def test_find_lane(self):
xodr_parser = XodrParser("modules/runtime/tests/data/urban_road.xodr")
params = ParameterServer()
world = World(params)
map_interface = MapInterface()
map_interface.SetOpenDriveMap(xodr_parser.map)
world.SetMap(map_interface)
lane_sw = map_interface.FindLane(Point2d(46, 180))
assert lane_sw.lane_type == XodrLaneType.sidewalk
lane_rl = map_interface.FindLane(Point2d(52, 130))
assert lane_rl.lane_type == XodrLaneType.driving
lane_no_lane = map_interface.FindLane(Point2d(120, 140))
assert lane_no_lane == None
xodr_parser = XodrParser(
"modules/runtime/tests/data/city_highway_straight.xodr")
np.set_printoptions(precision=8)
params = ParameterServer()
world = World(params)
map_interface = MapInterface()
map_interface.SetOpenDriveMap(xodr_parser.map)
world.SetMap(map_interface)
point = Point2d(5114, 5072)
viewer = MPViewer(params=params, use_world_bounds=True)
viewer.drawWorld(world)
viewer.drawPoint2d(point, 'red', 1.0)
viewer.show(block=True)
time.sleep(0.1)
lane_sw = map_interface.FindLane(point)
self.assertIsNotNone(lane_sw, "This point is clearly on a lane!")
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_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_definition_from_track(track, end):
states = list(dict_utils.get_item_iterator(track.motion_states))
motion_state = states[-1][1]
bark_state = bark_state_from_motion_state(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 create_cpp_plan_view(self, plan_view, header):
new_plan_view = PlanView()
# create plan view..
for geometry in plan_view["geometries"]:
starting_point = Point2d(float(geometry["x"]),
float(geometry["y"]))
if geometry["geometry"]["type"] == "line":
new_plan_view.AddLine(starting_point, float(geometry["hdg"]),
float(geometry["length"]))
if geometry["geometry"]["type"] == "arc":
new_plan_view.AddArc(starting_point, float(geometry["hdg"]),
float(geometry["length"]),
float(geometry["geometry"]["curvature"]),
0.25) # TODO: s_inc
if geometry["geometry"]["type"] == "spiral":
new_plan_view.AddSpiral(
starting_point, float(geometry["hdg"]),
float(geometry["length"]),
float(geometry["geometry"]["curv_start"]),
float(geometry["geometry"]["curv_end"]), 2) # TODO: s_inc
# now use header/ offset to modify plan view
if "offset" in header:
off_x = header["offset"]["x"]
off_y = header["offset"]["y"]
off_hdg = header["offset"]["hdg"]
logger.info("Transforming PlanView with given offset",
header["offset"])
new_plan_view.ApplyOffsetTransform(off_x, off_y, off_hdg)
return new_plan_view
def test_road(self):
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()
# Spiral
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().ToArray()
def test_write_params_agent(self):
params = ParameterServer()
behavior = BehaviorConstantVelocity(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_road(self):
newRoad = Road()
newRoad.id = 1
newRoad.name = "Autobahn A9"
newPlanView = PlanView()
newPlanView.add_line(Point2d(0, 0), 1.57079632679, 10)
newRoad.plan_view = newPlanView
line = newRoad.plan_view.get_reference_line().toArray()
# Spiral
p = Point2d(line[-1][0], line[-1][1])
newRoad.plan_view.add_spiral(p, 1.57079632679, 50.0, 0.0, 0.3, 0.4)
line = newRoad.plan_view.get_reference_line().toArray()
def test_between_lanes(self):
xodr_parser = XodrParser(
"modules/runtime/tests/data/city_highway_straight.xodr")
np.set_printoptions(precision=8)
params = ParameterServer()
world = World(params)
map_interface = MapInterface()
map_interface.SetOpenDriveMap(xodr_parser.map)
world.SetMap(map_interface)
# Simple test
point_close = Point2d(5114.68262, 5086.44971)
lane_sw = map_interface.FindLane(point_close)
self.assertIsNotNone(
lane_sw,
"This point is still in the left lane! XodrLane boundary is 5114.683"
)
switched_lane = False
lng_coord = 5086.44971
i = 5114.5
lane_sw = map_interface.FindLane(Point2d(i, lng_coord))
assert lane_sw != None
prev = lane_sw.lane_id
prev_i = i
while (i < 5117.5):
lane_sw = map_interface.FindLane(Point2d(i, lng_coord))
self.assertIsNotNone(
lane_sw,
"Should always be on at least one lane! Currently at ({}, {})".
format(i, lng_coord))
if prev != lane_sw.lane_id:
# print(prev)
# print(prev_i)
# print(lane_sw.lane_id)
# print(i)
self.assertFalse(switched_lane,
"XodrLane switch should only happens once!")
switched_lane = True
prev_i = i
prev = lane_sw.lane_id
i = i + 0.01
self.assertTrue(switched_lane,
"Eventually should have switched lanes!")
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_draw_agents(self):
params = ParameterServer()
behavior = BehaviorConstantVelocity(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_Crossing8Course(self):
xodr_parser = XodrParser(
"modules/runtime/tests/data/Crossing8Course.xodr")
params = ParameterServer()
world = World(params)
map_interface = MapInterface()
map_interface.SetOpenDriveMap(xodr_parser.map)
world.SetMap(map_interface)
start_point = Point2d(0, -11)
lanes_near_start = map_interface.find_nearest_lanes(start_point, 1)
assert (len(lanes_near_start) == 1)
goal_point = Point2d(-191.789, -50.1725)
lanes_near_goal = map_interface.find_nearest_lanes(goal_point, 1)
assert (len(lanes_near_goal) == 1)
time.sleep(
2
) # if this is not here, the second unit test is not executed (maybe parsing takes too long?)
def __find_first_ts_on_map__(self, id_ego):
traj = trajectory_from_track(self._track_dict[id_ego])
for state in traj:
point_agent = Point2d(state[1], state[2])
lane_list = self._map_interface.find_nearest_lanes(point_agent, 3)
for lane in lane_list:
polygon = self._map_interface.GetRoadgraph(
).GetLanePolygonForLaneId(lane.lane_id)
if Collide(polygon, point_agent):
time_ego_first = state[0] * 1e3 # use timestamp in ms
return time_ego_first
return None
def test_line(self):
pv = PlanView()
# Line
pv.AddLine(Point2d(0, 0), 1.57079632679, 10)
line = pv.GetReferenceLine().ToArray()
# Spiral
p = Point2d(line[-1][0], line[-1][1])
pv.AddSpiral(p, 1.57079632679, 50.0, 0.0, 0.3, 0.4)
line = pv.GetReferenceLine().ToArray()
offset = XodrLaneOffset(1.5, 0, 0, 0)
lane_width = XodrLaneWidth(0.0, 59.9, offset)
lane = XodrLane.CreateLaneFromLaneWidth(-1, pv.GetReferenceLine(),
lane_width, 0.5)
print(lane)
lane = XodrLane.CreateLaneFromLaneWidth(1, pv.GetReferenceLine(),
lane_width, 0.5)
print(lane)
def test_line(self):
pv = PlanView()
# Line
pv.add_line(Point2d(0, 0), 1.57079632679, 10)
line = pv.get_reference_line().toArray()
# Spiral
p = Point2d(line[-1][0], line[-1][1])
pv.add_spiral(p, 1.57079632679, 50.0, 0.0, 0.3, 0.4)
line = pv.get_reference_line().toArray()
offset = LaneOffset(1.5, 0, 0, 0)
lane_width = LaneWidth(0.0, 59.9, offset)
lane = Lane.create_lane_from_lane_width(-1,
pv.get_reference_line(), lane_width, 0.5)
print(lane)
lane = Lane.create_lane_from_lane_width(
1, pv.get_reference_line(), lane_width, 0.5)
print(lane)
def test_two_roads_one_lane(self):
params = ParameterServer()
world = World(params)
xodr_map = MakeXodrMapOneRoadTwoLanes()
map_interface = MapInterface()
map_interface.SetOpenDriveMap(xodr_map)
world.SetMap(map_interface)
start_point = Point2d(0, -11)
lanes_near_start = map_interface.find_nearest_lanes(start_point, 1)
assert(len(lanes_near_start) == 1)
goal_point = Point2d(-191.789, -50.1725)
lanes_near_goal = map_interface.find_nearest_lanes(goal_point, 1)
assert(len(lanes_near_goal) == 1)
viewer = MPViewer(params=params, use_world_bounds=True)
viewer.drawWorld(world)
time.sleep(2) # if this is not here, the second unit test is not executed (maybe parsing takes too long?)
def test_world(self):
# create agent
params = ParameterServer()
behavior = BehaviorConstantVelocity(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)
behavior_model2 = BehaviorConstantVelocity(param_server)
execution_model2 = ExecutionModelInterpolate(param_server)
dynamic_model2 = SingleTrackModel(param_server)
# Map Definition
xodr_parser = XodrParser("modules/runtime/tests/data/Crossing8Course.xodr")
map_interface = MapInterface()
map_interface.SetOpenDriveMap(xodr_parser.map)
world.SetMap(map_interface)
# Agent Definition
agent_2d_shape = CarLimousine()
init_state = np.array([0, -15, -13, 3.14 * 3.0 / 4.0, 50 / 3.6])
goal_polygon = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
goal_polygon = goal_polygon.Translate(Point2d(-63, -61))
agent_params = param_server.addChild("agent1")
agent1 = Agent(init_state, behavior_model, dynamic_model,
execution_model, agent_2d_shape, agent_params,
GoalDefinitionPolygon(goal_polygon), map_interface)
world.AddAgent(agent1)
agent_2d_shape2 = CarLimousine()
init_state2 = np.array([0, -15, -13, 3.14 * 3.0 / 4.0, 5.2])
agent_params2 = param_server.addChild("agent2")
agent2 = Agent(init_state2, behavior_model2, dynamic_model2, execution_model2,
agent_2d_shape2, agent_params2,
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)
# # plot plan_view # road_id = roadgraph.GetRoadForLaneId(lane_id) # road = map_interface.GetOpenDriveMap().GetRoad(road_id) # plan_view_reference = road.plan_view.GetReferenceLine() # # plot polygon with center line # viewer.drawWorld(world) # color = list(np.random.choice(range(256), size=3)/256) # viewer.drawPolygon2d(lane_polygon, color, 1.0) # viewer.drawLine2d(plan_view_reference, color="red") # viewer.saveFig(output_dir + "/" + "roadgraph_laneid_" + str(lane_id) + ".png") # viewer.show() # viewer.clear() comb_all = [] start_point = [Point2d(-115+1117, -158+1107)] end_point_list = [Point2d(27+1117, -158+1107)] comb = list(itertools.product(start_point, end_point_list)) comb_all = comb_all + comb # OpenDrive8 # comb_all = [] # start_point = [Point2d(1003, 1007)] # end_point_list = [Point2d(892, 1008)] # comb = list(itertools.product(start_point, end_point_list)) # comb_all = comb_all + comb # starting on the left # three_way_plain # comb_all = []
# plot plan_view
road_id = roadgraph.GetRoadForLaneId(lane_id)
road = map_interface.GetOpenDriveMap().GetRoad(road_id)
plan_view_reference = road.plan_view.GetReferenceLine()
# plot polygon with center line
viewer.drawWorld(world)
color = list(np.random.choice(range(256), size=3) / 256)
viewer.drawPolygon2d(lane_polygon, color, 1.0)
viewer.drawLine2d(plan_view_reference, color="red")
viewer.saveFig(output_dir + "/" + "roadgraph_laneid_" + str(lane_id) +
".png")
viewer.show()
viewer.clear()
comb_all = []
start_point = [Point2d(1004, 1003), Point2d(1004, 1006)]
end_point_list = [Point2d(886, 1008)]
comb = list(itertools.product(start_point, end_point_list))
comb_all = comb_all + comb
# OpenDrive8
# comb_all = []
# start_point = [Point2d(1003, 1007)]
# end_point_list = [Point2d(892, 1008)]
# comb = list(itertools.product(start_point, end_point_list))
# comb_all = comb_all + comb
# starting on the left
# three_way_plain
# comb_all = []
# start_point = [Point2d(-30, -2)]
def test_python_behavior_model(self):
# World Definition
scenario_param_file = "macro_actions_test.json" # must be within examples params folder
params = ParameterServer(filename=os.path.join(
"modules/world/tests/params/", scenario_param_file))
world = World(params)
# Define two behavior models one python one standard c++ model
behavior_model = PythonDistanceBehavior(params)
execution_model = ExecutionModelInterpolate(params)
dynamic_model = SingleTrackModel(params)
behavior_model2 = BehaviorConstantVelocity(params)
execution_model2 = ExecutionModelInterpolate(params)
dynamic_model2 = SingleTrackModel(params)
# Define the map interface and load a testing map
map_interface = MapInterface()
xodr_map = MakeXodrMapOneRoadTwoLanes()
map_interface.SetOpenDriveMap(xodr_map)
world.SetMap(map_interface)
# Define the agent shapes
agent_2d_shape = CarRectangle()
init_state = np.array([0, 3, -5.25, 0, 20])
# Define the goal definition for agents
center_line = Line2d()
center_line.AddPoint(Point2d(0.0, -1.75))
center_line.AddPoint(Point2d(100.0, -1.75))
max_lateral_dist = (0.4, 0.5)
max_orientation_diff = (0.08, 0.1)
velocity_range = (5.0, 20.0)
goal_definition = GoalDefinitionStateLimitsFrenet(
center_line, max_lateral_dist, max_orientation_diff,
velocity_range)
# define two agents with the different behavior models
agent_params = params.AddChild("agent1")
agent = Agent(init_state, behavior_model, dynamic_model,
execution_model, agent_2d_shape, agent_params,
goal_definition, map_interface)
world.AddAgent(agent)
init_state2 = np.array([0, 25, -5.25, 0, 15])
agent2 = Agent(init_state2, behavior_model2, dynamic_model2,
execution_model2, agent_2d_shape, agent_params,
goal_definition, 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", 0.2]
sim_real_time_factor = params["simulation"][
"real_time_factor", "execution in real-time or faster", 1]
# Draw map
video_renderer = VideoRenderer(renderer=viewer,
world_step_time=sim_step_time)
for _ in range(0, 20):
world.Step(sim_step_time)
viewer.clear()
video_renderer.drawWorld(world)
video_renderer.drawGoalDefinition(goal_definition, "red", 0.5,
"red")
time.sleep(sim_step_time / sim_real_time_factor)
video_renderer.export_video(filename="./test_video_intermediate",
remove_image_dir=True)
execution_model = ExecutionModelInterpolate(param_server)
dynamic_model = SingleTrackModel(param_server)
# Map Definition
xodr_parser = XodrParser("modules/runtime/tests/data/Crossing8Course.xodr")
map_interface = MapInterface()
map_interface.SetOpenDriveMap(xodr_parser.map)
world.SetMap(map_interface)
# Agent Definition
agent_2d_shape = CarLimousine()
init_state = np.array([0, -15, -13, 3.14 * 5.0 / 4.0, 10 / 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,
GoalDefinitionPolygon(goal_polygon), # goal_lane_id
map_interface)
world.AddAgent(agent)
self.cosimulation_viewer.show()
sim = Cosimulation()
try:
sim.launch_carla_server()
sim.connect_carla_server()
sim.spawn_npc_agents(10)
# [TIME_POSITION, X_POSITION, Y_POSITION, THETA_POSITION, VEL_POSITION, ...]
ego_initial = np.array([0, 90, -197, 0, 0])
goal_polygon = Polygon2d(
[0, 0, 0],
[Point2d(-1, -1),
Point2d(-1, 1),
Point2d(1, 1),
Point2d(1, -1)])
goal_polygon = goal_polygon.Translate(Point2d(2, -300))
bp_lib = sim.carla_client.get_blueprint_library()
bp = bp_lib.filter("vehicle.dodge_charger.police")[0]
tf = sim.carla_client.generate_tranformation(x=ego_initial[1],
y=ego_initial[2],
z=0.3,
pitch=0,
yaw=ego_initial[3],
roll=0)
carla_ego_id = sim.carla_client.spawn_actor(bp, tf)
def test_uct_single_agent(self):
try:
from bark.models.behavior import BehaviorUCTSingleAgentMacroActions
except:
print("Rerun with --define planner_uct=true")
return
# World Definition
scenario_param_file = "macro_actions_test.json" # must be within examples params folder
params = ParameterServer(filename=os.path.join(
"modules/world/tests/params/", scenario_param_file))
world = World(params)
# Model Definitions
behavior_model = BehaviorUCTSingleAgentMacroActions(params)
execution_model = ExecutionModelInterpolate(params)
dynamic_model = SingleTrackModel(params)
behavior_model2 = BehaviorConstantVelocity(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, -5.25, 0, 20])
agent_params = params.AddChild("agent1")
# goal_polygon = Polygon2d(
# [1, 1, 0], [Point2d(0, 0), Point2d(0, 2), Point2d(2, 2), Point2d(2, 0)])
# goal_definition = GoalDefinitionPolygon(goal_polygon)
# goal_polygon = goal_polygon.Translate(Point2d(90, -2))
center_line = Line2d()
center_line.AddPoint(Point2d(0.0, -1.75))
center_line.AddPoint(Point2d(100.0, -1.75))
max_lateral_dist = (0.4, 0.5)
max_orientation_diff = (0.08, 0.1)
velocity_range = (5.0, 20.0)
goal_definition = GoalDefinitionStateLimitsFrenet(
center_line, max_lateral_dist, max_orientation_diff,
velocity_range)
agent = Agent(init_state, behavior_model, dynamic_model,
execution_model, agent_2d_shape, agent_params,
goal_definition, map_interface)
world.AddAgent(agent)
init_state2 = np.array([0, 25, -5.25, 0, 0])
agent2 = Agent(init_state2, behavior_model2, dynamic_model2,
execution_model2, agent_2d_shape, agent_params,
goal_definition, 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", 0.2]
sim_real_time_factor = params["simulation"][
"real_time_factor", "execution in real-time or faster", 1]
# Draw map
video_renderer = VideoRenderer(renderer=viewer,
world_step_time=sim_step_time)
for _ in range(0, 5):
world.Step(sim_step_time)
viewer.clear()
video_renderer.drawWorld(world)
video_renderer.drawGoalDefinition(goal_definition)
time.sleep(sim_step_time / sim_real_time_factor)
video_renderer.export_video(filename="./test_video_intermediate",
remove_image_dir=True)
def test_evaluator_drivable_area(self):
# World Definition
params = ParameterServer()
world = World(params)
# Model Definitions
behavior_model = BehaviorConstantVelocity(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)
self.cosimulation_viewer.show()
sim = Cosimulation()
try:
sim.launch_carla_server()
sim.connect_carla_server()
sim.spawn_npc_agents(1)
# [TIME_POSITION, X_POSITION, Y_POSITION, THETA_POSITION, VEL_POSITION, ...]
ego_initial = np.array([0, 200, 0, 0, 0])
goal_polygon = Polygon2d(
[0, 0, 0],
[Point2d(-2, -2),
Point2d(-2, 2),
Point2d(2, 2),
Point2d(2, -2)])
goal_polygon = goal_polygon.Translate(Point2d(0, 0))
bp_lib = sim.carla_client.get_blueprint_library()
bp = bp_lib.filter("vehicle.dodge_charger.police")[0]
tf = sim.carla_client.generate_tranformation(x=ego_initial[1],
y=ego_initial[2],
z=0.3,
pitch=0,
yaw=math.degrees(
ego_initial[3]),
roll=0)