import numpy as np
from gym_urbandriving.utils import PIDController
from gym_urbandriving.agents import PursuitAgent
from gym_urbandriving.planning import VelocityMPCPlanner,GeometricPlanner,VelocityNeuralPlanner
import gym_urbandriving as uds
[docs]class SteeringSupervisor(PursuitAgent):
"""
Superivsor agent which implements the planning stack to obtain steering level supervision of
which the car should follow.
Attributes
----------
agent_num : int
Index of this agent in the world.
Used to access its object in state.dynamic_objects
"""
def __init__(self, agent_num=0):
"""
Initializes the SteeringSupervisor Class
Parameters
----------
agent_num: int
The number which specifies the agent in the dictionary state.dynamic_objects['controlled_cars']
"""
self.agent_num = agent_num
#Move to JSON
self.PID_acc = PIDController(1.0, 0, 0)
self.PID_steer = PIDController(2.0, 0, 0)
self.not_initiliazed = True
[docs] def eval_policy(self, state):
"""
Returns action based on current world state
Parameters
----------
state : PositionState
State of the world, unused
action : float
Target velocity for car to travel at
Returns
-------
SteeringAction
"""
if self.not_initiliazed:
geoplanner = GeometricPlanner(state, inter_point_d=40.0, planning_time=0.1)
geoplanner.plan_for_agents(state,type_of_agent='controlled_cars',agent_num=self.agent_num)
self.not_initiliazed = False
target_vel = VelocityNeuralPlanner().plan(state, self.agent_num,type_of_agent = "controlled_cars")
state.dynamic_objects['controlled_cars'][str(self.agent_num)].trajectory.set_vel(target_vel)
return super(SteeringSupervisor, self).eval_policy(state,type_of_agent = 'controlled_cars')