torchdrivesim.goals

Definitions of goal conditions. Currently, we support waypoint conditions.

Attributes

TensorPerAgentType

Classes

WaypointGoal

Waypoints can be given either per agent type or directly as tensors. The waypoint tensor can contain M waypoints

Module Contents

torchdrivesim.goals.TensorPerAgentType

class torchdrivesim.goals.WaypointGoal(waypoints: TensorPerAgentType, mask: TensorPerAgentType | None = None)

Waypoints can be given either per agent type or directly as tensors. The waypoint tensor can contain M waypoints for each collection of N waypoints that progressively get enabled. The provided mask should indicated which waypoints are padding elements. A waypoint is marked as succesful when the agent state reaches the center of the waypoint within some distance.

Parameters:

• waypoints – a functor of BxAxNxMx2 waypoint tensors [x, y]

• mask – a functor of BxAxNxM boolean tensor indicating whether a given waypoint element is present and not a padding.

waypoints

mask

state

_default_mask() → torch.Tensor

_default_state() → torch.Tensor

get_masks()

Returns the waypoint mask according to the current state value.

get_waypoints()

Returns the waypoints according to the current state value.

copy()

Duplicates this object, allowing for independent subsequent execution.

to(device: torch.device)

Modifies the object in-place, putting all tensors on the device provided.

extend(n: int, in_place: bool = True)

Multiplies the first batch dimension by the given number. This is equivalent to introducing extra batch dimension on the right and then flattening.

select_batch_elements(idx: torch.Tensor, in_place: bool = True)

Picks selected elements of the batch. The input is a tensor of indices into the batch dimension.

step(agent_states: TensorPerAgentType, time: int = 0, threshold: float = 2.0) → None

Advances the state of the waypoints.

_update_mask(state, mask, new_mask)

Helper function to update the global masks on the current state with the new_mask values.

Parameters:

• state – BxAx1 int tensor

• mask – BxAxNxM boolean tensor

• new_mask – BxAxM boolean tensor

Returns:

BxAxNxM boolean tensor updated with the new_mask values

_advance_state(state, agent_overlap)

Helper function to advance the state when agents overlap with waypoints.

Parameters:

• state – BxAx1 int tensor

• agent_overlap – BxAxM boolean tensor

Returns:

BxAxM boolean tensor indicating the state of the waypoints

_agent_waypoint_overlap(agent_states: torch.Tensor, waypoints: torch.Tensor, threshold=2.0) → torch.Tensor

Helper function to detect if agent states overlap with the current waypoints.

Parameters:

• agent_states – BxAx2 tensor of agents states - x,y

• waypoints – BxAxMx2 tensor of waypoint

Returns:

BxAxM boolean tensor indicating which agents overlap with the waypoints