US 10,642,275 B2Granted

Occulsion aware planning and control

AssigneeZoox, Inc.

InventorsWilliam Anthony Silva, Dragomir Dimitrov Anguelov, Benjamin Isaac Zwiebel, Juhana Kangaspunta

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Abstract

Techniques are discussed for controlling a vehicle, such as an autonomous vehicle, based on occluded areas in an environment. An occluded area can represent areas where sensors of the vehicle are unable to sense portions of the environment due to obstruction by another object. An occlusion grid representing the occluded area can be stored as map data or can be dynamically generated. An occlusion grid can include occlusion fields, which represent discrete two- or three-dimensional areas of driveable environment. An occlusion field can indicate an occlusion state and an occupancy state, determined using LIDAR data and/or image data captured by the vehicle. An occupancy state of an occlusion field can be determined by ray casting LIDAR data or by projecting an occlusion field into segmented image data. The vehicle can be controlled to traverse the environment when a sufficient portion of the occlusion grid is visible and unoccupied.

Claims20

Dependent claims

A system comprising:

one or more processors that cause the system to perform operations comprising:
controlling an autonomous vehicle to traverse to a first location in an environment;
capturing, as sensor data, at least LIDAR data or image data of the environment using a sensor of the autonomous vehicle;
determining, based at least in part on accessing map data or on identifying an obstacle in the environment, an occlusion grid comprising a plurality of occlusion fields, an occlusion field of the occlusion grid indicating an occlusion state and an occupancy state of a corresponding region of the environment;
determining, as an occluded region and based at least in part on the sensor data, that at least a first portion of the occlusion grid is occluded at a first time;
determining, as an un-occluded region and based at least in part on the sensor data, that at least a second portion of the occlusion grid is un-occluded at the first time;
determining, based at least in part on an extent and occupancy of the second portion, a confidence level associated with safely traversing a trajectory through the environment;
controlling the autonomous vehicle to stay at the first location or to traverse to a second location based at least in part on the confidence level being below a threshold level;
determining, based at least in part on the sensor data, that a third portion of the occlusion grid is visible and unoccupied at a second time;
and
controlling the autonomous vehicle based at least in part on the third portion of the occlusion grid being visible and unoccupied at the second time.

8 more elements

A method comprising:

controlling an autonomous vehicle to traverse to a first location in an environment;
determining, based at least in part on accessing map data or on identifying an obstacle in the environment, an occlusion grid;
determining, as an occluded region, that at least a first portion of the occlusion grid is occluded at a first time;
determining, as an un-occluded region, that at least a second portion of the occlusion grid is visible at the first time;
determining an occupancy state of the un-occluded region;
controlling the autonomous vehicle to stay at the first location or traverse to a second location based at least in part on an extent of the un-occluded region and the occupancy state of the un-occluded region;
determining that a third portion of the occlusion grid is un-occluded and unoccupied at a second time;
and
controlling the autonomous vehicle based at least in part on the third portion of the occlusion grid being un-occluded and unoccupied at the second time.

6 more elements

A non-transitory computer-readable medium storing instructions that, when executed, cause one or more processors to perform operations comprising:

controlling a robotic platform to traverse to a first location in an environment;
determining a location of the robotic platform with respect to a map, the map comprising map data;
determining, based at least in part on accessing the map data associated with the location or on identifying an obstacle in the environment proximate to the robotic platform, an occlusion grid;
determining, as an occluded region, that at least a first portion of the occlusion grid is occluded at a first time;
determining, as an un-occluded region, that at least a second portion of the occlusion grid is visible at the first time;
determining an occupancy state of the un-occluded region;
controlling the robotic platform to stay at the first location or traverse to a second location based at least in part on an extent of the un-occluded region and the occupancy state of the un-occluded region;
determining that a third portion of the occlusion grid is un-occluded and unoccupied at a second time;
and
controlling the robotic platform based at least in part on the third portion being un-occluded and unoccupied at the second time.

7 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/011,468Filed 18 Jun 2018
Published: US 2019/0384302 A119 Dec 2019
Granted: 5 May 2020
Term ends: Not estimatedThe source records don’t support an estimate.

Classification31 codes

B60W 60/00276
Using trajectory prediction for other traffic participants: for two or more other traffic participants
B60W 30/18154
Related to particular drive situations: approaching an intersection
B60W 30/18159
Related to particular drive situations: traversing an intersection
B60W 60/00272
Using trajectory prediction for other traffic participants: relying on extrapolation of current movement