GitHub PyPI Open In Colab

Inverted AI provides an API for controlling non-playable characters (NPCs) in autonomous driving simulations, available as either a REST API or a Python library built on top of it. Using the API requires an access key - contact us to get yours. This page describes how to get started quickly. For more in-depth understanding, see the API usage guide, and detailed documentation for the REST API and the Python library. To understand the underlying technology and why it’s necessary for autonomous driving simulations, visit the Inverted AI website.

Getting started#

Installation#

For installing the Python package from PyPI:

pip install --upgrade invertedai

The Python client library is open source, so you can also download it and build locally.

Minimal example#

Conceptually, the API is used to establish synchronous co-simulation between your own simulator running locally on your machine and the NPC engine running on Inverted AI servers. The basic integration in Python looks like this.

from typing import List
import numpy as np
import imageio
import invertedai as iai

# iai.add_apikey('')  # specify your key here or through the IAI_API_KEY variable


class LocalSimulator:
    """
    Mock up of a local simulator, where you control the ego vehicle. This example only supports single ego vehicle.
    """
    def __init__(self, ego_state: iai.AgentState, npc_states: List[iai.AgentState]):
        self.ego_state = ego_state
        self.npc_states = npc_states

    def _step_ego(self):
        """
        The simple motion model drives forward with constant speed.
        The ego agent ignores the map and NPCs for simplicity.
        """
        dt = 0.1
        dx = self.ego_state.speed * dt * np.cos(self.ego_state.orientation)
        dy = self.ego_state.speed * dt * np.sin(self.ego_state.orientation)

        self.ego_state = iai.AgentState(
            center=iai.Point(x=self.ego_state.center.x + dx, y=self.ego_state.center.y + dy),
            orientation=self.ego_state.orientation,
            speed=self.ego_state.speed,
        )

    def step(self, predicted_npc_states):
        self._step_ego()  # ego vehicle moves first so that it doesn't see future NPC movement
        self.npc_states = predicted_npc_states
        return self.ego_state


iai_simulation = iai.BasicCosimulation(  # instantiate a stateful wrapper for Inverted AI API
    location='canada:vancouver:ubc_roundabout',  # select one of available locations
    agent_count=5,  # how many vehicles in total to use in the simulation
    ego_agent_mask=[True, False, False, False, False],  # first vehicle is ego, rest are NPCs
    get_birdview=True,  # provides simple visualization - don't use in production
)
local_simulation = LocalSimulator(iai_simulation.ego_states[0], iai_simulation.npc_states)
images = [iai_simulation.birdview.decode()]  # images storing visualizations of subsequent states
for _ in range(100):  # how many simulation steps to execute (10 steps is 1 second)
    # query the API for subsequent NPC predictions, informing it how the ego vehicle acted
    iai_simulation.step([local_simulation.ego_state])
    # collect predictions for the next time step
    predicted_npc_behavior = iai_simulation.npc_states
    # execute predictions in your simulator, using your actions for the ego vehicle
    updated_ego_agent_state = local_simulation.step(predicted_npc_behavior)
    # save the visualization - requires np and cv2
    images.append(iai_simulation.birdview.decode())
# save the visualization to disk
imageio.mimsave("iai-example.gif", np.array(images), format="GIF-PIL")

To quickly check out how Inverted AI NPCs behave, try our Colab, where all agents are NPCs, or go to our github repository to execute it locally. When you’re ready to try our NPCs with a real simulator, see the example CARLA integration. The examples are currently only provided in Python, but if you want to use the API from another language, you can use the REST API directly.

Further resources#

The following pages will help you integrate the Inverted AI API with your own simulator.

REFERENCES#