US 10,882,522 B2Granted

Systems and methods for agent tracking

AssigneeToyota Research Institute, Inc.

InventorsGuy Rosman, Jonathan DeCastro, Nikos Arechiga Gonzalez, John Joseph Leonard, Luke S. Fletcher, Daniel Stonier

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Abstract

System, methods, and other embodiments described herein relate to modeling dynamic agents in a surrounding environment of an ego vehicle. In one embodiment, a method includes, in response to receiving sensor data including present observations of a road agent of the dynamic agents in the surrounding environment, identifying previous observations of the road agent from an electronic data store. The method includes estimating a future state of the road agent using at least the present observations and the previous observations of the road agent to compute the future state according to a probabilistic model comprised of a transition model that accounts for dynamic behaviors of the road agent to characterize transitions between states, and an agent model that accounts for actions of the road agent. The method includes controlling one or more vehicle systems of the ego vehicle according to the future state of the road agent.

Claims20

Dependent claims

A prediction system for modeling dynamic agents in a surrounding environment of an ego vehicle, comprising:

one or more processors;
a memory communicably coupled to the one or more processors and storing:
an input module including instructions that when executed by the one or more processors cause the one or more processors to, in response to receiving sensor data including present observations of a road agent of the dynamic agents that are being tracked in the surrounding environment, identify previous observations of the road agent from an electronic data store;
and
a tracking module including instructions that when executed by the one or more processors cause the one or more processors to predict a presence of a latent agent using a probabilistic model and at least the present observations of the road agent, and to estimate a future state of the road agent using at least the present observations, the presence of the latent agent, and the previous observations of the road agent to compute the future state according to the probabilistic model that is comprised of a transition model that accounts for dynamic behaviors of the road agent to characterize transitions between states, and an agent model that accounts for actions of the road agent, wherein the present observations indicate at least a partial observation of the road agent, and
wherein the tracking module includes instructions to control one or more vehicle systems of the ego vehicle according to the future state of the road agent.

3 more elements

A non-transitory computer-readable medium for modeling dynamic agents in a surrounding environment of an ego vehicle and including instructions that when executed by one or more processors cause the one or more processors to:

in response to receiving sensor data including present observations of a road agent of the dynamic agents that are being tracked in the surrounding environment, identify previous observations of the road agent from an electronic data store;
predict a presence of a latent agent using a probabilistic model and at least the present observations of the road agent;
estimate a future state of the road agent using at least the present observations, the presence of the latent agent, and the previous observations of the road agent to compute the future state according to the probabilistic model that is comprised of a transition model that accounts for dynamic behaviors of the road agent to characterize transitions between states, and an agent model that accounts for actions of the road agent, wherein the present observations indicate at least a partial observation of the road agent;
and
control one or more vehicle systems of the ego vehicle according to the future state of the road agent.

2 more elements

A method of modeling dynamic agents in a surrounding environment of an ego vehicle, comprising:

in response to receiving sensor data including present observations of a road agent of the dynamic agents that are being tracked in the surrounding environment, identifying previous observations of the road agent from an electronic data store;
predicting a presence of a latent agent using a probabilistic model and at least the present observations of the road agent;
estimating a future state of the road agent using at least the present observations, the presence of the latent agent, and the previous observations of the road agent to compute the future state according to the probabilistic model that is comprised of a transition model that accounts for dynamic behaviors of the road agent to characterize transitions between states, and an agent model that accounts for actions of the road agent, and wherein the present observations indicate at least a partial observation of the road agent;
and
controlling one or more vehicle systems of the ego vehicle according to the future state of the road agent.

2 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/130,458Filed 13 Sep 2018
Published: US 2020/0086863 A119 Mar 2020
Granted: 5 Jan 2021
Term ends: Not estimatedThe source records don’t support an estimate.

Classification15 codes

B60W 30/0956
Predicting travel path or likelihood of collision: the prediction being responsive to traffic or environmental parameters
G06N 3/006
Artificial life, i.e. computing arrangements simulating life: based on simulated virtual individual or collective life forms, e.g. social…
B60W 30/09
Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences: taking automatic action…
B60W 60/00272
Using trajectory prediction for other traffic participants: relying on extrapolation of current movement