Module Dataset

ScenarioIterator

ScenarioIterator class

class crgeo.dataset.iteration.scenario_iterator.ScenarioIterator(directory: Path | str, loop: bool = False, skip_subvariants: bool = False, verbose: int = 2, shuffle: bool = False, save_scenario_pickles: bool = True, load_scenario_pickles: bool = True, preprocessors: List[Tuple[BaseScenarioPreprocessor | Callable[[commonroad.scenario.scenario.Scenario, commonroad.planning.planning_problem.PlanningProblemSet | None], Tuple[commonroad.scenario.scenario.Scenario, commonroad.planning.planning_problem.PlanningProblemSet | None]], int] | BaseScenarioPreprocessor | Callable[[commonroad.scenario.scenario.Scenario, commonroad.planning.planning_problem.PlanningProblemSet | None], Tuple[commonroad.scenario.scenario.Scenario, commonroad.planning.planning_problem.PlanningProblemSet | None]]] | None = None, prefilters: Sequence[BaseScenarioFilterer] | None = None, postfilters: Sequence[BaseScenarioFilterer] | None = None, inverse_filters: bool = False, raise_exceptions: bool = True, max_scenarios: int | None = None, seed: int | None = None, skip_scenarios: Set[str] | None = None)

Class for iterating over CommonRoad scenarios found in folder.

Example usage:

scenario_iterator = ScenarioIterator(
    directory='scenarios/highD'
)
for scenario in scenario_iterator:
    print(scenario)

property cycle: int

Current iteration cycle

static get_preprocessor_pipeline_combinations(preprocessors: Sequence[Tuple[BaseScenarioPreprocessor, int]]) → List[List[BaseScenarioPreprocessor]]

property input_scenario_counter: int

property num_preprocessing_pipelines: int

property output_scenario_counter: int

property output_scenarios_per_scenario: int

shuffle(seed: int | None = None, reset: bool = True) → None

BaseScenarioPreprocessor

BaseScenarioPreprocessor class

class crgeo.dataset.preprocessing.base_scenario_preprocessor.BaseScenarioPreprocessor

Base class for preprocessing scenario. As the first part of data preparation pipeline(preprocess -> dataset collector -> post-process), preprocessors only deal with scenarios and planning problems, enabling operations on lanelet network, obstacles and planning problem setup and provide customized scenarios and planning problems for dataset collector.

Current preprocessors can be easily extended by writing another child class of BaseScenarioPreprocessor and overwriting the abstractmethod “_process”

property call_count: int

static cast(preprocessors: List[Tuple[BaseScenarioPreprocessor | Callable[[commonroad.scenario.scenario.Scenario, commonroad.planning.planning_problem.PlanningProblemSet | None], Tuple[commonroad.scenario.scenario.Scenario, commonroad.planning.planning_problem.PlanningProblemSet | None]], int] | BaseScenarioPreprocessor | Callable[[commonroad.scenario.scenario.Scenario, commonroad.planning.planning_problem.PlanningProblemSet | None], Tuple[commonroad.scenario.scenario.Scenario, commonroad.planning.planning_problem.PlanningProblemSet | None]]]) → Sequence[Tuple[BaseScenarioPreprocessor, int]]

property name: str

BaseScenarioFilterer

BaseScenarioFilterer class

class crgeo.dataset.preprocessing.base_scenario_filterer.BaseScenarioFilterer(name: str | None = None)

Base class for filtering scenarios.

filter_scenario(scenario: commonroad.scenario.scenario.Scenario, raise_exceptions: bool = False) → bool

property name

TrafficExtractor

TrafficExtractor class

BaseFeatureComputer

BaseFeatureComputer class

BaseEdgeDrawer

BaseEdgeDrawer class

BaseDataPostprocessor

BaseDataPostprocessor class

CommonRoadData

CommonRoadData class

CommonRoadDataTemporal

CommonRoadDataTemporal class

BaseDatasetCollector

BaseDatasetCollector class

ScenarioDatasetCollector

ScenarioDatasetCollector class

CommonRoadDataset

CommonRoadDataset class