def test_relevant_agents(self):
params = ParameterServer()
map = "bark/runtime/tests/data/city_highway_straight.xodr"
params["EvaluatorRss"]["MapFilename"] = map
map_interface = EvaluatorRSSTests.load_map(map)
world = World(params)
world.SetMap(map_interface)
goal_polygon_1 = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
goal_polygon_1 = goal_polygon_1.Translate(Point2d(5.5, 120))
goal_polygon_2 = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
goal_polygon_2 = goal_polygon_2.Translate(Point2d(1.8, 120))
ego_state = np.array([0, 5.5, 10, np.pi / 2, 10])
other_1_state = np.array([0, 1.8, -10, np.pi / 2, 15])
other_2_state = np.array([0, 1.8, -120, np.pi / 2, 10])
ego = TestAgent(ego_state, goal_polygon_1, map_interface, params)
other_1 = TestAgent(other_1_state, goal_polygon_2, map_interface,
params)
other_2 = TestAgent(other_2_state, goal_polygon_2, map_interface,
params)
world.AddAgent(ego)
world.AddAgent(other_1)
world.AddAgent(other_2)
viewer = MPViewer(params=params, use_world_bounds=True)
viewer.drawWorld(world)
viewer.show(block=False)
evaluator_rss = EvaluatorRSS(ego.id, params)
responses = evaluator_rss.PairwiseEvaluate(world)
self.assertEqual(1, len(responses))
self.assertTrue(responses[other_1.id])
self.assertFalse(other_2.id in responses)
def test_lateral_highway_unsafe(self):
"""
Checking Lateral Responses (true means safe)
"""
params = ParameterServer()
map = "bark/runtime/tests/data/city_highway_straight.xodr"
params["EvaluatorRss"]["MapFilename"] = map
map_interface = EvaluatorRSSTests.load_map(map)
world = World(params)
world.SetMap(map_interface)
goal_polygon_1 = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
goal_polygon_1 = goal_polygon_1.Translate(Point2d(5.5, 120))
goal_polygon_2 = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
goal_polygon_2 = goal_polygon_2.Translate(Point2d(1.8, 120))
# Hard coded
ego_state = np.array([0, 5.0, 10, np.pi / 2, 10]) # straight north
other_state = np.array([0, 3.1, 0, np.pi / 2, 10]) # straight north
ego = TestAgent(ego_state, goal_polygon_1, map_interface, params)
other = TestAgent(other_state, goal_polygon_2, map_interface, params)
world.AddAgent(ego)
world.AddAgent(other)
world.UpdateAgentRTree()
viewer = MPViewer(params=params, use_world_bounds=True)
viewer.drawWorld(world)
viewer.show(block=False)
evaluator_rss = EvaluatorRSS(ego.id, params)
self.assertEqual(
False,
evaluator_rss.PairwiseDirectionalEvaluate(world)[other.id][1])
def test_lateral_same_direction(self):
map = "bark/runtime/tests/data/city_highway_straight.xodr"
map_interface = EvaluatorRSSTests.load_map(map)
world = self.defaults["world"].Copy()
world.SetMap(map_interface)
goal_polygon_1 = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
goal_polygon_1 = goal_polygon_1.Translate(Point2d(5.5, 120))
goal_polygon_2 = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
goal_polygon_2 = goal_polygon_2.Translate(Point2d(1.8, 120))
# Hard coded
ego_state = np.array([0, 5.5, 10, 0, 10])
other_state = np.array([0, 1.8, -10, 0, 15])
ego = Agent(ego_state, self.defaults["ego_behavior"],
self.defaults["ego_dynamic"],
self.defaults["ego_execution"], self.defaults["ego_shape"],
self.defaults["agent_params"],
GoalDefinitionPolygon(goal_polygon_1), map_interface)
other = Agent(other_state, self.defaults["other_behavior"],
self.defaults["other_dynamic"],
self.defaults["other_execution"],
self.defaults["other_shape"],
self.defaults["agent_params"],
GoalDefinitionPolygon(goal_polygon_2), map_interface)
world.AddAgent(ego)
world.AddAgent(other)
evaluator_rss = EvaluatorRss(ego.id, map,
self.defaults["default_vehicle_dynamics"])
for _ in range(7):
world.Step(1)
self.assertEqual(
True,
evaluator_rss.PairwiseDirectionalEvaluate(world)[other.id][1])
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_lane_change(self):
# World Definition
params = ParameterServer()
world = World(params)
# Model Definitions
behavior_model = BehaviorMobil(params)
execution_model = ExecutionModelInterpolate(params)
dynamic_model = SingleTrackModel(params)
behavior_model2 = BehaviorIDMLaneTracking(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()
agent_2d_shape = CarRectangle()
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), map_interface)
world.AddAgent(agent)
init_state2 = np.array([0, 15, -1.75, 0, 2])
agent2 = Agent(init_state2, behavior_model2, dynamic_model2,
execution_model2, agent_2d_shape, agent_params,
GoalDefinitionPolygon(goal_polygon), map_interface)
world.AddAgent(agent2)
# viewer
viewer = MPViewer(params=params, use_world_bounds=True)
# World Simulation
sim_step_time = params["simulation"]["step_time",
"Step-time in simulation", 0.05]
sim_real_time_factor = params["simulation"][
"real_time_factor", "execution in real-time or faster", 100]
# Draw map
for _ in range(0, 10):
viewer.clear()
world.Step(sim_step_time)
viewer.drawWorld(world)
viewer.show(block=False)
time.sleep(sim_step_time / sim_real_time_factor)
def goal(self, world):
goal_polygon = Polygon2d(
[-1000, -1000, -1000],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
return GoalDefinitionPolygon(goal_polygon)
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 ShapeFromTrack(track, wheelbase=2.7):
offset = wheelbase / 2.0
length = track.length
width = track.width
pose = [0.0, 0.0, 0.0]
points = [[length / 2.0 + offset, -width / 2.0],
[length / 2.0 + offset, width / 2.0],
[-length / 2.0 + offset, width / 2.0],
[-length / 2.0 + offset, -width / 2.0],
[length / 2.0 + offset, -width / 2.0]]
poly = Polygon2d(pose, points)
return poly
def ShapeFromTrack(track, wheelbase=2.7):
offset = wheelbase / 2.0
length = track.length
width = track.width
pose = [0.0, 0.0, 0.0]
p1 = [length / 2.0 + offset, -width / 2.0]
p2 = [length / 2.0 + offset, width / 2.0]
p3 = [-length / 2.0 + offset, width / 2.0]
p4 = [-length / 2.0 + offset, -width / 2.0]
points = [p1, p2, p3, p4, p1]
poly = Polygon2d(pose, points)
return poly
def GoalDefinitionFromTrack(track, end):
states = list(dict_utils.get_item_iterator(track.motion_states))
motion_state = states[-1][1]
bark_state = BarkStateFromMotionState(motion_state)
goal_polygon = Polygon2d(
np.array([0.0, 0.0, 0.0]),
[Point2d(-1.5, 0),
Point2d(-1.5, 8),
Point2d(1.5, 8),
Point2d(1.5, 0)])
goal_polygon = goal_polygon.Translate(
Point2d(bark_state[0, int(StateDefinition.X_POSITION)],
bark_state[0, int(StateDefinition.Y_POSITION)]))
goal_definition = GoalDefinitionPolygon(goal_polygon)
return goal_definition
def test_write_params_agent(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)
])
init_state = np.zeros(4)
agent = Agent(init_state, behavior, dynamic, execution, shape,
params.AddChild("agent"))
params.Save("written_agents_param_test.json")
def test_longitude_ego_follow_other(self):
map = "bark/runtime/tests/data/city_highway_straight.xodr"
map_interface = EvaluatorRSSTests.load_map(map)
world = self.defaults["world"].Copy()
world.SetMap(map_interface)
goal_polygon = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
goal_polygon = goal_polygon.Translate(Point2d(1.8, 120))
# The safety distance seems more conservative than in the paper
# Hard coded
ego_state = np.array([0, 1.8, -114.9, 0, 10])
other_state = np.array([0, 1.8, -72.95, 0, 7])
ego = Agent(ego_state, self.defaults["ego_behavior"],
self.defaults["ego_dynamic"],
self.defaults["ego_execution"], self.defaults["ego_shape"],
self.defaults["agent_params"],
GoalDefinitionPolygon(goal_polygon), map_interface)
other = Agent(other_state, self.defaults["other_behavior"],
self.defaults["other_dynamic"],
self.defaults["other_execution"],
self.defaults["other_shape"],
self.defaults["agent_params"],
GoalDefinitionPolygon(goal_polygon), map_interface)
world.AddAgent(ego)
world.AddAgent(other)
evaluator_rss = EvaluatorRss(ego.id,
map,
self.defaults["default_vehicle_dynamics"],
checking_relevent_range=1.5)
world.Step(0.01)
self.assertEqual(
True,
evaluator_rss.PairwiseDirectionalEvaluate(world)[other.id][0])
world.Step(0.01)
self.assertEqual(
False,
evaluator_rss.PairwiseDirectionalEvaluate(world)[other.id][0])
def test_lateral_merging(self):
map = "bark/runtime/tests/data/DR_DEU_Merging_MT_v01_centered.xodr"
map_interface = EvaluatorRSSTests.load_map(map)
world = self.defaults["world"].Copy()
world.SetMap(map_interface)
goal_polygon = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
goal_polygon = goal_polygon.Translate(Point2d(-16, 108))
# Hard coded
ego_state = np.array([0, 68, 108, 0, 5])
other_state = np.array([0, 64, 104, 0, 5])
ego = Agent(ego_state, self.defaults["ego_behavior"],
self.defaults["ego_dynamic"],
self.defaults["ego_execution"], self.defaults["ego_shape"],
self.defaults["agent_params"],
GoalDefinitionPolygon(goal_polygon), map_interface)
other = Agent(other_state, self.defaults["other_behavior"],
self.defaults["other_dynamic"],
self.defaults["other_execution"],
self.defaults["other_shape"],
self.defaults["agent_params"],
GoalDefinitionPolygon(goal_polygon), map_interface)
world.AddAgent(ego)
world.AddAgent(other)
evaluator_rss = EvaluatorRss(ego.id, map,
self.defaults["default_vehicle_dynamics"])
world.Step(1)
self.assertEqual(
True,
evaluator_rss.PairwiseDirectionalEvaluate(world)[other.id][1])
world.Step(1)
self.assertEqual(
False,
evaluator_rss.PairwiseDirectionalEvaluate(world)[other.id][1])
def test_longitude_highway_unsafe(self):
"""
Checking Longitudinal Responses (true means safe)
"""
params = ParameterServer()
map = "bark/runtime/tests/data/city_highway_straight.xodr"
params["EvaluatorRss"]["MapFilename"] = map
map_interface = EvaluatorRSSTests.load_map(map)
world = World(params)
world.SetMap(map_interface)
goal_polygon = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
goal_polygon = goal_polygon.Translate(Point2d(1.8, 120))
# The safety distance seems more conservative than in the paper
# Hard coded
ego_state = np.array([0, 1.8, -60.0, np.pi / 2, 10])
other_state = np.array([0, 1.8, -68.0, np.pi / 2, 10])
ego = TestAgent(ego_state, goal_polygon, map_interface, params)
other = TestAgent(other_state, goal_polygon, map_interface, params)
world.AddAgent(ego)
world.AddAgent(other)
world.UpdateAgentRTree()
viewer = MPViewer(params=params, use_world_bounds=True)
viewer.drawWorld(world)
viewer.show(block=False)
evaluator_rss = EvaluatorRSS(ego.id, params)
pw_directional_evaluation_return = evaluator_rss.PairwiseDirectionalEvaluate(
world)
self.assertEqual(False, pw_directional_evaluation_return[other.id][0])
def test_lateral_merging_safe(self):
"""
Checking Lateral Responses (true means safe)
"""
params = ParameterServer()
map = "bark/runtime/tests/data/DR_DEU_Merging_MT_v01_centered.xodr"
params["EvaluatorRss"]["MapFilename"] = map
map_interface = EvaluatorRSSTests.load_map(map)
world = World(params)
world.SetMap(map_interface)
goal_polygon = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
goal_polygon = goal_polygon.Translate(Point2d(-15.4, 108.6))
# Hard coded
ego_state = np.array([0, 68.1, 108, -np.pi, 5])
other_state = np.array([0, 64.1, 105, -np.pi, 5])
ego = TestAgent(ego_state, goal_polygon, map_interface, params)
other = TestAgent(other_state, goal_polygon, map_interface, params)
world.AddAgent(ego)
world.AddAgent(other)
world.UpdateAgentRTree()
viewer = MPViewer(params=params, use_world_bounds=True)
viewer.drawWorld(world)
viewer.show(block=False)
evaluator_rss = EvaluatorRSS(ego.id, params)
world.AddEvaluator("rss", evaluator_rss)
pw_directional_evaluation_return = evaluator_rss.PairwiseDirectionalEvaluate(
world)
self.assertEqual(True, pw_directional_evaluation_return[other.id][1])
def GoalDefinitionFromTrack(track, end, xy_offset):
goal_size = 12.0
states = list(dict_utils.get_item_iterator(track.motion_states))
# Goal position is spatial position of last state
motion_state = states[-1][1]
bark_state = BarkStateFromMotionState(motion_state, xy_offset=xy_offset)
goal_polygon = Polygon2d(np.array([0.5 * goal_size, 0.5 * goal_size, 0.0]),
[
Point2d(0.0, 0.0),
Point2d(goal_size, 0.0),
Point2d(goal_size, goal_size),
Point2d(0.0, goal_size),
Point2d(0.0, 0.0)
])
goal_point = Point2d(
bark_state[0, int(StateDefinition.X_POSITION)] - 0.5 * goal_size,
bark_state[0, int(StateDefinition.Y_POSITION)] - 0.5 * goal_size)
goal_polygon = goal_polygon.Translate(goal_point)
goal_definition = GoalDefinitionPolygon(goal_polygon)
return goal_definition
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_world(self):
# create agent
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)
])
init_state = np.array([0, 0, 0, 0, 5])
agent = Agent(init_state, behavior, dynamic, execution, shape,
params.AddChild("agent"))
road_map = OpenDriveMap()
newXodrRoad = XodrRoad()
newXodrRoad.id = 1
newXodrRoad.name = "Autobahn A9"
newPlanView = PlanView()
newPlanView.AddLine(Point2d(0, 0), 1.57079632679, 10)
newXodrRoad.plan_view = newPlanView
line = newXodrRoad.plan_view.GetReferenceLine().ToArray()
p = Point2d(line[-1][0], line[-1][1])
newXodrRoad.plan_view.AddSpiral(p, 1.57079632679, 50.0, 0.0, 0.3, 0.4)
line = newXodrRoad.plan_view.GetReferenceLine()
lane_section = XodrLaneSection(0)
lane = XodrLane()
lane.line = line
lane_section.AddLane(lane)
newXodrRoad.AddLaneSection(lane_section)
road_map.AddRoad(newXodrRoad)
r = Roadgraph()
map_interface = MapInterface()
map_interface.SetOpenDriveMap(road_map)
map_interface.SetRoadgraph(r)
world = World(params)
world.AddAgent(agent)
def test_relevent_agents(self):
map = "bark/runtime/tests/data/city_highway_straight.xodr"
map_interface = EvaluatorRSSTests.load_map(map)
world = self.defaults["world"].Copy()
world.SetMap(map_interface)
goal_polygon_1 = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
goal_polygon_1 = goal_polygon_1.Translate(Point2d(5.5, 120))
goal_polygon_2 = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
goal_polygon_2 = goal_polygon_2.Translate(Point2d(1.8, 120))
# Hard coded
ego_state = np.array([0, 5.5, 10, 0, 10])
other_1_state = np.array([0, 1.8, -10, 0, 15])
other_2_state = np.array([0, 1.8, -120, 0, 10])
ego = Agent(ego_state, self.defaults["ego_behavior"],
self.defaults["ego_dynamic"],
self.defaults["ego_execution"], self.defaults["ego_shape"],
self.defaults["agent_params"],
GoalDefinitionPolygon(goal_polygon_1), map_interface)
other_1 = Agent(other_1_state, self.defaults["other_behavior"],
self.defaults["other_dynamic"],
self.defaults["other_execution"],
self.defaults["other_shape"],
self.defaults["agent_params"],
GoalDefinitionPolygon(goal_polygon_2), map_interface)
other_2_behavior = BehaviorConstantAcceleration(
self.defaults["agent_params"])
other_2_dynamic = SingleTrackModel(self.defaults["agent_params"])
other_2_execution = ExecutionModelInterpolate(
self.defaults["agent_params"])
other_2 = Agent(other_2_state, other_2_behavior, other_2_dynamic,
other_2_execution, self.defaults["other_shape"],
self.defaults["agent_params"],
GoalDefinitionPolygon(goal_polygon_2), map_interface)
world.AddAgent(ego)
world.AddAgent(other_1)
world.AddAgent(other_2)
evaluator_rss = EvaluatorRss(ego.id, map,
self.defaults["default_vehicle_dynamics"])
world.Step(1)
responses = evaluator_rss.PairwiseEvaluate(world)
self.assertEqual(1, len(responses)) # Test GetRelevantAgents
self.assertTrue(responses[other_1.id])
self.assertFalse(other_2.id in responses)
# end_point_list = [Point2d(153, 172), Point2d(172, 187), Point2d(188, 168)]
# comb = list(itertools.product(start_point, end_point_list))
# comb_all = comb_all + comb
# # starting on the top
# start_point = [Point2d(168, 187)]
# end_point_list = [Point2d(153, 172), Point2d(168, 153), Point2d(188, 168)]
# comb = list(itertools.product(start_point, end_point_list))
# comb_all = comb_all + comb
print("comb_all", comb_all)
for cnt, (start_p, end_p) in enumerate(comb_all):
polygon = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
start_polygon = polygon.Translate(start_p)
goal_polygon = polygon.Translate(end_p)
rc = map_interface.GenerateRoadCorridor(start_p, goal_polygon)
if rc:
roads = rc.roads
road_ids = list(roads.keys())
print(road_ids, rc.road_ids)
viewer.drawWorld(world)
viewer.drawRoadCorridor(rc, "blue")
viewer.saveFig(output_dir + "/" + "roadcorridor_" + str(cnt) + ".png")
viewer.show()
viewer.clear()
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)