A temporal prediction model (222) for semantic intent understanding is described. An agent (202) (e.g. a moving object such as a pedestrian, an animal, a cyclist, a vehicle, etc.) in an environment can be detected in sensor data collected from sensors on a vehicle. The sensors can include light detection and ranging (LIDAR) sensors, radio detection and ranging (RADAR) sensors, ultrasonic transducers, sound navigation and ranging (SONAR) sensors, location sensors (e.g., GPS, compass, etc.), inertial sensors (e.g., inertial measurement units, accelerometers, magnetometers, gyroscopes, etc.), cameras, wheel encoders, microphones, environment sensors. Computing device(s) associated with the vehicle determine, based partly on the sensor data, attribute(s) of the agent (e.g., classification, position, velocity, etc.), and generate, based partly on the attribute(s) and a temporal prediction model (222), semantic intent(s) of the agent (e.g., crossing a road, staying straight, etc.), which correspond to candidate trajectory(s) (238) of the agent. The candidate trajectory(s) (238) are associated with weight(s) representing likelihood(s) that the agent will perform respective intent(s). The computing device(s) uses one (or more) of the candidate trajectory(s) (238) to determine a vehicle trajectory along which a vehicle is to drive.
TEMPORAL PREDICTION MODEL FOR SEMANTIC INTENT UNDERSTANDING