def __init__(self,
params=ParameterServer(),
eval_agent=None,
bark_eval_fns=None,
bark_ml_eval_fns=None):
self._eval_agent = eval_agent
self._params = params["ML"]["GeneralEvaluator"]
self._bark_eval_fns = bark_eval_fns or {
"goal_reached" : lambda: EvaluatorGoalReached(),
"collision" : lambda: EvaluatorCollisionEgoAgent(),
"step_count" : lambda: EvaluatorStepCount(),
"drivable_area" : lambda: EvaluatorDrivableArea()
}
self._bark_ml_eval_fns = bark_ml_eval_fns or {
"collision_functor" : CollisionFunctor(self._params),
"goal_functor" : GoalFunctor(self._params),
"low_speed_goal_reached_functor" : LowSpeedGoalFunctor(self._params),
"drivable_area_functor" : DrivableAreaFunctor(self._params),
"step_count_functor" : StepCountFunctor(self._params),
"smoothness_functor" : SmoothnessFunctor(self._params),
"min_max_vel_functor" : MinMaxVelFunctor(self._params),
# "pot_center_functor": PotentialCenterlineFunctor(self._params),
# "pot_vel_functor": PotentialVelocityFunctor(self._params),
"pot_goal_center_functor": PotentialGoalCenterlineFunctor(self._params),
# "pot_goal_switch_vel_functor": PotentialGoalSwitchVelocityFunctor(self._params)
# "state_action_logging_functor": StateActionLoggingFunctor(self._params)
}
def _add_evaluators(self):
evaluators = {}
evaluators["goal_reached"] = EvaluatorGoalReached()
evaluators["collision"] = EvaluatorCollisionEgoAgent()
evaluators["step_count"] = EvaluatorStepCount()
evaluators["drivable_area"] = EvaluatorDrivableArea()
return evaluators
def _add_evaluators(self):
"""Evaluators that will be set in the BARK world"""
self._evaluators["goal_reached"] = EvaluatorGoalReached()
self._evaluators["collision"] = EvaluatorCollisionEgoAgent()
self._evaluators["step_count"] = EvaluatorStepCount()
self._evaluators["drivable_area"] = EvaluatorDrivableArea()
def test_evaluator_drivable_area(self):
# World Definition
params = ParameterServer()
world = World(params)
# Model Definitions
behavior_model = BehaviorConstantAcceleration(params)
execution_model = ExecutionModelInterpolate(params)
dynamic_model = SingleTrackModel(params)
# Map Definition
map_interface = MapInterface()
xodr_map = MakeXodrMapOneRoadTwoLanes()
map_interface.SetOpenDriveMap(xodr_map)
world.SetMap(map_interface)
#open_drive_map = world.map.GetOpenDriveMap()
#agent_2d_shape = CarLimousine()
agent_2d_shape = Polygon2d(
[1.25, 1, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(3, 1),
Point2d(3, -1)])
init_state = np.array([0, 3, -1.75, 0, 5])
agent_params = params.AddChild("agent1")
goal_polygon = Polygon2d(
[1, 1, 0],
[Point2d(0, 0),
Point2d(0, 2),
Point2d(2, 2),
Point2d(2, 0)])
goal_polygon = goal_polygon.Translate(Point2d(50, -2))
agent = Agent(
init_state,
behavior_model,
dynamic_model,
execution_model,
agent_2d_shape,
agent_params,
GoalDefinitionPolygon(goal_polygon), # goal_lane_id
map_interface)
world.AddAgent(agent)
evaluator = EvaluatorDrivableArea()
world.AddEvaluator("drivable_area", evaluator)
info = world.Evaluate()
self.assertFalse(info["drivable_area"])
viewer = MPViewer(params=params, use_world_bounds=True)
# Draw map
viewer.drawGoalDefinition(goal_polygon,
color=(1, 0, 0),
alpha=0.5,
facecolor=(1, 0, 0))
viewer.drawWorld(world)
viewer.drawRoadCorridor(agent.road_corridor)
viewer.show(block=False)