def run(self, carID):
"""
Executes the Traci client and the environment representation
"""
ego = carID
step = 0
while traci.simulation.getTime() < 60:
traci.simulationStep()
fw = Framework()
if carID in traci.vehicle.getIDList():
ego_trajectory = fw.getTrajectory(carID)
intersections = fw.getIntersectingTrajectories(
carID, ego_trajectory)
if intersections is not None:
#if len(ego_trajectory) > 1:
framework = fw.getTensorFramework(carID, intersections,
ego_trajectory)
auto = Autonomous(carID, framework, ego_trajectory)
distances = auto.getDistanceToIntercept()
tfc = Traffic(distances)
vehicles = tfc.getConflictoryTraffic()
distances_tfc = tfc.getDistanceToIntercept(vehicles)
safe = Safety(carID, framework, distances, distances_tfc)
trc_safety = safe.getTrafficSafetyMeasures()
#print(len(distances), len(trc_safety))
ego_safety = safe.getEgoSafetyMeasures()
#print(ego_safety)
prio = safe.getPriotizedTraffic(ego_safety, trc_safety)
rel = safe.getRelevantTraffic(prio)
#print(rel)
# for i in range(len(rel)):
# if rel[i][1]:
# safe.getEgoSecurityDistance(carID, rel[i][1][0][0])
env = Tensor(carID, distances, rel, ego_safety,
ego_trajectory)
env.createEnvironmentTensor()
#print(env.createEnvironmentTensor())
else:
corridor = np.full((200, 3), fill_value=-1.0)
corridor[0:200, 2] = 100 / np.maximum(
traci.vehicle.getSpeed(self.carID), 0.001)
#print(corridor)
step += 1
traci.close()
sys.stdout.flush()
def getTensor(self):
fw = Framework()
if self.carID in traci.vehicle.getIDList():
ego_trajectory = fw.getTrajectory(self.carID)
intersections = fw.getIntersectingTrajectories(
self.carID, ego_trajectory)
if intersections is not None:
#if len(ego_trajectory) > 1:
framework = fw.getTensorFramework(self.carID, intersections,
ego_trajectory)
auto = Autonomous(self.carID, framework, ego_trajectory)
distances = auto.getDistanceToIntercept()
tfc = Traffic(distances)
vehicles = tfc.getConflictoryTraffic()
distances_tfc = tfc.getDistanceToIntercept(vehicles)
self.distances_tfc = distances_tfc
safe = Safety(self.carID, framework, distances, distances_tfc)
trc_safety = safe.getTrafficSafetyMeasures()
# print(len(distances), len(trc_safety))
ego_safety = safe.getEgoSafetyMeasures()
# print(ego_safety)
prio = safe.getPriotizedTraffic(ego_safety, trc_safety)
rel = safe.getRelevantTraffic(prio)
#print('\n', prio)
#print(rel)
self.rel = rel
# print(rel)
# for i in range(len(rel)):
# if rel[i][1]:
# safe.getEgoSecurityDistance(carID, rel[i][1][0][0])
env = Tensor(self.carID, distances, rel, ego_safety,
ego_trajectory)
#print(env.createEnvironmentTensor())
return env.createEnvironmentTensor()
else:
corridor = np.full((200, 3), fill_value=-1.0)
#corridor[0:199, 2] = 0
corridor[0:200, 2] = 100 / np.maximum(
traci.vehicle.getSpeed(self.carID), 0.001)
#tensor = np.reshape(corridor[0:200], 600)
return corridor
