def test_database_from_github_release(self): db = BenchmarkDatabase( database_root="external/benchmark_database_release") evaluators = { "success": EvaluatorGoalReached, "collision": EvaluatorCollisionEgoAgent, "max_steps": EvaluatorStepCount } terminal_when = { "collision": lambda x: x, "max_steps": lambda x: x > 20 } params = ParameterServer( ) # only for evaluated agents not passed to scenario! behaviors_tested = { "IDM": BehaviorIDMClassic(params), "Const": BehaviorConstantVelocity(params) } benchmark_runner = BenchmarkRunner(benchmark_database=db, evaluators=evaluators, terminal_when=terminal_when, behaviors=behaviors_tested) benchmark_runner.run(2) print(benchmark_runner.dataframe.to_string())
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_database_multiprocessing_runner(self): dbs = DatabaseSerializer(test_scenarios=4, test_world_steps=5, num_serialize_scenarios=10) dbs.process("data/database1") local_release_filename = dbs.release(version="test") db = BenchmarkDatabase(database_root=local_release_filename) evaluators = { "success": "EvaluatorGoalReached", "collision": "EvaluatorCollisionEgoAgent", "max_steps": "EvaluatorStepCount" } terminal_when = { "collision": lambda x: x, "max_steps": lambda x: x > 5 } params = ParameterServer( ) # only for evaluated agents not passed to scenario! behaviors_tested = { "IDM": BehaviorIDMClassic(params), "Const": BehaviorConstantVelocity(params) } benchmark_runner = BenchmarkRunnerMP(benchmark_database=db, evaluators=evaluators, terminal_when=terminal_when, behaviors=behaviors_tested, log_eval_avg_every=10) result = benchmark_runner.run() params2 = ParameterServer() viewer = MPViewer(params=params2, x_range=[5060, 5160], y_range=[5070, 5150], use_world_bounds=True) rst, _ = benchmark_runner.run_benchmark_config(10, viewer=viewer) rst = benchmark_runner.run(maintain_history=True) self.assertEqual(len(rst.get_histories()), 40) rst, scenario_history = benchmark_runner.run_benchmark_config( 11, viewer=None, maintain_history=True) print(scenario_history) viewer = MPViewer(params=params2, x_range=[5060, 5160], y_range=[5070, 5150], use_world_bounds=True) viewer.drawWorld(world=scenario_history[5].GetWorldState(), eval_agent_ids=scenario_history[5].eval_agent_ids) viewer.show(block=True) df = result.get_data_frame() print(df) self.assertEqual( len(df.index), 40) # 2 Behaviors * 10 Serialize Scenarios * 2 scenario sets
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_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_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)
def test_database_runner(self): dbs = DatabaseSerializer(test_scenarios=2, test_world_steps=2, num_serialize_scenarios=5) cwd = os.getcwd() dbs.process("data/database1") local_release_filename = dbs.release(version="test") db = BenchmarkDatabase(database_root=local_release_filename) evaluators = {"success" : "EvaluatorGoalReached", "collision" : "EvaluatorCollisionEgoAgent", "max_steps": "EvaluatorStepCount"} terminal_when = {"collision" :lambda x: x, "max_steps": lambda x : x>5} params = ParameterServer() # only for evaluated agents not passed to scenario! behaviors_tested = {"IDM": BehaviorIDMClassic(params), "Const" : BehaviorConstantVelocity(params)} benchmark_runner = BenchmarkRunner(benchmark_database=db, evaluators=evaluators, terminal_when=terminal_when, behaviors=behaviors_tested, log_eval_avg_every=1) result = benchmark_runner.run() df = result.get_data_frame() print(df) self.assertEqual(len(df.index), 20) # 2 Behaviors * 10 Serialize Scenarios * 2 scenario sets
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)
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)
from bark.world.agent import Agent from bark.world.map import MapInterface from bark.geometry.standard_shapes import CarLimousine from bark.geometry import Point2d, Polygon2d # Parameters Definitions param_server = ParameterServer( filename="examples/params/od8_const_vel_one_agent.json") # set parameter that is accessible in Python as well as cpp # param_server.setReal("wheel_base", 0.8) # World Definition world = World(param_server) # Model Definitions behavior_model = BehaviorConstantVelocity(param_server) 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],
def test_database_run_and_analyze(self): dbs = DatabaseSerializer(test_scenarios=4, test_world_steps=5, num_serialize_scenarios=10) dbs.process("database") local_release_filename = dbs.release(version="test") db = BenchmarkDatabase(database_root=local_release_filename) evaluators = { "success": "EvaluatorGoalReached", "collision": "EvaluatorCollisionEgoAgent", "max_steps": "EvaluatorStepCount" } terminal_when = { "collision": lambda x: x, "max_steps": lambda x: x > 5 } params = ParameterServer( ) # only for evaluated agents not passed to scenario! behaviors_tested = { "IDM": BehaviorIDMClassic(params), "Const": BehaviorConstantVelocity(params) } benchmark_runner = BenchmarkRunnerMP(benchmark_database=db, evaluators=evaluators, terminal_when=terminal_when, behaviors=behaviors_tested, log_eval_avg_every=10) result = benchmark_runner.run(maintain_history=True) result.dump(os.path.join("./benchmark_results.pickle")) result_loaded = BenchmarkResult.load( os.path.join("./benchmark_results.pickle")) params2 = ParameterServer() fig = plt.figure(figsize=[10, 10]) viewer = MPViewer(params=params2, center=[5112, 5165], y_length=120, enforce_y_length=True, axis=fig.gca()) analyzer = BenchmarkAnalyzer(benchmark_result=result_loaded) configs = analyzer.find_configs(criteria={ "behavior": lambda x: x == "IDM", "success": lambda x: not x }) configs_const = analyzer.find_configs(criteria={ "behavior": lambda x: x == "Const", "success": lambda x: not x }) #analyzer.visualize(configs_idx_list = configs, # viewer = viewer, real_time_factor=10, fontsize=12) plt.close(fig) fig, (ax1, ax2) = plt.subplots(1, 2) viewer1 = MPViewer(params=params2, center=[5112, 5165], y_length=120, enforce_y_length=True, axis=ax1) viewer2 = MPViewer(params=params2, center=[5112, 5165], y_length=120, enforce_y_length=True, axis=ax2) analyzer.visualize(configs_idx_list=[configs[1:3], configs_const[1:3]], viewer=[viewer1, viewer2], viewer_names=["IDM", "ConstVelocity"], real_time_factor=1, fontsize=12)