--- a/trafficintelligence/prediction.py	Tue May 02 07:12:26 2023 -0400
+++ b/trafficintelligence/prediction.py	Tue May 02 17:11:24 2023 -0400
@@ -72,15 +72,25 @@
             self.predictedPositions[nTimeSteps] = self.predictedPositions[nTimeSteps-1]+self.initialVelocity
         return self.predictedPositions[nTimeSteps]
 
-class PredictedPolyTrajectoryConstant(PredictedTrajectory):
+class PredictedPolyTrajectoryConstantVelocity(PredictedTrajectory):
     '''Predicted trajectory of an object with an outline represented by a polygon
-    Simple method that just translates the polygon corners'''
+    Simple method that just translates the polygon corners (set of moving.Point)'''
     def __init__(self, polyCorners, initialVelocity, probability = 1.):
         self.probability = probability
-        self.predictedPositions = {0: polyCorners}
+        self.predictedPositions = {0: moving.pointsToShapely(polyCorners)}
         self.initialVelocity = initialVelocity
-        self.predictedTrajectories = [PredictedTrajectoryConstant(p, initialVelocity) for p in polyCorners]
-        
+        self.predictedTrajectories = [PredictedTrajectoryConstantVelocity(p, initialVelocity) for p in polyCorners]
+
+    def predictPosition(self, nTimeSteps):
+        if nTimeSteps > 0 and not nTimeSteps in self.predictedPositions:
+            self.predictedPositions[nTimeSteps] = moving.pointsToShapely([t.predictPosition(nTimeSteps) for t in self.predictedTrajectories])
+        return self.predictedPositions[nTimeSteps]
+
+    def hasCollisionWith(self, other, t, collisionDistanceThreshold):
+        poly1 = self.predictPosition(t)
+        poly2 = other.predictPosition(t)
+        return poly1.overlaps(poly2), poly1, poly2
+
 class PredictedTrajectoryPrototype(PredictedTrajectory):
     '''Predicted trajectory that follows a prototype trajectory
     The prototype is in the format of a moving.Trajectory: it could be
@@ -196,23 +206,6 @@
         collision, p1, p2 = predictedTrajectory1.hasCollisionWith(predictedTrajectory2, t, collisionDistanceThreshold)
     return collision, t, p1, p2
 
-def computeCollisionTimePolygon(predictedTrajectories1, predictedTrajectories2, timeHorizon):
-    '''Computes the first instant 
-    at which two objects represented by a series of points (eg box)
-    Computes all the times including timeHorizon
-    
-    User has to check the first variable collision to know about a collision'''
-    t = 1
-    poly1 = moving.pointsToShapely([t1.predictPosition(t) for t1 in predictedTrajectories1])
-    poly2 = moving.pointsToShapely([t2.predictPosition(t) for t2 in predictedTrajectories2])
-    collision = poly1.overlaps(poly2)
-    while t < timeHorizon and not collision:
-        t += 1
-        poly1 = moving.pointsToShapely([t1.predictPosition(t) for t1 in predictedTrajectories1])
-        poly2 = moving.pointsToShapely([t2.predictPosition(t) for t2 in predictedTrajectories2])
-        collision = poly1.overlaps(poly2)
-    return collision, t
-
 def savePredictedTrajectoriesFigure(currentInstant, obj1, obj2, predictedTrajectories1, predictedTrajectories2, timeHorizon, printFigure = True):
     from matplotlib.pyplot import figure, axis, title, clf, savefig
     if printFigure:
@@ -314,7 +307,7 @@
 
 
 class PredictionParameters(object):
-    def __init__(self, name, maxSpeed, useCurvilinear = False):
+    def __init__(self, name, maxSpeed = None, useCurvilinear = False):
         self.name = name
         self.maxSpeed = maxSpeed
         self.useCurvilinear = useCurvilinear
@@ -325,6 +318,9 @@
     def generatePredictedTrajectories(self, obj, instant):
         return None
 
+    def computeCollisionPoint(self, p1, p2, probability, t):
+        return SafetyPoint((p1+p2)*0.5, probability, t)
+
     def computeCrossingsCollisionsAtInstant(self, currentInstant, obj1, obj2, collisionDistanceThreshold, timeHorizon, computeCZ = False, debug = False):
         '''returns the lists of collision points and crossing zones'''
         predictedTrajectories1 = self.generatePredictedTrajectories(obj1, currentInstant)
@@ -339,7 +335,7 @@
             for et2 in predictedTrajectories2:
                 collision, t, p1, p2 = computeCollisionTime(et1, et2, collisionDistanceThreshold, timeHorizon)
                 if collision:
-                    collisionPoints.append(SafetyPoint((p1+p2)*0.5, et1.probability*et2.probability, t))
+                    collisionPoints.append(self.computeCollisionPoint(p1, p2, et1.probability*et2.probability, t))
                 elif computeCZ: # check if there is a crossing zone
                     # TODO same computation as PET with metric + concatenate past trajectory with future trajectory
                     cz = None
@@ -412,6 +408,19 @@
     def generatePredictedTrajectories(self, obj, instant):
         return [PredictedTrajectoryConstantVelocity(obj.getPositionAtInstant(instant), obj.getVelocityAtInstant(instant))]
 
+class ConstantPolyPredictionParameters(PredictionParameters):
+    def __init__(self):
+        PredictionParameters.__init__(self, 'constant velocity for polygon representation')
+
+    def generatePredictedTrajectories(self, obj, instant):
+        return [PredictedPolyTrajectoryConstantVelocity(obj.getBoundingPolygon(instant), obj.getVelocityAtInstant(instant))]
+
+    def computeCollisionPoint(self, p1, p2, probability, t):
+        polyRepr1 = p1.representative_point()
+        polyRepr2 = p2.representative_point()
+        p = moving.Point(polyRepr1.x+polyRepr2.x, polyRepr1.y+polyRepr2.y)
+        return SafetyPoint(p, probability, t)
+    
 class NormalAdaptationPredictionParameters(PredictionParameters):
     def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False):
         '''An example of acceleration and steering distributions is
@@ -469,7 +478,6 @@
         else:
             print('Object {} has no features'.format(obj.getNum()))
             return None
-
         
 class EvasiveActionPredictionParameters(PredictionParameters):
     def __init__(self, maxSpeed, nPredictedTrajectories, accelerationDistribution, steeringDistribution, useFeatures = False):
@@ -509,7 +517,6 @@
                                                                          self.maxSpeed))
         return predictedTrajectories
 
-
 class CVDirectPredictionParameters(PredictionParameters):
     '''Prediction parameters of prediction at constant velocity
     using direct computation of the intersecting point