360-degree LiDAR tailoring

Abstract

A method for 360 degree LiDAR clipping is provided that may include: receiving, using at least one processor, data characterizing detection of a target by a LiDAR sensor, where the target is located within a field of view of the detection of the sensor, and where the target and the sensor are coupled to or adjacent to a portion of a vehicle; determining, using at least one processor, a modified field of view of the sensor, where the modified field of view is narrower than the field of view, and where the determining is based at least on identifying one or more regions in the field of view that include the target; and providing, using at least one processor, a calibration data set including data associated with the modified field of view. A system and a computer program product are also provided.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the benefit of priority and the benefit of the filing date of U.S. Provisional Patent Application Serial No. 63 / 379,724, filed on October 14, 2022, U.S. Provisional Patent Application Serial No. 63 / 437,529, filed on January 6, 2023, and U.S. Patent Application Serial No. 18 / 327,593, filed on June 1, 2023. The entire contents of these patent applications are incorporated herein by reference. Background Art

[0003] Three - hundred - sixty - degree time - of - flight LiDARs can be used on vehicles. The full field of view of these LiDARs is rarely available, and each sensor requires cropping of the azimuth and elevation emission positions. Brief Description of the Drawings

[0004] Figure 1 is an example environment of a vehicle that can implement one or more components of an autonomous system;

[0005] Figure 2 is a diagram of one or more systems of a vehicle including an autonomous system;

[0006] Figure 3 is Figure 1 and Figure 2 a diagram of one or more devices and / or components of one or more systems;

[0007] Figure 4 is a diagram of certain components of an autonomous system;

[0008] Figures 5A - 5D is a diagram of an implementation of a process for 360 - degree LiDAR cropping; and

[0009] Figure 6 is a flowchart of a process for 360 - degree LiDAR cropping;

[0010] Figure 7 is a diagram of a LiDAR system with a specific elevation and azimuth;

[0011] Figure 8A is a diagram of an example implementation of the nominal sensor field of view of a LiDAR system from a front view;

[0012] Figure 8B is a diagram of an example implementation of the nominal sensor field of view of a LiDAR system from a top view;

[0013] Figure 9A is a diagram showing the blind spots of a LiDAR system at the ground plane in a nominal position;

[0014] Figure 9B A diagram showing the blind spots of a LiDAR system at the ground plane at tolerance positions; and

[0015] Figure 10 A flowchart of a process for 360-degree LiDAR cropping. DETAILED DESCRIPTION

[0016] 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. However, it will be apparent that the embodiments described in the present disclosure may be practiced without these specific details. In some instances, well-known structures and devices are illustrated in block diagram form to avoid unnecessarily obscuring aspects of the present disclosure.

[0017] 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. Further, 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 cannot be combined with other elements in some embodiments.

[0018] In addition, 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 mean that connections, relationships, or associations cannot 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. Further, 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.

[0019] Although terms such as "first", "second", and / or "third" 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.

[0020] 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 indicates 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 "comprises", "comprising", "includes", and / or "including" are used in this specification, they 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.

[0021] As used herein, the terms "communicate" and "communicating" refer to at least one of receiving, receiving, transmitting, conveying, and / or providing 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 may 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 intermediate 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 may refer to a network packet (e.g., a data packet, etc.) that includes data.

[0022] As used herein, depending on the context, the term "if" may optionally be interpreted to mean "when", "upon", "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 interpreted to mean "upon determining...", "in response to determining that", or "upon detecting [the stated condition or event]" and / or "in response to detecting [the stated condition or event]", etc. Additionally, as used herein, the terms "has", "have", or "having", etc. are intended to be open-ended terms. Further, unless otherwise expressly stated, the phrase "based on" is intended to mean "at least partially based on".

[0023] 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.

[0024] General Overview

[0025] In some aspects and / or embodiments, the systems, methods, and computer program products described herein include and / or implement cropping the field of view of a LiDAR system. The field of view of the LiDAR system is cropped (e.g., during a calibration process) to remove unavailable portions of the field of view of the LiDAR system (e.g., portions of the field of view corresponding to a vehicle, a mirror on the vehicle, and / or a tire of the vehicle, etc.). When cropping the field of view, a target is positioned adjacent to the vehicle to identify the boundaries of the field of view of the LiDAR sensor, and the LiDAR sensor generates data associated with a point cloud. The out-of-boundary portions of the field of view (e.g., those portions that overlap with the vehicle) can be removed (e.g., during a calibration process) by configuring the LiDAR sensor to crop the point cloud based on the boundaries of the field of view before transmitting the point cloud for downstream use (e.g., by an autonomous vehicle system as described herein). Additionally or alternatively, the autonomous system can be configured to crop the point cloud when receiving the data associated with the point cloud. This cropping process can be performed for each vehicle (e.g., based on each vehicle leaving the production line, based on a specific vehicle during maintenance, etc.), and provides a uniquely cropped field of view based on the specific dimensions and tolerances of each vehicle.

[0026] Implementations of the systems, methods, and computer program products described herein include techniques for 360 - degree LiDAR cropping. Some advantages of these systems and techniques include the following. The ability to crop the field of view of a LiDAR system on a per - vehicle basis reduces blind spots that may result from sensor mounting tolerances. Additionally, the ability to crop the field of view of a LiDAR system on a per - vehicle basis relaxes the constraints on the frame design, which can allow for the use of less expensive frames. Further, since the cropping systems and methods described herein can be automated, assembly time and assembly line inspections can be reduced. Also, with the implementation of the techniques described herein, unwanted LiDAR echoes (e.g., echoes from highly reflective surfaces such as mirrors, glass, or certain paints) can be avoided.

[0027] In some embodiments, the system can reduce blind spots from a common crop for all production vehicles. In some embodiments, the system can include an automated solution for the vehicle to learn the vehicle body and automatically detect the crop angle.

[0028] Now refer to Figure 1 , illustrative example environment 100, in which vehicles including autonomous systems and vehicles not including autonomous systems operate. As illustrated, 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.

[0029] 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, for example, cars, buses, trucks, and / or trains. 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 vehicles 102 include an autonomous system (e.g., an autonomous system that is the same as or similar to autonomous system 202).

[0030] Objects 104a - 104n (individually referred to as object 104 and collectively referred to 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., a building, a sign, a 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 area 108.

[0031] Routes 106a - 106n (individually referred to as route 106 and collectively as routes 106) are each associated with (e.g., define) a series of actions (also referred to as a trajectory) along which an AV can be navigated. 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 target state (e.g., a state corresponding to a second spatio - temporal location different from the first) or a target zone (e.g., a subspace of acceptable states (e.g., a termination state)). In some embodiments, the first state includes a location where one or more individuals will board the AV, and the second state or zone 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), which 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, etc. 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 target, 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 target state or zone.

[0032] Region 108 includes a physical area (e.g., a geographical area) in which vehicle 102 can be navigated. In an example, region 108 includes at least one state (e.g., a country, a province, an individual state among multiple states included in a country, etc.), at least a part of a state, at least one city, at least a part 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 driveway, a section of a parking lot, a section of an open space 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.

[0033] A vehicle-to-infrastructure (V2I) device 110 (sometimes referred to as a vehicle-to-infrastructure or vehicle-to-everything (V2X) device) includes at least one device configured to communicate with a vehicle 102 and / or a V2I system 118. In some embodiments, the V2I device 110 is configured to communicate with the vehicle 102, a remote AV system 114, a queue management system 116, and / or the V2I system 118 via a network 112. In some embodiments, the 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), lane markings, streetlights, a parking meter, etc. In some embodiments, the V2I device 110 is configured to communicate directly with the vehicle 102. Additionally or alternatively, in some embodiments, the V2I device 110 is configured to communicate with the vehicle 102, the remote AV system 114, and / or the queue management system 116 via the V2I system 118. In some embodiments, the V2I device 110 is configured to communicate with the V2I system 118 via the network 112.

[0034] The network 112 includes one or more wired and / or wireless networks. In an example, the 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.

[0035] 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 an autonomous system, autonomous vehicle computing, and / or software implemented by autonomous vehicle computing). 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.

[0036] 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 ride-sharing 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.).

[0037] 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.).

[0038] Provided Figure 1 The number and arrangement of the illustrated elements are by way of example. Compared with Figure 1 the illustrated elements, there may be additional elements, fewer elements, different elements, and / or elements in a different arrangement. Additionally or alternatively, at least one element of the environment 100 may perform one or more functions described as being performed by Figure 1 at least one different element. 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.

[0039] Now referring to Figure 2 , the vehicle 200 (which may be the same as or similar to Figure 1 the 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., which enable 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 incorporates 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 herein by reference. In some embodiments, the vehicle 200 is associated with an autonomous queue manager and / or a ridesharing company.

[0040] 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 the vehicle 200 has traveled, 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 may 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 compute 202f, a drive-by-wire (DBW) system 202h, and a safety controller 202g.

[0041] The camera 202a includes at least one device configured to communicate with the communication device 202e, the autonomous vehicle compute 202f, and / or the safety controller 202g via a bus (e.g., a bus 302 that is the same as 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 an output. In some examples, the camera 202a generates camera data that includes image data associated with the image. In such an example, the image data may 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 may 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 on (e.g., positioned on) the vehicle for capturing images for the purpose of stereoscopic vision (stereo vision). In some examples, the camera 202a includes generating image data and transmitting the image data to the autonomous vehicle compute 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.

[0042] 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 providing 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.

[0043] 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 also 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 signal processing system associated with the LiDAR sensor 202b (e.g., signal processing system 202i) 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 signal 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.

[0044] 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 signal 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 signal 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.

[0045] 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 may 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.

[0046] 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 may 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).

[0047] 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, 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).

[0048] 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 take precedence over (e.g., override) the control signals generated and / or transmitted by the autonomous vehicle computing 202f.

[0049] 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.).

[0050] The signal processing system 202i includes at least one device configured to communicate with the LiDAR sensor 202b. In some embodiments, at least one signal processing system associated with the LiDAR sensor 202b (e.g., the signal processing system 202i) generates an image (e.g., a point cloud and / or a combined point cloud, etc.) representing an object included in the field of view of the LiDAR sensor 202b. In some examples, at least one signal processing system 202i 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. In some embodiments, the signal processing system 202i receives uncropped data (e.g., the full field of view of the LiDAR sensor) and applies a filter to the uncropped data. In some embodiments, the filter crops the field of view of the LiDAR sensor based on a calibration data set. In some embodiments, the cropped field of view is then sent to the LiDAR sensor such that the LiDAR sensor operates in view of the cropped field of view.

[0051] 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 a control signal from the DBW system 202h, and the powertrain control system 204 causes the vehicle 200 to perform longitudinal vehicle movement (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 movement (such as making a left turn and / or making a right turn, etc.). In an example, the powertrain control system 204 causes the energy provided to the motor of the vehicle (e.g., fuel and / or electricity, etc.) to increase, remain the same, or decrease, thereby causing at least one wheel of the vehicle 200 to rotate or not rotate.

[0052] 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 to the left or to the right to cause the vehicle 200 to turn left or right. In other words, the steering control system 206 causes the activities required to regulate the y-axis component of the vehicle movement.

[0053] The braking system 208 includes at least one device configured to actuate one or more brakes to decelerate and / or hold the vehicle 200 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 onto 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, among others.

[0054] In some embodiments, the vehicle 200 includes at least one platform sensor (not explicitly illustrated) configured to measure or infer 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 proximal to the vehicle 200 within Figure 2 the vehicle 200, the braking system 208 can be located anywhere within the vehicle 200.

[0055] 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 a system of the vehicle 102); and / or one or more devices of the network 112 (e.g., one or more devices of a system of the network 112). In some embodiments, one or more devices of the vehicle 102 (e.g., one or more devices of a system of the vehicle 102), and / or one or more devices of the network 112 (e.g., one or more devices of a system of the network 112) include at least one device 300 and / or at least one component of the device 300. As Figure 3 shown, the device 300 includes a bus 302, a processor 304, a memory 306, a storage component 308, an input interface 310, an output interface 312, and a communication interface 314.

[0056] The bus 302 includes components that enable communication among the components of the licensing device 300. In some cases, the 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.). The 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 the processor 304 (e.g., flash memory, magnetic memory, and / or optical memory, etc.).

[0057] The storage component 308 stores data and / or software related to the operation and use of the device 300. In some examples, the 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, RAM, PROM, EPROM, FLASH-EPROM, NV-RAM, and / or another type of computer-readable medium, and corresponding drives.

[0058] The input interface 310 includes components that enable the licensing device 300 to receive information such as via 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, the 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.). The output interface 312 includes components for providing output information from the device 300 (e.g., a display, a speaker, and / or one or more light emitting diodes (LEDs), etc.).

[0059] In some embodiments, the communication interface 314 includes transceiver-like components that enable the licensing 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 (e.g., a transceiver and / or separate receiver and transmitter, etc.). In some examples, the communication interface 314 enables the licensing device 300 to receive information from and / or provide information to another device. In some examples, the 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.

[0060] In some embodiments, device 300 performs one or more processes described herein. Device 300 performs these processes based on software instructions executed by a processor 304 that are stored on a computer-readable medium such as a memory 306 and / or a storage component 308. 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.

[0061] 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 a 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, hardwired circuitry is used instead of or in combination with software instructions to perform one or more processes described herein. Thus, unless otherwise expressly stated, the embodiments described herein are not limited to any particular combination of hardware circuitry and software.

[0062] The memory 306 and / or the storage component 308 includes a data store or at least one data structure (e.g., a database, etc.). Device 300 is capable of receiving information from the data store or at least one data structure in the memory 306 or the storage component 308, storing information in the data store or at least one data structure, communicating information to the data store or at least one data structure, or searching for information stored in the data store or at least one data structure. In some examples, the information includes network data, input data, output data, or any combination thereof.

[0063] In some embodiments, 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 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 that, 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 device 300), causes device 300 (e.g., at least one component of device 300) to perform one or more processes described herein. In some embodiments, a module is implemented in software, firmware, and / or hardware, etc.

[0064] Provided Figure 3 The number and arrangement of the illustrated components are provided as an example. In some embodiments, Figure 3Compared with the illustrated components, the apparatus 300 may include additional components, fewer components, different components, or components arranged differently. Additionally or alternatively, a set of components (e.g., one or more components) of the apparatus 300 may perform one or more functions described as being performed by another component or another set of components of the apparatus 300.

[0065] Now referring to Figure 4 , 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 localization system 406 (sometimes referred to as a localization 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 localization system 406, the control system 408, and the database 410 are included in and / or implemented in an automatic navigation system of a vehicle (e.g., the autonomous vehicle computing 202f of the vehicle 200). Additionally or alternatively, in some embodiments, the perception system 402, the planning system 404, the localization system 406, the control system 408, and the database 410 are included in one or more independent systems (e.g., one or more systems the same or similar to the autonomous vehicle computing 400, etc.). In some examples, the perception system 402, the planning system 404, the localization system 406, the control system 408, and the database 410 are included in one or more independent systems located in a 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 the same or similar to the remote AV system 114, a queue management system the same or similar to the queue management system 116, and / or a V2I system the same or similar to the V2I system 118, etc.).

[0066] 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.

[0067] 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 toward 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 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 an 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.

[0068] 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 the combined point cloud with two-dimensional (2D) and / or three-dimensional (3D) maps of the area stored in the database 410. Then, based on the positioning system 406 comparing the at least one point cloud or the combined point cloud with the map, the positioning system 406 determines the location 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 the roadway geometry, a map describing the connection properties of the road network, a map describing the physical properties of the 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 types of other driving signals, etc.). In some embodiments, the map is generated in real time based on the data received by the perception system.

[0069] 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 location 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 location of the vehicle. In some examples, based on the positioning system 406 determining the location of the vehicle, the positioning system 406 generates data associated with the location of the vehicle. In such examples, the data associated with the location of the vehicle includes data associated with one or more semantic properties corresponding to the location of the vehicle.

[0070] 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 operational 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.

[0071] 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.).

[0072] 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 operation-related data and / or software and using the autonomous vehicle computing 400 of at least one system (e.g., associated with 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 area. 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, unpaved 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.

[0073] 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.

[0074] Now refer to Figures 5A - 5D, which shows an implementation 500 of a technique for 360-degree LiDAR cropping. In some embodiments, implementation 500 includes a vehicle 512 having one or more parts (e.g., mirror 514). The vehicle 512 includes a LiDAR sensor 516 mounted thereon. The full field of view of the LiDAR sensor 516 is not available because a portion of the field of view overlaps with the body or parts of the vehicle (e.g., mirror 514). To determine which beams of the LiDAR sensor 516 need to be turned off or ignored, a calibration process is implemented to crop the field of view. The calibration process includes placing a target 518 against the vehicle, and in some embodiments, using a robotic arm 520 to hold and place the target 518. In some implementations, the robotic arm 520 is part of the manufacturing line for the vehicle 512. In some embodiments, in addition to or instead of the robotic arm 520, an operator also positions the target 518 against a portion of the vehicle. For example, at a repair facility, target placement is more likely to be done manually, while in a manufacturing environment, implementing robotic target placement may be advantageous. In some embodiments, the target and / or sensor is coupled to (e.g., removably or permanently attached to) the body of the vehicle 512. In some embodiments, the target and / or sensor is adjacent to the body of the vehicle 512. In Figures 5A - 5C , a part of the vehicle is a mirror. In some embodiments, a part of the vehicle is a side panel, a fender, etc. In some embodiments, the robotic arm is registered to the vehicle and / or components involved in the cropping process.

[0075] As Figures 5A - 5C shown, the target 518 conforms to the shape of the part of the vehicle (e.g., mirror 516) against which the target is placed. In some embodiments, as Figure 5D shown, the target is a colored profiler that conforms to the shape of the vehicle. The target includes alternating regions of high reflectivity and low reflectivity to assist in calibration. In some examples, the alternating regions of high reflectivity and low reflectivity are alternating black and white bands extending perpendicular to the fingers 706 of the profiler. The target 518 is placed within the detection field of the LiDAR sensor (e.g., beams from the LiDAR sensor can reach the target). The alternating regions of high reflectivity (702) and low reflectivity (704) provide multiple measurement points (e.g., measurements are taken from the respective bands on the target) to improve the confidence of the measurements made by the LiDAR sensor for determining the cropping. For example, a signal processing system implemented to determine the cropping (e.g., signal processing system 202i associated with LiDAR sensor 202b) expects to see a high reflectivity region, a low reflectivity region, and another high reflectivity region from right to left in the point cloud generated by the LiDAR sensor. In some embodiments, if the pattern is not observed, an error is flagged.

[0076] In some embodiments, the target has another reflectivity pattern. For example, the target can have a low-high-low reflectivity pattern.

[0077] In some embodiments, the measurements taken (e.g., LiDAR data) are sent to a signal processing system 202i for interpretation, and the signal processing system 202i outputs LiDAR calibration data for cropping the field of view. In some embodiments, the signal processing system 202i is part of the autonomous system 202. In some embodiments, the signal processing system 202i is the same as or similar to Figure 3 device 300. In some embodiments, the LiDAR calibration data is sent to the firmware of the LiDAR sensor to effect cropping. In some embodiments, the LiDAR calibration data for ignoring the echoes of one or more beams in the LiDAR beam is received and implemented via one or more software processes. In some embodiments, the calibration data includes instructions for turning off and / or ignoring the echoes of one or more beams of the LiDAR sensor, which causes the LiDAR sensor to transmit or forego transmitting a signal at a certain point and location (e.g., during a burst or sweep of laser emission). In some embodiments, deactivating a particular beam helps reduce the impact of scatter from that beam in cases where those particular beams remain on and echoes are ignored.

[0078] Refer to Figure 5C , beam 522 is emitted by the LiDAR sensor 516 (shown in Figures 5A - 5B ) and cropped (represented by arrow 524). Beam 522 is cropped to maximize the coverage of the beam without covering the vehicle 512. Since the cropping is done on a per-vehicle basis, the tolerances of the LiDAR sensor (e.g., small differences that occur during sensor installation) are taken into account when determining the cropping and thus the field of view (FoV) of the sensor. In some embodiments, yaw tolerances are taken into account when determining the cropping.

[0079] In some embodiments, for a LiDAR sensor mounted on the side of a vehicle, three different target placements occur to determine the cropped FoV, one in the front, one in the rear, and one for a mirror. In other embodiments, any suitable number of target placements may occur based on the particular geometry of the vehicle on which the LiDAR sensor is mounted. For example, the front placement of the target for a side-mounted LiDAR sensor is adjacent to the front fender, and the rear placement is along the rear quarter panel. The placement of the target in these key areas allows the geometry of other parts of the vehicle to be inferred at least in part due to the rigidity of other parts of the vehicle.

[0080] In some embodiments, for LiDAR sensors mounted on the front or rear of a vehicle, two different target placements occur to determine the cropped FoV, one on the left side of the sensor and one on the right side. In other embodiments, any suitable number of target placements may occur based on the specific geometry of the vehicle on which the LiDAR sensor is mounted.

[0081] Now refer to Figure 6 , which shows a flowchart of a process 600 for 360-degree LiDAR cropping. In some embodiments, one or more of the steps described with respect to process 600 are performed by an autonomous system (e.g., fully and / or partially, etc.). In some embodiments, a robotic arm (e.g., Figures 5A - 5C 's robotic arm 520), a LiDAR sensor (e.g., LiDAR sensor 516), and a signal processing system (e.g., signal processing system 202i associated with LiDAR sensor 202b) coordinate to perform an automated process that includes placing a target using the robotic arm, making measurements using the LiDAR sensor, processing the LiDAR data using the signal processing system and determining calibration, and (via firmware or software) implementing calibration on the LiDAR sensor to create a modified field of view. Additionally or alternatively, in some embodiments, one or more of the steps described with respect to process 600 are performed by another device or group of devices (e.g., fully and / or partially, etc.) separate from or including the autonomous system.

[0082] Process 600 includes placing a target (e.g., Figures 5A - 5C 's target 518) against an area of interest on the vehicle (e.g., Figures 5A - 5C 's mirror 514) (block 602). In some embodiments, the target may be shaped to conform to the shape of the area of interest on the vehicle.

[0083] Process 600 also includes initializing (block 604) the LiDAR sensor (e.g., Figures 5A - 5C 's LiDAR sensor 516) and collecting data. The LiDAR sensor emits beams and collects a point cloud that includes the beams that have hit the target.

[0084] Process 600 also includes transmitting (block 606) the collected data to a signal processing system, e.g., Figure 2 's signal processing system 202i. The collected data includes data related to the beams emitted by the LiDAR sensor that have hit the target (e.g., Figures 5A - 5C 's target 518). In some embodiments, the signal processing system is a perception system (e.g., Figure 4Part of the perception system 402 of the autonomous vehicle computing 400, which receives a complete data set (e.g., an uncropped data set) and applies a filter to the complete data set before running the data through the perception pipeline.

[0085] Processing 600 also includes outputting (block 608) calibration information for cropping the LiDAR field of view by a signal processing system (e.g., signal processing system 202i associated with the LiDAR sensor 202b). The calibration information is based on the collected data. Calibration data is generated by looking for patterns of targets and determining where the vehicle part is and where the beams of the LiDAR sensor will contact the vehicle based on the pattern and the measurements taken. In some embodiments, the calibration data includes instructions for turning off and / or ignoring the echoes of one or more than one beam of the LiDAR sensor. In some embodiments, the calibration information is sent to the firmware of the LiDAR sensor for implementation, and the beams can be turned on or off. In other embodiments, the calibration information is implemented via software, and the output of one or more than one beam of the LiDAR sensor is ignored. In some embodiments, turning off a particular beam helps reduce the impact of scatter from those beams in cases where those beams remain on and the echoes are ignored.

[0086] Processing 600 also includes removing (block 610) targets (e.g., targets 518 from Figures 5A - 5C from the region of interest (e.g., mirror 514).

[0087] Processing 600 also includes collecting (block 612) data using the LiDAR sensor. This data collection step includes, for example, generating a new point cloud using the LiDAR sensor after the calibration information is received and implemented by a signal processing system associated with the LiDAR sensor (e.g., signal processing system 202i associated with the LiDAR sensor 202b) or the firmware of the LiDAR sensor.

[0088] Processing 600 also includes confirming (block 614) that the vehicle does not interfere with the field of view. For example, this step can include checking that the applied cropping calibration is correct (e.g., the vehicle is not in the modified field of view).

[0089] In some embodiments, the system can reduce blind spots from a general trim for all production vehicles. In some embodiments, the system includes an automated solution where the vehicle learns the vehicle body and automatically detects the trim angle. In some embodiments, the system is part of a fully automated production line for a vehicle platform. In these embodiments, the robot and / or target is equipped with one or more additional sensors configured to provide feedback, for example, when the target has been placed in the proper position on the vehicle body. After receiving feedback that the target has been properly placed, the remaining steps regarding Figure 6 and / or Figure 10 shown and described can be carried out. The movement of the robot and thus the movement of the target can be programmed such that all proper target placements along the vehicle body are carried out. In some embodiments, multiple robots and / or targets are used to complete all required placements against the vehicle body. In some embodiments, side-mounted robots are used for target placement. In some embodiments, an overhead robot is used for target placement.

[0090] In some embodiments, the known shape of the vehicle body and the known position of the LiDAR sensor thereon can be used to determine a placement protocol that includes instructions for placing the target at specific locations by the automated system. In some embodiments, the placement protocol is executed by the automated system and moves the target to specific locations in a predetermined order and / or within a predetermined amount of time.

[0091] Figure 7 is a diagram showing certain elevation angles of a LiDAR system. In the example, the LiDAR system 700 is a 360-degree mechanically rotating LiDAR with 128 channels. The LiDAR system 700 has a range of 0.1 to 50 meters and a ranging capability of 0.1 to 20 meters at 10% reflectivity. The LiDAR system 700 has a distance accuracy of approximately + / -3 cm. The LiDAR system 700 has a 360-degree horizontal field of view and a vertical field of view of 105.2° (-52.6° to +52.6°). In some embodiments, the resolution can be as fine as 0.4°. The LiDAR system 700 is single-echo (e.g., using the first echo or the last echo) or dual-echo (e.g., using the first echo and the last echo). Generally, a 360° LiDAR has discrete measurement azimuth and elevation angles. In some embodiments, these firing positions can be turned on or off in the sensor firmware depending on their availability in the sensor application. These firing positions can be turned on, off, or ignored based on their interaction with the target and / or the vehicle.

[0092] Figure 8AIt is a diagram of an exemplary implementation 800 of the nominal sensor field of view of a LiDAR system from the rear view of a vehicle. As shown, the clipped beam 802 approximates the contour of the vehicle body. Figure 8B It is a diagram of an exemplary implementation 800 of the nominal sensor field of view of a LiDAR system from the top view of a vehicle. As shown, the clipped beam 802 approximates the contour of the vehicle body.

[0093] Figure 9A It is a diagram showing the blind spot of a LiDAR system at the ground plane at the nominal position. In implementation 900, at the rear wheel 904 of the vehicle 902, the gap 906 between the coverage of the LiDAR beam 908 and the vehicle 902 is the blind spot of the LiDAR sensor. The blind spot is nominally about 216 mm from the rear wheel 904.

[0094] Figure 9B It is a diagram showing the blind spot of a LiDAR system at the ground plane at the tolerance position. In implementation 950, at the rear wheel 954 of the vehicle 952, the gap 956 between the coverage of the LiDAR beam 958 and the vehicle 952 is the blind spot of the LiDAR sensor. When the yaw error causes the short-range LiDAR to rotate away from the rear quarter panel, the blind spot opens. As previously mentioned, the blind spot is nominally about 216 mm and increases to about 382 mm with the tolerance stack near the rear wheel. In another embodiment, for example, if the tolerance swings in the other direction (e.g., when the yaw error causes the short-range LiDAR to rotate towards the rear quarter panel), the LiDAR sensor will see the vehicle body and read that there is an occlusion at that position. If this occurs, the vehicle will think there is an object in the near field even if no object is present. In this case, this false object detection will prevent the vehicle from moving when there is no object. For example, in the embodiment of a robot taxi, this false detection may cause the robot taxi not to be able to drive out by a passenger.

[0095] The above systems and methods help to eliminate or reduce such blind spots so that no blind spots and false occlusion detections occur. This directly applies to cameras and LiDAR. Generally, for sensing and safety reasons, a smaller value of the blind spot is preferably known.

[0096] Reference Figure 10 , which shows a flowchart of a process 1000 for 360-degree LiDAR clipping. The process includes receiving (block 1002) a characterization of a target (e.g., Figures 5A - 5C by a LiDAR sensor (e.g., Figures 5A - 5Ddata for detecting a target 518), where the target is within the detection field of view of the sensor (e.g., the beam from the sensor can reach the target), and where the target and the sensor (e.g., attached, removable, or non-removable) are coupled to or adjacent to a part of a vehicle (e.g., a mirror (e.g., Figures 5A - 5C mirror 514), side panel, fender, etc.).

[0097] The processing also includes determining (block 1004) a modified field of view of the sensor, where the modified field of view is narrower than the field of view (e.g., does not include the vehicle), and where the determination is at least based on identifying one or more regions in the field of view that include the target (e.g., determining the boundary between the target and a part of the vehicle).

[0098] In some embodiments, determining the modified field of view includes identifying a calibration azimuth angle based on data characterizing the detection of the target, where the calibration azimuth angle is associated with the boundary between the target and a part of the vehicle, and where the calibration data set includes the calibration azimuth angle. In some embodiments, the identification of the calibration azimuth angle is based on a difference in reflectivity of a beam between the target and a part of the vehicle, where the beam is generated by the sensor. In some embodiments, the processing also includes generating data characterizing the detection of the target. Generating the data includes steering the beam from the sensor at a plurality of azimuth angles relative to the sensor and detecting the reflection of at least a portion of the beam.

[0099] The processing also includes providing (block 1006) a calibration data set that includes data associated with the modified field of view (e.g., instructions for turning off the beam, ignoring data from selected beams, etc.).

[0100] In some embodiments, the processing also includes conforming the target to a part of the vehicle.

[0101] In some embodiments, the target includes at least a first region of high reflectivity and a second region of low reflectivity.

[0102] In some embodiments, the processing also includes receiving data representing the calibration azimuth angle by the sensor and modifying the field of view of the sensor based on the calibration azimuth angle by removing at least a portion of the field of view that is at an angle greater than the calibration azimuth angle. In some embodiments, at least a portion of the field of view includes the portion of the field of view that overlaps with the vehicle.

[0103] Further non-limiting aspects or embodiments are set forth in the numbered clauses below:

[0104] Clause 1: A method includes: using at least one processor to receive data characterizing the detection of a target by a LiDAR sensor, where the target is within the field of view of the sensor's detection, and where the target and the sensor are coupled to or adjacent to a part of a vehicle; using the at least one processor to determine a modified field of view of the sensor, where the modified field of view is narrower than the field of view, and where the determination is at least based on identifying one or more regions within the field of view that include the target; and using the at least one processor to provide a calibration data set including data associated with the modified field of view.

[0105] Clause 2: The method according to Clause 1, where determining the modified field of view includes: identifying a calibration azimuth angle based on data characterizing the detection of the target, where the calibration azimuth angle is associated with a boundary between the target and the part of the vehicle, and where the calibration data set includes the calibration azimuth angle.

[0106] Clause 3: The method according to Clause 2, where identifying the calibration azimuth angle is based on a difference in reflectivity of a beam between the target and the part of the vehicle, where the beam is generated by the sensor.

[0107] Clause 4: The method according to Clause 3, further including generating data characterizing the detection of the target, where the generating includes: directing beams from the sensor at a plurality of azimuth angles relative to the sensor; and detecting reflections of at least a portion of the beams.

[0108] Clause 5: The method according to any one of Clauses 1-4, further including conforming the target to the part of the vehicle.

[0109] Clause 6: The method according to any one of Clauses 1-5, further including: receiving, by the sensor, data representing the calibration azimuth angle; and modifying the field of view of the sensor based on the calibration azimuth angle by removing at least a portion of the field of view located at angles greater than the calibration azimuth angle.

[0110] Clause 7: The method according to Clause 6, where the at least a portion of the field of view includes a portion of the field of view that overlaps with the vehicle.

[0111] Clause 8: The method according to any one of Clauses 1-7, where the LiDAR sensor has a 360-degree field of view.

[0112] Clause 9: The method according to any one of Clauses 1-8, where the target includes at least a first region of high reflectivity and a second region of low reflectivity.

[0113] Clause 10: The method according to any one of Clauses 1-9, wherein providing the calibration data set includes providing instructions for the LiDAR sensor to generate a point cloud based on the modified field of view.

[0114] Clause 11: The method according to any one of Clauses 1-10, wherein determining the calibration data set includes preprocessing the point cloud corresponding to the field of view using the at least one processor.

[0115] Clause 12: A system, comprising: at least one processor, and at least one memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations, the operations including: receiving, using at least one processor, data representing the detection of a target by a LiDAR sensor, wherein the target is within the field of view of the sensor's detection, and wherein the target and the sensor are coupled to or adjacent to a part of a vehicle; determining, using the at least one processor, a modified field of view of the sensor, wherein the modified field of view is narrower than the field of view, and wherein the determination is at least based on identifying one or more regions within the field of view that include the target; and providing, using the at least one processor, a calibration data set including data associated with the modified field of view.

[0116] Clause 13: The system according to Clause 12, wherein determining the modified field of view includes: identifying a calibration azimuth angle based on data representing the detection of the target, wherein the calibration azimuth angle is associated with a boundary between the target and the part of the vehicle, and wherein the calibration data set includes the calibration azimuth angle.

[0117] Clause 14: The system according to Clause 13, wherein identifying the calibration azimuth angle is based on a difference in reflectivity of a beam between the target and the part of the vehicle, wherein the beam is generated by the sensor.

[0118] Clause 15: The system according to Clause 14, further comprising generating data representing the detection of the target, wherein the generating includes: directing beams from the sensor at a plurality of azimuth angles relative to the sensor; and detecting reflections of at least a portion of the beams.

[0119] Clause 16: The system according to any one of Clauses 12-15, wherein the target is configured to conform to the part of the vehicle.

[0120] Clause 17: The system according to any one of Clauses 12 - 16 further includes: receiving, by the sensor, data representing the calibrated azimuth angle; and modifying the field of view of the sensor based on the calibrated azimuth angle by removing at least a portion of the field of view that is at an angle greater than the calibrated azimuth angle.

[0121] Clause 18: The system according to Clause 17, wherein the at least a portion of the field of view includes a portion of the field of view that overlaps with the vehicle.

[0122] Clause 19: The system according to any one of Clauses 12 - 18, wherein the LiDAR sensor has a 360 - degree field of view.

[0123] Clause 20: The system according to any one of Clauses 12 - 19, wherein the target at least includes a first region with high reflectivity and a second region with low reflectivity.

[0124] Clause 21: At least one non - transitory computer - readable medium, which includes instructions stored thereon, the instructions when executed by the at least one processor cause the at least one processor to perform operations, the operations including: receiving, by the at least one processor, data characterizing the detection of a target by a LiDAR sensor, wherein the target is within the detection field of view of the sensor, and wherein the target and the sensor are coupled to or adjacent to a portion of a vehicle; determining, by the at least one processor, a modified field of view of the sensor, wherein the modified field of view is narrower than the field of view, and wherein the determination is at least based on identifying one or more regions in the field of view that include the target; and providing, by the at least one processor, a calibration data set including data associated with the modified field of view.

[0125] Clause 22: The non - transitory computer - readable medium according to Clause 21, wherein determining the modified field of view includes: identifying a calibrated azimuth angle based on the data characterizing the detection of the target, wherein the calibrated azimuth angle is associated with the boundary between the target and the portion of the vehicle, and wherein the calibration data set includes the calibrated azimuth angle.

[0126] Clause 23: The non - transitory computer - readable medium according to Clause 22, wherein identifying the calibrated azimuth angle is based on a difference in reflectivity of a beam between the target and the portion of the vehicle, wherein the beam is generated by the sensor.

[0127] Article 24: The non-transitory computer-readable medium according to Article 23 further includes generating data representative of the detection of the target, wherein the generating includes: guiding a beam from the sensor at a plurality of azimuth angles relative to the sensor; and detecting reflections of at least a portion of the beam.

[0128] Article 25: The non-transitory computer-readable medium according to any one of Articles 21-24, wherein the target is configured to conform to the portion of the vehicle.

[0129] Article 26: The non-transitory computer-readable medium according to Article 25 further includes: receiving, by the sensor, data representing the calibration azimuth angle; and modifying the field of view of the sensor based on the calibration azimuth angle by removing at least a portion of the field of view located at an angle greater than the calibration azimuth angle.

[0130] Article 27: The non-transitory computer-readable medium according to Article 26, wherein the at least a portion of the field of view includes a portion of the field of view that overlaps with the vehicle.

[0131] Article 28: The non-transitory computer-readable medium according to any one of Articles 21-27, wherein the LIDAR sensor has a 360-degree field of view.

[0132] Article 29: The non-transitory computer-readable medium according to any one of Articles 21-28, wherein the target includes at least a first region of high reflectivity and a second region of low reflectivity.

[0133] In the foregoing description, aspects and embodiments of the present disclosure have been described with reference to numerous specific details, which may vary depending on the 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 as issued from this application in the specific form of the issued claims, including any subsequent amendments. Any definition explicitly set forth herein for terms to be included in such claims shall govern the meaning of such terms as used in the claims. Additionally, when the phrase "further includes" is used in the foregoing specification or the appended claims, the text following that 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: Receiving, by at least one processor, data representative of a detection of a target by a LiDAR sensor, wherein the target is within a field of view of the sensor's detection, and wherein the target and the sensor are coupled to or adjacent to a part of a vehicle; Determining, by the at least one processor, a modified field of view of the sensor, wherein the modified field of view is narrower than the field of view, and wherein the determining is at least based on identifying one or more regions within the field of view that include the target; and Providing, by the at least one processor, a calibration data set that includes data associated with the modified field of view.

2. The method according to claim 1, wherein Determining the modified field of view includes: Identifying a calibration azimuth angle based on the data representative of the detection of the target, wherein the calibration azimuth angle is associated with a boundary between the target and the part of the vehicle, and wherein the calibration data set includes the calibration azimuth angle.

3. The method according to claim 2, wherein, Identifying the calibration azimuth angle is based on a difference in reflectivity of a beam between the target and the part of the vehicle, wherein the beam is generated by the sensor.

4. The method according to claim 3 further includes generating the data representative of the detection of the target, wherein, The generating includes: Steering a beam from the sensor at a plurality of azimuth angles relative to the sensor; and Detecting reflections of at least a portion of the beam.

5. The method according to any one of the preceding claims, further comprising conforming the target to the part of the vehicle.

6. The method according to any one of the preceding claims, further comprising: Receiving, by the sensor, data representative of the calibration azimuth angle; Modifying the field of view of the sensor based on the calibration azimuth angle by removing at least a portion of the field of view that is at an angle greater than the calibration azimuth angle.

7. The method according to claim 6, wherein, The at least a portion of the field of view includes a portion of the field of view that overlaps with the vehicle.

8. The method according to any one of the preceding claims, wherein, The LiDAR sensor has a 360-degree field of view.

9. The method according to any one of the preceding claims, wherein The target includes at least a first region of high reflectivity and a second region of low reflectivity.

10. The method according to any one of the preceding claims, wherein, Providing the calibration data set includes providing instructions for the LiDAR sensor to generate a point cloud based on the modified field of view.

11. The method according to any one of the preceding claims, wherein, Determining the calibration data set includes preprocessing a point cloud corresponding to the field of view using the at least one processor.

12. A system, comprising: At least one computer-readable medium that stores computer-executable instructions; One or more processors configured to execute the computer-executable instructions, the execution performing the method according to claim 1.

13. A non-transitory computer-readable storage medium comprising at least one program for execution by one or more processors of a first device, the at least one program including instructions that, when executed by the one or more processors, cause the first device to perform the method according to claim 1.

Images