exact rectangles
                     (
                         cur_bloated_tube,
                         cache_tube_update,
@@ -666,6 +667,8 @@ def compute_full_reachtube(
         # print(f">>>>>>>> Number of transitions happening: {num_transitions}")
         self.num_transitions = num_transitions
 
+        #print(len(self.nodes))
+
         return self.reachtube_tree
 
     @staticmethod
@@ -789,11 +792,15 @@ def get_transition_verify_opt(
         guard_hits = []
         guard_hit = False
         reduction_rate = 10
-        reduction_queue = [(0, trace_length, trace_length)]
+        reduction_queue = []
+        for i in range(trace_length):
+            reduction_queue.append((i,i+1,1))
+        #reduction_queue = [(0, trace_length, trace_length)]
         # for idx, end_idx,combine_len in reduction_queue:
         hits = []
         while reduction_queue:
-            idx, end_idx, combine_len = reduction_queue.pop()
+            #don't over approximate rectangles
+            idx, end_idx, combine_len = reduction_queue.pop(0)
             reduction_needed = False
             # print((idx, combine_len))
             any_contained = False