Beispiel #1
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)
Beispiel #2
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    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)
Beispiel #3
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def make_initial_world(primitives):
    # must be within examples params folder
    params = ParameterServer()
    world = World(params)

    # Define two behavior models
    behavior_model = BehaviorMPContinuousActions(params)
    primitive_mapping = {}
    for prim in primitives:
        idx = behavior_model.AddMotionPrimitive(
            np.array(prim))  # adding action
        primitive_mapping[idx] = prim

    behavior_model.ActionToBehavior(0)  # setting initial action

    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)

    return world
Beispiel #4
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    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)
Beispiel #5
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    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)
Beispiel #6
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    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])
Beispiel #7
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  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)
Beispiel #8
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    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)
Beispiel #9
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  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)
Beispiel #10
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    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])
Beispiel #11
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    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])
Beispiel #12
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 def _build_world_state(self):
     param_server = ParameterServer(json=self._json_params)
     world = World(param_server)
     if self._map_interface is None:
         self.CreateMapInterface(self.full_map_file_name)
         world.SetMap(self._map_interface)
     else:
         world.SetMap(self._map_interface)
     for agent in self._agent_list:
         agent.GenerateRoadCorridor(self._map_interface)
         world.AddAgent(agent)
     world.UpdateAgentRTree()
     return world
Beispiel #13
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    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)
Beispiel #14
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    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)
Beispiel #15
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    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(
            os.path.dirname(__file__), "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 = BehaviorConstantAcceleration(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)
Beispiel #16
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    def test_uct_single_agent(self):
        try:
            from bark.core.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(
            os.path.dirname(__file__), "params/", scenario_param_file))

        world = World(params)

        # Model Definitions
        behavior_model = BehaviorUCTSingleAgentMacroActions(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()
        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)
Beispiel #17
0
    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)