Systems and methods for path planning with latent state inference and graphical relationships

A method for path planning with latent state inference and graphical relationships, the method comprising:

• receiving sensor data associated with a plurality of agents, wherein the plurality of agents includes an ego agent;
• mapping the sensor data to a latent state distribution to identify latent states of the plurality of agents;
• predicting future trajectories of the plurality of agents at a given time based on sensor data and the latent states of the plurality of agents;
• generating a graphical representation based on the sensor data and a graphical representation neural network, wherein temporal information is applied to the sensor data so the graphical representation includes spatial-temporal relationships between the ego agent and the agents of the plurality of agents;
• generating a motion plan for the ego agent based on the predicted future trajectories and the graphical representation;
• and
• causing the ego agent to execute the motion plan.

A system for path planning with latent state inference and graphical relationships, the system comprising:

• an inference module configured to:
• receive sensor data associated with a plurality of agents, wherein the plurality of agents includes an ego agent;
• and
• map the sensor data to a latent state distribution to identify latent states of the plurality of agents, wherein the latent states identify agents of the plurality of agents as cooperative or aggressive with respect to the ego agent;
• a policy module configured to predict future trajectories of the plurality of agents at a given time based on the sensor data and the latent states of the plurality of agents;
• a graphical representation module configured generate a graphical representation based on the sensor data and a graphical representation neural network;
• a planning module configured to generate a motion plan for the ego agent based on the predicted future trajectories and the graphical representation;
• and
• an execution module configured to cause the ego agent to execute the motion plan, and receive sensor data to confirm the plurality of agents yield to the ego agent as predicted by the policy module, wherein the inference module maps actions of the plurality of agents to the latent distribution.