def initialize_params(self, params):
self._local_params = \
self._params["Scenario"]["Generation"]["DeterministicScenarioGeneration"]
self.goal_frame_center = self._local_params["goal_frame"][
"center_pose", "Center pose of the goal frames", [0, 0, 0]]
self.goal_frame_points = self._local_params["goal_frame"][
"polygon_points", "Points of the goal frame polygon",
[[-0.2, 1], [0.2, 1], [0.2, -1], [-0.2, -1], [-0.2, 1]]]
self.goal_frame_poses = self._local_params[
"goal_poses",
"A list with x,y, theta specyfing the sequence of the goal frames",
[[5, 10, 3.14 / 2 + 0.01], [10, 15, 3.14 /
4], [20, 10, 3.14 /
2], [15, 2, 3.14 / 2 - 0.7]]]
json_converter = ModelJsonConversion()
_agent_params = json_converter.agent_to_json(
self.default_drone_model())
if not isinstance(_agent_params, dict):
_agent_params = _agent_params.ConvertToDict()
self.drone_params = self._local_params[
"drones", "list of dictionaries with drone definitions",
[{
"drone_model": _agent_params
}]]
self.ego_agent_id = self._local_params["ego_agent_id",
"ID of the ego-agent", 0]
def initialize_params(self, params):
params_temp = \
self._params["Scenario"]["Generation"]["UniformVehicleDistribution"]
self._random_seed = 1000 # since ScenarioGeneration UniformVehicleDistribution
# will soon be deprecated we introduce this hack
self._map_file_name = params_temp[
"MapFilename", "Path to the open drive map",
"bark/runtime/tests/data/city_highway_straight.xodr", ]
self._ego_goal_end = params_temp[
"EgoGoalEnd", "The center of the ego agent's goal region polygon",
[5117, 5200]]
self._ego_goal_start = params_temp[
"EgoGoalStart", "The coordinates of the start of the ego goal,\
if empty only ego goal end is used as center of polygon ", []]
self._ego_goal_state_limits = params_temp[
"EgoGoalStateLimits",
"x,y and theta limits around center line of lane between start and end applied to both lateral sides \
(only valid if start and end goal of ego are given)", [0.1, 0, 0.08]]
self._ego_route = params_temp[
"EgoRoute",
"A list of two points defining start and end point of initial ego driving corridor. \
If empty, then one of the other agents is selected as ego agents.",
[[5117.5, 5100], [5117.5, 5200]]]
self._others_source = params_temp[
"OthersSource",
"A list of points around which other vehicles spawn. \
Points should be on different lanes. XodrLanes must be near these points \
(<0.5m) Provide a list of lists with x,y-coordinates",
[[5114.626, 5061.8305]]]
self._others_sink = params_temp[
"OthersSink",
"A list of points defining end of other vehicles routes.\
Points should be on different lanes and match the order of the\
source points. XodrLanes must be near these points (<0.5m) \
Provide a list of lists with x,y-coordinates", [[5114.626, 5193.1725]]]
assert len(self._others_sink) == len(self._others_source)
self._vehicle_distance_range = params_temp["VehicleDistanceRange",
"Distance range between vehicles in meter given as tuple from which" + \
"distances are sampled uniformly",
(10, 20)]
self._other_velocity_range = params_temp["OtherVehicleVelocityRange",
"Lower and upper bound of velocity in km/h given as tuple from which" + \
" velocities are sampled uniformly",
(20,30)]
self._ego_velocity_range = params_temp["EgoVehicleVelocityRange",
"Lower and upper bound of velocity in km/h given as tuple from which" + \
" velocities are sampled uniformly",
(20,30)]
json_converter = ModelJsonConversion()
self._agent_params = params_temp[
"VehicleModel", "How to model the other agents",
json_converter.agent_to_json(self.default_agent_model())]
if not isinstance(self._agent_params, dict):
self._agent_params = self._agent_params.ConvertToDict()
np.random.seed(self._random_seed)