def test_one_agent_at_goal_state_limits(self):
param_server = ParameterServer()
# Model Definition
behavior_model = BehaviorConstantVelocity(param_server)
execution_model = ExecutionModelInterpolate(param_server)
dynamic_model = SingleTrackModel(param_server)
# Agent Definition
agent_2d_shape = CarLimousine()
init_state = np.array(
[0, -191.789, -50.1725, 3.14 * 3.0 / 4.0, 150 / 3.6])
agent_params = param_server.AddChild("agent1")
goal_polygon = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
goal_polygon = goal_polygon.Translate(Point2d(-191.789, -50.1725))
agent = Agent(
init_state, behavior_model, dynamic_model, execution_model,
agent_2d_shape, agent_params,
GoalDefinitionStateLimits(
goal_polygon,
(3.14 * 3.0 / 4.0 - 0.08, 3.14 * 3.0 / 4.0 + 0.08)), None)
world = World(param_server)
world.AddAgent(agent)
evaluator = EvaluatorGoalReached(agent.id)
world.AddEvaluator("success", evaluator)
info = world.Evaluate()
self.assertEqual(info["success"], True)
def test_planning_time(self):
param_server = ParameterServer()
# Model Definition
behavior_model = BehaviorConstantAcceleration(param_server)
execution_model = ExecutionModelInterpolate(param_server)
dynamic_model = SingleTrackModel(param_server)
# Agent Definition
agent_2d_shape = CarLimousine()
init_state = np.array([0, -191.789,-50.1725, 3.14*3.0/4.0, 150/3.6])
agent_params = param_server.AddChild("agent1")
goal_polygon = Polygon2d([0, 0, 0],
[Point2d(-4,-4),
Point2d(-4,4),
Point2d(4,4),
Point2d(4,-4)])
goal_polygon = goal_polygon.Translate(Point2d(-191.789,-50.1725))
agent = Agent(init_state,
behavior_model,
dynamic_model,
execution_model,
agent_2d_shape,
agent_params,
GoalDefinitionPolygon(goal_polygon),
None)
world = World(param_server)
world.AddAgent(agent)
evaluator = EvaluatorPlanningTime(agent.id)
world.AddEvaluator("time", evaluator)
info = world.Evaluate()
self.assertEqual(info["time"], 0.0)
def test_number_of_agents(self):
# World Definition
params = ParameterServer()
world = World(params)
# Model Definitions
behavior_model = BehaviorConstantAcceleration(params)
execution_model = ExecutionModelInterpolate(params)
dynamic_model = SingleTrackModel(params)
behavior_model2 = BehaviorConstantAcceleration(params)
execution_model2 = ExecutionModelInterpolate(params)
dynamic_model2 = SingleTrackModel(params)
# Map Definition
map_interface = MapInterface()
xodr_map = MakeXodrMapOneRoadTwoLanes()
map_interface.SetOpenDriveMap(xodr_map)
world.SetMap(map_interface)
agent_2d_shape = CarLimousine()
init_state = np.array([0, 13, -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), map_interface)
world.AddAgent(agent)
init_state2 = np.array([0, 16, -1.75, 0, 5])
agent2 = Agent(init_state2, behavior_model2, dynamic_model2,
execution_model2, agent_2d_shape, agent_params,
GoalDefinitionPolygon(goal_polygon), map_interface)
world.AddAgent(agent2)
evaluator = EvaluatorNumberOfAgents(agent.id)
world.AddEvaluator("num_agents", evaluator)
info = world.Evaluate()
self.assertEqual(info["num_agents"], len(world.agents))
# do it once more
self.assertEqual(info["num_agents"], len(world.agents))
world.RemoveAgentById(agent2.id)
info = world.Evaluate()
# evaluator should still hold two
self.assertNotEqual(info["num_agents"], len(world.agents))
self.assertEqual(info["num_agents"], 2)
world.Step(0.1)
info = world.Evaluate()
# evaluator should still hold two
self.assertEqual(info["num_agents"], 2)
def test_one_agent_at_goal_sequential(self):
param_server = ParameterServer()
# Model Definition
dynamic_model = SingleTrackModel(param_server)
behavior_model = BehaviorMPContinuousActions(param_server)
idx = behavior_model.AddMotionPrimitive(np.array([1, 0]))
behavior_model.ActionToBehavior(idx)
execution_model = ExecutionModelInterpolate(param_server)
# Agent Definition
agent_2d_shape = CarLimousine()
init_state = np.array([0, 0, 0, 0, 0])
agent_params = param_server.AddChild("agent1")
goal_frame = Polygon2d([0, 0, 0],
[Point2d(-1,-1),
Point2d(-1,1),
Point2d(1,1),
Point2d(1,-1)])
goal_polygon1 = goal_frame.Translate(Point2d(10, 0))
goal_polygon2 = goal_frame.Translate(Point2d(20, 0))
goal_polygon3 = goal_frame.Translate(Point2d(30, 0))
goal_def1 = GoalDefinitionStateLimits(goal_polygon1, [-0.08, 0.08])
goal_def2 = GoalDefinitionStateLimits(goal_polygon2, [-0.08, 0.08])
goal_def3 = GoalDefinitionStateLimits(goal_polygon3, [-0.08, 0.08])
goal_definition = GoalDefinitionSequential([goal_def1,
goal_def2,
goal_def3])
self.assertEqual(len(goal_definition.sequential_goals),3)
agent = Agent(init_state,
behavior_model,
dynamic_model,
execution_model,
agent_2d_shape,
agent_params,
goal_definition,
None)
world = World(param_server)
world.AddAgent(agent)
evaluator = EvaluatorGoalReached(agent.id)
world.AddEvaluator("success", evaluator)
# just drive with the single motion primitive should be successful
for _ in range(0,1000):
world.Step(0.2)
info = world.Evaluate()
if info["success"]:
break
self.assertEqual(info["success"], True)
self.assertAlmostEqual(agent.state[int(StateDefinition.X_POSITION)], 30, delta=0.5)
def test_gap_distance_front(self):
# World Definition
params = ParameterServer()
world = World(params)
gap = 10
# Model Definitions
behavior_model = BehaviorConstantAcceleration(params)
execution_model = ExecutionModelInterpolate(params)
dynamic_model = SingleTrackModel(params)
behavior_model2 = BehaviorConstantAcceleration(params)
execution_model2 = ExecutionModelInterpolate(params)
dynamic_model2 = SingleTrackModel(params)
# Map Definition
map_interface = MapInterface()
xodr_map = MakeXodrMapOneRoadTwoLanes()
map_interface.SetOpenDriveMap(xodr_map)
world.SetMap(map_interface)
agent_2d_shape = CarLimousine()
init_state = np.array([0, 13, -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), map_interface)
world.AddAgent(agent)
init_state2 = np.array([0, 13 + gap, -1.75, 0, 5])
agent2 = Agent(init_state2, behavior_model2, dynamic_model2,
execution_model2, agent_2d_shape, agent_params,
GoalDefinitionPolygon(goal_polygon), map_interface)
world.AddAgent(agent2)
world.Step(0.1)
evaluator = EvaluatorGapDistanceFront(agent.id)
world.AddEvaluator("gap", evaluator)
info = world.Evaluate()
self.assertAlmostEqual(info["gap"],
gap - agent_2d_shape.front_dist -
agent_2d_shape.rear_dist,
places=4)
def __init__(self, init_state, goal_polygon, map_interface, params):
behavior_model = BehaviorConstantAcceleration(params)
execution_model = ExecutionModelInterpolate(params)
dynamic_model = SingleTrackModel(params)
agent_2d_shape = CarLimousine()
agent_params = params.AddChild("agent")
super(TestAgent,
self).__init__(init_state, behavior_model, dynamic_model,
execution_model, agent_2d_shape, agent_params,
GoalDefinitionPolygon(goal_polygon),
map_interface)
def setUp(self):
param_server = ParameterServer()
world = World(param_server)
self.defaults = dict()
self.defaults["world"] = world
self.defaults["ego_behavior"] = BehaviorConstantAcceleration(
param_server)
self.defaults["ego_dynamic"] = SingleTrackModel(param_server)
self.defaults["ego_execution"] = ExecutionModelInterpolate(
param_server)
self.defaults["ego_shape"] = CarLimousine()
self.defaults["other_behavior"] = BehaviorConstantAcceleration(
param_server)
self.defaults["other_dynamic"] = SingleTrackModel(param_server)
self.defaults["other_execution"] = ExecutionModelInterpolate(
param_server)
self.defaults["other_shape"] = CarLimousine()
self.defaults["agent_params"] = param_server.addChild("agent")
self.defaults["default_vehicle_dynamics"] = [
1.7, -1.7, -1.69, -1.67, 0.2, -0.8, 0.1, 1.
]
def test_draw_agents(self):
params = ParameterServer()
behavior = BehaviorConstantAcceleration(params)
execution = ExecutionModelInterpolate(params)
dynamic = SingleTrackModel(params)
shape = Polygon2d([1.25, 1, 0], [
Point2d(0, 0),
Point2d(0, 2),
Point2d(4, 2),
Point2d(4, 0),
Point2d(0, 0)
])
shape2 = CarLimousine()
init_state = [0, 3, 2, 1]
init_state2 = [0, 0, 5, 4]
agent = Agent(init_state, behavior, dynamic, execution, shape,
params.AddChild("agent"))
agent2 = Agent(init_state2, behavior, dynamic, execution, shape2,
params.AddChild("agent"))
def test_one_agent_at_goal_state_limits_frenet(self):
param_server = ParameterServer()
# Model Definition
behavior_model = BehaviorConstantVelocity(param_server)
execution_model = ExecutionModelInterpolate(param_server)
dynamic_model = SingleTrackModel(param_server)
# Agent Definition
agent_2d_shape = CarLimousine()
agent_params = param_server.AddChild("agent1")
center_line = Line2d()
center_line.AddPoint(Point2d(5.0, 5.0))
center_line.AddPoint(Point2d(10.0, 10.0))
center_line.AddPoint(Point2d(20.0, 10.0))
max_lateral_dist = (0.4, 1)
max_orientation_diff = (0.08, 0.1)
velocity_range = (20.0, 25.0)
goal_definition = GoalDefinitionStateLimitsFrenet(
center_line, max_lateral_dist, max_orientation_diff,
velocity_range)
# not at goal x,y, others yes
agent1 = Agent(np.array([0, 6, 8, 3.14 / 4.0, velocity_range[0]]),
behavior_model, dynamic_model, execution_model,
agent_2d_shape, agent_params, goal_definition, None)
# at goal x,y and others
agent2 = Agent(np.array([0, 5.0, 5.5, 3.14 / 4.0, velocity_range[1]]),
behavior_model, dynamic_model, execution_model,
agent_2d_shape, agent_params, goal_definition, None)
# not at goal x,y,v yes but not orientation
agent3 = Agent(
np.array(
[0, 5, 5.5, 3.14 / 4.0 + max_orientation_diff[1] + 0.001,
20]), behavior_model, dynamic_model, execution_model,
agent_2d_shape, agent_params, goal_definition, None)
# not at goal x,y, orientation but not v
agent4 = Agent(
np.array([
0, 5, 4.5, 3.14 / 4 - max_orientation_diff[0],
velocity_range[0] - 0.01
]), behavior_model, dynamic_model, execution_model, agent_2d_shape,
agent_params, goal_definition, None)
# at goal x,y, at lateral limit
agent5 = Agent(
np.array([
0, 15, 10 - max_lateral_dist[0] + 0.05, 0, velocity_range[1]
]), behavior_model, dynamic_model, execution_model, agent_2d_shape,
agent_params, goal_definition, None)
# not at goal x,y slightly out of lateral limit
agent6 = Agent(
np.array([
0, 15, 10 + max_lateral_dist[0] + 0.05,
3.14 / 4 + max_orientation_diff[0], velocity_range[0]
]), behavior_model, dynamic_model, execution_model, agent_2d_shape,
agent_params, goal_definition, None)
# not at goal x,y,v yes but not orientation
agent7 = Agent(
np.array(
[0, 5, 5.5, 3.14 / 4.0 - max_orientation_diff[0] - 0.001,
20]), behavior_model, dynamic_model, execution_model,
agent_2d_shape, agent_params, goal_definition, None)
world = World(param_server)
world.AddAgent(agent1)
world.AddAgent(agent2)
world.AddAgent(agent3)
world.AddAgent(agent4)
world.AddAgent(agent5)
world.AddAgent(agent6)
world.AddAgent(agent7)
evaluator1 = EvaluatorGoalReached(agent1.id)
evaluator2 = EvaluatorGoalReached(agent2.id)
evaluator3 = EvaluatorGoalReached(agent3.id)
evaluator4 = EvaluatorGoalReached(agent4.id)
evaluator5 = EvaluatorGoalReached(agent5.id)
evaluator6 = EvaluatorGoalReached(agent6.id)
evaluator7 = EvaluatorGoalReached(agent7.id)
world.AddEvaluator("success1", evaluator1)
world.AddEvaluator("success2", evaluator2)
world.AddEvaluator("success3", evaluator3)
world.AddEvaluator("success4", evaluator4)
world.AddEvaluator("success5", evaluator5)
world.AddEvaluator("success6", evaluator6)
world.AddEvaluator("success7", evaluator7)
info = world.Evaluate()
self.assertEqual(info["success1"], False)
self.assertEqual(info["success2"], True)
self.assertEqual(info["success3"], False)
self.assertEqual(info["success4"], False)
self.assertEqual(info["success5"], True)
self.assertEqual(info["success6"], False)
self.assertEqual(info["success7"], False)