US 11,834,070 B2
Probabilistic simulation sampling from agent data
Johan Engstrom, Los Gatos, CA (US); Emmanuel Christophe, Mountain View, CA (US); Joseph Lee, Menlo Park, CA (US); Isaac Supeene, South San Francisco, CA (US); and Razvan Mathias, San Jose, CA (US)
Assigned to Waymo LLC, Mountain View, CA (US)
Filed by Waymo LLC, Mountain View, CA (US)
Filed on Jul. 8, 2021, as Appl. No. 17/370,924.
Prior Publication US 2023/0011497 A1, Jan. 12, 2023
Int. Cl. B60W 60/00 (2020.01); B60W 30/095 (2012.01); G06V 40/20 (2022.01)
CPC B60W 60/0011 (2020.02) [B60W 30/0956 (2013.01); G06V 40/20 (2022.01); B60W 2554/4046 (2020.02); B60W 2554/4049 (2020.02)] 20 Claims
OG exemplary drawing
 
1. A method performed by one or more computers, the method comprising:
identifying an instance of a navigation interaction that includes an autonomous vehicle and one or more agents navigating in an environment;
generating a plurality of simulated interactions corresponding to the instance, comprising, for each simulated interaction:
identifying one or more of the agents;
for each identified agent and for each of one or more properties that characterize behavior of the identified agent, obtaining data representing a probability distribution over a set of possible values for the property, wherein the one or more properties comprise a reaction time of the identified agent to a stimulus;
sampling a respective value from each of the probability distributions; and
simulating the navigation interaction using a computer simulation of the environment that, for each identified agent, selects actions performed by the identified agent at a plurality of time steps using behavior predictions for other agents in the computer simulation, and wherein the simulating comprises:
selecting, based on the sampled value for the reaction time of the identified agent, a time window after a time step at which the stimulus occurred; and
selecting actions performed by the identified agent for the time steps in the time window using prior behavior predictions from time steps prior to the stimulus occurring;
determining, for each simulated interaction, whether a particular event occurred during the simulated interaction; and
determining a likelihood that the particular event would occur during the navigation interaction based at least in part on whether the particular event occurred during each of the simulated interactions.