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from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Optional, Tuple
from numpy import isin
import torch
from risk_biased.mpc_planner.planner_cost import TrackingCost
from risk_biased.utils.cost import BaseCostTorch
from risk_biased.utils.risk import AbstractMonteCarloRiskEstimator
def get_rotation_matrix(angle, device):
c = torch.cos(angle)
s = torch.sin(angle)
rot_matrix = torch.stack(
(torch.stack((c, s), -1), torch.stack((-s, c), -1)), -1
).to(device)
return rot_matrix
class AbstractState(ABC):
"""
State representation using an underlying tensor. Position, Velocity, and Angle can be accessed.
"""
@property
@abstractmethod
def position(self) -> torch.Tensor:
"""Extract position information from the state tensor
Returns:
position_tensor of size (..., 2)
"""
@property
@abstractmethod
def velocity(self) -> torch.Tensor:
"""Extract velocity information from the state tensor
Returns:
velocity_tensor of size (..., 2)
"""
@property
@abstractmethod
def angle(self) -> torch.Tensor:
"""Extract velocity information from the state tensor
Returns:
velocity_tensor of size (..., 1)
"""
@abstractmethod
def get_states(self, dim: int) -> torch.Tensor:
"""Return the underlying states tensor with dim 2, 4 or 5 ([x, y], [x, y, vx, vy], or [x, y, angle, vx, vy])."""
@abstractmethod
def rotate(self, angle: float, in_place: bool) -> AbstractState:
"""Rotate the state by the given angle
Args:
angle: in radiants
in_place: wether to change the object itself or return a rotated copy
Returns:
rotated self or rotated copy of self
"""
@abstractmethod
def translate(self, translation: torch.Tensor, in_place: bool) -> AbstractState:
"""Translate the state by the given tranlation
Args:
translation: translation vector in 2 dimensions
in_place: wether to change the object itself or return a rotated copy
"""
# Define overloading operators to behave as a tensor for some operations
def __getitem__(self, key) -> AbstractState:
"""
Use get item on the underlying tensor to get the item at the given key.
Allways returns a velocity state so that if the underlying time sequence is reduced to one step, the velocity is still accessible.
"""
if isinstance(key, int):
key = (key, Ellipsis, slice(None, None, None))
elif Ellipsis not in key:
key = (*key, Ellipsis, slice(None, None, None))
else:
key = (*key, slice(None, None, None))
return to_state(
torch.cat(
(
self.position[key],
self.velocity[key],
),
dim=-1,
),
self.dt,
)
@property
def shape(self):
return self._states.shape[:-1]
def to_state(in_tensor: torch.Tensor, dt: float) -> AbstractState:
if in_tensor.shape[-1] == 2:
return PositionSequenceState(in_tensor, dt)
elif in_tensor.shape[-1] == 4:
return PositionVelocityState(in_tensor, dt)
else:
assert in_tensor.shape[-1] > 4
return PositionAngleVelocityState(in_tensor, dt)
class PositionSequenceState(AbstractState):
"""
State representation with an underlying tensor defining only positions.
"""
def __init__(self, states: torch.Tensor, dt: float) -> None:
super().__init__()
assert (
states.shape[-1] == 2
) # Check that the input tensor defines only the position
assert (
states.ndim > 1 and states.shape[-2] > 1
) # Check that the input tensor defines a sequence of positions (otherwise velocity cannot be computed)
self.dt = dt
self._states = states.clone()
@property
def position(self) -> torch.Tensor:
return self._states
@property
def velocity(self) -> torch.Tensor:
vel = (self._states[..., 1:, :] - self._states[..., :-1, :]) / self.dt
vel = torch.cat((vel[..., 0:1, :], vel), dim=-2)
return vel.clone()
@property
def angle(self) -> torch.Tensor:
vel = self.velocity
angle = torch.arctan2(vel[..., 1:2], vel[..., 0:1])
return angle
def get_states(self, dim: int = 2) -> torch.Tensor:
if dim == 2:
return self._states.clone()
elif dim == 4:
return torch.cat((self._states.clone(), self.velocity), dim=-1)
elif dim == 5:
return torch.cat((self._states.clone(), self.angle, self.velocity), dim=-1)
else:
raise RuntimeError(f"State dimension must be either 2, 4, or 5. Got {dim}")
def rotate(self, angle: float, in_place: bool = False) -> PositionSequenceState:
"""Rotate the state by the given angle in radiants"""
rot_matrix = get_rotation_matrix(angle, self._states.device)
if in_place:
self._states = (rot_matrix @ self._states.unsqueeze(-1)).squeeze(-1)
return self
else:
return to_state(
(rot_matrix @ self._states.unsqueeze(-1).clone()).squeeze(-1), self.dt
)
def translate(
self, translation: torch.Tensor, in_place: bool = False
) -> PositionSequenceState:
"""Translate the state by the given tranlation"""
if in_place:
self._states[..., :2] += translation.expand_as(self._states[..., :2])
return self
else:
return to_state(
self._states[..., :2].clone()
+ translation.expand_as(self._states[..., :2]),
self.dt,
)
class PositionVelocityState(AbstractState):
"""
State representation with an underlying tensor defining position and velocity.
"""
def __init__(self, states: torch.Tensor, dt) -> None:
super().__init__()
assert states.shape[-1] == 4
self._states = states.clone()
self.dt = dt
@property
def position(self) -> torch.Tensor:
return self._states[..., :2]
@property
def velocity(self) -> torch.Tensor:
return self._states[..., 2:4]
@property
def angle(self) -> torch.Tensor:
vel = self.velocity
angle = torch.arctan2(vel[..., 1:2], vel[..., 0:1])
return angle
def get_states(self, dim: int = 4) -> torch.Tensor:
if dim == 2:
return self._states[..., :2].clone()
elif dim == 4:
return self._states.clone()
elif dim == 5:
return torch.cat(
(
self._states[..., :2].clone(),
self.angle,
self._states[..., 2:].clone(),
),
dim=-1,
)
else:
raise RuntimeError(f"State dimension must be either 2, 4, or 5. Got {dim}")
def rotate(
self, angle: torch.Tensor, in_place: bool = False
) -> PositionVelocityState:
"""Rotate the state by the given angle in radiants"""
rot_matrix = get_rotation_matrix(angle, self._states.device)
rotated_pos = (rot_matrix @ self.position.unsqueeze(-1)).squeeze(-1)
rotated_vel = (rot_matrix @ self.velocity.unsqueeze(-1)).squeeze(-1)
if in_place:
self._states = torch.cat((rotated_pos, rotated_vel), dim=-1)
return self
else:
return to_state(torch.cat((rotated_pos, rotated_vel), dim=-1), self.dt)
def translate(
self, translation: torch.Tensor, in_place: bool = False
) -> PositionVelocityState:
"""Translate the state by the given tranlation"""
if in_place:
self._states[..., :2] += translation.expand_as(self._states[..., :2])
return self
else:
return to_state(
torch.cat(
(
self._states[..., :2].clone()
+ translation.expand_as(self._states[..., :2]),
self._states[..., 2:].clone(),
),
dim=-1,
),
self.dt,
)
class PositionAngleVelocityState(AbstractState):
"""
State representation with an underlying tensor representing position angle and velocity.
"""
def __init__(self, states: torch.Tensor, dt: float) -> None:
super().__init__()
assert states.shape[-1] == 5
self._states = states.clone()
self.dt = dt
@property
def position(self) -> torch.Tensor:
return self._states[..., :2].clone()
@property
def velocity(self) -> torch.Tensor:
return self._states[..., 3:5].clone()
@property
def angle(self) -> torch.Tensor:
return self._states[..., 2:3].clone()
def get_states(self, dim: int = 5) -> torch.Tensor:
if dim == 2:
return self._states[..., :2].clone()
elif dim == 4:
return torch.cat(
(self._states[..., :2].clone(), self._states[..., 3:].clone()), dim=-1
)
elif dim == 5:
return self._states.clone()
else:
raise RuntimeError(f"State dimension must be either 2, 4, or 5. Got {dim}")
def rotate(
self, angle: float, in_place: bool = False
) -> PositionAngleVelocityState:
"""Rotate the state by the given angle in radiants"""
rot_matrix = get_rotation_matrix(angle, self._states.device)
rotated_pos = (rot_matrix @ self.position.unsqueeze(-1)).squeeze(-1)
rotated_angle = self.angle + angle
rotated_vel = (rot_matrix @ self.velocity.unsqueeze(-1)).squeeze(-1)
if in_place:
self._states = torch.cat(rotated_pos, rotated_angle, rotated_vel, -1)
return self
else:
return to_state(
torch.cat(rotated_pos, rotated_angle, rotated_vel, -1), self.dt
)
def translate(
self, translation: torch.Tensor, in_place: bool = False
) -> PositionAngleVelocityState:
"""Translate the state by the given tranlation"""
if in_place:
self._states[..., :2] += translation.expand_as(self._states[..., :2])
return self
else:
return to_state(
torch.cat(
(
self._states[..., :2]
+ translation.expand_as(self._states[..., :2]),
self._states[..., 2:],
),
dim=-1,
),
self.dt,
)
def get_interaction_cost(
ego_state_future: AbstractState,
ado_state_future_samples: AbstractState,
interaction_cost_function: BaseCostTorch,
) -> torch.Tensor:
"""Computes interaction cost samples from predicted ado future trajectories and a batch of ego
future trajectories
Args:
ego_state_future: ((num_control_samples), num_agents, num_steps_future) ego state future
future trajectory
ado_state_future_samples: (num_prediction_samples, num_agents, num_steps_future)
predicted ado state trajectory samples
interaction_cost_function: interaction cost function between ego and (stochastic) ado
dt: time differential between two discrete timesteps in seconds
Returns:
(num_control_samples, num_agents, num_prediction_samples) interaction cost tensor
"""
if len(ego_state_future.shape) == 2:
x_ego = ego_state_future.position.unsqueeze(0)
v_ego = ego_state_future.velocity.unsqueeze(0)
else:
x_ego = ego_state_future.position
v_ego = ego_state_future.velocity
num_control_samples = ego_state_future.shape[0]
ado_position_future_samples = ado_state_future_samples.position.unsqueeze(0).expand(
num_control_samples, -1, -1, -1, -1
)
v_samples = ado_state_future_samples.velocity.unsqueeze(0).expand(
num_control_samples, -1, -1, -1, -1
)
interaction_cost, _ = interaction_cost_function(
x1=x_ego.unsqueeze(1),
x2=ado_position_future_samples,
v1=v_ego.unsqueeze(1),
v2=v_samples,
)
return interaction_cost.permute(0, 2, 1)
def evaluate_risk(
risk_level: float,
cost: torch.Tensor,
weights: torch.Tensor,
risk_estimator: Optional[AbstractMonteCarloRiskEstimator] = None,
) -> torch.Tensor:
"""Returns a risk tensor given costs and optionally a risk level
Args:
risk_level (optional): a risk-level float. If 0.0, risk-neutral expectation will be
returned. Defaults to 0.0.
cost: (num_control_samples, num_agents, num_prediction_samples) cost tensor
weights: (num_control_samples, num_agents, num_prediction_samples) probability weight of the cost tensor
risk_estimator (optional): a Monte Carlo risk estimator. Defaults to None.
Returns:
(num_control_samples, num_agents) risk tensor
"""
num_control_samples, num_agents, _ = cost.shape
if risk_level == 0.0:
risk = cost.mean(dim=-1)
else:
assert risk_estimator is not None, "no risk estimator is specified"
risk = risk_estimator(
risk_level * torch.ones(num_control_samples, num_agents),
cost,
weights=weights,
)
return risk
def evaluate_control_sequence(
control_sequence: torch.Tensor,
dynamics_model,
ego_state_history: AbstractState,
ego_state_target_trajectory: AbstractState,
ado_state_future_samples: AbstractState,
sample_weights: torch.Tensor,
interaction_cost_function: BaseCostTorch,
tracking_cost_function: TrackingCost,
risk_level: float = 0.0,
risk_estimator: Optional[AbstractMonteCarloRiskEstimator] = None,
) -> Tuple[float, float]:
"""Returns the risk and tracking cost evaluation of the given control sequence
Args:
control_sequence: (num_steps_future, control_dim) tensor of control sequence
dynamics_model: dynamics model for control
ego_state_target_trajectory: (num_steps_future) tensor of ego target
state trajectory
ado_state_future_samples: (num_prediction_samples, num_agents, num_steps_future)
of predicted ado trajectory samples states
sample_weights: (num_prediction_samples, num_agents) tensor of probability weights of the samples
intraction_cost_function: interaction cost function between ego and (stochastic) ado
tracking_cost_function: deterministic tracking cost that does not involve ado
risk_level: risk_level (optional): a risk-level float. If 0.0, risk-neutral expectation
is used. Defaults to 0.0.
risk_estimator (optional): a Monte Carlo risk estimator. Defaults to None.
Returns:
tuple of (interaction risk, tracking_cost)
"""
ego_state_current = ego_state_history[..., -1]
ego_state_future = dynamics_model.simulate(ego_state_current, control_sequence)
# state starts with x, y, angle, vx, vy
tracking_cost = tracking_cost_function(
ego_state_future.position,
ego_state_target_trajectory.position,
ego_state_target_trajectory.velocity,
)
interaction_cost = get_interaction_cost(
ego_state_future,
ado_state_future_samples,
interaction_cost_function,
)
interaction_risk = evaluate_risk(
risk_level,
interaction_cost,
sample_weights.permute(1, 0).unsqueeze(0).expand_as(interaction_cost),
risk_estimator,
)
# TODO: averaging over agents but we might want to reduce a different way
return (interaction_risk.mean().item(), tracking_cost.mean().item())
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