Gatik Ai Inc.

Gautam Narang

Apeksha Kumavat

Arjun Narang

Michael Smart

Marko Ilievski

Kinh Tieu

Amir Yazdani

Andreas Bauer

Brian McLean

Siddharth Ram

Dharmateja Kadem

Gunnar Newquist

Alexander Schmitt

Engin Burak Anil

Marko Illevski

In variants, the vehicle can provide redundant drive by wire subsystems that are operable in both an autonomous (AV) mode and manual driving modes.

Drive-by-wire vehicle architecture

A method for behavior prediction of vehicles in a scene can include: recording a set of observations, determining a scene graph, determining a set of scene features, predicting agent behavior based on the scene graph, and/or controlling an autonomous vehicle. The method functions to determine vehicle controls for an autonomous vehicle based on elements in the surrounding environment and relationships between the elements.

Efficient behavior prediction

A method for determining scene risk for an autonomous vehicle includes receiving scene data, predicting risk scores using a trained risk prediction model, determining an appropriate autonomous vehicle behavior based on these risk scores, and controlling the vehicle accordingly. In some implementations, the method includes training the risk prediction model by generating initial risk scores from a labeled training dataset, learning an intermediate model from these scores, and training the risk prediction model using multimodal training data and secondary risk scores.

Method and system for autonomous vehicle control

In variants, a method for generating synthetic data can include: determining an initial dataset, determining characteristics of the initial dataset, generating a set of prompts based on the characteristics, prompting a model to generate the synthetic data using the set of prompts, and training an AV model using the synthetic data.

System and method for augmenting autonomous vehicle training data

A system for context-aware decision making of an autonomous agent includes a computing system having a context selector and a map. A method for context-aware decision making of an autonomous agent includes receiving a set of inputs, determining a context associated with an autonomous agent based on the set of inputs, and optionally any or all of: labeling a map; selecting a learning module (context-specific learning module) based on the context; defining an action space based on the learning module; selecting an action from the action space; planning a trajectory based on the action S; and/or any other suitable processes.

Method and system for context-aware decision making of an autonomous agent

A system for deterministic trajectory selection based on uncertainty estimation includes a set of one or more computing systems. A method for deterministic trajectory selection includes receiving a set of inputs; determining a set of outputs; determining uncertainty parameters associated with any or all of the set of inputs and/or any or all of the set of outputs; and evaluating the uncertainty parameters and optionally triggering a process and/or action in response.

Method and system for deterministic trajectory selection based on uncertainty estimation for an autonomous agent

A system for operating an autonomous agent with a remote operator includes a remote subsystem, a local subsystem, and a communication subsystem. A method for operating an autonomous agent with a remote operator includes receiving and processing a set of inputs; presenting a set of outputs to a remote subsystem; receiving an input from a remote operator; and operating the autonomous agent. Additionally or alternatively, the method can include any or all of: detecting a failure and/or triggering a failure response; detecting a particular scenario associated with the autonomous agent; triggering a remote operator request; processing remote operator input; and/or any other processes.

Method and system for operating an autonomous agent with a remote operator

A system for addressing failure in an autonomous agent includes a driving subsystem, a control subsystem, a central computing subsystem, and an autonomous vehicle (AV) sensor subsystem. The system can optionally additionally include a power subsystem, a vehicle chassis subsystem, a communication subsystem, a distributed computing and/or processing subsystem, a supplementary sensor subsystem, and/or any other components. A method for addressing failure can include any or all of: detecting and responding to a failure; and operating the vehicle. Additionally or alternatively, the methodcan include any other processes.

Method and system for addressing failure in an autonomous agent

A system for expanding the operational design domain (ODD) of an autonomous agent includes a decision-making platform (equivalently referred to herein as a decision-making architecture). A method for expanding the operational design domain (ODD) includes determining a decision-making architecture for a first domain and adapting the decision-making architecture to a second domain. Additionally or alternatively, the methodcan include implementing the decision-making architecture Sand/or any other processes.

Method and system for expanding the operational design domain of an autonomous agent

A system for data-driven, modular decision making and trajectory generation includes a computing system. A method for data-driven, modular decision making and trajectory generation includes: receiving a set of inputs; selecting a learning module such as a deep decision network and/or a deep trajectory network from a set of learning modules; producing an output based on the learning module; repeating any or all of the above processes; and/or any other suitable processes. Additionally or alternatively, the method can include training any or all of the learning modules; validating one or more outputs; and/or any other suitable processes and/or combination of processes.

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Palo Alto, CA, United States of America

Gautam Narang