Using a bird's eye view feature map, augmented with semantic information, to detect an object in an environment

A bird's eye view feature map, augmented with semantic information, can be used to detect an object in an environment. A point cloud data set augmented with the semantic information that is associated with identities of classes of objects can be obtained. Features can be extracted from the point cloud data set. Based on the features, an initial bird's eye view feature map can be produced. Because operations performed on the point cloud data set to extract the features or to produce the initial bird's eye view feature map can have an effect of diminishing an ability to distinguish the semantic information in the initial bird's eye view feature map, the initial bird's eye view feature map can be augmented with the semantic information to produce an augmented bird's eye view feature map. Based on the augmented bird's eye view feature map, the object in the environment can be detected.

TECHNICAL FIELD

The disclosed technologies are directed to using a bird's eye view feature map, augmented with semantic information, to detect an object in an environment.

BACKGROUND

Computer vision is a phrase that can be used to describe an interdisciplinary scientific field directed to developing processes that enable a computer to derive information from a visual representation of an environment in order to support one or more operations to be performed by the computer. Specifically, for a computer that is a component of an autonomous mobile agent, abilities: (1) to detect one or more objects in an environment and (2) to distinguish one object from another object can be features that are important for one or more operations to be performed by the autonomous mobile agent. The visual representation of the environment can have different forms. A two-dimensional image produced, for example, by a camera can be characterized as having a large amount of data about a visual appearance of the environment, but having little information about one or more distances between the camera and the one or more objects in the environment. In contrast, a point cloud data set produced, for example, by a lidar device can be characterized as having a small amount of data about the visual appearance of the environment, but having precise information about the one or more distances between the lidar device and the one or more objects in the environment.

SUMMARY

In an embodiment, a system for detecting an object in an environment can include one or more processors and a memory. The memory can be communicably coupled to the one or more processors. The memory can store a feature extraction module, a feature map production module, and an object detection module. The feature extraction module can include instructions that when executed by the one or more processors cause the one or more processors to obtain a point cloud data set augmented with semantic information associated with identities of classes of objects. The feature extraction module can include instructions that when executed by the one or more processors cause the one or more processors to extract features from the point cloud data set. The feature map production module can include instructions that when executed by the one or more processors cause the one or more processors to produce, based on the features, an initial bird's eye view feature map. The feature map production module can include instructions that when executed by the one or more processors cause the one or more processors to augment the initial bird's eye view feature map with the semantic information to produce an augmented bird's eye view feature map. The object detection module can include instructions that when executed by the one or more processors cause the one or more processors to detect, based on the augmented bird's eye view feature map, the object in the environment.

In another embodiment, a method for detecting an object in an environment can include obtaining, by a processor, a point cloud data set augmented with semantic information associated with identities of classes of objects. The method can include extracting, by the processor, features from the point cloud data set. The method can include producing, by the processor and based on the features, an initial bird's eye view feature map. The method can include augmenting, by the processor, the initial bird's eye view feature map with the semantic information to produce an augmented bird's eye view feature map. The method can include detecting, by the processor and based on the augmented bird's eye view feature map, the object in the environment.

In another embodiment, a non-transitory computer-readable medium for detecting an object in an environment can include instructions that when executed by one or more processors cause the one or more processors to obtain a point cloud data set augmented with semantic information associated with identities of classes of objects. The non-transitory computer-readable medium can include instructions that when executed by the one or more processors cause the one or more processors to extract features from the point cloud data set. The non-transitory computer-readable medium can include instructions that when executed by the one or more processors cause the one or more processors to produce, based on the features, an initial bird's eye view feature map. The non-transitory computer-readable medium can include instructions that when executed by the one or more processors cause the one or more processors to augment the initial bird's eye view feature map with the semantic information to produce an augmented bird's eye view feature map. The non-transitory computer-readable medium can include instructions that when executed by the one or more processors cause the one or more processors to detect, based on the augmented bird's eye view feature map, the object in the environment.

DETAILED DESCRIPTION

The disclosed technologies can use a bird's eye view feature map, augmented with semantic information, to detect an object in an environment. A point cloud data set augmented with semantic information associated with identities of classes of objects can be obtained. For example: (1) a two-dimensional image of the object in the environment can be obtained, (2) the two-dimensional image can be segmented to produce the semantic information, (3) an initial point cloud data set that corresponds to the two-dimensional image can be obtained (e.g., the initial point cloud data set can be obtained by a lidar device and a corresponding relationship between data of the initial point cloud data set and pixels of the two-dimensional image can be obtained by a three-dimensional to two-dimensional projection), and (4) the semantic information can be mapped to points of the initial point cloud data set to produce the point cloud data set augmented with the semantic information. Features can be extracted from the point cloud data set augmented with the semantic information. Based on the features, an initial bird's eye view feature map can be produced. Using a bird's eye view can facilitate abilities: (1) to distinguish, in the environment, one object from another object and (2) to determine one or more distances between a device that produced the initial point cloud data set and the one or more objects in the environment. Because operations performed on data in the point cloud data set to extract the features, to produce the initial bird's eye view feature map, or both can have an effect of diminishing an ability to distinguish the semantic information in data for the initial bird's eye view feature map, the initial bird's eye view feature map can be augmented with the semantic information to produce an augmented bird's eye view feature map. Based on the augmented bird's eye view feature map, the object in the environment can be detected. Advantageously, because points produced by lidar devices, for example, can be relatively small in comparison with a size of a human being, the disclosed technologies can improve an ability to detect an object that has a small size or for which an initial point cloud data set has only a few points of data.

FIG. 1is a diagram that illustrates an example of a two-dimensional image100of an environment102, according to the disclosed technologies. For illustrative purposes, the environment102can be a portion of a floor of a warehouse. The two-dimensional image100can include, for example, two objects: a first object104(e.g., a box) and a second object106(e.g., a barrel). For example, from a viewpoint of a device (e.g., a camera) that produced the two-dimensional image100, a portion of the first object104(e.g., the box) is occluded by the second object106(e.g., the barrel) so that an ability to distinguish the first object104(e.g., the box) from the second object106(e.g., the barrel) may be difficult.

FIG. 2is a diagram that illustrates an example of a point cloud200that corresponds to the two-dimensional image100, according to the disclosed technologies. The point cloud200can include, for example, Point #1through Point #32.

FIG. 3is a diagram that illustrates an example of a process300that produced the point cloud200, according to the disclosed technologies. For example, in the process300, a device302(e.g., a lidar device) that produced the point cloud200can include a laser. The device302can cause the laser to emit pulses of a coherent light. Beams of the coherent light can be reflected from points on the first object104(e.g., the box) and the second object106(e.g., the barrel). The device302can measure times of flight between emissions of the pulses and receptions of the beams reflected from the points. Distances between the device302and the points can be determined from the times of flight. The device302can cause the beams of coherent light to sweep through an arc defined, for example, with respect to a horizontal plane. A position of a point can be defined by a coordinate system that includes, for example, a distance between the device302and the point (φ, a degree of angular rotation along the arc (φ), and a height above the horizontal plane (z). For example, the point cloud200can include points arrayed along the arc in an inclusive range from 68° to 115°.

FIG. 4is a set of tables of an example of an initial point cloud data set400that corresponds to the point cloud200, according to the disclosed technologies. For example,FIG. 4can include a table for each of Point #1through Point #32. Each table can include data402that define a position of a corresponding point in the point cloud200. With reference toFIGS. 2 and 3, for example, the position of the corresponding point can be defined by the distance between the device302(illustrated inFIG. 3) and the point (ρ), the degree of angular rotation along the arc (φ), and the height above the horizontal plane (z). Alternatively, the position of the corresponding point can be defined by any other coordinate system including, for example, a Cartesian coordinate system.

Returning toFIG. 1, for example, one or more of a segmentation process, a classifier, or the like can be used to produce semantic information from the two-dimensional image100. The semantic information can be associated with identities of classes of objects. For example, because the two-dimensional image100was produced in the environment102of a warehouse, the one or more of the segmentation process, the classifier, or the like can be configured to identify the classes of objects likely to be located within a warehouse such as, for example: (1) a first class for a box and (2) a second class for a barrel. With reference toFIG. 2, for example, the semantic information can be mapped to points of the point cloud200to produce a point cloud data set augmented with semantic information.

FIGS. 5A and 5Bare a set of tables of an example of a point cloud data set augmented with semantic information500, according to the disclosed technologies. For example,FIGS. 5A and 5Bcan include a table for each of Point #1through Point #32. Each table can include the data402that define the position of the corresponding point in the point cloud200. Each table can also include data502for corresponding class information mapped to the corresponding point. Additionally, for example, the semantic information can include: (1) a first color (e.g., red) for the first class for the box and (2) a second color (e.g., blue) for the second class for the barrel. For example, each table can include data504for corresponding code information (e.g., color) associated with the corresponding class. With reference toFIGS. 2, 5A, and 5B, for example, causing Point #1through Point #32to be presented in a manner so that each point includes its corresponding color can improve an ability to distinguish the first object104(e.g., the box) from the second object106(e.g., the barrel) in the point cloud200. For example, with the class information and the code information (e.g., color) mapped to the points of the point cloud200: (1) the points associated with the first class for the box (e.g., red) can include: Point #1through Point #6, Point #8through Point #11, Point #17through Point #20, and Point #26and (2) the points associated with the second class for the barrel (e.g., blue) can include: Point #7, Point #12through Point #16, Point #21through Point #25, Point #27through Point #31, and Point #32.

FIG. 6is a block diagram that illustrates an example of a system600for detecting an object in an environment, according to the disclosed technologies. The system600can include, for example, a processor602and a memory604. The memory604can be communicably coupled to the processor602. For example, the memory604can store a feature extraction module606, a feature map production module608, and an object detection module610. For example, the system600can be disposed in an autonomous mobile agent. For example, the autonomous mobile agent can include an autonomous vehicle, a mobile robot, or the like.

The feature extraction module606can include instructions that function to control the processor602to obtain a point cloud data set augmented with semantic information associated with identities of classes of objects. For example, the point cloud data set augmented with the semantic information can be the point cloud data set augmented with the semantic information500illustrated inFIGS. 5A and 5B. Additionally or alternatively, for example, the semantic information can include probability distributions of predictions of the identities of the classes of objects. That is, because: (1) the disclosed technologies are directed to detect an object in an environment and (2) the semantic information is produced early in a process of the disclosed technologies, the semantic information may be inaccurate and, therefore, the semantic information may be better characterized as probability distributions of predictions of the identities of the classes of objects. Additionally, for example, the semantic information can include: (1) a first color for a first class of the classes of objects and (2) a second color for a second class of the classes of objects. For example, the semantic information can include: (1) a first color (e.g., red) for the first class for the box and (2) a second color (e.g., blue) for the second class for the barrel as illustrated inFIGS. 5A and 5B.

In a first implementation, the system600can further include a communications device612. The communications device612can be communicably coupled to the processor602. The instructions to obtain the point cloud data set can include instructions that function to control the processor602to receive, via the communications device612, the point cloud data set.

In a second implementation, the instructions to obtain the point cloud data set can include instructions that function to control the processor602to obtain a two-dimensional image of the object in the environment. For example, the two-dimensional image can be the two-dimensional image100illustrated inFIG. 1. For example, the system600can further include a camera614. The camera614can be configured to produce the two-dimensional image.

In the second implementation, the instructions to obtain the point cloud data set can include instructions that function to control the processor602to segment the two-dimensional image to produce the semantic information.

In the second implementation, the instructions to obtain the point cloud data set can include instructions that function to control the processor602to obtain an initial point cloud data set that corresponds to the two-dimensional image. For example, the initial point cloud data set can be the initial point cloud data set400, illustrated inFIG. 4, of the point cloud200illustrated inFIG. 2. For example, the system600can further include a lidar device616. The lidar device616can be configured to produce the initial point cloud data set.

In a variation of the second implementation, the instructions to obtain the point cloud data set can include instructions that function to control the processor602to correct a misalignment between the two-dimensional image and the initial point cloud data set. For example, the misalignment can be due to parallax between a device that produced the two-dimensional image (e.g., the camera614) and a device that produced the initial point cloud data set (e.g., the lidar device616), a miscalibration between the device that produced the two-dimensional image (e.g., the camera614) and the device that produced the initial point cloud data set (e.g., the lidar device616), or the like.

In the second implementation, the instructions to obtain the point cloud data set can include instructions that function to control the processor602to map the semantic information to points of the initial point cloud data set to produce the point cloud data set augmented with the semantic information. For example, the point cloud data set augmented with the semantic information can be the point cloud data set augmented with the semantic information500illustrated inFIGS. 5A and 5B.

In the second implementation, for example, the system600can further include a data store618. The data store618can be communicably coupled to the processor602. The data store618can be configured to store one or more of the two-dimensional image620or the initial point cloud data set622.

The feature extraction module606can include instructions that function to control the processor602to extract features from the point cloud data set. With reference toFIGS. 5A and 5B, for example, the instructions to extract the features from the point cloud data set can include instructions initially to segregate the points associated with the first class for the box (e.g., Point #1through Point #6, Point #8through Point #11, Point #17through Point #20, and Point #26) from the points associated with the second class for the barrel (e.g., Point #7, Point #12through Point #16, Point #21through Point #25, Point #27through Point #31, and Point #32). For example, the instructions to extract the features from the point cloud data set can include instructions thereafter to perform one or more analyses of the data for the points associated with the first class for the box separate from one or more analyses of the data for the points associated with the second class for the barrel.

For example, regarding the first class for the box, the one or more analyses can determine that: (1) Point #1through Point #6and Point #10form a first plane (at a height above the horizontal plane (z) of 6.00 in.), (2) Point #17and Point #26form a second plane (at a height above the horizontal plane (z) of 0 in.), (3) Point #2, Point #8through Point #10, Point #17through Point #19, and Point #26form a third plane, (4) Point #6, Point #10, Point #11, Point #19, Point #20, and Point #26form a fourth plane, (5) because Point #2and Point #10are disposed in both the first plane and the third plane, Point #2and Point #10are likely disposed on a first linear edge, and (6) because Point #6and Point #10are disposed in both the first plane and the fourth plane, Point #6and Point #10are likely disposed on a second linear edge.

For example, regarding the second class for the barrel, the one or more analyses can determine that: (1) Point #7, Point #12through Point #16, and Point #23form a first plane (at a height above the horizontal plane (z) of 6.00 in.), (2) Point #27, Point #31, and Point #32form a second plane (at a height above the horizontal plane (z) of 0 in.), (3) a circle having a center at Point #14and a radius of 1.50 ft. intersects Point #7, Point #12, Point #16, and Point #23, (4) a line formed by Point #22and Point #28intersects the circle, (5) a line formed by Point #24and Point #30intersects the circle, and (6) because Point #12, Point #16, and Point #23are disposed in both the first plane and the circle, Point #12, Point #16, and Point #23are likely disposed on an arc-shaped edge with a center at Point #14and a radius of 1.50 ft.

Returning toFIG. 6, in a third implementation, the instructions to extract the features from the point cloud data set can include instructions that function to control the processor602to determine a width of the point cloud data set, a depth of the point cloud data set, and a height of the point cloud data set. With reference toFIGS. 5A and 5B, for example, the processor602can determine that the width (w) of the point cloud data set500is 5 ft. 6 in., the depth (d) of the point cloud data set500is 4 ft. 6 in., and the height (z) of the point cloud data set500is 6 in.

Returning toFIG. 6, in the third implementation, the instructions to extract the features from the point cloud data set can include instructions that function to control the processor602to discretize the point cloud data set into cells delineated by a grid defined with respect to the width and the depth. Such a grid can be useful to correct a misalignment between the two-dimensional image and the initial point cloud data set. For example, the misalignment can be due to parallax between a device that produced the two-dimensional image (e.g., the camera614) and a device that produced the initial point cloud data set (e.g., the lidar device616), a miscalibration between the device that produced the two-dimensional image (e.g., the camera614) and the device that produced the initial point cloud data set (e.g., the lidar device616), or the like.

In the third implementation, the instructions to extract the features from the point cloud data set can include instructions that function to control the processor602to operate a two-dimensional convolutional neural network to process the cells.

In the third implementation, for example, the semantic information can: (1) include probability distributions of predictions of the identities of the classes of objects and (2) be averaged over at least some of the cells.

The feature map production module608can include instructions that function to control the processor602to produce, based on the features, an initial bird's eye view feature map.FIG. 7is a diagram that illustrates an example of an initial bird's eye view feature map700, according to the disclosed technologies. For example, the initial bird's eye view feature map700can include a first linear edge702, a second linear edge704, and an arc-shaped edge706.

Returning toFIG. 6, for example, one or more of the instructions to extract the features from the point cloud data set or the instructions to produce the initial bird's eye view feature map can include instructions that function to control the processor602to perform operations on data in the point cloud data set that have an effect of diminishing an ability to distinguish the semantic information in data for the initial bird's eye view feature map. For example, because the instructions to extract the features from the point cloud data set can include instructions initially to segregate the points associated with the first class for the box from the points associated with the second class for the barrel, but thereafter to perform one or more analyses of the data for the points associated with the first class for the box separate from one or more analyses of the data for the points associated with the second class for the barrel, the features extracted from the point cloud data set can lack the semantic information so that the initial bird's eye view feature map, produced from the features, can also lack the semantic information.

The feature map production module608can include instructions that function to control the processor602to augment the initial bird's eye view feature map with the semantic information to produce an augmented bird's eye view feature map.

For example, the system600can further include the data store618. The data store618can be communicably coupled to the processor602. The data store618can be configured to store one or more of the point cloud data set624, the initial bird's eye view feature map626, or the augmented bird's eye view feature map628.FIG. 8is a diagram that illustrates an example of a bird's eye view feature map augmented with the semantic information800, according to the disclosed technologies. For example, the bird's eye view feature map augmented with the semantic information800can include the first linear edge702, the second linear edge704, the arc-shaped edge706, the semantic information (e.g., the first color (e.g., red)) for the first class for the box for Point #1through Point #6and Point #10, and the semantic information (e.g., the second color (e.g., blue)) for the first class for the barrel for Point #7, Point #12through Point #16, and Point #23.

Returning toFIG. 6, the object detection module610can include instructions that function to control the processor602to detect, based on the augmented bird's eye view feature map628, the object in the environment. With reference toFIGS. 1, 6, and 8, for example, the object detection module610can detect, based on the bird's eye view feature map augmented with the semantic information800, the first object104(e.g., the box) even though from the viewpoint of the device (e.g., the camera614) that produced the two-dimensional image100, a portion of the first object104(e.g., the box) is occluded by the second object106(e.g., the barrel).

For example, in the system600, one or more of the instructions to obtain the point cloud data set, the instructions to extract the features, the instructions to produce the initial bird's eye view feature map, the instructions to augment the initial bird's eye view feature map, or the instructions to detect the object in the environment can include instructions that function to control the processor602to operate a neural network.

FIGS. 9A and 9Binclude a flow diagram that illustrates an example of a method900that is associated with detecting an object in an environment, according to the disclosed technologies. The method900is described from the perspective of the system600illustrated inFIG. 6. Although the method900is described in combination with the system600, one of skill in the art understands, in light of the description herein, that the method900is not limited to being implemented by the system600. Rather, the system600is an example of a system that may be used to implement the method900. For example, the method900can be performed by an autonomous mobile agent. For example, the autonomous mobile agent can include an autonomous vehicle, a mobile robot, or the like.

InFIG. 9A, in the method900, at an operation902, the feature extraction module606can obtain a point cloud data set augmented with semantic information associated with identities of classes of objects. Additionally or alternatively, for example, the semantic information can include probability distributions of predictions of the identities of the classes of objects. Additionally, for example, the semantic information can include: (1) a first color for a first class of the classes of objects and (2) a second color for a second class of the classes of objects.

In a first implementation, the operation902can include an operation904. At the operation904, the feature extraction module606can receive, via the communications device612, the point cloud data set.

In a second implementation, the operation902can include an operation906, an operation908, an operation910, and an operation912. Additionally, for example, the operation902can include an operation914.

At the operation906, the feature extraction module606can obtain the two-dimensional image620of the object in the environment. For example, the camera614can be configured to produce the two-dimensional image. For example, the data store618can be configured to store the two-dimensional image620.

At the operation908, the feature extraction module606can segment the two-dimensional image620to produce the semantic information.

At the operation910, the feature extraction module606can obtain the initial point cloud data set622that corresponds to the two-dimensional image620. For example, the lidar device616can be configured to produce the initial point cloud data set622. For example, the data store618can be configured to store the initial point cloud data set622.

At the operation914, the feature extraction module606can correct a misalignment between the two-dimensional image620and the initial point cloud data set622. For example, the misalignment can be due to parallax between a device that produced the two-dimensional image620(e.g., the camera614) and a device that produced the initial point cloud data set622(e.g., the lidar device616), a miscalibration between the device that produced the two-dimensional image620(e.g., the camera614) and the device that produced the initial point cloud data set622(e.g., the lidar device616), or the like.

At the operation912, the feature extraction module606can map the semantic information to points of the initial point cloud data set622to produce the point cloud data set624augmented with the semantic information. For example, the data store618can be configured to store the point cloud data set624.

InFIG. 9B, in the method900, at the operation916, the feature extraction module606can extract features from the point cloud data set624.

In a third implementation, the operation916can include an operation918, an operation920, and an operation922.

At the operation918, the feature extraction module606can determine a width of the point cloud data set624, a depth of the point cloud data set624, and a height of the point cloud data set624.

At the operation920, the feature extraction module606can discretize the point cloud data set624into cells delineated by a grid defined with respect to the width and the depth. Such a grid can be useful to correct a misalignment between the two-dimensional image and the initial point cloud data set. For example, the misalignment can be due to parallax between a device that produced the two-dimensional image (e.g., the camera614) and a device that produced the initial point cloud data set (e.g., the lidar device616), a miscalibration between the device that produced the two-dimensional image (e.g., the camera614) and the device that produced the initial point cloud data set (e.g., the lidar device616), or the like.

At the operation922, the feature extraction module606can operate a two-dimensional convolutional neural network to process the cells.

In the third implementation, for example, the semantic information can: (1) include probability distributions of predictions of the identities of the classes of objects and (2) be averaged over at least some of the cells.

At an operation924, the feature map production module608can produce, based on the features, the initial bird's eye view feature map626. For example, the data store618can be configured to store the initial bird's eye view feature map626.

For example, one or more of the operation916or the operation924can perform operations on data in the point cloud data set624that have an effect of diminishing an ability to distinguish the semantic information in data for the initial bird's eye view feature map626.

At an operation926, the feature map production module608can augment the initial bird's eye view feature map626with the semantic information to produce the augmented bird's eye view feature map628. For example, the data store618can be configured to store the augmented bird's eye view feature map628.

At an operation928, the object detection module610can detect, based on the augmented bird's eye view feature map628, the object in the environment.

For example, one or more of the operation902, the operation916, the operation924, the operation926, or the operation928can operate a neural network.

FIG. 10includes a block diagram that illustrates an example of elements disposed on a vehicle1000, according to the disclosed technologies. As used herein, a “vehicle” can be any form of powered transport. In one or more implementations, the vehicle1000can be an automobile. While arrangements described herein are with respect to automobiles, one of skill in the art understands, in light of the description herein, that embodiments are not limited to automobiles.

In some embodiments, the vehicle1000can be configured to switch selectively between an automated mode, one or more semi-automated operational modes, and/or a manual mode. Such switching can be implemented in a suitable manner, now known or later developed. As used herein, “manual mode” can refer that all of or a majority of the navigation and/or maneuvering of the vehicle1000is performed according to inputs received from a user (e.g., human driver). In one or more arrangements, the vehicle1000can be a conventional vehicle that is configured to operate in only a manual mode.

In one or more embodiments, the vehicle1000can be an automated vehicle. As used herein, “automated vehicle” can refer to a vehicle that operates in an automated mode. As used herein, “automated mode” can refer to navigating and/or maneuvering the vehicle1000along a travel route using one or more computing systems to control the vehicle1000with minimal or no input from a human driver. In one or more embodiments, the vehicle1000can be highly automated or completely automated. In one embodiment, the vehicle1000can be configured with one or more semi-automated operational modes in which one or more computing systems perform a portion of the navigation and/or maneuvering of the vehicle along a travel route, and a vehicle operator (i.e., driver) provides inputs to the vehicle1000to perform a portion of the navigation and/or maneuvering of the vehicle1000along a travel route.

For example, Standard J3016, Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles, issued by the Society of Automotive Engineers (SAE) International on Jan. 16, 2014, and most recently revised on Jun. 15, 2018, defines six levels of driving automation. These six levels include: (1) level 0, no automation, in which all aspects of dynamic driving tasks are performed by a human driver; (2) level 1, driver assistance, in which a driver assistance system, if selected, can execute, using information about the driving environment, either steering or acceleration/deceleration tasks, but all remaining driving dynamic tasks are performed by a human driver; (3) level 2, partial automation, in which one or more driver assistance systems, if selected, can execute, using information about the driving environment, both steering and acceleration/deceleration tasks, but all remaining driving dynamic tasks are performed by a human driver; (4) level 3, conditional automation, in which an automated driving system, if selected, can execute all aspects of dynamic driving tasks with an expectation that a human driver will respond appropriately to a request to intervene; (5) level 4, high automation, in which an automated driving system, if selected, can execute all aspects of dynamic driving tasks even if a human driver does not respond appropriately to a request to intervene; and (6) level 5, full automation, in which an automated driving system can execute all aspects of dynamic driving tasks under all roadway and environmental conditions that can be managed by a human driver.

The vehicle1000can include various elements. The vehicle1000can have any combination of the various elements illustrated inFIG. 10. In various embodiments, it may not be necessary for the vehicle1000to include all of the elements illustrated inFIG. 10. Furthermore, the vehicle1000can have elements in addition to those illustrated inFIG. 10. While the various elements are illustrated inFIG. 10as being located within the vehicle1000, one or more of these elements can be located external to the vehicle1000. Furthermore, the elements illustrated may be physically separated by large distances. For example, as described, one or more components of the disclosed system can be implemented within the vehicle1000while other components of the system can be implemented within a cloud-computing environment, as described below. For example, the elements can include one or more processors1010, one or more data stores1015, a sensor system1020, an input system1030, an output system1035, vehicle systems1040, one or more actuators1050, one or more automated driving modules1060, a communications system1070, and the system600for detecting an object in an environment.

In one or more arrangements, the one or more processors1010can be a main processor of the vehicle1000. For example, the one or more processors1010can be an electronic control unit (ECU). For example, functions and/or operations of the processors of the processor602(illustrated inFIG. 6) can be realized by the one or more processors1010.

The one or more data stores1015can store, for example, one or more types of data. For example, functions and/or operations of the memory604, the data store618, or both (illustrated inFIG. 6) can be realized by the one or more data stores1015. The one or more data store1015can include volatile memory and/or non-volatile memory. Examples of suitable memory for the one or more data stores1015can include Random-Access Memory (RAM), flash memory, Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), registers, magnetic disks, optical disks, hard drives, any other suitable storage medium, or any combination thereof. The one or more data stores1015can be a component of the one or more processors1010. Additionally or alternatively, the one or more data stores1015can be operatively connected to the one or more processors1010for use thereby. As used herein, “operatively connected” can include direct or indirect connections, including connections without direct physical contact. As used herein, a statement that a component can be “configured to” perform an operation can be understood to mean that the component requires no structural alterations, but merely needs to be placed into an operational state (e.g., be provided with electrical power, have an underlying operating system running, etc.) in order to perform the operation.

In one or more arrangements, the one or more data stores1015can store map data1016. The map data1016can include maps of one or more geographic areas. In some instances, the map data1016can include information or data on roads, traffic control devices, road markings, structures, features, and/or landmarks in the one or more geographic areas. The map data1016can be in any suitable form. In some instances, the map data1016can include aerial views of an area. In some instances, the map data1016can include ground views of an area, including 360-degree ground views. The map data1016can include measurements, dimensions, distances, and/or information for one or more items included in the map data1016and/or relative to other items included in the map data1016. The map data1016can include a digital map with information about road geometry. The map data1016can be high quality and/or highly detailed.

In one or more arrangements, the map data1016can include one or more terrain maps1017. The one or more terrain maps1017can include information about the ground, terrain, roads, surfaces, and/or other features of one or more geographic areas. The one or more terrain maps1017can include elevation data of the one or more geographic areas. The map data1016can be high quality and/or highly detailed. The one or more terrain maps1017can define one or more ground surfaces, which can include paved roads, unpaved roads, land, and other things that define a ground surface.

In one or more arrangements, the map data1016can include one or more static obstacle maps1018. The one or more static obstacle maps1018can include information about one or more static obstacles located within one or more geographic areas. A “static obstacle” can be a physical object whose position does not change (or does not substantially change) over a period of time and/or whose size does not change (or does not substantially change) over a period of time. Examples of static obstacles can include trees, buildings, curbs, fences, railings, medians, utility poles, statues, monuments, signs, benches, furniture, mailboxes, large rocks, and hills. The static obstacles can be objects that extend above ground level. The one or more static obstacles included in the one or more static obstacle maps1018can have location data, size data, dimension data, material data, and/or other data associated with them. The one or more static obstacle maps1018can include measurements, dimensions, distances, and/or information for one or more static obstacles. The one or more static obstacle maps1018can be high quality and/or highly detailed. The one or more static obstacle maps1018can be updated to reflect changes within a mapped area.

In one or more arrangements, the one or more data stores1015can store sensor data1019. As used herein, “sensor data” can refer to any information about the sensors with which the vehicle1000can be equipped including the capabilities of and other information about such sensors. The sensor data1019can relate to one or more sensors of the sensor system1020. For example, in one or more arrangements, the sensor data1019can include information about one or more lidar sensors1024of the sensor system1020.

In some arrangements, at least a portion of the map data1016and/or the sensor data1019can be located in one or more data stores1015that are located onboard the vehicle1000. Alternatively or additionally, at least a portion of the map data1016and/or the sensor data1019can be located in one or more data stores1015that are located remotely from the vehicle1000.

The sensor system1020can include one or more sensors. As used herein, a “sensor” can refer to any device, component, and/or system that can detect and/or sense something. The one or more sensors can be configured to detect and/or sense in real-time. As used herein, the term “real-time” can refer to a level of processing responsiveness that is perceived by a user or system to be sufficiently immediate for a particular process or determination to be made, or that enables the processor to keep pace with some external process.

In arrangements in which the sensor system1020includes a plurality of sensors, the sensors can work independently from each other. Alternatively, two or more of the sensors can work in combination with each other. In such a case, the two or more sensors can form a sensor network. The sensor system1020and/or the one or more sensors can be operatively connected to the one or more processors1010, the one or more data stores1015, and/or another element of the vehicle1000(including any of the elements illustrated inFIG. 10). The sensor system1020can acquire data of at least a portion of the external environment of the vehicle1000(e.g., nearby vehicles). The sensor system1020can include any suitable type of sensor. Various examples of different types of sensors are described herein. However, one of skill in the art understands that the embodiments are not limited to the particular sensors described herein.

The sensor system1020can include one or more vehicle sensors1021. The one or more vehicle sensors1021can detect, determine, and/or sense information about the vehicle1000itself. In one or more arrangements, the one or more vehicle sensors1021can be configured to detect and/or sense position and orientation changes of the vehicle1000such as, for example, based on inertial acceleration. In one or more arrangements, the one or more vehicle sensors1021can include one or more accelerometers, one or more gyroscopes, an inertial measurement unit (IMU), a dead-reckoning system, a global navigation satellite system (GNSS), a global positioning system (GPS), a navigation system1047, and/or other suitable sensors. The one or more vehicle sensors1021can be configured to detect and/or sense one or more characteristics of the vehicle1000. In one or more arrangements, the one or more vehicle sensors1021can include a speedometer to determine a current speed of the vehicle1000.

Alternatively or additionally, the sensor system1020can include one or more environment sensors1022configured to acquire and/or sense driving environment data. As used herein, “driving environment data” can include data or information about the external environment in which a vehicle is located or one or more portions thereof. For example, the one or more environment sensors1022can be configured to detect, quantify, and/or sense obstacles in at least a portion of the external environment of the vehicle1000and/or information/data about such obstacles. Such obstacles may be stationary objects and/or dynamic objects. The one or more environment sensors1022can be configured to detect, measure, quantify, and/or sense other things in the external environment of the vehicle1000such as, for example, lane markers, signs, traffic lights, traffic signs, lane lines, crosswalks, curbs proximate the vehicle1000, off-road objects, etc.

Various examples of sensors of the sensor system1020are described herein. The example sensors may be part of the one or more vehicle sensors1021and/or the one or more environment sensors1022. However, one of skill in the art understands that the embodiments are not limited to the particular sensors described.

In one or more arrangements, the one or more environment sensors1022can include one or more radar sensors1023, one or more lidar sensors1024, one or more sonar sensors1025, and/or one more cameras1026. In one or more arrangements, the one or more cameras1026can be one or more high dynamic range (HDR) cameras or one or more infrared (IR) cameras. For example, the one or more cameras1026can be used to record the reality of a state of an item of information that can appear in the digital map. For example, functions and/or operations of the lidar device616(illustrated inFIG. 6) can be realized by the one or more lidar sensors1024. For example, functions and/or operations of the camera614(illustrated inFIG. 6) can be realized by the one or more cameras1026.

The input system1030can include any device, component, system, element, arrangement, or groups thereof that enable information/data to be entered into a machine. The input system1030can receive an input from a vehicle passenger (e.g., a driver or a passenger). The output system1035can include any device, component, system, element, arrangement, or groups thereof that enable information/data to be presented to a vehicle passenger (e.g., a driver or a passenger).

Various examples of the one or more vehicle systems1040are illustrated inFIG. 10. However, one of skill in the art understands that the vehicle1000can include more, fewer, or different vehicle systems. Although particular vehicle systems can be separately defined, each or any of the systems or portions thereof may be otherwise combined or segregated via hardware and/or software within the vehicle1000. For example, the one or more vehicle systems1040can include a propulsion system1041, a braking system1042, a steering system1043, a throttle system1044, a transmission system1045, a signaling system1046, and/or the navigation system1047. Each of these systems can include one or more devices, components, and/or a combination thereof, now known or later developed.

The navigation system1047can include one or more devices, applications, and/or combinations thereof, now known or later developed, configured to determine the geographic location of the vehicle1000and/or to determine a travel route for the vehicle1000. The navigation system1047can include one or more mapping applications to determine a travel route for the vehicle1000. The navigation system1047can include a global positioning system, a local positioning system, a geolocation system, and/or a combination thereof.

The one or more actuators1050can be any element or combination of elements operable to modify, adjust, and/or alter one or more of the vehicle systems1040or components thereof responsive to receiving signals or other inputs from the one or more processors1010and/or the one or more automated driving modules1060. Any suitable actuator can be used. For example, the one or more actuators1050can include motors, pneumatic actuators, hydraulic pistons, relays, solenoids, and/or piezoelectric actuators.

The one or more processors1010and/or the one or more automated driving modules1060can be operatively connected to communicate with the various vehicle systems1040and/or individual components thereof. For example, the one or more processors1010and/or the one or more automated driving modules1060can be in communication to send and/or receive information from the various vehicle systems1040to control the movement, speed, maneuvering, heading, direction, etc. of the vehicle1000. The one or more processors1010and/or the one or more automated driving modules1060may control some or all of these vehicle systems1040and, thus, may be partially or fully automated.

The one or more processors1010and/or the one or more automated driving modules1060may be operable to control the navigation and/or maneuvering of the vehicle1000by controlling one or more of the vehicle systems1040and/or components thereof. For example, when operating in an automated mode, the one or more processors1010and/or the one or more automated driving modules1060can control the direction and/or speed of the vehicle1000. The one or more processors1010and/or the one or more automated driving modules1060can cause the vehicle1000to accelerate (e.g., by increasing the supply of fuel provided to the engine), decelerate (e.g., by decreasing the supply of fuel to the engine and/or by applying brakes) and/or change direction (e.g., by turning the front two wheels). As used herein, “cause” or “causing” can mean to make, force, compel, direct, command, instruct, and/or enable an event or action to occur or at least be in a state where such event or action may occur, either in a direct or indirect manner. The communications system1070can include one or more receivers1071and/or one or more transmitters1072. The communications system1070can receive and transmit one or more messages through one or more wireless communications channels. For example, the one or more wireless communications channels can be in accordance with the Institute of Electrical and Electronics Engineers (IEEE) 802.11p standard to add wireless access in vehicular environments (WAVE) (the basis for Dedicated Short-Range Communications (DSRC)), the 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) Vehicle-to-Everything (V2X) (LTE-V2X) standard (including the LTE Uu interface between a mobile communication device and an Evolved Node B of the Universal Mobile Telecommunications System), the 3GPP fifth generation (5G) New Radio (NR) Vehicle-to-Everything (V2X) standard (including the 5G NR Uu interface), or the like. For example, the communications system1070can include “connected car” technology. “Connected car” technology can include, for example, devices to exchange communications between a vehicle and other devices in a packet-switched network. Such other devices can include, for example, another vehicle (e.g., “Vehicle to Vehicle” (V2V) technology), roadside infrastructure (e.g., “Vehicle to Infrastructure” (V2I) technology), a cloud platform (e.g., “Vehicle to Cloud” (V2C) technology), a pedestrian (e.g., “Vehicle to Pedestrian” (V2P) technology), or a network (e.g., “Vehicle to Network” (V2N) technology. “Vehicle to Everything” (V2X) technology can integrate aspects of these individual communications technologies. For example, functions and/or operations of the communications device612(illustrated inFIG. 6) can be realized by the communications system1070.

The vehicle1000can include one or more modules, at least some of which are described herein. The modules can be implemented as computer-readable program code that, when executed by the one or more processors1010, implement one or more of the various processes described herein. One or more of the modules can be a component of the one or more processors1010. Alternatively or additionally, one or more of the modules can be executed on and/or distributed among other processing systems to which the one or more processors1010can be operatively connected. The modules can include instructions (e.g., program logic) executable by the one or more processors1010. Alternatively or additionally, the one or more data store1015may contain such instructions.

The vehicle1000can include one or more automated driving modules1060. The one or more automated driving modules1060can be configured to receive data from the sensor system1020and/or any other type of system capable of capturing information relating to the vehicle1000and/or the external environment of the vehicle1000. In one or more arrangements, the one or more automated driving modules1060can use such data to generate one or more driving scene models. The one or more automated driving modules1060can determine position and velocity of the vehicle1000. The one or more automated driving modules1060can determine the location of obstacles, obstacles, or other environmental features including traffic signs, trees, shrubs, neighboring vehicles, pedestrians, etc.

The one or more automated driving modules1060can be configured to receive and/or determine location information for obstacles within the external environment of the vehicle1000for use by the one or more processors1010and/or one or more of the modules described herein to estimate position and orientation of the vehicle1000, vehicle position in global coordinates based on signals from a plurality of satellites, or any other data and/or signals that could be used to determine the current state of the vehicle1000or determine the position of the vehicle1000with respect to its environment for use in either creating a map or determining the position of the vehicle1000in respect to map data.

The one or more automated driving modules1060can be configured to determine one or more travel paths, current automated driving maneuvers for the vehicle1000, future automated driving maneuvers and/or modifications to current automated driving maneuvers based on data acquired by the sensor system1020, driving scene models, and/or data from any other suitable source such as determinations from the sensor data1019. As used herein, “driving maneuver” can refer to one or more actions that affect the movement of a vehicle. Examples of driving maneuvers include: accelerating, decelerating, braking, turning, moving in a lateral direction of the vehicle1000, changing travel lanes, merging into a travel lane, and/or reversing, just to name a few possibilities. The one or more automated driving modules1060can be configured to implement determined driving maneuvers. The one or more automated driving modules1060can cause, directly or indirectly, such automated driving maneuvers to be implemented. As used herein, “cause” or “causing” means to make, command, instruct, and/or enable an event or action to occur or at least be in a state where such event or action may occur, either in a direct or indirect manner. The one or more automated driving modules1060can be configured to execute various vehicle functions and/or to transmit data to, receive data from, interact with, and/or control the vehicle1000or one or more systems thereof (e.g., one or more of vehicle systems1040). For example, functions and/or operations of an automotive navigation system can be realized by the one or more automated driving modules1060.

Detailed embodiments are disclosed herein. However, one of skill in the art understands, in light of the description herein, that the disclosed embodiments are intended only as examples. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one of skill in the art to variously employ the aspects herein in virtually any appropriately detailed structure. Furthermore, the terms and phrases used herein are not intended to be limiting but rather to provide an understandable description of possible implementations. Various embodiments are illustrated inFIGS. 1-4, 5A, 5B, 6-8, 9A, 9B, and 10, but the embodiments are not limited to the illustrated structure or application.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments. In this regard, each block in flowcharts or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). One of skill in the art understands, in light of the description herein, that, in some alternative implementations, the functions described in a block may occur out of the order depicted by the figures. For example, two blocks depicted in succession may, in fact, be executed substantially concurrently, or the blocks may be executed in the reverse order, depending upon the functionality involved.

The systems, components and/or processes described above can be realized in hardware or a combination of hardware and software and can be realized in a centralized fashion in one processing system or in a distributed fashion where different elements are spread across several interconnected processing systems. Any kind of processing system or another apparatus adapted for carrying out the methods described herein is suitable. A typical combination of hardware and software can be a processing system with computer-readable program code that, when loaded and executed, controls the processing system such that it carries out the methods described herein. The systems, components, and/or processes also can be embedded in a computer-readable storage, such as a computer program product or other data programs storage device, readable by a machine, tangibly embodying a program of instructions executable by the machine to perform methods and processes described herein. These elements also can be embedded in an application product that comprises all the features enabling the implementation of the methods described herein and that, when loaded in a processing system, is able to carry out these methods.

Furthermore, arrangements described herein may take the form of a computer program product embodied in one or more computer-readable media having computer-readable program code embodied, e.g., stored, thereon. Any combination of one or more computer-readable media may be utilized. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. As used herein, the phrase “computer-readable storage medium” means a non-transitory storage medium. A computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the computer-readable storage medium would include, in a non-exhaustive list, the following: a portable computer diskette, a hard disk drive (HDD), a solid-state drive (SSD), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. As used herein, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

Generally, modules, as used herein, include routines, programs, objects, components, data structures, and so on that perform particular tasks or implement particular data types. In further aspects, a memory generally stores such modules. The memory associated with a module may be a buffer or may be cache embedded within a processor, a random-access memory (RAM), a ROM, a flash memory, or another suitable electronic storage medium. In still further aspects, a module as used herein, may be implemented as an application-specific integrated circuit (ASIC), a hardware component of a system on a chip (SoC), a programmable logic array (PLA), or another suitable hardware component that is embedded with a defined configuration set (e.g., instructions) for performing the disclosed functions.

Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber, cable, radio frequency (RF), etc., or any suitable combination of the foregoing. Computer program code for carrying out operations for aspects of the disclosed technologies may be written in any combination of one or more programming languages, including an object-oriented programming language such as Java™, Smalltalk, C++, or the like, and conventional procedural programming languages such as the “C” programming language or similar programming languages. The program code may execute entirely on a user's computer, partly on a user's computer, as a stand-alone software package, partly on a user's computer and partly on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

The terms “a” and “an,” as used herein, are defined as one or more than one. The term “plurality,” as used herein, is defined as two or more than two. The term “another,” as used herein, is defined as at least a second or more. The terms “including” and/or “having,” as used herein, are defined as comprising (i.e., open language). The phrase “at least one of . . . or . . . ” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. For example, the phrase “at least one of A, B, or C” includes A only, B only, C only, or any combination thereof (e.g., AB, AC, BC, or ABC).