Add informed/uninformed search tutorials

GSMP/motion_automata/README_TREESEARCH.md

+Tutorials on Tree Search Algorithms
+============
+This is a short guide on how to use the search algorithms in tree_search.py for lectures and tutorials.
+
+Set up
+============
+For using the code, you have to install the tools as described in the README in the root folder. For using the code in
+Pycharm, create a new project in the folder `./GSMP/motion_automata`, to avoid path issues.
+
+# Documentation
+For running the search algorithms, you can currently specify three different configurations which mainly influence the
+visualization of the search and are defined in `config.py`:
+
+### Default: 
+
+* This configuration should only be used in the jupyter notebook tutorial (`cr_uninformed_search_tutorial` and
+`cr_informed_search_tutorial`). It first runs the algorithm and stores the state of
+each motion primitive, i.e., explored/frontier/currently exploring etc., for each step. Then it plots these steps using
+ipywidgets, so the student can step through the search in the jupyter notebook. 
+
+* The color code for plotting is taken from the AIMA tutorial, but can be changed in `config.py`. 
+
+* By setting PLOT_LEGEND to True, a legend with all states of
+motion primitives (see `MotionPrimitiveStatus` in `helper_tree_search.py`) is plotted. 
+
+* To reduce the number of steps, you can turn off that motion primitives with collisions are plotted one after the other by setting PLOT_COLLISION_STEPS
+to False.
+
+### StudentScript: 
+
+This configuration uses the same parameters as the Default configuration, but it can be run in Pycharm,
+e.g. when executing `main.py`. It plots the different steps while executing the search algorithm. It should help
+students to easily go through the code.

GSMP/motion_automata/automata/HelperFunctions.py

@@ -7,9 +7,10 @@ sys.path.append(os.path.join(path_commonroad_search, "GSMP/tools/commonroad-coll
 sys.path.append(os.path.join(path_commonroad_search, "GSMP/tools/commonroad-road-boundary"))
 sys.path.append(os.path.join(path_commonroad_search, "GSMP/motion_automata/vehicle_model"))
 
-
 import time
 from multiprocessing import Manager, Process
+import enum
+import ipywidgets as widgets
 
 import numpy as np
 from math import atan2
@@ -17,12 +18,16 @@ from math import atan2
 import matplotlib.pyplot as plt
 from IPython import display
 from ipywidgets import widgets
+from IPython.display import display
 
 # import CommonRoad-io modules
 from commonroad.visualization.draw_dispatch_cr import draw_object
 from commonroad.common.file_reader import CommonRoadFileReader
 from commonroad.scenario.trajectory import Trajectory, State
+from commonroad.scenario.scenario import Scenario
 from commonroad.geometry.shape import Rectangle
+from commonroad.planning.planning_problem import PlanningProblem
+from commonroad.scenario.obstacle import StaticObstacle, ObstacleRole, ObstacleType, DynamicObstacle
 from commonroad.prediction.prediction import TrajectoryPrediction
 from commonroad_cc.collision_detection.pycrcc_collision_dispatch import create_collision_object
 from commonroad_cc.visualization.draw_dispatch import draw_object as draw_it
@@ -32,6 +37,8 @@ from commonroad.common.solution_writer import CommonRoadSolutionWriter, VehicleT
 from automata.MotionAutomata import MotionAutomata
 from automata.MotionPrimitive import MotionPrimitive
 from automata.States import FinalState
+from typing import List, Dict, Tuple
+
 
 # ================================== scenario ================================== #
 def load_scenario(scenario_path):
@@ -42,8 +49,8 @@ def load_scenario(scenario_path):
 
 
 # ================================== automata ================================== #
-def generate_automata(veh_type_id: int):
-    """ 
+def generate_automata(veh_type_id, mp_file=None, search_tutorial=False):
+    """
     1. We first create a Motion Automata object, then load pre-computed motion primitives into it. 
     Note that every motion primitive is a short trajectory with varied initial states that lead to different final states.
 
@@ -51,10 +58,15 @@ def generate_automata(veh_type_id: int):
     which contains all connectable primitives from the focused primitive. 
     If the velocity and steering angel of the start state of primitive B match 
     those of the final state of primitive A, we say B is connectable from A.
+
+    :param veh_type_id: id for the vehicle model in the motion primitives
+    :param mp_file: optional parameter for specifying the path to a motion primitives xml file
+    :param search_tutorial: boolean value to specify if the search is used for a tutorial ->
+    motion primitives are sorted by their y-coordinate in the final state
     """
 
     # the primitives vary for different vehicle models 1, 2 and 3.
-    assert veh_type_id in (1,2,3), "Input vehicle type id is not valid! Must be either 1, 2 or 3."
+    assert veh_type_id in (1, 2, 3), "Input vehicle type id is not valid! Must be either 1, 2 or 3."
 
     # step 1
     automata = MotionAutomata(veh_type_id)
@@ -62,22 +74,38 @@ def generate_automata(veh_type_id: int):
     print("Reading motion primitives...")
     try:
         prefix = '../../GSMP/motion_automata/motion_primitives/'
+        if mp_file is not None:
+            automata.readFromXML(prefix + mp_file)
+        else:
+            if veh_type_id == 1:
+                automata.readFromXML(
+                    prefix + 'V_0.0_20.0_Vstep_1.0_SA_-0.91_0.91_SAstep_0.23_T_0.5_Model_FORD_ESCORT.xml')
+            elif veh_type_id == 2:
+                automata.readFromXML(
+                    prefix + 'V_0.0_20.0_Vstep_1.0_SA_-1.066_1.066_SAstep_0.27_T_0.5_Model_BMW320i.xml')
+            elif veh_type_id == 3:
+                automata.readFromXML(
+                    prefix + 'V_0.0_20.0_Vstep_1.0_SA_-1.023_1.023_SAstep_0.26_T_0.5_Model_VW_VANAGON.xml')
 
-        if veh_type_id == 1:
-            automata.readFromXML(prefix + 'V_0.0_20.0_Vstep_1.0_SA_-0.91_0.91_SAstep_0.23_T_0.5_Model_FORD_ESCORT.xml')
-        elif veh_type_id == 2:
-            automata.readFromXML(prefix + 'V_0.0_20.0_Vstep_1.0_SA_-1.066_1.066_SAstep_0.27_T_0.5_Model_BMW320i.xml')
-        elif veh_type_id == 3:
-            automata.readFromXML(prefix + 'V_0.0_20.0_Vstep_1.0_SA_-1.023_1.023_SAstep_0.26_T_0.5_Model_VW_VANAGON.xml')
     except Exception:
         prefix = '../../../GSMP/motion_automata/motion_primitives/'
 
-        if veh_type_id == 1:
-            automata.readFromXML(prefix + 'V_0.0_20.0_Vstep_1.0_SA_-0.91_0.91_SAstep_0.23_T_0.5_Model_FORD_ESCORT.xml')
-        elif veh_type_id == 2:
-            automata.readFromXML(prefix + 'V_0.0_20.0_Vstep_1.0_SA_-1.066_1.066_SAstep_0.27_T_0.5_Model_BMW320i.xml')
-        elif veh_type_id == 3:
-            automata.readFromXML(prefix + 'V_0.0_20.0_Vstep_1.0_SA_-1.023_1.023_SAstep_0.26_T_0.5_Model_VW_VANAGON.xml')
+        if mp_file is not None:
+            automata.readFromXML(prefix + mp_file)
+        else:
+            if veh_type_id == 1:
+                automata.readFromXML(
+                    prefix + 'V_0.0_20.0_Vstep_1.0_SA_-0.91_0.91_SAstep_0.23_T_0.5_Model_FORD_ESCORT.xml')
+            elif veh_type_id == 2:
+                automata.readFromXML(
+                    prefix + 'V_0.0_20.0_Vstep_1.0_SA_-1.066_1.066_SAstep_0.27_T_0.5_Model_BMW320i.xml')
+            elif veh_type_id == 3:
+                automata.readFromXML(
+                    prefix + 'V_0.0_20.0_Vstep_1.0_SA_-1.023_1.023_SAstep_0.26_T_0.5_Model_VW_VANAGON.xml')
+
+    # additional step for search examples -> sorting motion primitives by max. y-coordinate
+    if search_tutorial:
+        automata.sort_primitives()
 
     # step 2
     automata.createConnectivityLists()
@@ -90,36 +118,34 @@ def generate_automata(veh_type_id: int):
 
     return automata
 
-def add_initial_state_to_automata(automata, planning_problem, flag_print_states = True):
+
+def add_initial_state_to_automata(automata, planning_problem):
     # set initial steering angle to zero
-	planning_problem.initial_state.steering_angle = 0.0
+    planning_problem.initial_state.steering_angle = 0.0
 
     # the initial velocity of the planning problem may be any value, we need to obtain the closest velocity to it
     # from StartStates of the primitives in order to get the feasible successors of the planning problem
-	velocity_closest = automata.getClosestStartVelocity(planning_problem.initial_state.velocity)
-	planning_problem.initial_state.velocity = velocity_closest
+    velocity_closest = automata.getClosestStartVelocity(planning_problem.initial_state.velocity)
+    planning_problem.initial_state.velocity = velocity_closest
 
-	initial_state = planning_problem.initial_state
-	goal_region = planning_problem.goal.state_list[0]
+    initial_state = planning_problem.initial_state
 
-    # if flag_print_states:
-    #     print("Initial State:", initial_state)
-    #     print("Goal Region:", goal_region)
-
-	# turn intial state into a motion primitive to check for connectivity to subsequent motion primitives
-	final_state_primitive = FinalState(x=initial_state.position[0],
-                                       y=initial_state.position[1], 
+    # turn initial state into a motion primitive to check for connectivity to subsequent motion primitives
+    final_state_primitive = FinalState(x=initial_state.position[0],
+                                       y=initial_state.position[1],
                                        steering_angle=initial_state.steering_angle,
-    								   velocity=initial_state.velocity, 
-                                       orientation=initial_state.orientation, 
+                                       velocity=initial_state.velocity,
+                                       orientation=initial_state.orientation,
                                        time_step=initial_state.time_step)
 
-	initial_motion_primitive = MotionPrimitive(startState=None, finalState=final_state_primitive, timeStepSize=0, trajectory=None)
+    initial_motion_primitive = MotionPrimitive(startState=None, finalState=final_state_primitive, timeStepSize=0,
+                                               trajectory=None)
+
+    # create connectivity list for this imaginary motion primitive
+    automata.createConnectivityListPrimitive(initial_motion_primitive)
 
-	# create connectivity list for this imaginary motion primitive
-	automata.createConnectivityListPrimitive(initial_motion_primitive)
+    return automata, initial_motion_primitive
 
-	return automata, initial_motion_primitive
 
 # ================================== search for solution ================================== #
 """
@@ -133,6 +159,7 @@ More information on python Prcess could be found at [1](https://docs.python.org/
 3. To execute the search, we need to define an individual wrapper function and set it as the target of the new Process.
 """
 
+
 def execute_search(motion_planner, initial_motion_primitive, path_shared, dict_status_shared, max_tree_depth):
     """
     definition of the wrapper function for search
@@ -148,49 +175,51 @@ def execute_search(motion_planner, initial_motion_primitive, path_shared, dict_s
     else:
         display.clear_output(wait=True)
         path_shared.value = result[0]
-    
+
     if len(result[1]) > 0:
         print("Found primitives")
-        
+
     # for primitive in result[1]:
     #     print('\t', primitive)
 
-def start_search(scenario, planning_problem, automata, motion_planner, initial_motion_primitive, flag_plot_intermediate_results=True, flag_plot_planning_problem=True):
+
+def start_search(scenario, planning_problem, automata, motion_planner, initial_motion_primitive,
+                 flag_plot_intermediate_results=True, flag_plot_planning_problem=True):
     # create Manager object to manage shared variables between different Processes
     process_manager = Manager()
 
     # create shared variables between Processes
-    path_shared        = process_manager.Value('path_shared', None)
+    path_shared = process_manager.Value('path_shared', None)
     dict_status_shared = process_manager.Value('dict_status_shared', None)
 
     # initialize status with a dictionary.
     # IMPORTANT NOTE: if the shared variable contains complex object types (list, dict, ...), to change its value,
     # we need to do it via another object (here dict_status) and pass it to the shared variable at the end
-    dict_status = {'cost_current': 0, 'path_current':[]}
+    dict_status = {'cost_current': 0, 'path_current': []}
     dict_status_shared.value = dict_status
 
     # maximum number of concatenated primitives
-    max_tree_depth = 100 
+    max_tree_depth = 100
 
     # create and start the Process for search
-    process_search = Process(target=execute_search, 
-                             args=(motion_planner, 
-                                   initial_motion_primitive, 
-                                   path_shared, 
-                                   dict_status_shared, 
+    process_search = Process(target=execute_search,
+                             args=(motion_planner,
+                                   initial_motion_primitive,
+                                   path_shared,
+                                   dict_status_shared,
                                    max_tree_depth))
     process_search.start()
 
     if flag_plot_intermediate_results:
         # create a figure, plot the scenario and planning problem
-        fig = plt.figure(figsize=(9, 9))   
+        fig = plt.figure(figsize=(9, 9))
         plt.clf()
         draw_object(scenario)
-        if flag_plot_planning_problem: 
+        if flag_plot_planning_problem:
             draw_object(planning_problem)
         plt.gca().set_aspect('equal')
         plt.gca().set_axis_off()
-        plt.margins(0,0)
+        plt.margins(0, 0)
         plt.show()
 
     refresh_rate_plot = 5.0
@@ -200,14 +229,14 @@ def start_search(scenario, planning_problem, automata, motion_planner, initial_m
         # break the loop if process_search is not alive anymore
         if not process_search.is_alive():
             break
-        
+
         # print current status
         string_status = "Cost so far:", round(dict_status_shared.value['cost_current'], 2)
         string_status += "Time step:", round(dict_status_shared.value['path_current'][-1].time_step, 2)
-        string_status += "Orientation:",round(dict_status_shared.value['path_current'][-1].orientation, 2)
+        string_status += "Orientation:", round(dict_status_shared.value['path_current'][-1].orientation, 2)
         string_status += "Velocity:", round(dict_status_shared.value['path_current'][-1].velocity, 2)
-        print (string_status, end='\r')
-        
+        print(string_status, end='\r')
+
         # if there is a path to be visualized
         if len(dict_status_shared.value['path_current']) > 0:
             if flag_plot_intermediate_results:
@@ -217,28 +246,28 @@ def start_search(scenario, planning_problem, automata, motion_planner, initial_m
                     draw_object(planning_problem)
                 plt.gca().set_aspect('equal')
                 plt.gca().set_axis_off()
-                plt.margins(0,0)
+                plt.margins(0, 0)
                 plt.show()
-            
+
             # create a Trajectory from the current states of the path
             # Trajectory is essentially a list of states
             trajectory = Trajectory(initial_time_step=0, state_list=dict_status_shared.value['path_current'])
-            
+
             # create an occupancy Prediction via the generated trajectory and shape of ego vehicle
             # the Prediction includes a Trajectory and Shape of the object
             prediction = TrajectoryPrediction(trajectory=trajectory, shape=automata.egoShape)
-            
-            # create a colission object from prediction
+
+            # create a collision object from prediction
             # here it is used for visualization purpose
             collision_object = create_collision_object(prediction)
-            
+
             # draw this collision object
             draw_it(collision_object, draw_params={'collision': {'facecolor': 'magenta'}})
 
         # visualize current trajectory
-        fig.canvas.draw()    
+        fig.canvas.draw()
 
-    # wait till process_search terminates
+        # wait till process_search terminates
     process_search.join()
     time.sleep(0.5)
     print("Search finished.")
@@ -250,22 +279,5 @@ def start_search(scenario, planning_problem, automata, motion_planner, initial_m
 
     return path_shared.value, dict_status_shared.value
 
-# ================================== visualization ================================== #
-def get_state_at_time(t):
-    for state in path_shared.value:
-        # return the first state that has time_step >= given t
-        if state.time_step >= t:
-            return state
-    # else return last state
-    return path[-1]
-
-def draw_state(t):
-    print("current time step: ", t)
-    draw_figure()
-
-    if path_shared is not None:
-        state = get_state_at_time(t)
-        trajectory = Trajectory(initial_time_step=int(state.time_step),state_list=[state])
-        prediction = TrajectoryPrediction(trajectory=trajectory, shape=automata.egoShape)
-        collision_object = create_collision_object(prediction)
-        draw_it(collision_object)
\ No newline at end of file
+
+

GSMP/motion_automata/automata/MotionAutomata.py

@@ -5,6 +5,7 @@ from automata.MotionPrimitiveParser import MotionPrimitiveParser
 from automata.MotionPrimitive import MotionPrimitive
 from commonroad.geometry.shape import Rectangle
 
+
 class MotionAutomata:
     """
     Class to handle motion primitives for motion planning
@@ -39,6 +40,9 @@ class MotionAutomata:
         self.setVehicleTypeIdPrimitives()
         return
 
+    def sort_primitives(self):
+        self.Primitives.sort(key=lambda x: x.finalState.y, reverse=False)
+
     def createConnectivityListPrimitive(self, primitive: MotionPrimitive) -> None:
         """
         Creates the successor list for a single primitive and stores them in a successor list of the given primitive.

GSMP/motion_automata/automata/MotionPrimitiveParser.py

@@ -24,7 +24,7 @@ class MotionPrimitiveParser:
         startSteeringAngle = float(startNode.find('steering_angle').text)
         startVelocity = float(startNode.find('velocity').text)
         startOrientation = float(startNode.find('orientation').text)
-        startTimeStep = float(startNode.find('time_step').text)
+        startTimeStep = int(startNode.find('time_step').text)
 
         startState = StartState(startX, startY, startSteeringAngle, startVelocity, startOrientation, startTimeStep)
 
@@ -35,13 +35,14 @@ class MotionPrimitiveParser:
         finalSteeringAngle = float(finalNode.find('steering_angle').text)
         finalVelocity = float(finalNode.find('velocity').text)
         finalOrientation = float(finalNode.find('orientation').text)
-        finalTimeStep = float(finalNode.find('time_step').text)
+        finalTimeStep = int(finalNode.find('time_step').text)
 
         finalState = FinalState(finalX, finalY, finalSteeringAngle, finalVelocity, finalOrientation, finalTimeStep)
 
         # create trajectory from path node and start/final states
         pathNode = xmlNode.find('Path')
-        timeStepSize = 1.0 / (len(pathNode) + 1)
+        duration = xmlNode.find('Duration')
+        timeStepSize = float(duration.text) / (len(pathNode) + 1)
 
         trajectory = MotionPrimitiveParser.createTrajectoryFromPathStates(pathNode, startState, finalState, timeStepSize)
 

GSMP/motion_automata/automata/helper_tree_search.py

+__author__ = "Anna-Katharina Rettinger"
+__copyright__ = "TUM Cyber-Physical Systems Group"
+__credits__ = ["CoPlan"]
+__version__ = "0.1"
+__maintainer__ = "Anna-Katharina Rettinger"
+__email__ = "commonroad-i06@in.tum.de"
+__status__ = "Beta"
+
+import enum
+import ipywidgets as widgets
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+from IPython import display
+from ipywidgets import widgets
+from IPython.display import display
+
+# import CommonRoad-io modules
+from commonroad.visualization.draw_dispatch_cr import draw_object
+from commonroad.scenario.trajectory import State
+from commonroad.scenario.scenario import Scenario
+from commonroad.geometry.shape import Rectangle
+from commonroad.planning.planning_problem import PlanningProblem
+from commonroad.scenario.obstacle import ObstacleType, DynamicObstacle
+
+# import Motion Automaton modules
+from automata.MotionPrimitive import MotionPrimitive
+from typing import List, Dict, Tuple
+
+
+@enum.unique
+class MotionPrimitiveStatus(enum.Enum):
+    IN_FRONTIER = 0
+    INVALID = 1
+    CURRENTLY_EXPLORED = 2
+    EXPLORED = 3
+    SOLUTION = 4
+
+
+def plot_legend(plotting_config):
+    if hasattr(plotting_config, 'LABELS'):
+        node_status, labels = plotting_config.LABELS
+    else:
+        node_status = [status.value for status in MotionPrimitiveStatus]
+        labels = ['Frontier', 'Collision', 'Currently Exploring', 'Explored', 'Final Solution']
+
+    custom_lines = []
+    for value in node_status:
+
+        custom_lines.append(Line2D([0], [0], color=plotting_config.PLOTTING_PARAMS[value][0],
+                                   linestyle=plotting_config.PLOTTING_PARAMS[value][1],
+                                   linewidth=plotting_config.PLOTTING_PARAMS[value][2]))
+    legend = plt.legend(handles=custom_lines, labels=labels, loc='lower left',
+                        bbox_to_anchor=(0.02, 0.51), prop={'size': 18})
+    legend.set_zorder(30)
+    plt.rcParams["legend.framealpha"] = 1.0
+    plt.rcParams["legend.shadow"] = True
+
+
+def plot_search_scenario(scenario, initial_state, ego_shape, planning_problem, config):
+    plt.figure(figsize=(22.5, 4.5))
+    plt.axis('equal')
+    plt.xlim(55, 97)
+    plt.ylim(-2.5, 5.5)
+    draw_params = {'scenario': {'lanelet': {'facecolor': '#F8F8F8'}}}
+    draw_object(scenario, draw_params=draw_params)
+    ego_vehicle = DynamicObstacle(obstacle_id=scenario.generate_object_id(), obstacle_type=ObstacleType.CAR,
+                                  obstacle_shape=ego_shape,
+                                  initial_state=initial_state)
+    draw_object(ego_vehicle)
+    draw_object(planning_problem)
+    if config.PLOT_LEGEND:
+        plot_legend(plotting_config=config)
+
+
+def initial_visualization(scenario, initial_state, ego_shape, planning_problem, config, path_fig):
+    if not config.JUPYTER_NOTEBOOK:
+        plot_search_scenario(scenario, initial_state, ego_shape, planning_problem, config)
+        if path_fig:
+            plt.rcParams['svg.fonttype'] = 'none'
+            plt.axis('off')
+            try:
+                plt.savefig(path_fig + 'initial_scenario.' + config.OUTPUT_FORMAT, format=config.OUTPUT_FORMAT,
+                            bbox_inches='tight')
+            except:
+                print('Saving was not successful')
+        else:
+            plt.show(block=False)
+
+
+def plot_state(state: State, color='red'):
+    plt.plot(state.position[0], state.position[1], color=color, marker='o', markersize=6)
+
+
+def plot_motion_primitive(mp: MotionPrimitive, color='red'):
+    """
+    Plots an object of class MotionPrimitive with marker at initial state and end state
+    @param mp: object of class Motion Primitive
+    @param color: color for plotting Motion Primitive
+    @return:
+    """
+    plot_state(state=mp.trajectory.state_list[0])
+    plot_state(state=mp.trajectory.state_list[-1])
+    x = []
+    y = []
+    for state in mp.trajectory.state_list:
+        x.append(state.position[0])
+        y.append(state.position[1])
+    plt.plot(x, y, color=color, marker="")
+
+
+def plot_primitive_path(mp: List[State], status: MotionPrimitiveStatus, plotting_params):
+    plt.plot(mp[-1].position[0], mp[-1].position[1], color=plotting_params[status.value][0], marker='o', markersize=8,
+             zorder=27)
+    x = []
+    y = []
+    for state in mp:
+        x.append(state.position[0])
+        y.append(state.position[1])
+    plt.plot(x, y, color=plotting_params[status.value][0], marker="", linestyle=plotting_params[status.value][1],
+             linewidth=plotting_params[status.value][2], zorder=25)
+
+
+def update_visualization(mp: List[State], status: MotionPrimitiveStatus, node_status: Dict[int, Tuple],
+                         path_fig, config, count):
+    assert isinstance(status, MotionPrimitiveStatus), "Status in not of type MotionPrimitiveStatus."
+
+    node_status.update({hash(mp[-1]): (mp, status)})
+    # only plot if run with python script
+    if not config.JUPYTER_NOTEBOOK:
+        plot_primitive_path(mp=mp, status=status, plotting_params=config.PLOTTING_PARAMS)
+        if path_fig:
+            plt.axis('off')
+            plt.rcParams['svg.fonttype'] = 'none'
+            try:
+                plt.savefig(path_fig + 'solution_step_' + str(count) + '.' + config.OUTPUT_FORMAT,
+                            format=config.OUTPUT_FORMAT, bbox_inches='tight')
+            except:
+                print('Saving was not successful')
+        else:
+            plt.pause(0.4)
+    return node_status
+
+
+def show_scenario(scenario_data: Tuple[Scenario, State, Rectangle, PlanningProblem], node_status: Dict[int, Tuple],
+                  config):
+    plot_search_scenario(scenario=scenario_data[0], initial_state=scenario_data[1], ego_shape=scenario_data[2],
+                         planning_problem=scenario_data[3], config=config)
+    for node in node_status.values():
+        plot_primitive_path(node[0], node[1], config.PLOTTING_PARAMS)
+
+    plt.show()
+
+
+def display_steps(scenario_data, config, algorithm, **args):
+    def slider_callback(iteration):
+        # don't show graph for the first time running the cell calling this function
+        try:
+            show_scenario(scenario_data, node_status=all_node_status[iteration], config=config)
+        except:
+            pass
+
+    def visualize_callback(Visualize):
+        if Visualize is True:
+            button.value = False
+            global all_node_status
+
+            if 'limit' in args:
+                path, primitives, all_node_status = algorithm(limit=args['limit'])
+            else:
+                path, primitives, all_node_status = algorithm()
+
+            slider.max = len(all_node_status) - 1
+
+            for i in range(slider.max + 1):
+                slider.value = i
+                # time.sleep(.5)
+
+    slider = widgets.IntSlider(min=0, max=1, step=1, value=0)
+    slider_visual = widgets.interactive(slider_callback, iteration=slider)
+    display(slider_visual)
+
+    button = widgets.ToggleButton(value=False)
+    button_visual = widgets.interactive(visualize_callback, Visualize=button)
+    display(button_visual)
\ No newline at end of file

GSMP/motion_automata/automata/node.py

+from commonroad.scenario.trajectory import State
+from typing import List
+from automata.MotionPrimitive import MotionPrimitive
+
+
+class Node: