Developing Behavior Models

The BehaviorModel class is the core component that defines how an agent acts/plans in the environment. In this tutorial, a simple Python behavior model is created that drives in a straight line. The video below provides additional insights on the development of behavior models for BARK.

All behavior models in BARK are derived from the BehaviorModel base-class. The base class implements three virtual functions that have to be (Plan, Clone) or can be overloaded (ActionToBehavior). The function ActionToBehavior does not need to be overloaded besides if the action of the behavior model is set externally.

The core functionality resides within the Plan function that outputs a state-space trajectory for an agent. The Clone function specifies how a behavior model is cloned, e.g., if members should be preserved or be reset.

A simple derived behavior model class is given by:

import numpy as np
from bark.core.models.behavior import BehaviorModel

class DerivedBehaviorModel(BehaviorModel):
  def __init__(self,
               params=None):
    BehaviorModel.__init__(self, params)

  def Plan(self, step_time, observed_world):
    pass

  def Clone(self):
    pass

  def ActionToBehavior(self, action):
    pass

However, this is not a valid behavior model as the Plan function does not return or set a trajectory and the clone function does not return an object. For a behavior model that drives in s straight line, the Plan function can be modified as follows:

def Plan(self, step_time, observed_world):
  trajectory_points = 10
  start_time = observed_world.GetWorldTime()
  ego_vehicle_state = observed_world.CurrentEgoState()
  trajectory_time = np.linspace(
    start_time, start_time + step_time, trajectory_points)
  x_position = np.linspace(
    0, 10, trajectory_points)
  # 10 time steps x (time, x, y, theta and velocity)
  trajectory = np.zeros(shape=(trajectory_points, 5))
  trajectory[:, 0] = trajectory_time
  trajectory[:, 1] = x_position
  trajectory[:, 2:4] = ego_vehicle_state[2:4]
  # store the trajectory and return it
  super().SetTrajectory(trajectory)
  return trajectory

To use the new newly generated behavior model in the scenario generation, the LaneCorridorConfig has to be modified slightly from the getting started to:

class CustomLaneCorridorConfig(LaneCorridorConfig):
  def __init__(self,
               params=None,
               **kwargs):
    super(CustomLaneCorridorConfig, self).__init__(params, **kwargs)

  def goal(self, world):
    road_corr = world.map.GetRoadCorridor(
      self._road_ids, XodrDrivingDirection.forward)
    lane_corr = self._road_corridor.lane_corridors[0]
    # defines goal polygon on the left lane
    return GoalDefinitionPolygon(lane_corr.polygon)

  def controlled_behavior_model(self, world):
    return DerivedBehaviorModel(self._params)

The ego vehicle's behavior model is now the DerivedBehaviorModel.