| from nuplan.planning.simulation.trajectory.trajectory_sampling import TrajectorySampling |
|
|
| from navsim.agents.abstract_agent import AbstractAgent |
| from navsim.common.dataclasses import AgentInput, Trajectory, Scene, SensorConfig |
|
|
|
|
| class HumanAgent(AbstractAgent): |
| """Privileged agent interface of human operator.""" |
|
|
| requires_scene = True |
|
|
| def __init__( |
| self, |
| trajectory_sampling: TrajectorySampling = TrajectorySampling(time_horizon=4, interval_length=0.5), |
| ): |
| """ |
| Initializes the human agent object. |
| :param trajectory_sampling: trajectory sampling specification |
| """ |
| self._trajectory_sampling = trajectory_sampling |
|
|
| def name(self) -> str: |
| """Inherited, see superclass.""" |
|
|
| return self.__class__.__name__ |
|
|
| def initialize(self) -> None: |
| """Inherited, see superclass.""" |
| pass |
|
|
| def get_sensor_config(self) -> SensorConfig: |
| """Inherited, see superclass.""" |
| return SensorConfig.build_no_sensors() |
|
|
| def compute_trajectory(self, agent_input: AgentInput, scene: Scene) -> Trajectory: |
| """ |
| Computes the ego vehicle trajectory. |
| :param current_input: Dataclass with agent inputs. |
| :return: Trajectory representing the predicted ego's position in future |
| """ |
| return scene.get_future_trajectory(self._trajectory_sampling.num_poses) |
|
|
