Efficient and optimal feature extraction from observations

Abstract

A method (900) for efficient and optimal feature extraction is provided that includes identifying (902) observations (602, 802A, 802B) that satisfy a predetermined configuration of an index (606, 606A, 606B, 830A, 832A, 834A, 836A, 838A, 840A, 830B, 832B, 834B, 836B, 838B, 840B). The method further includes extracting (904) feature values (804A, 804B) from the observations representative of an indicator of the predetermined configuration of indicators, and determining (906) parameters for feature extraction (708) based on the feature values extracted from the identified observations and corresponding tags (604, 806A, 806B, 806C-1, 806C-2) of the identified observations. A system (300) and a computer program product are also provided.

Description

Background Art

[0001] Machine learning systems use various techniques to learn from data sets. The data sets are labeled according to features associated with observations of the data sets. Brief Description of the Drawings

[0002] Figure 1 is an example environment of a vehicle that can implement one or more components including an autonomous system;

[0003] Figure 2 is a diagram of one or more systems of a vehicle including an autonomous system;

[0004] Figure 3 is Figure 1 and Figure 2 a diagram of one or more devices and / or components of one or more systems of

[0005] Figure 4A is a diagram of certain components of an autonomous system;

[0006] Figure 4B is a diagram of an implementation of a neural network;

[0007] Figure 4C and Figure 4D is a diagram illustrating an example operation of a CNN;

[0008] Figure 5 is a diagram of an implementation of a process for efficient and optimal feature extraction from observations.

[0009] Figure 6 Illustrates high-dimensional observations, features, and classifications.

[0010] Figure 7A is a block diagram of a feature extraction pipeline.

[0011] Figure 7B is a block diagram of a data set.

[0012] Figures 8A to 8C is an illustration of a feature extraction system in the context of route planning.

[0013] Figure 9 is a flowchart of a process for efficient and optimal feature extraction from observations. Detailed Description

[0014] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be apparent, however, that the embodiments described herein may be practiced without these specific details. In some instances, well-known structures and devices are illustrated in block diagram form in order to avoid unnecessarily obscuring aspects of the present disclosure.

[0015] In the drawings, for ease of description, a specific arrangement or order of illustrative elements (such as those representing systems, devices, modules, instruction blocks, and / or data elements, etc.) is illustrated. However, those skilled in the art will understand that unless explicitly described, the specific order or arrangement of the illustrative elements in the drawings is not intended to imply a required order or sequence of processing, or a separation of processing. Additionally, unless explicitly described, the inclusion of illustrative elements in the drawings is not intended to imply that such elements are required in all embodiments, nor that the features represented by such elements cannot be included in some embodiments or combined with other elements in some embodiments.

[0016] Furthermore, in the drawings, connecting elements (such as solid lines, dashed lines, or arrows, etc.) are used to illustrate connections, relationships, or associations between or among two or more other illustrative elements. The absence of any such connecting element is not intended to imply that no connection, relationship, or association can exist. In other words, some connections, relationships, or associations between elements are not illustrated in the drawings so as not to obscure the present disclosure. Additionally, for ease of illustration, a single connecting element may be used to represent multiple connections, relationships, or associations between elements. For example, if a connecting element represents the communication of a signal, data, or instruction (e.g., "software instruction"), those skilled in the art should understand that such an element may represent one or more signal paths (e.g., a bus) that may be required to affect the communication.

[0017] Although terms such as "first," "second," and / or "third," etc. are used to describe various elements, these elements should not be limited by these terms. The terms "first," "second," and / or "third" are only used to distinguish one element from another. For example, without departing from the scope of the described embodiments, a first contact may be referred to as a second contact, and similarly, a second contact may be referred to as a first contact. Both the first contact and the second contact are contacts, but they are not the same contact.

[0018] The terms used in the description of the various embodiments described herein are included for the purpose of describing particular embodiments only and are not intended to be limiting. As used in the description of the various embodiments and the appended claims, the singular forms "a", "an", and "the" are also intended to include the plural forms and may be used interchangeably with "one or more than one" or "at least one", unless the context clearly dictates otherwise. It will also be understood that the term "and / or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items. It will also be understood that when the terms "comprise", "comprising", "include", and / or "having" are used in this specification, specify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0019] As used herein, the terms "communicate" and "communicating" refer to at least one of receiving, receipt, transmission, conveyance, and / or provision of information (or information represented by, for example, data, signals, messages, instructions, and / or commands, etc.). For a unit (e.g., a device, a system, a component of a device or system, and / or a combination thereof, etc.) that is to communicate with another unit, this means that the unit can directly or indirectly receive information from the other unit and / or send (e.g., transmit) information to the other unit. This can refer to a direct or indirect connection that is inherently wired and / or wireless. Additionally, two units can communicate with each other even if the information transmitted between the first unit and the second unit is modified, processed, relayed, and / or routed. For example, even if the first unit receives information passively and does not actively transmit information to the second unit, the first unit can communicate with the second unit. As another example, if at least one intermediary unit (e.g., a third unit located between the first unit and the second unit) processes the information received from the first unit and transmits the processed information to the second unit, the first unit can communicate with the second unit. In some embodiments, a message can refer to a network packet (e.g., a data packet, etc.) that includes data.

[0020] As used herein, depending on the context, the term "if" may optionally be construed to mean "when", "at the time of", "in response to determining that", and / or "in response to detecting", etc. Similarly, depending on the context, the phrase "if it has been determined" or "if [the stated condition or event] is detected" may optionally be construed to mean "at the time of determining...", "in response to determining that", or "at the time of detecting [the stated condition or event]" and / or "in response to detecting [the stated condition or event]", etc. Further, as used herein, the terms "has", "have", or "having", etc. are intended to be open-ended terms. Additionally, unless otherwise expressly stated, the phrase "based on" is intended to mean "at least partially based on".

[0021] Reference will now be made in detail to the embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, it will be apparent to one of ordinary skill in the art that the various described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0022] General Overview

[0023] In some aspects and / or embodiments, the systems, methods, and computer program products described herein include and / or implement efficient and optimal feature extraction from observations. The observations are the identified observations that satisfy a predetermined configuration of a metric. Feature values representing the metric in the predetermined configuration are extracted from the observations. Parameters for feature extraction are determined based on the feature values extracted from the identified observations and the corresponding labels of the identified observations.

[0024] By implementation of the systems, methods, and computer program products described herein, techniques for efficient and optimal feature extraction are provided. This technique enables exploration and extraction of observations with high dimensions. This technique eliminates time-consuming manually designed representative features. This technique automatically extracts hidden features from each observation (e.g., planned trajectories in traffic), which form the input to a machine learning system. Then, the machine learning system can use these features to make higher-level decisions. Features are extracted without knowledge of the specific machine learning system that will consume the data. In this way, parameter tuning of the machine learning model is avoided because parameter tuning is cumbersome, inefficient, and a bottleneck for many trained systems.

[0025] Now refer to Figure 1, an exemplary environment 100 is illustrated, in which vehicles including autonomous systems and vehicles not including autonomous systems operate. As illustrated, the environment 100 includes vehicles 102a - 102n, objects 104a - 104n, routes 106a - 106n, area 108, vehicle - to - infrastructure (V2I) devices 110, network 112, remote autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118. Vehicles 102a - 102n, vehicle - to - infrastructure (V2I) devices 110, network 112, autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118 are interconnected via a wired connection, a wireless connection, or a combination of wired and wireless connections (e.g., establishing a connection for communication, etc.). In some embodiments, objects 104a - 104n are interconnected with at least one of vehicles 102a - 102n, vehicle - to - infrastructure (V2I) devices 110, network 112, autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118 via a wired connection, a wireless connection, or a combination of wired and wireless connections.

[0026] Vehicles 102a - 102n (individually referred to as vehicle 102 and collectively referred to as vehicles 102) include at least one device configured to transport goods and / or people. In some embodiments, vehicle 102 is configured to communicate with V2I device 110, remote AV system 114, queue management system 116, and / or V2I system 118 via network 112. In some embodiments, vehicle 102 includes cars, buses, trucks, and / or trains, etc. In some embodiments, vehicle 102 is the same as or similar to vehicle 200 described herein (see Figure 2 ). In some embodiments, vehicles 200 in the set of vehicles 200 are associated with an autonomous queue manager. In some embodiments, as described herein, vehicle 102 travels along corresponding routes 106a - 106n (individually referred to as route 106 and collectively referred to as routes 106). In some embodiments, one or more than one vehicle 102 includes an autonomous system (e.g., an autonomous system the same as or similar to autonomous system 202).

[0027] Objects 104a - 104n (individually referred to as object 104 and collectively as objects 104) include, for example, at least one vehicle, at least one pedestrian, at least one cyclist, and / or at least one structure (e.g., building, sign, fire hydrant, etc.). Each object 104 (e.g., located at a fixed location and over a period of time) is stationary or (e.g., having a speed and associated with at least one trajectory) moving. In some embodiments, object 104 is associated with a corresponding location in region 108.

[0028] Routes 106a - 106n (individually referred to as route 106 and collectively as routes 106) are each associated with (e.g., define) a sequence of actions (also referred to as a trajectory) that a connected AV can navigate along. Each route 106 begins at an initial state (e.g., a state corresponding to a first spatio - temporal location and / or speed, etc.) and ends at a final goal state (e.g., a state corresponding to a second spatio - temporal location different from the first spatio - temporal location) or a goal region (e.g., a subspace of acceptable states (e.g., termination states)). In some embodiments, the first state includes a location where one or more individuals will board the AV, and the second state or region includes one or more locations where one or more individuals boarding the AV will disembark. In some embodiments, route 106 includes multiple acceptable sequences of states (e.g., multiple sequences of spatio - temporal locations) that are associated with (e.g., define) multiple trajectories. In an example, route 106 includes only high - level actions or imprecise state locations, such as a series of connected roads indicating a direction change at a roadway intersection. Additionally or alternatively, route 106 can include more precise actions or states, such as, for example, a specific target lane or precise location within a lane region and a target rate at those locations. In an example, route 106 includes multiple precise state sequences along at least one high - level action with a finite look - ahead horizon to reach an intermediate goal, where the combination of successive iterations of the finite - horizon state sequences cumulatively corresponds to multiple trajectories that together form a high - level route terminating at the final goal state or region.

[0029] Region 108 includes a physical region (e.g., a geographic region) in which vehicle 102 can navigate. In an example, region 108 includes at least one state (e.g., a country, a province, an individual state among a plurality of states included in a country, etc.), at least a portion of a state, at least one city, at least a portion of a city, etc. In some embodiments, region 108 includes at least one named arterial road (referred to herein as a "road"), such as a highway, an interstate highway, a parkway, an urban street, etc. Additionally or alternatively, in some examples, region 108 includes at least one unnamed road, such as a lane, a section of a parking lot, a section of a vacant and / or undeveloped area, a dirt road, etc. In some embodiments, a road includes at least one lane (e.g., a portion of the road through which vehicle 102 can pass). In an example, a road includes at least one lane associated with (e.g., identified based on) at least one lane marking line.

[0030] A vehicle-to-infrastructure (V2I) device 110 (sometimes referred to as a vehicle-to-infrastructure (Vehicle-to-Infrastructure) or vehicle-to-everything (Vehicle-to-Everything) (V2X) device) includes at least one device configured to communicate with vehicle 102 and / or V2I system 118. In some embodiments, V2I device 110 is configured to communicate with vehicle 102, remote AV system 114, queue management system 116, and / or V2I system 118 via network 112. In some embodiments, V2I device 110 includes a radio frequency identification (RFID) device, a sign, a camera (e.g., a two-dimensional (2D) and / or three-dimensional (3D) camera), a lane marking, a streetlight, a parking meter, etc. In some embodiments, V2I device 110 is configured to communicate directly with vehicle 102. Additionally or alternatively, in some embodiments, V2I device 110 is configured to communicate with vehicle 102, remote AV system 114, and / or queue management system 116 via V2I system 118. In some embodiments, V2I device 110 is configured to communicate with V2I system 118 via network 112.

[0031] Network 112 includes one or more wired and / or wireless networks. In an example, network 112 includes a cellular network (e.g., a Long-Term Evolution (LTE) network, a Third Generation (3G) network, a Fourth Generation (4G) network, a Fifth Generation (5G) network, a Code Division Multiple Access (CDMA) network, etc.), a Public Land Mobile Network (PLMN), a Local Area Network (LAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN), a telephone network (e.g., a Public Switched Telephone Network (PSTN)), a private network, an ad-hoc network, an intranet, the Internet, a fiber-based network, a cloud computing network, etc., and / or a combination of some or all of these networks, etc.).

[0032] The remote AV system 114 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the network 112, the queue management system 116, and / or the V2I system 118 via the network 112. In an example, the remote AV system 114 includes a server, a server group, and / or other similar devices. In some embodiments, the remote AV system 114 is co-located with the queue management system 116. In some embodiments, the remote AV system 114 participates in the installation of some or all of the components of the vehicle (including autonomous systems, autonomous vehicle computing, and / or software implemented by autonomous vehicle computing, etc.). In some embodiments, the remote AV system 114 maintains (e.g., updates and / or replaces) these components and / or software during the life of the vehicle.

[0033] The queue management system 116 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the remote AV system 114, and / or the V2I system 118. In an example, the queue management system 116 includes a server, a server group, and / or other similar devices. In some embodiments, the queue management system 116 is associated with a ridesharing company (e.g., an organization for controlling the operation of multiple vehicles (e.g., vehicles including autonomous systems and / or vehicles not including autonomous systems), etc.).

[0034] In some embodiments, the V2I system 118 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the remote AV system 114, and / or the queue management system 116 via the network 112. In some examples, the V2I system 118 is configured to communicate with the V2I device 110 via a connection different from the network 112. In some embodiments, the V2I system 118 includes a server, a server group, and / or other similar devices. In some embodiments, the V2I system 118 is associated with a municipal authority or a private institution (e.g., a private institution for maintaining the V2I device 110, etc.).

[0035] Provide Figure 1The number and arrangement of the illustrated elements are provided by way of example. Compared with the Figure 1 illustrated elements, additional elements, fewer elements, different elements, and / or elements in a different arrangement may exist. Additionally or alternatively, at least one element of the environment 100 may perform one or more functions described as being performed by at least one different element of the Figure 1 . Additionally or alternatively, at least one set of elements of the environment 100 may perform one or more functions described as being performed by at least one different set of elements of the environment 100.

[0036] Now referring to Figure 2 , the vehicle 200 (which may be the same as or similar to the Figure 1 vehicle 102) includes an autonomous system 202, a powertrain control system 204, a steering control system 206, and a braking system 208, or is associated with the autonomous system 202, the powertrain control system 204, the steering control system 206, and the braking system 208. In some embodiments, the vehicle 200 is the same as the vehicle 102 (see Figure 1)Same or similar. In some embodiments, the autonomous system 202 is configured to endow the vehicle 200 with autonomous driving capabilities (e.g., implement at least one of the following driving automation or maneuver-based functions, features, and / or devices, etc., where the at least one driving automation or maneuver-based function, feature, and / or device enables the vehicle 200 to operate partially or fully without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that abandon reliance on human intervention, such as level 5 ADS-operated vehicles, etc.), highly autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in certain situations, such as level 4 ADS-operated vehicles, etc.), and / or conditionally autonomous vehicles (e.g., vehicles that abandon reliance on human intervention in limited situations, such as level 3 ADS-operated vehicles, etc.), etc.). In one embodiment, the autonomous system 202 includes the operational or tactical functionality required to operate the vehicle 200 in on-road traffic and continuously perform part or all of the dynamic driving task (DDT). In another embodiment, the autonomous system 202 includes an advanced driver assistance system (ADAS) that includes driver support features. The autonomous system 202 supports various levels of driving automation ranging from no driving automation (e.g., level 0) to full driving automation (e.g., level 5). For a detailed description of fully autonomous vehicles and highly autonomous vehicles, reference may be made to SAE International standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, the entire content of which is incorporated by reference. In some embodiments, the vehicle 200 is associated with an autonomous queue manager and / or a ridesharing company.

[0037] The autonomous system 202 includes a sensor suite that includes one or more devices such as a camera 202a, a LiDAR sensor 202b, a Radar sensor 202c, and a microphone 202d. In some embodiments, the autonomous system 202 may include more or fewer devices and / or different devices (e.g., ultrasonic sensors, inertial sensors, GPS receivers (discussed below), and / or odometer sensors for generating data associated with an indication of the distance traveled by the vehicle 200, etc.). In some embodiments, the autonomous system 202 uses one or more devices included in the autonomous system 202 to generate data associated with the environment 100 described herein. The data generated by one or more devices of the autonomous system 202 can be used by one or more systems described herein to observe the environment (e.g., environment 100) in which the vehicle 200 is located. In some embodiments, the autonomous system 202 includes a communication device 202e, an autonomous vehicle computing 202f, a drive-by-wire (DBW) system 202h, and a safety controller 202g.

[0038] The camera 202a includes at least one device configured to communicate with the communication device 202e, the autonomous vehicle computing 202f, and / or the safety controller 202g via a bus (e.g., a bus 302 that is the same or similar to Figure 3 the bus). The camera 202a includes at least one camera (e.g., a digital camera using an optical sensor such as a charge-coupled device (CCD), a thermal camera, an infrared (IR) camera, and / or an event camera, etc.) for capturing images including physical objects (e.g., cars, buses, curbs, and / or people, etc.). In some embodiments, the camera 202a generates camera data as output. In some examples, the camera 202a generates camera data including image data associated with the image. In this example, the image data can specify at least one parameter corresponding to the image (e.g., image characteristics such as exposure, brightness, etc., and / or an image timestamp, etc.). In such an example, the image can be in a format (e.g., RAW, JPEG, and / or PNG, etc.). In some embodiments, the camera 202a includes a plurality of independent cameras configured (e.g., positioned) on the vehicle for capturing images for the purpose of stereovision (stereo vision). In some examples, the camera 202a includes generating image data and transmitting the image data to the autonomous vehicle computing 202f and / or a queue management system (e.g., the same as Figure 1a plurality of cameras of the same or similar queue management system as the queue management system 116. In such an example, the autonomous vehicle computing 202f determines the depth to one or more objects in the fields of view of at least two of the plurality of cameras based on image data from at least two cameras. In some embodiments, the camera 202a is configured to capture images of objects within a distance relative to the camera 202a (e.g., up to 100 meters and / or up to 1 kilometer, etc.). Thus, the camera 202a includes features such as sensors and lenses optimized for sensing objects at one or more distances relative to the camera 202a.

[0039] In an embodiment, the camera 202a includes at least one camera configured to capture one or more images associated with one or more traffic lights, street signs, and / or other physical objects that provide visual navigation information. In some embodiments, the camera 202a generates traffic light data associated with one or more images. In some examples, the camera 202a generates TLD (Traffic Light Detection) data associated with one or more images including a format (e.g., RAW, JPEG, and / or PNG, etc.). In some embodiments, the camera 202a that generates TLD data is different from other systems incorporating cameras described herein in that the camera 202a may include one or more cameras having a wide field of view (e.g., a wide-angle lens, a fish-eye lens, and / or a lens having a viewing angle of about 120 degrees or greater, etc.) to generate images related to as many physical objects as possible.

[0040] The Light Detection and Ranging (LiDAR) sensor 202b includes being configured to communicate with the communication device 202e, the autonomous vehicle computing 202f, and / or the safety controller 202g via a bus (e.g., with Figure 3at least one device that communicates via a bus (e.g., a bus identical or similar to bus 302). The LiDAR sensor 202b includes a system configured to emit light from a light emitter (e.g., a laser emitter). The light emitted by the LiDAR sensor 202b includes light outside the visible spectrum (e.g., infrared light, etc.). In some embodiments, during operation, the light emitted by the LiDAR sensor 202b encounters a physical object (e.g., a vehicle) and is reflected back to the LiDAR sensor 202b. In some embodiments, the light emitted by the LiDAR sensor 202b does not penetrate the physical object it encounters. The LiDAR sensor 202b further includes at least one light detector that detects the light after the light emitted from the light emitter encounters a physical object. In some embodiments, at least one data processing system associated with the LiDAR sensor 202b generates an image (e.g., a point cloud and / or a combined point cloud, etc.) representing the objects included in the field of view of the LiDAR sensor 202b. In some examples, at least one data processing system associated with the LiDAR sensor 202b generates an image representing the boundary of a physical object and / or the surface of a physical object (e.g., the topology of the surface), etc. In such examples, the image is used to determine the boundary of the physical object in the field of view of the LiDAR sensor 202b.

[0041] A Radio Detection and Ranging (Radar) sensor 202c includes at least one device configured to communicate with a communication device 202e, an autonomous vehicle computer 202f, and / or a safety controller 202g via a bus (e.g., a bus identical or similar to Figure 3 bus 302). The Radar sensor 202c includes a system configured to emit (pulsed or continuous) radio waves. The radio waves emitted by the Radar sensor 202c include radio waves within a predetermined spectrum. In some embodiments, during operation, the radio waves emitted by the Radar sensor 202c encounter a physical object and are reflected back to the Radar sensor 202c. In some embodiments, the radio waves emitted by the Radar sensor 202c are not reflected by some objects. In some embodiments, at least one data processing system associated with the Radar sensor 202c generates a signal representing the objects included in the field of view of the Radar sensor 202c. For example, at least one data processing system associated with the Radar sensor 202c generates an image representing the boundary of a physical object and / or the surface of a physical object (e.g., the topology of the surface), etc. In some examples, the image is used to determine the boundary of the physical object in the field of view of the Radar sensor 202c.

[0042] The microphone 202d includes at least one device configured to communicate with the communication device 202e, the autonomous vehicle computing 202f, and / or the safety controller 202g via a bus (e.g., a bus the same as or similar to the Figure 3 bus 302). The microphone 202d includes one or more microphones (e.g., an array microphone and / or an external microphone, etc.) that capture an audio signal and generate data associated with (e.g., representing) the audio signal. In some examples, the microphone 202d includes a transducer device and / or a similar device. In some embodiments, one or more of the systems described herein can receive the data generated by the microphone 202d and determine the position (e.g., distance, etc.) of an object relative to the vehicle 200 based on the audio signal associated with the data.

[0043] The communication device 202e includes at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the autonomous vehicle computing 202f, the safety controller 202g, and / or the DBW (drive-by-wire) system 202h. For example, the communication device 202e can include a device the same as or similar to the Figure 3 communication interface 314. In some embodiments, the communication device 202e includes a vehicle-to-vehicle (V2V) communication device (e.g., a device for enabling wireless communication of data between vehicles).

[0044] The autonomous vehicle computing 202f includes at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the communication device 202e, the safety controller 202g, and / or the DBW system 202h. In some examples, the autonomous vehicle computing 202f includes devices such as a client device, a mobile device (e.g., a cellular phone and / or a tablet computer, etc.), and / or a server (e.g., a computing device including one or more central processing units and / or graphics processing units, etc.). In some embodiments, the autonomous vehicle computing 202f is the same as or similar to the autonomous vehicle computing 400 described herein. Additionally or alternatively, in some embodiments, the autonomous vehicle computing 202f is configured to communicate with an autonomous vehicle system (e.g., an autonomous vehicle system the same as or similar to the Figure 1 remote AV system 114), a queue management system (e.g., a queue management system the same as or similar to the Figure 1 queue management system 116), a V2I device (e.g., a V2I device the same as or similar to the Figure 1 V2I device 110), and / or a V2I system (e.g., a V2I system the same as or similar to the Figure 1communicate with a V2I system 118 that is the same as or similar to the V2I system).

[0045] The safety controller 202g includes at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the communication device 202e, the autonomous vehicle computing 202f, and / or the DBW system 202h. In some examples, the safety controller 202g includes one or more controllers (such as an electrical controller and / or an electromechanical controller, etc.) configured to generate and / or transmit control signals to operate one or more devices of the vehicle 200 (such as the powertrain control system 204, the steering control system 206, and / or the braking system 208, etc.). In some embodiments, the safety controller 202g is configured to generate control signals that are prior to (e.g., override) the control signals generated and / or transmitted by the autonomous vehicle computing 202f.

[0046] The DBW system 202h includes at least one device configured to communicate with the communication device 202e and / or the autonomous vehicle computing 202f. In some examples, the DBW system 202h includes one or more controllers (such as an electrical controller and / or an electromechanical controller, etc.) configured to generate and / or transmit control signals to operate one or more devices of the vehicle 200 (such as the powertrain control system 204, the steering control system 206, and / or the braking system 208, etc.). Additionally or alternatively, one or more controllers of the DBW system 202h are configured to generate and / or transmit control signals to operate at least one different device of the vehicle 200 (such as turn signals, headlights, door locks, and / or windshield wipers, etc.).

[0047] The powertrain control system 204 includes at least one device configured to communicate with the DBW system 202h. In some examples, the powertrain control system 204 includes at least one controller and / or actuator, etc. In some embodiments, the powertrain control system 204 receives control signals from the DBW system 202h, and the powertrain control system 204 causes the vehicle 200 to perform longitudinal vehicle movements (such as starting to move forward, stopping moving forward, starting to move backward, stopping moving backward, accelerating in a certain direction, decelerating in a certain direction, etc.), or perform lateral vehicle movements (such as making a left turn and / or making a right turn, etc.). In an example, the powertrain control system 204 increases, keeps the same, or decreases the energy (such as fuel and / or electricity, etc.) provided to the motor of the vehicle, thereby causing at least one wheel of the vehicle 200 to rotate or not rotate.

[0048] The steering control system 206 includes at least one device configured to rotate one or more wheels of the vehicle 200. In some examples, the steering control system 206 includes at least one controller and / or actuator, etc. In some embodiments, the steering control system 206 rotates two front wheels and / or two rear wheels of the vehicle 200 left or right to turn the vehicle 200 left or right. In other words, the steering control system 206 causes the activities required to regulate the y-axis component of the vehicle's movement.

[0049] The braking system 208 includes at least one device configured to actuate one or more brakes to decelerate the vehicle 200 and / or keep it stationary. In some examples, the braking system 208 includes at least one controller and / or actuator configured to close one or more calipers associated with one or more wheels of the vehicle 200 on the corresponding rotors of the vehicle 200. Additionally or alternatively, in some examples, the braking system 208 includes an automatic emergency braking (AEB) system and / or a regenerative braking system, etc.

[0050] In some embodiments, the vehicle 200 includes at least one platform sensor (not explicitly illustrated) for measuring or inferring the nature of the state or condition of the vehicle 200. In some examples, the vehicle 200 includes platform sensors such as a global positioning system (GPS) receiver, an inertial measurement unit (IMU), a wheel speed sensor, a wheel brake pressure sensor, a wheel torque sensor, an engine torque sensor, and / or a steering angle sensor. Although the braking system 208 is illustrated as being located Figure 2 proximal to the vehicle 200 in, the braking system 208 can be located anywhere in the vehicle 200.

[0051] Now refer to Figure 3 , a schematic diagram illustrating the device 300. As illustrated, the device 300 includes a processor 304, a memory 306, a storage component 308, an input interface 310, an output interface 312, a communication interface 314, and a bus 302. In some embodiments, the device 300 corresponds to: at least one device of the vehicle 102 (e.g., at least one device of the system of the vehicle 102); and / or one or more devices of the network 112 (e.g., one or more devices of the system of the network 112). In some embodiments, one or more devices of the vehicle 102 (e.g., one or more devices of the system of the vehicle 102), and / or one or more devices of the network 112 (e.g., one or more devices of the system of the network 112) include at least one device 300 and / or at least one component of the device 300. As Figure 3As shown, device 300 includes bus 302, processor 304, memory 306, storage component 308, input interface 310, output interface 312, and communication interface 314.

[0052] Bus 302 includes components that permit communication between the components of device 300. In some cases, processor 304 includes a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), and / or an accelerated processing unit (APU), etc.), a microphone, a digital signal processor (DSP), and / or any processing component that can be programmed to perform at least one function (e.g., a field programmable gate array (FPGA) and / or an application specific integrated circuit (ASIC), etc.). Memory 306 includes random access memory (RAM), read only memory (ROM), and / or another type of dynamic and / or static storage device that stores data and / or instructions for use by processor 304 (e.g., flash memory, magnetic memory, and / or optical memory, etc.).

[0053] Storage component 308 stores data and / or software related to the operation and use of device 300. In some examples, storage component 308 includes a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optical disk, and / or a solid state disk, etc.), a compact disk (CD), a digital versatile disk (DVD), a floppy disk, a cassette tape, a magnetic tape, a CD-ROM, a RAM, a PROM, an EPROM, a FLASH-EPROM, an NV-RAM, and / or another type of computer-readable medium, and corresponding drives.

[0054] Input interface 310 includes components that permit device 300 to receive information such as via a user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, and / or a camera, etc.). Additionally or alternatively, in some embodiments, input interface 310 includes sensors for sensing information (e.g., a global positioning system (GPS) receiver, an accelerometer, a gyroscope, and / or an actuator, etc.). Output interface 312 includes components for providing output information from device 300 (e.g., a display, a speaker, and / or one or more light emitting diodes (LEDs), etc.).

[0055] In some embodiments, communication interface 314 includes transceiver-like components (e.g., a transceiver and / or separate receiver and transmitter, etc.) that permit device 300 to communicate with other devices via a wired connection, a wireless connection, or a combination of a wired connection and a wireless connection. In some examples, communication interface 314 permits device 300 to receive information from another device and / or provide information to another device. In some examples, communication interface 314 includes an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, an interface and / or a cellular network interface, etc.

[0056] In some embodiments, the device 300 performs one or more processes described herein. The device 300 performs these processes based on software instructions stored in a computer-readable medium such as the memory 306 and / or the storage component 308 and executed by the processor 304. A computer-readable medium (e.g., a non-transitory computer-readable medium) is defined herein as a non-transitory memory device. A non-transitory memory device includes a storage space located within a single physical storage device or a storage space distributed across multiple physical storage devices.

[0057] In some embodiments, software instructions are read into the memory 306 and / or the storage component 308 from another computer-readable medium or from another device via the communication interface 314. When the software instructions stored in the memory 306 and / or the storage component 308 are executed, they cause the processor 304 to perform one or more processes described herein. Additionally or alternatively, instead of software instructions or in combination with software instructions, hardwired circuitry is used to perform one or more processes described herein. Thus, unless otherwise explicitly stated, the embodiments described herein are not limited to any particular combination of hardware circuitry and software.

[0058] The memory 306 and / or the storage component 308 includes a data storage portion or at least one data structure (e.g., a database, etc.). The device 300 is capable of receiving information from the data storage portion or at least one data structure in the memory 306 or the storage component 308, storing the information in the data storage portion or at least one data structure, communicating the information to the data storage portion or at least one data structure, or searching for the information stored in the data storage portion or at least one data structure. In some examples, the information includes network data, input data, output data, or any combination thereof.

[0059] In some embodiments, the device 300 is configured to execute software instructions stored in the memory 306 and / or the memory of another device (e.g., another device that is the same as or similar to the device 300). As used herein, the term "module" refers to at least one instruction stored in the memory 306 and / or the memory of another device, which when executed by the processor 304 and / or the processor of another device (e.g., another device that is the same as or similar to the device 300), causes the device 300 (e.g., at least one component of the device 300) to perform one or more processes described herein. In some embodiments, the module is implemented in software, firmware, and / or hardware, etc.

[0060] Provide Figure 3 The number and arrangement of the illustrated components are provided as examples. In some embodiments, Figure 3Compared with the illustrated components, device 300 may include additional components, fewer components, different components, or components arranged differently. Additionally or alternatively, a set of components of device 300 (e.g., one or more than one component) may perform one or more than one function described as being performed by another component or another set of components of device 300.

[0061] Now referring to Figure 4A , an example block diagram of an autonomous vehicle computing 400 (sometimes referred to as an "AV stack") is illustrated. As illustrated, the autonomous vehicle computing 400 includes a perception system 402 (sometimes referred to as a perception module), a planning system 404 (sometimes referred to as a planning module), a positioning system 406 (sometimes referred to as a positioning module), a control system 408 (sometimes referred to as a control module), and a database 410. In some embodiments, the perception system 402, the planning system 404, the positioning system 406, the control system 408, and the database 410 are included in and / or implemented in the automatic navigation system of the vehicle (e.g., the autonomous vehicle computing 202f of vehicle 200). Additionally or alternatively, in some embodiments, the perception system 402, the planning system 404, the positioning system 406, the control system 408, and the database 410 are included in one or more than one independent system (e.g., one or more than one system identical or similar to the autonomous vehicle computing 400, etc.). In some examples, the perception system 402, the planning system 404, the positioning system 406, the control system 408, and the database 410 are included in one or more than one independent system located in the vehicle and / or at least one remote system as described herein. In some embodiments, any and / or all of the systems included in the autonomous vehicle computing 400 are implemented in software (e.g., software instructions stored in a memory), computer hardware (e.g., via a microprocessor, a microcontroller, an application specific integrated circuit (ASIC), and / or a field programmable gate array (FPGA), etc.), or a combination of computer software and computer hardware. It will also be understood that, in some embodiments, the autonomous vehicle computing 400 is configured to communicate with remote systems (e.g., an autonomous vehicle system identical or similar to the remote AV system 114, a queue management system 116 identical or similar to the queue management system 116, and / or a V2I system 118 identical or similar to the V2I system, etc.).

[0062] In some embodiments, the perception system 402 receives data associated with at least one physical object in the environment (e.g., data used by the perception system 402 to detect at least one physical object), and classifies the at least one physical object. In some examples, the perception system 402 receives image data captured by at least one camera (e.g., camera 202a), the image being associated with one or more physical objects within the field of view of the at least one camera (e.g., representing the one or more physical objects). In such examples, the perception system 402 classifies the at least one physical object based on one or more groupings of physical objects (e.g., bicycles, vehicles, traffic signs, and / or pedestrians, etc.). In some embodiments, based on the classification of the physical object by the perception system 402, the perception system 402 transmits data associated with the classification of the physical object to the planning system 404.

[0063] In some embodiments, the planning system 404 receives data associated with a destination, and generates data associated with at least one route (e.g., route 106) along which a vehicle (e.g., vehicle 102) can travel towards the destination. In some embodiments, the planning system 404 periodically or continuously receives data from the perception system 402 (e.g., the data associated with the classification of the physical object as described above), and the planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by the perception system 402. In other words, the planning system 404 can perform tasks related to the tactical functions required to operate the vehicle 102 in road traffic. Tactical efforts involve maneuvering the vehicle in traffic during the journey, which includes but is not limited to deciding whether and when to overtake another vehicle, change lanes, or select an appropriate speed, acceleration, deceleration, etc. In some embodiments, the planning system 404 receives data associated with the updated position of the vehicle (e.g., vehicle 102) from the positioning system 406, and the planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by the positioning system 406.

[0064] In some embodiments, the positioning system 406 receives data associated with (e.g., representing) the location of a vehicle (e.g., vehicle 102) in an area. In some examples, the positioning system 406 receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensor 202b). In certain examples, the positioning system 406 receives data associated with at least one point cloud from multiple LiDAR sensors, and the positioning system 406 generates a combined point cloud based on the respective point clouds. In these examples, the positioning system 406 compares the at least one point cloud or combined point cloud with a two-dimensional (2D) and / or three-dimensional (3D) map of the area stored in the database 410. Then, based on the positioning system 406 comparing the at least one point cloud or combined point cloud with the map, the positioning system 406 determines the position of the vehicle in the area. In some embodiments, the map includes a combined point cloud of the area generated prior to the navigation of the vehicle. In some embodiments, the map includes, but is not limited to, a high-precision map of roadway geometry, a map describing the connectivity of the road network, a map describing the physical properties of roadways (such as traffic speed, traffic flow, the number of vehicle and bicycle traffic lanes, lane width, lane traffic direction, or the type and location of lane markings, or a combination thereof, etc.), and a map describing the spatial location of road features (such as crosswalks, traffic signs, or various other types of driving signal lights, etc.). In some embodiments, the map is generated in real time based on the data received by the perception system.

[0065] In another example, the positioning system 406 receives Global Navigation Satellite System (GNSS) data generated by a Global Positioning System (GPS) receiver. In some examples, the positioning system 406 receives GNSS data associated with the location of a vehicle in an area, and the positioning system 406 determines the latitude and longitude of the vehicle in the area. In such examples, the positioning system 406 determines the position of the vehicle in the area based on the latitude and longitude of the vehicle. In some embodiments, the positioning system 406 generates data associated with the position of the vehicle. In some examples, based on the positioning system 406 determining the position of the vehicle, the positioning system 406 generates data associated with the position of the vehicle. In such examples, the data associated with the position of the vehicle includes data associated with one or more semantic properties corresponding to the position of the vehicle.

[0066] In some embodiments, the control system 408 receives data associated with at least one trajectory from the planning system 404, and the control system 408 controls the operation of the vehicle. In some examples, the control system 408 receives data associated with at least one trajectory from the planning system 404, and the control system 408 controls the operation of the vehicle by generating and transmitting control signals to cause the powertrain control system (e.g., the DBW system 202h and / or the powertrain control system 204, etc.), the steering control system (e.g., the steering control system 206), and / or the braking system (e.g., the braking system 208) to operate. For example, the control system 408 is configured to perform operating functions such as lateral vehicle motion control or longitudinal vehicle motion control. Lateral vehicle motion control causes activities required to regulate the y-axis component of the vehicle motion. Longitudinal vehicle motion control causes activities required to regulate the x-axis component of the vehicle motion. In an example, in the case where the trajectory includes a left turn, the control system 408 transmits a control signal to cause the steering control system 206 to adjust the steering angle of the vehicle 200, thereby causing the vehicle 200 to turn left. Additionally or alternatively, the control system 408 generates and transmits control signals to cause other devices of the vehicle 200 (e.g., headlights, turn signals, door locks, and / or windshield wipers, etc.) to change states.

[0067] In some embodiments, the perception system 402, the planning system 404, the positioning system 406, and / or the control system 408 implement at least one machine learning model (e.g., at least one multi-layer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder, and / or at least one transformer, etc.). In some examples, the perception system 402, the planning system 404, the positioning system 406, and / or the control system 408 implement at least one machine learning model individually or in combination with one or more of the above systems. In some examples, the perception system 402, the planning system 404, the positioning system 406, and / or the control system 408 implement at least one machine learning model as part of a pipeline (e.g., a pipeline for identifying one or more objects located in the environment, etc.). The following are examples of Figures 4B to 4D the implementation of machine learning models.

[0068] The database 410 stores data transmitted to, received from, and / or updated by the perception system 402, the planning system 404, the positioning system 406, and / or the control system 408. In some examples, the database 410 includes a storage component for storing data and / or software related to operations and using the autonomous vehicle computing 400 of at least one system (e.g., related to Figure 3the same or similar storage components as the storage component 308). In some embodiments, the database 410 stores data associated with 2D and / or 3D maps of at least one region. In some examples, the database 410 stores data associated with 2D and / or 3D maps of a part of a city, multiple parts of multiple cities, multiple cities, counties, states, and / or countries (e.g., nations), etc. In such examples, a vehicle (e.g., a vehicle the same or similar to the vehicle 102 and / or the vehicle 200) can drive along one or more drivable areas (e.g., single-lane roads, multi-lane roads, highways, back roads, and / or off-road paths, etc.), and cause at least one LiDAR sensor (e.g., a LiDAR sensor the same or similar to the LiDAR sensor 202b) to generate data associated with an image representing the objects included in the field of view of the at least one LiDAR sensor.

[0069] In some embodiments, the database 410 can be implemented across multiple devices. In some examples, the database 410 is included in a vehicle (e.g., a vehicle the same or similar to the vehicle 102 and / or the vehicle 200), an autonomous vehicle system (e.g., an autonomous vehicle system the same or similar to the remote AV system 114), a queue management system (e.g., a queue management system the same or similar to Figure 1 the queue management system 116), and / or a V2I system (e.g., a V2I system the same or similar to Figure 1 the V2I system 118), etc.

[0070] Now refer to Figure 4B , a diagram illustrating the implementation of a machine learning model. More specifically, a diagram illustrating the implementation of a convolutional neural network (CNN) 420. For illustrative purposes, the following description of the CNN 420 will be with respect to implementing the CNN 420 via the perception system 402. However, it will be understood that in some examples, the CNN 420 (e.g., one or more components of the CNN 420) is implemented by other systems different from or in addition to the perception system 402, such as the planning system 404, the positioning system 406, and / or the control system 408, etc. Although the CNN 420 includes certain features as described herein, these features are provided for illustrative purposes and are not intended to limit the present disclosure.

[0071] The CNN 420 includes a plurality of convolutional layers including a first convolutional layer 422, a second convolutional layer 424, and a convolutional layer 426. In some embodiments, the CNN 420 includes a subsampling layer 428 (sometimes referred to as a pooling layer). In some embodiments, the subsampling layer 428 and / or other subsampling layers have dimensions that are smaller than the dimensions of the upstream system (i.e., the amount of nodes). By means of the subsampling layer 428 having dimensions that are smaller than the dimensions of the upstream layer, the CNN 420 combines the amount of data associated with the initial input and / or output of the upstream layer, thereby reducing the amount of computation required for the CNN 420 to perform downstream convolutional operations. Additionally or alternatively, by means of the subsampling layer 428 being associated with at least one subsampling function (e.g., being configured to perform at least one subsampling function) (as described below with respect to Figure 4C and Figure 4D ), the CNN 420 combines the amount of data associated with the initial input.

[0072] Based on the perception system 402 providing corresponding inputs and / or outputs associated with the first convolutional layer 422, the second convolutional layer 424, and the convolutional layer 426 respectively to generate corresponding outputs, the perception system 402 performs convolutional operations. In some examples, based on the perception system 402 providing data as inputs to the first convolutional layer 422, the second convolutional layer 424, and the convolutional layer 426, the perception system 402 implements the CNN 420. In such examples, based on the perception system 402 receiving data from one or more different systems (e.g., one or more systems of a vehicle identical or similar to the vehicle 102, a remote AV system identical or similar to the remote AV system 114, a queue management system identical or similar to the queue management system 116, and / or a V2I system identical or similar to the V2I system 118, etc.), the perception system 402 provides the data as inputs to the first convolutional layer 422, the second convolutional layer 424, and the convolutional layer 426. The following is a detailed description of Figure 4C including convolutional operations.

[0073] In some embodiments, the perception system 402 provides data associated with an input (referred to as an initial input) to the first convolutional layer 422, and the perception system 402 uses the first convolutional layer 422 to generate data associated with an output. In some embodiments, the perception system 402 provides the output generated by a convolutional layer as an input to a different convolutional layer. For example, the perception system 402 provides the output of the first convolutional layer 422 as an input to the subsampling layer 428, the second convolutional layer 424, and / or the convolutional layer 426. In such an example, the first convolutional layer 422 is referred to as an upstream layer, and the subsampling layer 428, the second convolutional layer 424, and / or the convolutional layer 426 are referred to as downstream layers. Similarly, in some embodiments, the perception system 402 provides the output of the subsampling layer 428 to the second convolutional layer 424 and / or the convolutional layer 426, and in this example, the subsampling layer 428 will be referred to as an upstream layer, and the second convolutional layer 424 and / or the convolutional layer 426 will be referred to as downstream layers.

[0074] In some embodiments, before the perception system 402 provides an input to the CNN 420, the perception system 402 processes data associated with the input provided to the CNN 420. For example, based on the perception system 402 normalizing sensor data (such as, for example, image data, LiDAR data, and / or Radar data, etc.), the perception system 402 processes data associated with the input provided to the CNN 420.

[0075] In some embodiments, based on the perception system 402 performing convolutional operations associated with each convolutional layer, the CNN 420 generates an output. In some examples, based on the perception system 402 performing convolutional operations associated with each convolutional layer and the initial input, the CNN 420 generates an output. In some embodiments, the perception system 402 generates an output and provides the output to the fully connected layer 430. In some examples, the perception system 402 provides the output of the convolutional layer 426 to the fully connected layer 430, where the fully connected layer 430 includes data associated with a plurality of eigenvalues referred to as F1, F2,..., FN. In this example, the output of the convolutional layer 426 includes data associated with a plurality of output eigenvalues representing predictions.

[0076] In some embodiments, based on the perception system 402 identifying an eigenvalue associated with the highest likelihood of being the correct prediction among a plurality of predictions, the perception system 402 identifies a prediction from among the plurality of predictions. For example, in the case where the fully connected layer 430 includes eigenvalues F1, F2, ..., FN and F1 is the largest eigenvalue, the perception system 402 identifies the prediction associated with F1 as the correct prediction among the plurality of predictions. In some embodiments, the perception system 402 trains the CNN 420 to generate predictions. In some examples, based on the perception system 402 providing training data associated with the prediction to the CNN 420, the perception system 402 trains the CNN 420 to generate predictions.

[0077] Now refer Figure 4C and Figure 4D , a diagram illustrating an example operation of the CNN 440 that utilizes the perception system 402. In some embodiments, the CNN 440 (e.g., one or more components of the CNN 440) is the same as or similar to the CNN 420 (e.g., one or more components of the CNN 420) (see Figure 4B ).

[0078] In step 450, the perception system 402 provides data associated with an image as input to the CNN 440 (step 450). For example, as illustrated, the perception system 402 provides data associated with an image to the CNN 440, where the image is a grayscale image represented as values stored in a two-dimensional (2D) array. In some embodiments, the data associated with the image may include data associated with a color image, which is represented as values stored in a three-dimensional (3D) array. Additionally or alternatively, the data associated with the image may include data associated with an infrared image and / or a Radar image, etc.

[0079] In step 455, the CNN 440 performs a first convolution function. For example, based on the CNN 440 providing the values representing the image as input to one or more neurons (not explicitly illustrated) included in the first convolutional layer 442, the CNN 440 performs the first convolution function. In this example, the values representing the image may correspond to the values of a region (sometimes referred to as a receptive field) representing the image. In some embodiments, each neuron is associated with a filter (not explicitly illustrated). The filter (sometimes referred to as a kernel) can be represented as an array of values corresponding in size to the values provided as input to the neuron. In one example, the filter can be configured to identify edges (e.g., horizontal lines, vertical lines, and / or straight lines, etc.). In successive convolutional layers, the filters associated with the neurons can be configured to successively identify more complex patterns (e.g., arcs and / or objects, etc.).

[0080] In some embodiments, based on the CNN 440, the values provided as input to each neuron among one or more neurons included in the first convolutional layer 442 are multiplied by the values of filters corresponding to each neuron among the same one or more neurons, and the CNN 440 performs a first convolutional function. For example, the CNN 440 may multiply the values provided as input to each neuron among one or more neurons included in the first convolutional layer 442 by the values of filters corresponding to each neuron among the same one or more neurons to generate a single value or an array of values as output. In some embodiments, the collective output of the neurons of the first convolutional layer 442 is referred to as the convolutional output. In some embodiments, when each neuron has the same filter, the convolutional output is referred to as a feature map.

[0081] In some embodiments, the CNN 440 provides the output of each neuron of the first convolutional layer 442 to the neurons of a downstream layer. For clarity, an upstream layer may be a layer that transmits data to a different layer (referred to as a downstream layer). For example, the CNN 440 may provide the output of each neuron of the first convolutional layer 442 to the corresponding neurons of a subsampling layer. In an example, the CNN 440 provides the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the first subsampling layer 444. In some embodiments, the CNN 440 adds a bias value to the aggregated set of all values provided to each neuron of the downstream layer. For example, the CNN 440 adds a bias value to the aggregated set of all values provided to each neuron of the first subsampling layer 444. In such an example, the CNN 440 determines the final value to be provided to each neuron of the first subsampling layer 444 based on the aggregated set of all values provided to each neuron and the activation function associated with each neuron of the first subsampling layer 444.

[0082] In step 460, the CNN 440 performs a first subsampling function. For example, based on the CNN 440 providing the values output by the first convolutional layer 442 to the corresponding neurons of the first subsampling layer 444, the CNN 440 may perform a first subsampling function. In some embodiments, the CNN 440 performs the first subsampling function based on an aggregation function. In an example, based on the CNN 440 determining the maximum input among the values provided to a given neuron (referred to as the max pooling function), the CNN 440 performs the first subsampling function. In another example, based on the CNN 440 determining the average input among the values provided to a given neuron (referred to as the average pooling function), the CNN 440 performs the first subsampling function. In some embodiments, based on the CNN 440 providing values to each neuron of the first subsampling layer 444, the CNN 440 generates an output, which is sometimes referred to as the subsampled convolutional output.

[0083] In step 465, CNN 440 performs a second convolution function. In some embodiments, CNN 440 performs the second convolution function in a manner similar to how CNN 440 performs the first convolution function as described above. In some embodiments, based on the values output by the first subsampling layer 444 being provided as inputs to one or more neurons (not explicitly illustrated) included in the second convolution layer 446, CNN 440 performs the second convolution function. In some embodiments, as described above, each neuron of the second convolution layer 446 is associated with a filter. As described above, the (one or more) filters associated with the second convolution layer 446 may be configured to identify more complex patterns compared to the filters associated with the first convolution layer 442.

[0084] In some embodiments, based on CNN 440 multiplying the values provided as inputs to each of the one or more neurons included in the second convolution layer 446 by the values of the filters corresponding to each of the one or more neurons, CNN 440 performs the second convolution function. For example, CNN 440 may multiply the values provided as inputs to each of the one or more neurons included in the second convolution layer 446 by the values of the filters corresponding to each of the one or more neurons to generate a single value or an array of values as output.

[0085] In some embodiments, CNN 440 provides the output of each neuron of the second convolution layer 446 to the neurons of a downstream layer. For example, CNN 440 may provide the output of each neuron of the first convolution layer 442 to the corresponding neurons of the subsampling layer. In an example, CNN 440 provides the output of each neuron of the first convolution layer 442 to the corresponding neurons of the second subsampling layer 448. In some embodiments, CNN 440 adds a bias value to the aggregated set of all values provided to each neuron of the downstream layer. For example, CNN 440 adds a bias value to the aggregated set of all values provided to each neuron of the second subsampling layer 448. In such an example, CNN 440 determines the final value provided to each neuron of the second subsampling layer 448 based on the aggregated set of all values provided to each neuron and the activation function associated with each neuron of the second subsampling layer 448.

[0086] At step 470, CNN 440 performs a second subsampling function. For example, based on the values output by the second convolutional layer 446 being provided to the respective neurons of the second subsampling layer 448 by CNN 440, CNN 440 may perform the second subsampling function. In some embodiments, based on CNN 440 using an aggregation function, CNN 440 performs the second subsampling function. In an example, as described above, based on CNN 440 determining the maximum input or average input among the values provided to a given neuron, CNN 440 performs the first subsampling function. In some embodiments, based on CNN 440 providing values to the respective neurons of the second subsampling layer 448, CNN 440 generates an output.

[0087] At step 475, CNN 440 provides the output of each neuron of the second subsampling layer 448 to the fully connected layer 449. For example, CNN 440 provides the output of each neuron of the second subsampling layer 448 to the fully connected layer 449 such that the fully connected layer 449 generates an output. In some embodiments, the fully connected layer 449 is configured to generate an output associated with a prediction (sometimes referred to as classification). The prediction may include an indication that an object included in the image provided as input to CNN 440 includes an object and / or a collection of objects, etc. In some embodiments, the perception system 402 performs one or more operations and / or provides data associated with the prediction to different systems described herein.

[0088] Now referring to Figure 5 , illustrated is a diagram of an implementation 500 of a process for efficient and optimal feature extraction from observations. In some embodiments, implementation 500 includes AV computing 506 implemented in an autonomous navigation system of a vehicle 502. AV computing 506 is the same as or similar to Figure 4A AV computing 400. AV computing 506 includes a planning system 504a (e.g., Figure 4A 404 of Figure 4A ) and a control system 504b (e.g., the control system 408 of

[0089] ). In an example, the planning system 504a determines a route (514) for the AV 502 to travel through an environment (e.g., Figure 1 environment 100 of

[0090] ). The route is transmitted (516) to the control system 504b, which controls the operation of the vehicle by generating and transmitting control signals to cause the powertrain control system (e.g., DBW system 202h and / or powertrain control system 204, etc.), the steering control system (e.g., steering control system 206), and / or the braking system (e.g., braking system 208) to operate.AV computation 506 executes a machine learning system to implement autonomous navigation of vehicle 502. In an example, the machine learning model is the same as or similar to the machine learning model described above with respect to Figures 4B to 4D The machine learning model includes various machine learning techniques such as supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning. The machine learning model is trained by learning from an existing data set that includes observations. In some embodiments, the observations are instances, scenarios, units, or samples of the data set. The observations are associated with labels. In an example, the labels are additional information known to be related to the observations, where the additional information is based on prior knowledge.

[0091] Features are values or characteristics of individual observations in the data set. In some embodiments, the features are inputs to the machine learning model, and the labels are outputs of the machine learning model. Modeling real-world decision problems typically involves using high-dimensional raw data objects (observations) as inputs. In safety-critical applications, engineers typically extract a set of manually designed representative features (latent features) from each observation (e.g., a planned trajectory in traffic), and this representative feature forms the input to the machine learning system. Then, the machine learning system can use these features to make higher-level decisions. In some embodiments, the machine learning model includes parameters that are configuration variables internal to the machine learning model. In some embodiments, training the machine learning model includes estimating the parameters using features extracted from a given data set. Traditionally, feature extraction involves some parameter tuning that depends on the specific machine learning system to be trained. Adjusting these parameters is cumbersome and inefficient, thus posing a bottleneck for many trained systems.

[0092] In some embodiments, latent features are extracted from the data set and used to train a machine learning system (such as the machine learning system of AV computation 506). Features are automatically extracted by selecting specific observations with a predetermined configuration of metrics. In some embodiments, the metrics are at least partially based on the domain of the data set. For example, the metrics are known criteria for evaluating the observations of the data set. In an example, the metrics are evaluation metrics for measuring the performance of the machine learning system. In an example, the predetermined configuration of the metrics refers to a pattern or predetermined occurrence of the metrics in the observations of the data set.

[0093] Figure 6 Shows high-dimensional observations, metrics, and classifications. As Figure 6As shown, dataset 620 and dataset 630 are specific examples of observations 602, metrics 606, and classifications 604. The observations 602 are evaluated to determine the classification 604. In the example, the classification 604 is the type of label associated with the observation. In some embodiments, the classification 604 is assigned to the observation 602 before identifying the machine learning model to be trained. The observations 602 and the classification 604 can be, for example, labeled datasets. In some embodiments, the metrics 606 are applied to evaluate the observations without relying on the underlying algorithm or machine learning system. In the example, the metrics are agnostic to the machine learning system being trained. In other words, the metrics are used to evaluate aspects of the observations independently of the specific machine learning model to be trained. Using a predetermined configuration of the metrics, features of the dataset are extracted.

[0094] In Figure 6 the example of, a dataset 620 including an observation 602A is illustrated. The observation includes a trajectory (e.g., Figure 1 route 106). In some embodiments, the trajectory includes a series of car poses and scene information (such as maps, lanes, other participants, and road signs, etc.). The trajectory is classified (e.g., labeled) as the best trajectory (604A) based on the state of the vehicle. The state can be, for example, overtaking, not overtaking, passing in front, passing behind. This technique enables efficient and optimal feature extraction from the observations. For example, the metric 606A is used to evaluate the observation 602A. A predetermined configuration of the metric 606A is used to extract hidden features, which are input into the machine learning model for training, testing, or validation of the machine learning model. In Figure 6 the example of, the metric 606A is a traffic safety metric. For example, the metric 606A includes the likelihood of a traffic conflict (e.g., the likelihood of a traffic conflict averaged over Y time steps), the lateral gap to other vehicles (e.g., the lateral gap to other vehicles within an X threshold), and overall forward movement, etc.

[0095] At dataset 630, an observation 602B is illustrated. The observation 602B includes a person. The person is classified (e.g., labeled) as a healthy individual or an unhealthy individual (604B) based on the state of the individual. This technique enables efficient and optimal feature extraction from the observations. For example, the metric 606B is used to evaluate the observation 602B. A predetermined configuration of the metric 606B is used to extract hidden features, which are used for training, testing, or validating the machine learning model. In Figure 6In the example, the metric 606B is an expert metric provided by a healthcare professional. For example, expert metrics include cardiovascular tone, respiratory score, activity score, heart health, age, and the number of doctor visits in the past X months, etc.

[0096] This technology includes an algorithm for efficiently learning and optimizing a feature from observations without retraining a machine learning model or relying on a machine learning system that utilizes hidden features. In the example, even when the feature extraction process is decoupled from the parameters of the machine learning system, the machine learning system that utilizes these extracted features achieves greater performance when compared to traditional machine learning systems. Traditionally, feature extraction from observations is done by extracting as many features as possible. For example, at dataset 620 and dataset 630, traditional techniques blindly extract as much information as possible from observations 602A and 602B, such as traffic information from a trajectory and biometric information from a person, etc. However, this is usually inefficient and suboptimal. In other traditional methods, the feature extraction process is usually manually adjusted after feature extraction and training of the machine learning system, which is very inefficient. This technology performs automatic feature extraction in a data-driven manner without prior knowledge of the model. As a result, the feature extraction process can be shown to be optimal and very suitable for training a machine learning system.

[0097] Figure 7A is a block diagram of the feature extraction pipeline 700A. For ease of description, the underlying data in the autonomous driving domain is used to describe the feature extraction pipeline 700A. However, this technology is directly applicable to classification systems in any domain as described regarding Figure 7B any domain as described. Figure 7B is a block diagram of the dataset 700B. In the example, the training data that can be used to optimize the described classifier is a collection of observations (z) 722 and relative classifications (y) 724. Figure 7B shows the dataset 700B from the domain, which has observations (z) 722 such as a driving scenario or a human body, and classifications (y) such as whether the driving scenario is good or whether the person is healthy, respectively. In the dataset 700B, there is no ground truth for determining the features of the observations.

[0098] Referring again to Figure 7A , the feature extraction pipeline 700A includes a driving scenario (Z) 702 (e.g., Figure 6 the observation 602 of Figure 6Classification 604). Classification (Y) 704 refers to each trajectory based on the driving scenario (Z) 702, and whether the driving scenario (Z) 702 is good or bad (e.g., safe or unsafe). This technology provides efficient and optimal feature extraction as guided by a predetermined configuration of metrics. For example, a traffic safety metric (e.g., metric (X) 706) is used to extract features from the driving scenario (Z) 702, which includes the driving trajectory and the surrounding traffic context. In an example, the driving scenario (Z) 702 consists of the state of the self-vehicle, the map, and the agents present in the scene. A set of metrics (X) 706 is obtained and used to operate the selection / classification (Y) 704 among a set of alternative trajectories proposed by one or more planning algorithms. The trajectory metric (X) is calculated according to the driving scenario (Z) 702 via a set of parametric functions presenting a set of parameters.

[0099] For example, the trajectory metric (X) is a binary metric called "lateral gap violation". The metric is evaluated according to a rule including a threshold parameter. For example, if the trajectory of the AV is within X meters laterally of another moving traffic agent, the metric returns 1 to indicate that a lateral gap violation has occurred. Otherwise, the metric is 0 to indicate that no lateral gap violation has occurred. Thus, the metric is governed by a parameter (X), which is determined before the machine learning model is selected, trained, tested, or validated. Traditionally, this parameter is manually selected. This technology automatically selects the best parameters from a dataset (e.g., observations and classifications) without any prior decision on the model evaluated by the metric. Regarding Figure 8A Feature extraction and parameter determination are further described.

[0100] Figure 8A This is an illustration of the feature extraction system 800B in the context of route planning. In Figure 8A the example, the observation 802A includes the scene 810A. The scene 810A has a known label 806A. In the scene 810A, as shown by the arrow 813A, the self-vehicle 812A (e.g., AV) is traveling in the same direction as the vehicle 814A traveling in the adjacent lane in the lane. The predetermined configurations of the metrics 830A, 832A, 834A, 836A, 838A, and 840A are shown. In Figure 8AIn the example of , scenario 810A (e.g., an observation) satisfies the predetermined configuration of metrics 830A, 832A, 834A, 836A, 838A, and 840A with a single non-zero value metric over the metrics 830A, 832A, 834A, 836A, 838A, and 840A found in the observation. For ease of description, the predetermined configuration of the metrics is described as a single non-zero value metric; however, any metric with a different value when compared to other metrics applied to the observation can be used. Additionally, for purposes of description, label 806A classifies scenario 810A as exhibiting good behavior by self-vehicle 812A.

[0101] To determine the best parameters for feature extraction, the feature extraction process is initialized by selecting an initial reasonable value for the feature corresponding to the single non-zero value metric 838A. Feature values are iteratively extracted from the observations in the dataset. For example, scenario 810A exhibits a lateral gap violation because the corresponding metric is a single non-zero value metric. Based on the known information about scenario 810A, a lateral gap feature value is extracted from scenario 810A, and since label 806A identifies scenario 810A as good behavior, the extracted lateral gap value is known not to be a violation of the lateral gap. In some embodiments, other features include lane change violations, traffic conflicts, acceleration violations, longitudinal gaps, and comfort costs.

[0102] The unchanged non-zero feature values (e.g., data points) are highly informative because, first, the classification of these features (most of which are zero except for one feature) is inferred from the label without any assumptions about how the model behaves, regardless of the machine learning system and machine learning system parameters subsequently used for training, testing, or validation. In some embodiments, the feature extraction parameters are optimally adjusted on these data points without any assumptions about the parameters of the machine learning system.

[0103] In Figure 8AIn the example, a lateral gap feature is extracted from the observation results, which is determined by checking whether the lateral distance between the ego vehicle 812A and the vehicle 814A is within a certain threshold (e.g., a feature extraction parameter or value). In this example, the scenario 810A contains a lateral gap violation. However, the label of the data set is "good behavior", so there should be no lateral gap violation. Thus, the lateral threshold parameter is optimally determined to be less than the lateral gap 816A. For example, the lateral gap 816A observed in the scenario 810A is 1.6 m. Since the scenario 810A is labeled as good behavior and there is no actual lane gap violation, it can be seen that 1.6 m is a safe gap. Multiple observations falling under the same category are analyzed to determine the optimal gap threshold that minimizes the error rate for all given scenarios. Thus, the feature extraction process is optimally adjusted without explicit knowledge of the machine learning system that converts the feature 804A into the label 806A.

[0104] In some embodiments, the feature extraction parameters are optimized. According to the previous example, it is determined that 1.6 m is outside the lateral gap violation threshold. When evaluating multiple observations of a predetermined configuration with the same metrics, different lateral gaps (1.2 m, 1.5 m, etc.) with different labels of whether the observation is safe / good are extracted. Some of the identified useful observations may even conflict with each other (e.g., according to the label, the observation of 1.2 m may be considered good, but the observation of 1.4 m may be considered undesirable; this is entirely possible because driving data may be noisy). In some embodiments, Bayesian optimization is used to reconcile these differences and select the best parameters for each feature, which minimizes the error rate over all data points. In some embodiments, the extracted feature values are used as inputs to a machine learning system for implementing trajectory suggestions, determining (hierarchical) trajectory cost functions, trajectory selection, and homotopy selection.

[0105] Figure 8B This is an illustration of the feature extraction system 800B in the context of route planning. In Figure 8B the example, the observation 802B includes the scenario 810B. The scenario 810B has a known label 806B. In the scenario 810B, as shown by the arrow 813B, the ego vehicle 812B (e.g., an AV) is traveling in the same lane as the vehicle 814B and in the same direction as the vehicle 814B. A predetermined configuration of metrics 830B, 832B, 834B, 836B, 838B, and 840B is shown. In Figure 8BIn the example of, scenario 810B (e.g., an observation) satisfies the predetermined configuration of metrics 830B, 832B, 834B, 836B, 838B, and 840B by having a single non-zero value metric among the metrics 830B, 832B, 834B, 836B, 838B, and 840B found in the observation. Label 806B classifies scenario 810B as exhibiting bad behavior by the self-vehicle 812B.

[0106] To determine the best parameters for feature extraction, the feature extraction process is initialized by selecting an initial reasonable value of the feature corresponding to the single non-zero value metric 836B. Feature values are iteratively extracted from the observations in the dataset. For example, scenario 810B exhibits a longitudinal gap violation because the corresponding metric 836B is a single non-zero value metric. Based on the known information related to scenario 810B, the longitudinal gap feature value is extracted from scenario 810B, and since label 806B identifies scenario 810B as bad behavior, the extracted longitudinal gap value is known to be a violation of the longitudinal gap. In some embodiments, other features include lane change violations, traffic conflicts, acceleration violations, lateral gaps, and comfort costs.

[0107] In Figure 8B the example of, the longitudinal gap feature is extracted from the observation, and the longitudinal gap feature is determined by checking whether the longitudinal distance between the self-vehicle 812B and the vehicle 814B is within a certain threshold (e.g., a feature extraction parameter or value). In this example, scenario 810B contains a longitudinal gap violation. The label 806B of the dataset is "bad behavior", so there should be a longitudinal gap violation. Thus, the longitudinal threshold parameter is optimally determined to be at least equal to the longitudinal gap 816B. For example, the longitudinal gap 816B observed in scenario 810B is 2.5m. Since scenario 810B is labeled as bad behavior and there is a longitudinal gap violation, it can be seen that 2.5m is an unsafe gap. Multiple observations falling under the same category are analyzed to determine the best gap threshold that minimizes the error rate for all given scenarios. Thus, the feature extraction process is optimally adjusted without explicit knowledge of the machine learning system that converts feature 804B into label 806B.

[0108] Figure 8C is an illustration of the feature extraction system 800C in the context of route planning. In Figure 8C the example of, the observation 802C includes scenario 810C and scenario 820C. Scenario 810C has a known label 806C-1. Scenario 820C has a known label 806C-2. Scenario 810C and scenario 820C illustrate emergency braking. As illustrated by arrow 813C, scenario 810C illustrates at -2.05m / s 2Emergency braking at a rate. As illustrated by arrow 815C, scenario 820C illustrates emergency braking at a rate of -1.8 m / s 2 Emergency braking at a rate.

[0109] In scenario 810C, vehicle 812C brakes at -2.05 m / s 2 and is tagged as uncomfortable (e.g., tag 806C-1), while in scenario 820C, vehicle 814C brakes at -1.8 m / s 2 and is tagged as acceptable or comfortable (e.g., tag 806C-2). In the example, the predetermined configuration of the metric includes a single non-zero value for the deceleration violation metric. In some embodiments, to determine the deceleration violation threshold for extracting the feature parameter, based on the extracted feature values and the corresponding tags 806C-1 and tag 806C-2, the threshold is determined to be between -1.8 m / s 2 and -2.05 m / s 2 In the example, additional data points enable the calculation of the optimal threshold for extracting the feature parameter. Then, these optimally extracted features can be used in a machine learning classification system.

[0110] Now referring to Figure 9 , illustrated is a flowchart of process 900 for efficient and optimal feature extraction from observations. In some embodiments, one or more of the steps described with respect to process 900 are performed by Figure 2 autonomous system 202 (e.g., fully and / or partially, etc.). Additionally or alternatively, in some embodiments, one or more of the steps described with respect to process 900 are performed by other devices or groups of devices separate from or including autonomous system 202 (such as Figure 2 AV computing 202f of Figure 1 remote AV system 114 of Figure 1 queue management system 116 of Figure 1 V2I system 118 of Figure 3 device 300 of Figure 7A AV computing 400 of FIG. 4, Figures 8A to 8C feature extraction pipeline 700A of

[0111] At block 902, observations that satisfy the predetermined configuration of the metric are identified. In the example, observations are identified from a tagged data set. In the example, the predetermined configuration of the metric is a single non-zero value metric among the predetermined configuration of metrics. In the example, an observation satisfies the predetermined configuration of the metric when the observation exhibits the characteristics of the metric. The metric is based, for example, on the domain of the observation.

[0112] At block 904, eigenvalue representing a single non-zero value metric is extracted from the observations. In an example, eigenvalues are iteratively extracted from respective observations.

[0113] At block 906, parameters for extraction are determined based on the eigenvalues extracted from the observations, where the observations are labeled. In an example, the eigenvalues are averaged to determine a threshold for the feature. In an example, the eigenvalues form a range of values, and features are extracted based on being within or outside the range of values according to a particular metric or rule.

[0114] In some embodiments, the present technology includes an algorithm that can efficiently learn the hidden feature from observations without the need to retrain a machine learning system that utilizes the optimized hidden feature or rely on the machine learning system. This process was previously difficult to optimize, but by identifying unique observations, the present technology can optimally learn the feature extraction process from these observations. In some embodiments, the optimization process can extract a set of hidden safety features from the observed driving data. These hidden features are compatible with different AV machine learning systems and can be used for planning and safety verification. In some embodiments, the pipeline has a direct application in the optimization of a trajectory selector in an autonomous vehicle planning algorithm, where the hidden feature can be interpreted as a cost and the output of the classification is a label indicating the best trajectory in a pair of trajectories. The selection pipeline can also be used to create sub-types for trajectories. In fact, selecting points where the feature vector has a particular form can help identify specific scenarios on the road. For example, trajectory sub-types include trajectories near or within intersections, trajectories in specific driving scenarios such as pick-up and drop-off areas (e.g., hotel lobbies, flight departure / arrival zones, event venues, etc.), and high-speed trajectories on highways. In an example, after feature (metric) extraction on the trajectory / driving scenario, trajectory sub-types are created. After extraction, scenarios with similar metrics / features are grouped together, and trajectories / driving scenarios belonging to the same sub-type are identified.

[0115] According to some non-limiting embodiments or examples, a method is provided that includes: using at least one processor, identifying observations that satisfy a predetermined configuration of a metric; using at least one processor, extracting from the observations eigenvalue representing the metric in the predetermined configuration of the metric; and using at least one processor, determining parameters for feature extraction based on the eigenvalue extracted from the identified observations and the corresponding label of the identified observations.

[0116] According to some non - limiting embodiments or examples, a system is provided that includes: at least one processor and at least one non - transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to: identify observations of a predetermined configuration that meet a metric; extract, from the observations, eigenvalue(s) of the metric(s) representing the predetermined configuration; and determine parameters for feature extraction based on the eigenvalue(s) extracted from the identified observations and the corresponding label(s) of the identified observations.

[0117] According to some non - limiting embodiments or examples, at least one non - transitory storage medium is provided that stores instructions that, when executed by at least one processor, cause the at least one processor to: identify observations of a predetermined configuration that meet a metric; extract, from the observations, eigenvalue(s) of the metric(s) representing the predetermined configuration; and determine parameters for feature extraction based on the eigenvalue(s) extracted from the identified observations and the corresponding label(s) of the identified observations.

[0118] Further non - limiting aspects or embodiments are set forth in the numbered clauses below:

[0119] Clause 1: A method includes: using at least one processor, identifying observations of a predetermined configuration that meet a metric; using the at least one processor, extracting eigenvalue(s) of the metric(s) representing the predetermined configuration from the observations; and using the at least one processor, determining parameters for feature extraction based on the eigenvalue(s) extracted from the identified observations and the corresponding label(s) of the identified observations.

[0120] Clause 2: The method according to Clause 1, wherein the predetermined configuration of the metric is a single non - zero value metric among the predetermined configuration of the metrics.

[0121] Clause 3: The method according to Clause 1 or 2, wherein the observations are identified in a labeled dataset.

[0122] Clause 4: The method according to any one of Clauses 1 to 3, wherein eigenvalue(s) are iteratively extracted from the observations.

[0123] Clause 5: The method according to any one of Clauses 1 to 4, wherein the parameter is an average or a range including the eigenvalue(s) iteratively extracted from the observations.

[0124] Clause 6: The method according to any one of Clauses 1 to 5, wherein the observations meet the predetermined configuration of the metric when the observations exhibit the characteristics of the metric.

[0125] Clause 7: The method according to any one of Clauses 1 to 6, wherein the metric is at least partially based on the domain of the observations.

[0126] Clause 8: A system, comprising: at least one processor, and at least one non-transitory storage medium storing instructions which, when executed by the at least one processor, cause the at least one processor to: identify observations that satisfy a predetermined configuration of a metric; extract from the observations eigenvalue of a metric representing the predetermined configuration; and determine parameters for feature extraction based on the eigenvalue extracted from the identified observations and the corresponding labels of the identified observations.

[0127] Clause 9: The system according to Clause 8, wherein the predetermined configuration of the metric is a single non-zero value metric among the predetermined configuration of the metrics.

[0128] Clause 10: The system according to Clause 8 or 9, wherein the observations are identified in a labeled dataset.

[0129] Clause 11: The system according to any one of Clauses 8 to 10, wherein the eigenvalue is iteratively extracted from the observations.

[0130] Clause 12: The system according to any one of Clauses 8 to 11, wherein the parameter is an average or range including the eigenvalue iteratively extracted from the observations.

[0131] Clause 13: The system according to any one of Clauses 8 to 12, wherein the observations satisfy the predetermined configuration of the metric when the observations exhibit the characteristics of the metric.

[0132] Clause 14: The system according to any one of Clauses 8 to 13, wherein the metric is at least partially based on the domain of the observations.

[0133] Clause 15: At least one non-transitory storage medium storing instructions which, when executed by at least one processor, cause the at least one processor to: identify observations that satisfy a predetermined configuration of a metric; extract from the observations eigenvalue of a metric representing the predetermined configuration; and determine parameters for feature extraction based on the eigenvalue extracted from the identified observations and the corresponding labels of the identified observations.

[0134] Clause 16: The at least one non-transitory storage medium according to Clause 15, wherein the predetermined configuration of the metric is a single non-zero value metric among the predetermined configuration of the metrics.

[0135] Clause 17: The at least one non-transitory storage medium according to Clause 15 or 16, wherein the observation is identified in the labeled dataset.

[0136] Clause 18: The at least one non-transitory storage medium according to any one of Clauses 15 to 17, wherein the eigenvalue is iteratively extracted from the observation.

[0137] Clause 19: The at least one non-transitory storage medium according to any one of Clauses 15 to 18, wherein the parameter is an average value or a range including the eigenvalue iteratively extracted from the observation.

[0138] Clause 20: The at least one non-transitory storage medium according to any one of Clauses 15 to 19, wherein when the observation exhibits the characteristics of the metric, the observation meets the predetermined configuration of the metric.

[0139] In the foregoing description, aspects and embodiments of the present disclosure have been described with reference to numerous specific details, which may vary according to implementation. Accordingly, the specification and drawings are to be regarded as illustrative rather than restrictive in a limiting sense. The sole and exclusive indication of the scope of the invention, and what the applicant desires to be the scope of the invention, is the literal and equivalent scope of the claims issued from this application in the specific form of the issued claims, including any subsequent amendments. Any definition of terms expressly set forth herein for inclusion in such claims shall be construed in the sense such terms are used in the claims. Additionally, when the term "further comprises" is used in the foregoing specification or the appended claims, the text following this phrase may be an additional step or entity, or a sub-step / sub-entity of a previously described step or entity.

Claims

1. A method, comprising: using at least one processor to identify observations of a predetermined configuration that meet the criteria; using the at least one processor to extract from the observations eigenvalue(s) of the criteria representative of the predetermined configuration; and using the at least one processor to determine, based on the eigenvalue(s) extracted from the identified observations and the corresponding label(s) of the identified observations, parameters for feature extraction.

2. The method according to claim 1, wherein the predetermined configuration of the criteria is a single non-zero value criterion among the criteria of the predetermined configuration.

3. The method according to claim 1, wherein the observations are identified in a labeled dataset.

4. The method according to claim 1, wherein eigenvalue(s) are iteratively extracted from the observations.

5. The method according to claim 1, wherein the parameters are an average value or a range including eigenvalue(s) iteratively extracted from the observations.

6. The method according to claim 1, wherein the observations meet the predetermined configuration of the criteria when the observations exhibit the characteristics of the criteria.

7. The method according to claim 1, wherein the criteria are at least partially based on the domain of the observations.

8. A system, comprising: at least one processor; and at least one non-transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to: identify observations of a predetermined configuration that meet the criteria; extract from the observations eigenvalue(s) of the criteria representative of the predetermined configuration; and determine, based on the eigenvalue(s) extracted from the identified observations and the corresponding label(s) of the identified observations, parameters for feature extraction.

9. The system according to claim 8, wherein the predetermined configuration of the criteria is a single non-zero value criterion among the criteria of the predetermined configuration.

10. The system according to claim 8, wherein the observations are identified in a labeled dataset.

11. The system according to claim 8, wherein eigenvalue(s) are iteratively extracted from the observations.

12. The system according to claim 8, wherein the parameters are an average value or a range including eigenvalue(s) iteratively extracted from the observations.

13. The system according to claim 8, wherein the observations meet the predetermined configuration of the criteria when the observations exhibit the characteristics of the criteria.

14. The system according to claim 8, wherein the criteria are at least partially based on the domain of the observations.

15. At least one non-transitory storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to: identify observations of a predetermined configuration that meet the criteria; extract from the observations eigenvalue(s) of the criteria representative of the predetermined configuration; and determine, based on the eigenvalue(s) extracted from the identified observations and the corresponding label(s) of the identified observations, parameters for feature extraction.

16. The at least one non-transitory storage medium according to claim 15, wherein The predetermined configuration of the metric is a single non-zero value metric among the predetermined configuration of metrics.

17. The at least one non-transitory storage medium according to claim 15, wherein, the observations are identified in the labeled dataset.

18. The at least one non-transitory storage medium according to claim 15, wherein, the eigenvalue is iteratively extracted from the observations.

19. The at least one non-transitory storage medium according to claim 15, wherein, the parameter is an average or range including the eigenvalue iteratively extracted from the observations.

20. The at least one non-transitory storage medium according to claim 15, wherein, when the observations exhibit the characteristics of the metric, the observations satisfy the predetermined configuration of the metric.

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