def __init__(self, master_kernel, sim_params):
"""See parent class."""
KernelVehicle.__init__(self, master_kernel, sim_params)
self.__ids = [] # ids of all vehicles
self.__human_ids = [] # ids of human-driven vehicles
self.__controlled_ids = [] # ids of flow-controlled vehicles
self.__controlled_lc_ids = [] # ids of flow lc-controlled vehicles
self.__rl_ids = [] # ids of rl-controlled vehicles
self.__observed_ids = [] # ids of the observed vehicles
# vehicles: Key = Vehicle ID, Value = Dictionary describing the vehicle
# Ordered dictionary used to keep neural net inputs in order
self.__vehicles = collections.OrderedDict()
# total number of vehicles in the network
self.num_vehicles = 0
# number of rl vehicles in the network
self.num_rl_vehicles = 0
# contains the parameters associated with each type of vehicle
self.type_parameters = {}
# list of vehicle ids located in each edge in the network
self._ids_by_edge = dict()
# number of vehicles that entered the network for every time-step
self._num_departed = []
self._departed_ids = []
# number of vehicles to exit the network for every time-step
self._num_arrived = []
self._arrived_ids = []
# contains conversion from Flow-ID to Aimsun-ID
self._id_aimsun2flow = {}
self._id_flow2aimsun = {}
# contains conversion from Flow-type to Aimsun-type
self._type_aimsun2flow = {}
self._type_flow2aimsun = {}
# number of vehicles of each type
self.num_type = {}
def __init__(self,
master_kernel,
sim_params):
"""See parent class."""
KernelVehicle.__init__(self, master_kernel, sim_params)
self.__ids = [] # ids of all vehicles
self.__human_ids = [] # ids of human-driven vehicles
self.__controlled_ids = [] # ids of flow-controlled vehicles
self.__controlled_lc_ids = [] # ids of flow lc-controlled vehicles
self.__rl_ids = [] # ids of rl-controlled vehicles
self.__observed_ids = [] # ids of the observed vehicles
# vehicles: Key = Vehicle ID, Value = Dictionary describing the vehicle
# Ordered dictionary used to keep neural net inputs in order
self.__vehicles = collections.OrderedDict()
# total number of vehicles in the network
self.num_vehicles = 0
# number of rl vehicles in the network
self.num_rl_vehicles = 0
# contains the parameters associated with each type of vehicle
self.type_parameters = {}
# list of vehicle ids located in each edge in the network
self._ids_by_edge = dict()
# number of vehicles that entered the network for every time-step
self._num_departed = []
self._departed_ids = []
# number of vehicles to exit the network for every time-step
self._num_arrived = []
self._arrived_ids = []
# contains conversion from Flow-ID to Aimsun-ID
self._id_aimsun2flow = {}
self._id_flow2aimsun = {}
# current number of vehicles of each type
self.num_type = {}
# total number of vehicles of each type (used for naming them)
self.total_num_type = {}
# type of vehicles that will be tracked
# note: vehicles added via the scenario (ie by calling the
# add_vehicle function) will also be tracked, even if their
# type is not specified here
self.tracked_vehicle_types = {"rl", "idm"} # TODO maybe generic
# all the vehicle tracking information that should be stored
# for the tracked vehicles info that can be tracked:
# CurrentPos, distance2End, xCurrentPos, yCurrentPos, zCurrentPos,
# xCurrentPosBack, yCurrentPosBack, zCurrentPosBack, CurrentSpeed,
# TotalDistance, SectionEntranceT, CurrentStopTime, stopped,
# idSection, segment, numberLane, idJunction, idSectionFrom,
# idLaneFrom, idSectionTo, idLaneTo
self.tracked_info_bitmap = self.make_bitmap_for_tracking({
'CurrentPos', 'distance2End',
'xCurrentPos', 'yCurrentPos', 'xCurrentPosBack', 'yCurrentPosBack',
'CurrentSpeed', 'numberLane',
'idSection', 'idJunction', 'idSectionFrom', 'idSectionTo'
})