US 10,503,174 B1 · Method and device for optimized resource allocation in autonomous driving on the basis of reinforcement learning using data from lidar, radar, and camera sensor

Abstract

A method for efficient resource allocation in autonomous driving by reinforcement learning is provided for reducing computation via a heterogeneous sensor fusion. This attention-based method includes steps of: a computing device instructing an attention network to perform a neural network operation by referring to attention sensor data, to calculate attention scores; instructing a detection network to acquire video data by referring to the attention scores and to generate decision data for the autonomous driving; instructing a drive network to operate the autonomous vehicle by referring to the decision data, to acquire circumstance data, and to generate a reward by referring to the circumstance data; and instructing the attention network to adjust parameters used for the neural network operation by referring to the reward. Thus, a virtual space where the autonomous vehicle optimizes the resource allocation can be provided by the method.

Claims18

Dependent claims

A method for efficient resource allocation in autonomous driving by reinforcement learning, comprising steps of:

(a) a computing device, if at least one attention sensor data is acquired, performing at least one neural network operation by referring to the attention sensor data, to thereby calculate one or more attention scores;
(b) the computing device acquiring at least one video data taken by at least part of one or more cameras installed on an autonomous vehicle by referring to the attention scores and to generate at least one decision data for the autonomous driving by referring to the video data;
(c) the computing device operating the autonomous vehicle by referring to the decision data, to acquire at least one circumstance data representing a change of circumstance of the autonomous vehicle in operation, and to generate at least one reward by referring to the circumstance data;
and
(d) the computing device adjusting at least part of one or more parameters used for the neural network operation by referring to the reward.

2 more elements

A computing device for efficient resource allocation in autonomous driving by reinforcement learning, comprising:

at least one memory that stores instructions;
and
at least one processor configured to execute the instructions to:
perform processes of (I) performing at least one neural network operation by referring to at least one attention sensor data, to thereby calculate one or more attention scores, (II) acquiring at least one video data taken by at least part of one or more cameras installed on an autonomous vehicle by referring to the attention scores and to generate at least one decision data for the autonomous driving by referring to the video data, (III) operating the autonomous vehicle by referring to the decision data, to acquire at least one circumstance data representing a change of circumstance of the autonomous vehicle in operation, and to generate at least one reward by referring to the circumstance data, and (IV) adjusting at least part of one or more parameters used for the neural network operation by referring to the reward.

1 more element

Dependencies are inferred from the claim text.

Method and device for optimized resource allocation in autonomous driving on the basis of reinforcement learning using data from lidar, radar, and camera sensor

Life of this patent

Method and device for optimized resource allocation in autonomous driving on the basis of reinforcement learning using data from lidar, radar, and camera sensor

Life of this patent

Abstract

Claims18