scenario_param_file ="highway_merging.json" # must be within examples params folder
param_server = ParameterServer(filename= os.path.join("examples/params/",scenario_param_file))
scenario_generation = UniformVehicleDistribution(num_scenarios=10, random_seed=0, params=param_server)
viewer = MPViewer(params=param_server, x_range=[5060, 5160], y_range=[5070,5150])
sim_step_time = param_server["simulation"]["step_time",
"Step-time used in simulation",
0.2]
sim_real_time_factor = param_server["simulation"]["real_time_factor",
"execution in real-time or faster", 1]
scenario, idx = scenario_generation.get_next_scenario()
# Rendering WITHOUT intermediate steps
video_renderer = VideoRenderer(renderer=viewer, world_step_time=sim_step_time)
for _ in range(0, 1):
scenario, idx = scenario_generation.get_next_scenario()
world_state = scenario.get_world_state()
for _ in range(0, 5):
video_renderer.drawWorld(world_state, scenario._eval_agent_ids, idx)
world_state.step(sim_step_time)
video_renderer.export_video(filename="examples/scenarios/test_video_step")
# Rendering WITH intermediate steps
video_renderer = VideoRenderer(renderer=viewer, world_step_time=sim_step_time, render_intermediate_steps=10)
world_state = scenario.get_world_state()
for _ in range(0, 10):
world_state.do_planning(sim_step_time)
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)
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)
# set you json config that contains a map and matching tracks.
param_server = ParameterServer(
filename=os.path.join("examples/params/interaction_example.json"))
scenario_generation = InteractionDatasetScenarioGeneration(num_scenarios=1,
random_seed=0,
params=param_server)
viewer = MPViewer(params=param_server, use_world_bounds=True)
sim_step_time = param_server["simulation"]["step_time",
"Step-time used in simulation", 0.2]
sim_real_time_factor = param_server["simulation"][
"real_time_factor", "execution in real-time or faster", 1]
scenario = scenario_generation.create_scenarios(param_server, 1)[0]
world_state = scenario.GetWorldState()
sim_time_steps = param_server["simulation"]["simulation_time_steps",
"Number of time steps to simulate",
50]
video_renderer = VideoRenderer(renderer=viewer, world_step_time=sim_step_time)
for _ in range(0, sim_time_steps):
world_state.DoPlanning(sim_step_time)
viewer.clear()
video_renderer.drawWorld(world_state)
world_state.DoExecution(sim_step_time)
# video_renderer.export_video(filename="./interaction_dataset", remove_image_dir=True)
filename=os.path.join("examples/params/", scenario_param_file))
scenario_generation = UniformVehicleDistribution(num_scenarios=10,
random_seed=0,
params=param_server)
viewer = MPViewer(params=param_server,
x_range=[5060, 5160],
y_range=[5070, 5150])
sim_step_time = param_server["simulation"]["step_time",
"Step-time used in simulation", 0.2]
sim_real_time_factor = param_server["simulation"][
"real_time_factor", "execution in real-time or faster", 1]
scenario, idx = scenario_generation.get_next_scenario()
# Rendering WITHOUT intermediate steps
video_renderer = VideoRenderer(renderer=viewer, world_step_time=sim_step_time)
env = Runtime(0.2, video_renderer, scenario_generation, render=True)
env.reset()
for _ in range(0, 5):
env.step()
video_renderer.export_video(filename="examples/scenarios/test_video_step")
# Rendering WITH intermediate steps
video_renderer = VideoRenderer(renderer=viewer,
world_step_time=sim_step_time,
render_intermediate_steps=10)
env = Runtime(0.2, video_renderer, scenario_generation, render=True)
env.reset()
for _ in range(0, 5):
env.step()
scenario_generation = UniformVehicleDistribution(num_scenarios=1,
random_seed=0,
params=param_server)
viewer = MPViewer(params=param_server,
x_range=[5060, 5160],
y_range=[5070, 5150])
sim_step_time = param_server["simulation"]["step_time",
"Step-time used in simulation", 0.2]
sim_real_time_factor = param_server["simulation"][
"real_time_factor", "execution in real-time or faster", 1]
scenario, idx = scenario_generation.get_next_scenario()
world_state = scenario.get_world_state()
world_state.agents[scenario._eval_agent_ids[
0]].behavior_model = BehaviorUCTSingleAgent(param_server)
param_server.save("examples/params/mcts_params_written.json")
# world_state.agents[scenario._eval_agent_ids[0]].behavior_model
video_renderer = VideoRenderer(renderer=viewer, world_step_time=sim_step_time)
for _ in range(0, 40): # run scenario for 100 steps
world_state.do_planning(sim_step_time)
video_renderer.drawWorld(world_state, scenario._eval_agent_ids)
world_state.do_execution(sim_step_time)
video_renderer.export_video(
filename="examples/scenarios/test_video_intermediate",
remove_image_dir=True)