User Guide#

Inverted AI API provides a service that controls non-playable characters (NPCs) in driving simulations. The two main functions are INITIALIZE, called at the beginning of the simulation, and DRIVE, called at each time step. Typically, the user runs their simulator locally, controlling the actions of the ego vehicle, and querying the API to obtain the behavior of NPCs. This page describes the high level concepts governing the interaction with the API. Please refer to specific pages for Python SDK, C++ SDK, REST API, Getting started, and Examples.

We follow the continuous space, discrete time approach used in most driving simulators. In the current version, the API only supports a time step of 100 ms, corresponding to 10 frames per second, and expects to run in a synchronous fashion. The latency of API calls varies with physical location of the client server and its network configuration, but generally the API should not be relied upon to provide real-time simulation. For optimal resource utilization, we recommend that you run multiple simulations in parallel, so that one can execute when another is waiting for the API reply. The technology underlying the API is based on ITRA and was optimized to handle simulations of up to 20 seconds (200 time steps) contained within an area of roughly 300 meters in diameter. The API backend has been provisioned to accommodate a large number of agents, where the maximum allowed varies per location.

Programming language support#

The core interface is a REST API, that can be called from any programming language. This is a low-level, bare-bones access mode that offers maximum flexibility to deploy in any environment. For convenience, we also provide a Python SDK, freely available on PyPI with minimal dependencies, which provides an abstraction layer on top of the REST API. Recently, we also released C++ SDK and in the future we intend to release similar libraries for other languages.

Maps and geofencing#

The API operates on a pre-defined collection of maps and currently there is no programmatic way to add additional locations. For each location there is a map, represented internally in the Lanelet2 format, which specifies lanelets, traffic lights, and a selection of static traffic signs (along with their relationship to specific lanelets). Each map comes with a canonical Euclidean coordinate frame in meters, which for OSM files is obtained by applying a specific UTM projector defined by lat/lon, and everything sent across the API is always specified in terms of this coordinate frame. To be able to perform co-simulation, you need to have the same map available in your simulator. For convenience, the map used on our end can be downloaded through LOCATION_INFO. The maps must be flat (we assume the world is 2D) and generally cover relatively small regions (a few hundred meters). For each map there is a designated supported area, defined as the interior of a convex polygon represented as a closed linestring, outside of which the realism of NPCs may significantly deteriorate. It’s valid to query the API outside of the supported area, but predictions obtained in this way may be unsatisfactory. The maps for each location are versioned using the standard semantic versioning scheme “major.minor.patch”, starting from “1.0.0” (or “0.1.0” if location is experimental). Note that different API keys may allow access to different locations. For a location that a given API key is allowed to access, LOCATION_INFO provides all the relevant information. Please contact us with requests to include additional locations.

Agent types and representations#

At the moment the API only supports vehicles, but future releases will also support pedestrians, bicycles, etc.. We assume that each vehicle is a rigid rectangle with a fixed length and width. The motion of each vehicle is constrained by the kinematic bicycle model, which further requires specifying the rear axis offset, that is the distance between the center of the vehicle and its rear axis. Front axis offset is not relevant, because it can not be fit from observational data, so we omit it. The three static agent attributes are: length, width, and rear offset. We represent the instantaneous state of each vehicle as four numbers: x and y position, orientation angle, and speed. We do not consider lateral velocity, vehicle lights, or any other information about vehicle state. DRIVE predicts the next state for each vehicle, rather than an action that can be executed in the local simulator and run through its dynamics model. The predicted motion is consistent with the kinematic bicycle model and accelerations are constrained to a reasonable range observed in real world traffic, but there is no guarantee that the corresponding motion could be realized through some action given a particular dynamics model in the local simulator. We recommend teleporting the NPCs to their new positions, since any discrepancies between predicted and realized states for NPCs may negatively affect the quality of subsequent predictions.

Traffic lights and other control signals#

Static traffic signals form a part of the map description and influence NPC predictions, but they are not exposed in the interface. Traffic light placement, in particular regarding which traffic light applies to which lanelet, forms a part of the map as well. Traffic light state changes dynamically and can be automatically managed by the server when calling the API. Each traffic light can be green, yellow, or red at any given point. Traffic light IDs are fixed and can be derived from the map, but for convenience we also provide traffic light IDs and the corresponding locations in LOCATION_INFO. For maps with traffic lights, on a call to INITIALIZE, the server generates a realistic configuration of all traffic lights, and returns the associated light states via ‘light_recurrent_states’. On each call to DRIVE, traffic lights’ states can be automatically managed by the server with ‘light_recurrent_states’. There is also the option to manually set light states with ‘traffic_lights_states’, but once this path is taken, it is on the client to continually provide ‘traffic_lights_states’ on all calls to DRIVE.

Handling agents and NPCs#

In the API, there is no distinction between agents, controlled by you, and NPCs, controlled by us, so we refer to them collectively as agents. In any simulation there can be zero or more characters of either kind. When calling DRIVE, the client needs to list all agents in simulation and we predict the next states for all of them. It is up to the client to decide which of those agents are NPCs and use the corresponding predictions in the local simulator. However, it is important to specify all agents when calling the API, since otherwise NPCs will not be able to react to omitted agents. Due to the recurrent nature of ITRA, we generally recommend that the customer is consistent about this choice throughout the simulation - predictions for agents whose state is updated differently from ITRA predictions may not be as good as when ITRA fully controls them.

Consistent simulation with a stateless API#

The API is stateless, so each call to DRIVE requires specifying both the static attributes and the dynamic state of each agent. However, ITRA is a recurrent model that uses the simulation’s history to make predictions, which we facilitate through the stateless API by passing around a recurrent state, which is a vector with unspecified semantics from the client’s perspective. Each call to DRIVE returns a new recurrent state for each agent, which must be passed for this agent to DRIVE on the subsequent call. Providing an incorrect recurrent state may silently lead to deteriorating performance, and in order to obtain valid values for the initial recurrent state, the simulation must always start with INITIALIZE. To initialize the simulation to a specific state, you can provide a sequence of historical states for all agents that will be used to construct the matching recurrent state. For best performance, at least 10 time steps should be provided. To simplify the process of passing the recurrent states around, we provide a stateful Co-simulation wrapper in the Python library that handles this internally.

Entering and exiting simulation#

In the simple case there is a fixed number of agents present throughout the entire simulation. However, it is also possible to dynamically introduce and remove agents, which is typically done when they enter and exit the supported area. Removing agents is easy, all it takes is removing the information for a given agent from the lists of agent attributes, agent states, and recurrent states. For convenience, DRIVE returns a boolean vector indicating which agents are within the supported area after the predicted step. Introducing agents into a running simulation is more complicated, due to the requirement to construct their recurrent state. When predictions for the new agents are not going to be consumed, its state can simply be appended to the relevant lists, with the recurrent state set to zeros. To obtain good predictions for such an agent, another call to INITIALIZE needs to be made, providing the recent history of all agents, including the new agent. This correctly initializes the recurrent state and DRIVE can be called from that point on normally. For best performance, each agent should initially be controlled by the client for at least 10 time steps before being handed off to ITRA as an NPC by calling INITIALIZE.

Reproducibility and control over predictions#

INITIALIZE and DRIVE optionally accept a random seed, which controls their stochastic behavior. With the same seed and the same inputs, the outputs will be approximately the same with high accuracy. Other than for the random seed, there is currently no mechanism to influence the behavior of predicted agents, such as by directing them to certain exits or setting their speed, but such mechanisms will be included in future releases.

Validation and debugging#

To facilitate development of integration without incurring the costs of API calls, we provide a way to mock API calls that returns locally computed simple responses in the correct format. This mock API also performs validation of message formats, including checking lengths of lists and bounds for numeric values, and those checks can also be optionally performed on the client side before paid API calls. All those features are only available in the Python library and not in the REST API. To enable the mock API, just set the environment variable IAI_MOCK_API to true according to Environment Variables. For further debugging and visualization, both INITIALIZE and DRIVE optionally return a rendered birdview image showing the simulation state after the call to them. This significantly increases the payload size and latency, so it should not be done in real integrations.