Sensor device, method and storage medium therefor

Abstract

The present invention relates to sensor apparatus and methods and storage media. Methods for low-level fusion for silicon photomultiplier based sensors, i.e., SiPM-based sensors, are provided that can include circumventing light detection and ranging sensor processing paths, i.e., LiDAR sensor processing paths, to provide high-confidence low-level fusion between camera (passive) imaging and LiDAR (active) imaging. Systems and computer program products are also provided.

Description

Technical Field

[0001] This invention relates to SiPM-based sensors for low-level fusion. Background Technology

[0002] LiDAR (Light Detection and Ranging) determines information based on light emitted by a transmitter, reflected by an object, and detected by a detector. This information includes data associated with the object, such as the distance to the object and the object's velocity. The detector is a photodetector that receives the light reflected by the object. The detector can be a solid-state photodetector, a photomultiplier tube, or any combination thereof. Summary of the Invention

[0003] According to an aspect of the present invention, a sensor device includes: a plurality of pixels connected in parallel, wherein at least one of the plurality of pixels includes: a first type of photodiode, wherein the first type of photodiode captures reflected light emitted in the environment and outputs three-dimensional position information, i.e., 3D position information and reflectivity information; and a second type of photodiode having a filter, wherein the second type of photodiode captures filtered reflections in the environment, wherein the color filter separates data in the filtered reflections into color information output by the second type of photodiode, and wherein the first type of photodiode and the second type of photodiode are juxtaposed on a common substrate; wherein the 3D position information and color information of the at least one pixel of the plurality of pixels are fused and output by the sensor device.

[0004] According to another aspect of the present invention, a method for a sensor device includes: capturing reflections from light emitted in the environment using a first type of photodiode to obtain position and reflectivity information; capturing color information using a second type of photodiode, wherein the second type of photodiode is coupled to a color filter to obtain the color information, and wherein the first type of photodiode and the second type of photodiode are juxtaposed on a common substrate; and fusing the position and reflectivity information with the color information on a pixel-by-pixel basis.

[0005] According to another aspect of the invention, a non-transitory computer-readable storage medium stores instructions thereon that, when executed by one or more processors, cause the one or more processors to perform the above-described method. Attached Figure Description

[0006] Figure 1 It is an example environment that can realize a vehicle that includes one or more components of an autonomous system;

[0007] Figure 2It is a diagram of one or more systems that include autonomous vehicles;

[0008] Figure 3 yes Figure 1 and Figure 2 A diagram of one or more devices and / or one or more system components;

[0009] Figure 4 It is a diagram of some components of an autonomous system;

[0010] Figure 5 This is a diagram illustrating an example implementation of processing for low-level fusion;

[0011] Figure 6 An example of a LiDAR (Light Detection and Ranging) system is shown;

[0012] Figure 7 The LiDAR system in operation is shown.

[0013] Figure 8 Additional details on the operation of the LiDAR system are shown;

[0014] Figure 9 This is a diagram of the pixel arrangement on a SiPM-based sensor; and

[0015] Figure 10 It is a flowchart for a process of fusing reflectivity and color information captured from a first type of photodiode and a second type of photodiode, respectively. Detailed Implementation

[0016] In the following description, numerous specific details are set forth for purposes of explanation in order to provide a thorough understanding of this disclosure. However, it will be apparent that the embodiments described herein can be practiced without these specific details. In some instances, well-known constructions and apparatuses are illustrated in block diagram form to avoid unnecessarily obscuring aspects of this disclosure.

[0017] In the accompanying drawings, for ease of description, specific arrangements or orders of schematic elements (such as those representing systems, devices, modules, instruction blocks, and / or data elements) are illustrated. However, those skilled in the art will understand that, unless explicitly described, the specific order or arrangement of schematic elements in the drawings is not intended to imply a requirement for a particular processing order or sequence, or separation of processes. Furthermore, unless explicitly described, the inclusion of schematic elements in the drawings is not intended to imply that such elements are required in all embodiments, nor is it intended to imply that features represented by such elements cannot be included in some embodiments or cannot be combined with other elements in some embodiments.

[0018] Furthermore, in the accompanying drawings, connecting elements (such as solid or dashed lines or arrows) are used to illustrate connections, relationships, or associations between or among two or more other schematic elements. The absence of any such connecting element does not imply 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 content of this 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 communication of signals, data, or instructions (e.g., "software instructions"), those skilled in the art will understand that such an element may represent one or more signal paths (e.g., a bus) that may be necessary to influence the communication.

[0019] Although the terms "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 used only 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 terminology used in the description of the various embodiments described herein is included for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various embodiments described 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” 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 “comprising,” “including,” “possessing,” and / or “having” are used in this specification, they specifically indicate the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude 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 "communication" and "to communicate" refer to at least one of the following: receiving, receiving, transmitting, conveying, and / or providing information (or information represented by, for example, data, signals, messages, instructions, and / or commands). For a unit (e.g., an apparatus, system, component of an apparatus or system, and / or combinations thereof) that wants to communicate with another unit, this means that the unit is able to receive information directly or indirectly from and / or send (e.g., transmit) information to the other unit. This can refer to a direct or indirect connection that is essentially wired and / or wireless. Furthermore, two units can communicate with each other even if the transmitted information can be modified, processed, relayed, and / or routed between the first and second units. For example, the first unit can communicate with the second unit even if it passively receives information and does not actively transmit information to the second unit. As another example, the first unit can communicate with the second unit if at least one intermediary unit (e.g., a third unit located between the first and second units) processes information received from the first unit and transmits the processed information to the second unit. In some embodiments, a message may refer to a network packet that includes data (e.g., a data packet, etc.).

[0022] As used herein, depending on the context, the term "if" may optionally be interpreted as "when," "in," "in response to being determined," and / or "in response to being detected," etc. Similarly, depending on the context, the phrases "if determined" or "if [the stated condition or event] is detected" may optionally be interpreted as "in response to being determined," "in response to being determined," "or" "in response to being detected," and / or "in response to being detected," etc. Furthermore, as used herein, the terms "have," "possess," or "own," etc., are intended to be open-ended terms. Additionally, unless explicitly stated otherwise, the phrase "based on" is intended to mean "at least partially based on."

[0023] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. Numerous specific details are set forth in the following detailed description in order to provide a thorough understanding of the various embodiments described. However, it will be apparent to those skilled in the art that the various embodiments described can be practiced without these specific details. In other instances, well-known methods, processes, 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 silicon photomultiplier tube (SiPM)-based sensors for low-level fusion. In some embodiments, a SiPM-based sensor with low-level fusion may be part of a light detection and ranging (LiDAR) device. Typically, low-level fusion refers to determining the correspondence between different types of sensor data on a pixel-by-pixel basis. A SiPM-based sensor may be configured as a single-pixel sensor or an array of pixels operable to capture light reflected by objects in the environment. Each SiPM pixel is implemented using one or more microcells. In embodiments, a SiPM pixel may consist of different types of subpixels. In embodiments, photodiode (PD) type subpixels are used in individual or some pixels within a SiPM pixel. In embodiments, different color filters (e.g., red / green / blue (RGB)) are applied to each pixel. In embodiments, RGB filters are arranged to capture their respective color data. In embodiments, specific deep trenches and subpixel patterns are used. Optical trenches are used to reduce crosstalk between subpixels of the SiPM.

[0026] Automotive time-of-flight (ToF) optical detection and ranging (LiDAR) systems use laser signals to determine the rate and distance of stationary and moving objects (e.g., other vehicles, pedestrians, obstacles). LiDAR systems make these measurements by comparing the emitted transmitted signal with the reflected returned signal. For many applications, long-range detection capability is desirable. Typically, SiPM-based time-of-flight (TOF) LiDARs offer long detection ranges with high pixel throughput. In some examples, SiPM-based LiDARs achieve higher sensitivity for low light throughput compared to avalanche photodiode (APD)-based LiDARs, which can provide even longer-range detection capabilities. However, the point cloud coordination accuracy of SiPM-based LiDARs is limited by the positional repeatability of the scanning system, which fundamentally limits the accuracy of low-level (early) fusion of camera and LiDAR. Low-level fusion refers to fusing LiDAR with raw camera data at the pixel (or image) level. For example, a LiDAR point cloud (e.g., three-dimensional (3D)) is projected onto a two-dimensional (2D) camera image, and then the point cloud is checked to see if it belongs to an object (2D) bounding box. By means of the implementation of the systems, methods, and computer program products described herein, the techniques used in SiPM-based sensors for low-level fusion provide high dynamic range (DR) sensor output, which provides accurate return signal strength and object reflectivity information in LiDAR applications. The data output by the SiPM-based sensor makes it possible to determine intensity in the presence of sensor saturation. Therefore, this technique improves the functionality of LiDAR sensors and vehicles including LiDAR sensors. These and other embodiments can also circumvent LiDAR sensor processing paths to provide high-confidence low-level fusion between camera (passive) imaging and LiDAR (active) imaging. Passive imaging, for example, refers to measuring reflected light (e.g., sunlight emitted from the sun) that is not introduced by an imaging or sensing device. Active imaging, for example, uses an introduced light source or illumination to, for example, actively send pulses and measure backscattered or reflected light. These and other embodiments can provide cost-effective solutions for low-level camera and LiDAR fusion, as well as simplified calibration processes for multi-type sensor fusion.

[0027] Now for reference Figure 1Example environment 100 is illustrated, 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) device 110, network 112, remote autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118. Vehicles 102a-102n, vehicle-to-infrastructure (V2I) device 110, network 112, autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118 are interconnected via wired connections, wireless connections, or a combination of wired and wireless connections (e.g., establishing connections 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 wired connection, wireless connection, or a combination of wired and wireless connection.

[0028] Vehicles 102a-102n (specifically referred to as vehicle 102 and collectively as vehicle 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 associated with vehicle 200 described herein (see Figure 2 The vehicles 102 are the same as or similar to autonomous vehicles 202. In some embodiments, vehicles 200 in a group of vehicles 200 are associated with an autonomous queue manager. In some embodiments, as described herein, vehicles 102 travel along corresponding routes 106a-106n (each individually referred to as route 106 and collectively as route 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).

[0029] Objects 104a-104n (each individually referred to as object 104 and collectively as object 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 for a period of time) is either 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.

[0030] Routes 106a-106n (each individually referred to as Route 106 and collectively as Route 106) are each associated with (e.g., defining) a series of actions (also referred to as trajectories) along which the connecting AV can navigate. Each Route 106 begins with an initial state (e.g., a state corresponding to a first spatiotemporal location and / or speed, etc.) and ends with a final target state (e.g., a state corresponding to a second spatiotemporal location different from the first spatiotemporal location) or a target area (e.g., a subspace of an acceptable state (e.g., a termination state)). In some embodiments, a first state includes a location where one or more individuals will board the AV, and a second state or area includes a location where one or more individuals boarding the AV will disembark. In some embodiments, Route 106 includes multiple acceptable state sequences (e.g., multiple spatiotemporal location sequences) associated with multiple trajectories (e.g., defining multiple trajectories). In the example, Route 106 includes only high-level actions or imprecise state locations, such as a series of connecting roads indicating a change of direction at a roadway intersection. Additionally or alternatively, route 106 may include more precise actions or states, such as, for example, specific target lanes or precise locations within a lane area and target rates at those locations. In the example, route 106 includes multiple precise state sequences along at least one high-level action with a finite look-ahead horizon leading to an intermediate target, wherein the cumulative combination of successive iterations of the finite horizon state sequences corresponds to multiple trajectories that collectively form a high-level route terminating at a final target state or region.

[0031] Region 108 includes a physical area (e.g., a geographic region) that the vehicle 102 can navigate. In the example, region 108 includes at least one state (e.g., a country, a province, a single state among multiple 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, interstate highway, park road, city street, etc. Additionally or alternatively, in some examples, region 108 includes at least one unnamed road, such as a driving lane, a section of a parking lot, a section of vacant land and / or undeveloped area, dirt road, etc. In some embodiments, a road includes at least one lane (e.g., a portion of the road that the vehicle 102 can traverse). In the example, a road includes at least one lane associated with at least one lane marking (e.g., identified based on at least one lane marking).

[0032] The Vehicle-to-Infrastructure (V2I) device 110 (sometimes referred to as a Vehicle-to-Everything (V2X) device) includes at least one device configured to communicate with vehicle 102 and / or V2I infrastructure system 118. In some embodiments, the 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, the V2I device 110 includes radio frequency identification (RFID) devices, signs, cameras (e.g., two-dimensional (2D) and / or three-dimensional (3D) cameras), lane markings, streetlights, parking meters, etc. In some embodiments, the V2I device 110 is configured to communicate directly with vehicle 102. Additionally or alternatively, in some embodiments, the 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.

[0033] Network 112 includes one or more wired and / or wireless networks. In the example, network 112 includes cellular networks (e.g., Long Term Evolution (LTE) networks, third-generation (3G) networks, fourth-generation (4G) networks, fifth-generation (5G) networks, Code Division Multiple Access (CDMA) networks, etc.), Public Land Mobile Networks (PLMNs), Local Area Networks (LANs), Wide Area Networks (WANs), Metropolitan Area Networks (MANs), telephone networks (e.g., Public Switched Telephone Networks (PSTN)), private networks, self-organizing networks, intranets, the Internet, fiber-based networks, cloud computing networks, etc., and / or combinations of some or all of these networks.

[0034] The remote AV system 114 includes at least one device configured to communicate with vehicle 102, V2I device 110, network 112, queue management system 116, and / or V2I system 118 via network 112. In examples, the remote AV system 114 includes a server, server group, and / or other similar devices. In some embodiments, the remote AV system 114 is located in the same location as 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. In some embodiments, the remote AV system 114 maintains (e.g., updates and / or replaces) these components and / or software during the lifespan of the vehicle.

[0035] The queue management system 116 includes at least one device configured to communicate with vehicle 102, V2I device 110, remote AV system 114, and / or V2I infrastructure system 118. In examples, the queue management system 116 includes servers, server groups, 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 and / or not including autonomous systems)).

[0036] 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 a 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 municipality or private entity (e.g., a private entity maintaining the V2I device 110).

[0037] supply Figure 1 The number and arrangement of the elements are shown as examples. (and) Figure 1 Compared to the illustrated elements, there may be additional elements, fewer elements, different elements, and / or elements arranged differently. Additionally or alternatively, at least one element of environment 100 may be described as being composed of… Figure 1 One or more functions performed by at least one different element of environment 100. Additionally or alternatively, at least one group of elements of environment 100 may perform one or more functions described as performed by at least one different group of elements of environment 100.

[0038] Now for reference Figure 2 The vehicle 200 includes an autonomous system 202, a powertrain control system 204, a steering control system 206, and a braking system 208. In some embodiments, the vehicle 200 and the vehicle 102 (see...) Figure 1 The vehicle 200 is similar to or the same as the vehicle in question. In some embodiments, the vehicle 200 has autonomous capabilities (e.g., implementing at least one function, feature, and / or device that 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 human intervention) and / or highly autonomous vehicles (e.g., vehicles that abandon human intervention in certain situations)). For a detailed description of fully autonomous and highly autonomous vehicles, refer to SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, the entire contents of which are incorporated herein by reference. In some embodiments, the vehicle 200 is associated with an autonomous queue manager and / or a ride-sharing company.

[0039] Autonomous system 202 includes a sensor suite comprising one or more devices such as camera 202a, LiDAR sensor 202b, radar sensor 202c, and microphone 202d. In some embodiments, 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 vehicle 200). In some embodiments, autonomous system 202 uses one or more devices included in autonomous system 202 to generate data associated with environment 100 as described herein. The data generated by one or more devices of autonomous system 202 may be used by one or more systems as described herein to observe the environment in which vehicle 200 is located (e.g., environment 100). In some embodiments, autonomous system 202 includes communication device 202e, autonomous vehicle computing 202f, and safety controller 202g.

[0040] Camera 202a includes components configured to communicate with communication device 202e, autonomous vehicle computing 202f, and / or safety controller 202g via a bus (e.g., with...). Figure 3At least one device that communicates with the same or similar bus as bus 302. Camera 202a includes at least one camera (e.g., a digital camera using a light sensor such as a charge-coupled device (CCD), complementary metal-oxide-semiconductor (CMOS) image sensor (CIS), thermal camera, infrared (IR) camera, and / or event camera, etc.) for capturing images of physical objects (e.g., cars, buses, curbs, and / or people, etc.). In some embodiments, camera 202a generates camera data as output. In some examples, camera 202a generates camera data including image data associated with an image. In this 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 image timestamp, etc.). In such examples, the image may be in a format (e.g., RAW, JPEG, and / or PNG, etc.). In some embodiments, camera 202a includes multiple independent cameras configured (e.g., positioned on) a vehicle to capture images for stereoscopic imaging (stereoscopic vision). In some examples, camera 202a includes generating image data and transmitting that image data to autonomous vehicle computing 202f and / or a queue management system (e.g., with...). Figure 1 The queue management system 116 (same as or similar to a queue management system) has multiple cameras. In such an example, the autonomous vehicle calculation 202f determines the depth of one or more objects in the fields of view of at least two of the multiple cameras based on image data from at least two cameras. In some embodiments, camera 202a is configured to capture images of objects within a distance relative to camera 202a (e.g., up to 100 meters and / or up to 1 kilometer, etc.). Therefore, camera 202a includes features such as sensors and lenses optimized for sensing objects at one or more distances relative to camera 202a.

[0041] In embodiments, 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, camera 202a generates traffic light data associated with one or more images. In some examples, camera 202a generates TLD data associated with one or more images, including formats such as RAW, JPEG, and / or PNG. In some embodiments, camera 202a, which generates TLD data, differs from other camera-included systems described herein in that camera 202a may include one or more cameras with a wide field of view (e.g., wide-angle lens, fisheye lens, and / or a lens with an angle of view of about 120 degrees or greater) to generate images associated with as many physical objects as possible.

[0042] The light detection and ranging (LiDAR) sensor 202b includes components configured to communicate with a communication device 202e, an autonomous vehicle computing unit 202f, and / or a safety controller 202g via a bus (e.g., with...). Figure 3 At least one device that communicates with the same or similar bus (bus 302). The LiDAR sensor 202b may be a SiPM-based LiDAR. The LiDAR sensor 202b includes a system configured to emit light from a 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 photodetector that detects the light after it has encountered a physical object. In some embodiments, at least one data processing system associated with the LiDAR sensor 202b generates an image (e.g., point cloud and / or combined point cloud, etc.) representing 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 boundaries of a physical object and / or the surface of the physical object (e.g., the topology of the surface). In such examples, the image is used to determine the boundaries of the physical object within the field of view of the LiDAR sensor 202b.

[0043] The radio detection and ranging (radar) sensor 202c includes components 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 3 At least one device that communicates with the same or similar bus (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 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 boundaries of physical objects and / or the surfaces of physical objects (e.g., surface topology). In some examples, this image is used to determine the boundaries of physical objects in the field of view of the radar sensor 202c.

[0044] Microphone 202d includes components configured to communicate with communication device 202e, autonomous vehicle computing 202f, and / or safety controller 202g via a bus (e.g., with...). Figure 3 At least one device that communicates with the same or similar bus as bus 302. Microphone 202d includes one or more microphones (e.g., array microphones and / or external microphones, etc.) that capture audio signals and generate data associated with (e.g., representing) the audio signals. In some examples, microphone 202d includes transducer devices and / or similar devices. In some embodiments, one or more systems described herein can receive data generated by microphone 202d and determine the position (e.g., distance, etc.) of an object relative to vehicle 200 based on the audio signal associated with the data.

[0045] The communication device 202e includes at least one device configured to communicate with a camera 202a, a LiDAR sensor 202b, a radar sensor 202c, a microphone 202d, an autonomous vehicle computing system 202f, a safety controller 202g, and / or a drive-by-wire (DBW) system 202h. For example, the communication device 202e may include communication with… Figure 3 The communication device 202e is the same as or similar to the 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).

[0046] The autonomous vehicle computing 202f includes at least one device configured to communicate with a camera 202a, a LiDAR sensor 202b, a radar sensor 202c, a microphone 202d, a communication device 202e, a security controller 202g, and / or a DBW system 202h. In some examples, the autonomous vehicle computing 202f includes devices such as client devices, mobile devices (e.g., cellular phones and / or tablets) and / or servers (e.g., computing devices including one or more central processing units and / or graphics processing units). 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., with...). Figure 1 Remote AV systems 114 are the same as or similar to autonomous vehicle systems), queue management systems (e.g., with...). Figure 1 The queue management system 116 is the same as or similar to the queue management system 116), and V2I devices (e.g., with Figure 1 V2I devices (same as or similar to V2I devices 110) and / or V2I systems (e.g., with V2I devices 110) Figure 1 The V2I system 118 communicates with the same or similar V2I system.

[0047] The safety controller 202g includes at least one device configured to communicate with a camera 202a, a LiDAR sensor 202b, a radar sensor 202c, a microphone 202d, a communication device 202e, an autonomous vehicle computing system 202f, and / or a DBW system 202h. In some examples, the safety controller 202g includes one or more controllers (electrical controllers and / or electromechanical controllers, etc.) configured to generate and / or transmit control signals to operate the vehicle 200 (e.g., powertrain control system 204, steering control system 206, and / or 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 system 202f.

[0048] 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 (e.g., electrical controllers and / or electromechanical controllers, etc.) configured to generate and / or transmit control signals to operate the vehicle 200, including one or more devices (e.g., powertrain control system 204, steering control system 206, and / or 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 (e.g., turn signals, headlights, door locks, and / or windshield wipers, etc.) of the vehicle 200.

[0049] 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 start moving forward, stop moving forward, start moving backward, stop moving backward, accelerate in a certain direction, decelerate in a certain direction, make a left turn and / or make a right turn, etc. In examples, the powertrain control system 204 increases, keeps the same, or decreases the energy (e.g., fuel and / or electricity, etc.) supplied to the motor of the vehicle, thereby causing at least one wheel of the vehicle 200 to rotate or not rotate.

[0050] 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 causes the two front wheels and / or the two rear wheels of the vehicle 200 to turn left or right, thereby causing the vehicle 200 to turn left or right.

[0051] The braking system 208 includes at least one device configured to actuate one or more brakes to decelerate and / or keep 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 on the respective rotor 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.

[0052] 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 conditions 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 rate sensor, a wheel brake pressure sensor, a wheel torque sensor, an engine torque sensor, and / or a steering angle sensor.

[0053] Now for reference Figure 3 A schematic diagram of device 300 is illustrated. As illustrated, device 300 includes a processor 304, a memory 306, a storage device 308, an input interface 310, an output interface 312, a communication interface 314, and a bus 302. In some embodiments, device 300 corresponds to: at least one device of vehicle 102 (e.g., at least one device of system of vehicle 102); and / or one or more devices of network 112 (e.g., one or more devices of system of network 112). In some embodiments, one or more devices of vehicle 102 (e.g., one or more devices of system of vehicle 102), and / or one or more devices of network 112 (e.g., one or more devices of system of network 112) include at least one device 300 and / or at least one component of device 300. Figure 3 As shown, the device 300 includes a bus 302, a processor 304, a memory 306, a storage device 308, an input interface 310, an output interface 312, and a communication interface 314.

[0054] Bus 302 includes components for communication between the components of the licensed device 300. In some embodiments, processor 304 is implemented in hardware, software, or a combination of hardware and software. In some examples, processor 304 includes a processor (e.g., a central processing unit (CPU), graphics processing unit (GPU), and / or accelerated processing unit (APU), 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 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 (e.g., flash memory, magnetic memory, and / or optical memory, etc.) that stores data and / or instructions for use by processor 304.

[0055] Storage device 308 stores data and / or software related to the operation and use of device 300. In some examples, storage device 308 includes hard disks (e.g., magnetic disks, optical disks, magneto-optical disks, and / or solid-state disks), compact discs (CDs), digital versatile discs (DVDs), floppy disks, cassette tapes, magnetic tapes, CD-ROMs, RAM, PROMs, EPROMs, FLASH-EPROMs, NV-RAMs, and / or other types of computer-readable media, and corresponding drives.

[0056] Input interface 310 includes components that enable the device 300 to receive information, such as via user input (e.g., a touchscreen display, keyboard, keypad, mouse, buttons, switches, microphone, and / or camera). Additionally or alternatively, in some embodiments, input interface 310 includes sensors for sensing information (e.g., a Global Positioning System (GPS) receiver, accelerometer, gyroscope, and / or actuator). Output interface 312 includes components for providing output information from device 300 (e.g., a display, speaker, and / or one or more light-emitting diodes (LEDs)).

[0057] In some embodiments, the communication interface 314 includes transceiver-like components (e.g., a transceiver and / or separate receivers and transmitters) that enable the licensing device 300 to communicate with other devices via a wired connection, a wireless connection, or a combination of wired and wireless connections. 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, etc. Interfaces and / or cellular network interfaces, etc.

[0058] In some embodiments, device 300 performs one or more of the processes described herein. Device 300 performs these processes based on software instructions stored in a computer-readable medium, such as memory 306 and / or storage device 308, executed by processor 304. Computer-readable medium (e.g., non-transitory computer-readable medium) is defined herein as a non-transitory memory device. A non-transitory memory device includes storage space located within a single physical storage device or storage space distributed across multiple physical storage devices.

[0059] In some embodiments, software instructions are read from another computer-readable medium or from another device via communication interface 314 into memory 306 and / or storage device 308. When executed, the software instructions stored in memory 306 and / or storage device 308 cause processor 304 to perform one or more processes described herein. Additionally or alternatively, hard-wired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Therefore, unless explicitly stated otherwise, the embodiments described herein are not limited to any particular combination of hardware circuitry and software.

[0060] Memory 306 and / or storage device 308 include a data storage unit or at least one data structure (e.g., a database). Device 300 is capable of receiving information from the data storage unit or at least one data structure in memory 306 or storage device 308, storing information in the data storage unit or at least one data structure, communicating information to the data storage unit or at least one data structure, or searching for information stored in the data storage unit or at least one data structure. In some examples, the information includes network data, input data, output data, or any combination thereof.

[0061] In some embodiments, device 300 is configured to execute software instructions stored in the memory of memory 306 and / or another device (e.g., another device identical or similar to device 300). As used herein, the term "module" refers to at least one instruction stored in the memory of memory 306 and / or the other device, which, when executed by the processor of processor 304 and / or the processor of another device (e.g., another device identical or similar to device 300), causes device 300 (e.g., at least one component of device 300) to perform one or more processes as described herein. In some embodiments, modules are implemented in software, firmware, and / or hardware, etc.

[0062] supply Figure 3 The number and arrangement of components are illustrated as examples. In some embodiments, with Figure 3 Compared to the illustrated components, device 300 may include additional components, fewer components, different components, or components arranged differently. Additionally or alternatively, a group of components of device 300 (e.g., one or more components) may perform one or more functions described as being performed by another component or another group of components of device 300.

[0063] Now for reference Figure 4The diagram illustrates an example block diagram of an autonomous vehicle computing 400 (sometimes referred to as an "AV stack"). 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, planning system 404, positioning system 406, control system 408, and database 410 are included in and / or implemented in the vehicle's automatic navigation system (e.g., the autonomous vehicle computing 202f of vehicle 200). Additionally or alternatively, in some embodiments, the perception system 402, planning system 404, positioning system 406, control system 408, and database 410 are included in one or more separate systems (e.g., one or more systems that are the same as or similar to the autonomous vehicle computing 400, etc.). In some examples, the perception system 402, planning system 404, positioning system 406, control system 408, and database 41 are included in one or more independent systems located within 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 memory), computer hardware (e.g., via microprocessors, microcontrollers, application-specific integrated circuits (ASICs), and / or field-programmable gate arrays (FPGAs), 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., autonomous vehicle systems identical or similar to remote AV system 114, queue management systems identical or similar to queue management systems 116, and / or V2I systems identical or similar to V2I system 118, etc.).

[0064] 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) that is 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 at least one physical object based on one or more groups of physical objects (e.g., bicycles, vehicles, traffic signs, and / or pedestrians, etc.). In some embodiments, based on the classification of physical objects by the perception system 402, the perception system 402 transmits data associated with the classification of the physical objects to the planning system 404.

[0065] 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 sensing system 402 (e.g., the data associated with the classification of physical objects 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 sensing system 402. In some embodiments, the planning system 404 receives data associated with the updated location 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.

[0066] In some embodiments, positioning system 406 receives data associated with (e.g., representing) a location of a vehicle (e.g., vehicle 102) in an area. In some examples, 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 some examples, positioning system 406 receives data associated with at least one point cloud from multiple LiDAR sensors, and positioning system 406 generates a composite point cloud based on the individual point clouds. In these examples, positioning system 406 compares the at least one point cloud or composite point cloud with a two-dimensional (2D) and / or three-dimensional (3D) map of the area stored in database 410. Then, based on the comparison of the at least one point cloud or composite point cloud with the map, positioning system 406 determines the location of the vehicle in the area. In some embodiments, the map includes a composite point cloud of the area generated prior to navigation of the vehicle. In some embodiments, the map includes, but is not limited to, a high-precision map of the geometry of the roadway, a map describing the connectivity of the road network, a map describing the physical properties of the roadway (such as traffic speed, traffic flow, the number of vehicle and bicycle lanes, lane width, lane traffic direction, or the type and location of lane markings, or combinations thereof), and a map describing the spatial locations of road features (such as pedestrian crossings, traffic signs, or various types of other traffic lights). In some embodiments, the map is generated in real time based on data received by the sensing system.

[0067] In another example, positioning system 406 receives Global Navigation Satellite System (GNSS) data generated by a Global Positioning System (GPS) receiver. In some examples, positioning system 406 receives GNSS data associated with the location of a vehicle in an area, and positioning system 406 determines the latitude and longitude of the vehicle in the area. In such examples, positioning system 406 determines the location of the vehicle in the area based on the latitude and longitude of the vehicle. In some embodiments, positioning system 406 generates data associated with the location of the vehicle. In some examples, based on the location of the vehicle determined by positioning system 406, 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.

[0068] 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 operate 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). In an example, where the trajectory includes a left turn, the control system 408 transmits control signals 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 change the state of other devices of the vehicle 200 (e.g., headlights, turn signals, door locks, and / or windshield wipers, etc.).

[0069] In some embodiments, the perception system 402, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model (e.g., at least one multilayer 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, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model individually or in combination with one or more of the aforementioned systems. In some examples, the perception system 402, planning system 404, positioning system 406, and / or 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.).

[0070] Database 410 stores data transmitted to, received from, and / or updated by the sensing system 402, planning system 404, positioning system 406, and / or control system 408. In some examples, database 410 includes storage devices for storing operation-related data and / or software, and for at least one system that uses autonomous vehicles to compute 400 (e.g., with...). Figure 3(The storage device 308 is the same as or similar to the storage device 308). In some embodiments, database 410 stores data associated with 2D and / or 3D maps of at least one area. In some examples, 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., countries). In such examples, a vehicle (e.g., the same as or similar to vehicle 102 and / or vehicle 200) can drive along one or more drivable areas (e.g., single-lane roads, multi-lane roads, highways, remote roads, and / or off-road roads, etc.) and causes at least one LiDAR sensor (e.g., the same as or similar to LiDAR sensor 202b) to generate data associated with images representing objects included in the field of view of the at least one LiDAR sensor.

[0071] In some embodiments, database 410 may be implemented across multiple devices. In some examples, database 410 includes a vehicle (e.g., a vehicle identical or similar to vehicle 102 and / or vehicle 200), an autonomous vehicle system (e.g., an autonomous vehicle system identical or similar to remote AV system 114), and a queue management system (e.g., with...). Figure 1 Queue management system 116 (same as or similar to queue management system) and / or V2I system (e.g., with Figure 1 Among the V2I systems (118 similar to or similar V2I systems), etc.

[0072] Now for reference Figure 5 A diagram illustrating an implementation 500 for low-level fusion processing is provided. In some embodiments, implementation 500 includes a sensing system 504a, a planning system 504b, and a control system 504c. In some embodiments, the sensing system 504a and... Figure 4 The system 504b is the same as or similar to system 402. In some embodiments, the planning system 504b is the same as... Figure 4 The planning system 404 is the same as or similar to the planning system 404. In some embodiments, the control system 504c is the same as... Figure 4 The control system is the same as or similar to that of 408.

[0073] The sensing system 504a can use the hardware of the vehicle 502 to capture data in the environment of the vehicle 502. In the example, the vehicle hardware includes means for capturing information processed to identify objects in the environment (e.g., Figure 2 The LiDAR sensor 202b; Figure 6The LiDAR system 504a (602) can classify objects, which are grouped into types such as pedestrians, bicycles, cars, traffic signs, etc. The captured information can also be used to generate scene descriptions associated with the surrounding environment. The perception system 504a can continuously or periodically transmit object classification 508 and other data to the planning system 504b. The intelligent LiDAR used herein can achieve object classification with a higher level of accuracy compared to conventional technologies. The planning system 504b uses data from the perception system 504a, etc., to generate a route. This route is transmitted (510) to the control system 504c. The control system 504c enables the vehicle to navigate the route generated by the planning system 504b.

[0074] Figure 6 An example of a LiDAR system 602 is shown. The LiDAR system 602 emits light 604a-c from one or more light emitters 606 (e.g., laser emitters). The light emitted by the LiDAR system is typically not in the visible spectrum; for example, infrared light is typically used. A portion of the emitted light 604b encounters a physical object 608 (e.g., a vehicle) and is reflected back to the LiDAR system 602. The LiDAR system 602 also has one or more photodetectors 610 (e.g., photodiodes, pin photodiodes, APDs, SPADs, and SiPMs) to detect the reflected light. In an embodiment, one or more data processing systems associated with the LiDAR system generate an image 612 representing the field of view 614 of the LiDAR system. Image 612 includes information representing the boundaries and reflectivity 616 of the physical object 608. Thus, image 612 is used to determine the boundaries 616 of one or more physical objects near the AV.

[0075] Figure 7 The LiDAR system 602 is shown in operation. Figure 7 In the example, vehicle 102 receives camera system output in the form of image 702 and LiDAR system output in the form of LiDAR data points 704. In use, the data processing system of vehicle 102 compares image 702 with data points 704. Specifically, physical objects 706 identified in image 702 are also identified in data points 704. Thus, vehicle 102 perceives the boundaries of physical objects based on the contours and density of data points 704.

[0076] Figure 8 Additional details of the operation of the LiDAR system 602 are shown. As described above, the vehicle 102 detects the boundaries and reflectivity of physical objects based on the characteristics of the data points detected by the LiDAR system 602. Figure 8As shown, flat objects such as ground 802 will reflect light 804a-d emitted from LiDAR system 602 in a consistent manner. While vehicle 102 is traveling on ground 802, if nothing is obstructing the path, LiDAR system 602 will continue to detect light reflected from the next effective surface point 806. However, if object 808 obstructs the path, light 804e-f emitted by LiDAR system 602 will reflect from point 810a-b in a manner inconsistent with the expected consistency. Based on this information, vehicle 102 can determine the presence of object 808.

[0077] In this embodiment, the SiPM is an array of SPADs. Typically, SPADs are capable of detecting single photons that provide countable, short-duration trigger pulses. However, due to the high avalanche build-up rate and low timing jitter of the device, SPADs can also be used to obtain the arrival time of incident photons. SPADs are biased well above their reverse bias breakdown voltage and have a structure capable of operating without damage or excessive noise. Typically, photodiodes operating in Geiger mode employ a breakdown mechanism to achieve high gain and are referred to as SPADs.

[0078] Typically, the reflections observed by the SiPM micro-elements (e.g., SPADs) are summed to generate an output signal. Internal and / or external circuitry calculates the optical power and arrival time of the reflected light by measuring a current signal (e.g., measuring the rise time, peak value, area, and shape of the signal). For example, timing calculations may be based on the rise, peak value, and shape of the signal, and power calculations may be based on the peak value, area, and shape of the signal. In an embodiment, the output circuitry counts the number of fired micro-elements (e.g., active SiPM devices). In an embodiment, the output circuitry measures the start time of the pulses emitted by the micro-elements to obtain the photon arrival time. Typically, the dynamic range of a SiPM can be limited by the number of micro-elements (SPADs) in each SiPM pixel, which fundamentally limits the intensity / reflectivity accuracy.

[0079] Figure 9This is a diagram of a pixel arrangement 902 on a SiPM-based sensor 900. Pixels 904a-904d depicted on pixel arrangement 902 are four pixels of size at least n×m in the overall pixel arrangement 902 (e.g., in adjacent 2×2 cell blocks). Specifically, pixels 904a-904d include pixel (n,m)904a, pixel (n,m+1)904b, pixel (n+1,m)904c, and pixel (n+1,m+1)904d. Each pixel in pixels 904a-904d includes one or more regions of a Geiger pattern (GM) element 906, a photodiode 908 with an optical filter 910, and an optical trench 912.

[0080] In some embodiments, the SiPM-based sensor 900 operates by reading out both the photodiode 908 and the GM micro-element 906 to generate dual output signals. This results in the simultaneous output of two separate anodes. In some embodiments, the output of the SiPM-based sensor 900 is a continuous analog output (e.g., current output). In this way, the SiPM-based sensor 900 operates as an analog device while simultaneously reading out the current outputs of multiple pixels of the SiPM. In some embodiments, the output of the SiPM-based sensor 900 is a distinguishable and therefore countable individual pulse (e.g., digital output). The pulses output by the SiPM are counted to generate an output signal. The output of the SiPM-based sensor 900 according to this technology enables signal generation across a dynamic range from a single photon to thousands of photons detected by the GM micro-element 906 and the photodiode 908. Typically, the GM micro-elements 906 and 908 can have different optical filters, such as the photodiode 908 with an RGB filter and the GM micro-element 906 with a near-infrared (NIR) filter. When the GM micro-element 906 and the photodiode 908 have the same optical filter (e.g., NIR filters with the same or different transmission characteristics), the output signal of the photodiode 908 will help increase the dynamic range.

[0081] In the example, one or more SiPM-based pixels 904 of the SiPM-based sensor 900 are series combinations of GM micro-elements 906 and photodiodes with optical filters 910. The SiPM-based sensor 900 is a photodetector that senses, times, and quantizes light (or optical) signals down to the single-photon level. Typically, the SiPM pixel 904 comprises multiple micro-elements in an array sharing a common output (e.g., anode and cathode). In embodiments, each micro-element is a series combination of a single-photon avalanche photodiode (SPAD) and a quenching circuit (e.g., a resistor). All micro-elements are connected in parallel and independently detect photons, and the resulting SiPM-based sensor 900 has multiple pixels 904, some of which have multiple anodes. In some embodiments, the SiPM-based sensor 900 may include one or more SiPM-based pixels 904 capable of independently detecting photons or optical return signals. Each pixel 904 may include a sub-region of multiple GM elements 906 (e.g., SPADs) and one (typically) or more photodiodes 908.

[0082] During operation in low-flux events, when a microelement detects a photon, an electron-hole pair is generated. When a sufficiently high electric field is generated in the depletion region, the generated charge carriers (electrons or holes) are accelerated to carry enough kinetic energy to generate secondary electron-hole pairs (collision ionization). A macroscopic (avalanche) current flows through the microelement until it is quenched by passive quenching (e.g., a quenching resistor) or active circuitry. The sums of the currents from the individual excited microelements are combined to form a pseudo-analog output (a time-sequential superposition of the current pulses from the excited microelements) used to calculate the magnitude of the photon flux. Quenching reduces the reverse voltage applied to the SiPM to below its breakdown voltage, thereby stopping the avalanche of current. The SiPM is then recharged back to the bias voltage and can be used to detect subsequent photons.

[0083] In one embodiment, each photodiode 908 with an optical filter 910 is configured to filter to accept one of the RGB colors (e.g., similar to an imaging sensor). Figure 9 In the arrangement of photodiodes 908 with optical filters 910 shown, the photodiode 908 with optical filter 910r is configured to receive red light, the photodiode 908 with optical filter 910g is configured to receive green light, and the photodiode 908 with optical filter 910b is configured to receive blue light. In each of the pixels 904a-904d, an optical trench 912 isolates the photodiode 908 from other light-sensitive areas (e.g., SPAD 906). In this embodiment, the optical trench 912 is optional.

[0084] Applying a sub-region of a photodiode with an RGB filter to each (or some) SiPM pixel enables (e.g., for...) Figure 10 In the described process, the same hardware is used to simultaneously capture point cloud data and camera-like data. Because the technology of this disclosure captures both point cloud and camera-type data simultaneously, the calibration process is altered. For example, using a single sensor to capture multiple types of data can streamline calibration.

[0085] Traditionally, calibration is performed between 3D point cloud data and 2D camera data to generate a correspondence between data output by separate hardware sensors. For example, conventional calibration typically uses one or more transformations to fuse the data. Data fusion usually enables accurate interpretation of objects in the environment. The SiPM sensor according to this technology includes hardware for capturing point cloud data, which is co-located on the same substrate as the hardware for capturing 2D imaging data. By being co-located on the same substrate, one or more transformations for fusing multiple data types output by the SiPM device are eliminated. Specifically, the transformation mapping points from the LiDAR coordinate system to the camera coordinate system is at least partially based on known intrinsic parameters characterizing the SiPM-based sensor.

[0086] In the example, the pixels of the sensor according to this technology include a first type of photodiode (e.g., a GM micro-element 906 with internal gain) and a second type of photodiode (e.g., a photodiode 908 with an optical filter 910). The second type of photodiode may have different internal gains (e.g., PD, PIN PD, or APD). The first and second type of photodiodes capture light emitted in the environment. The data captured by the first type of photodiode is output by the sensor and processed to generate 3D position (e.g., distance data) and reflectance data. In the example, the 3D position and reflectance data may be point cloud data. In some cases, the second type of photodiode is physically coupled to a color filter (e.g., visible or NIR). For example, the color filter is an RGB color filter that removes other colors in the reflection while allowing a predetermined color to pass through, thereby enabling the corresponding photodiode to capture color information. The color filter is implemented using a filter array with a predefined pattern on top of one or more photodiodes. The data captured by the second type of photodiode coupled to the color filter is output by the sensor and processed to generate 2D image data. Location information and 2D color image information are fused pixel-by-pixel before being read out by external circuitry. In this way, this technique achieves low-level data fusion on a pixel-by-pixel basis before outputting sensor data.

[0087] A photodiode 908 with an optical filter 910 can introduce noise and / or optical crosstalk. For example, noise and / or optical crosstalk can include interactions between the photodiode 908 and the micro-element 906, which are introduced by photon and electron leakage caused by optical refraction and minority carriers. In embodiments, trenches fabricated into the substrate can mitigate noise and / or crosstalk between sub-pixels. For example, trenches (e.g., optical trench 912) can be implemented as non-light-emitting regions between adjacent sub-pixels.

[0088] Now for reference Figure 10 This describes a process 1000 for fusing 3D position and 2D image information captured from a first type of photodiode and a second type of photodiode, respectively. In some embodiments, one or more steps of the process 1000 are performed by an autonomous system 202 (e.g., entirely and / or partially). Additionally or alternatively, in some embodiments, one or more steps of the process 1000 are performed by other devices or groups of devices separate from or including device 300 (e.g., entirely and / or partially). A first type of photodiode (e.g., a sub-region of a SPAD array) can provide both 3D position (distance) and reflectivity information. Using a first type of photodiode (e.g., GM micro-element 906) and a second type of photodiode (e.g., photodiode 908 with filter 910), 3D position, reflectivity, and 2D image can be fused on a pixel-by-pixel basis.

[0089] In 1002, a first-type photodiode (e.g., GM Micro 906) is used to detect light from the environment (e.g., Figure 1 The sensing system 504a captures reflections (e.g., captured information) of light emitted in the environment 100 to obtain location and reflectivity information. For example, the sensing system 504a can use the hardware of the vehicle 502 to capture information about the environment of the vehicle 502. In some examples, the captured information may include information about objects that are grouped into types such as pedestrians, bicycles, cars, traffic signs, etc. The captured information may include reflections such as reflected light 804a-d emitted from the LiDAR system 602. In these examples, the reflected light may be light 804e-f emitted by the LiDAR system 602 toward the object 808 and reflected from points 810a-b. In some embodiments, the photodiode of the first type is a single-photon avalanche diode (SPAD).

[0090] In 1004, color information is captured using a second type of photodiode (e.g., a photodiode 908 having an optical filter 910). The second type of photodiode is coupled to a color filter to obtain color information (e.g., from a photodiode having an optical filter 910r configured to receive red light, a photodiode having an optical filter 910g configured to receive green light, and a photodiode having an optical filter 910b configured to receive blue light). The first type of photodiode and the second type of photodiode are juxtaposed on a common substrate (e.g., a SiPM-based sensor 900). In some embodiments, the second type of photodiode (or photodetector) is a photodiode (PD) or a PIN photodiode (PIN).

[0091] In 1006, (e.g., by sensing system 504a) distance (3D position) information and color (2D image) information are fused on a pixel-by-pixel basis. For example, distance and reflectivity data can be fused with image data by SiPM-based sensor 900 before being processed by sensing system 504a.

[0092] In some embodiments, the process 1000 further includes applying a deep trench pattern (e.g., an optical trench) to a common substrate to reduce crosstalk between the second type of photodiode and the first type of photodiode. For example, the optical trench 912 can be incorporated into a SiPM-based sensor 900 to prevent crosstalk between the GM element 906 and the photodiode 908 having an optical filter 910.

[0093] In some embodiments, the fused position and reflectivity information, along with color information, is provided to the control system of the autonomous vehicle. For example, the SiPM-based sensor 900 can be directly accessed by the control system 504c.

[0094] In the preceding description, aspects and embodiments of this disclosure have been described with reference to numerous specific details, which may vary from implementation to implementation. Therefore, the specification and drawings should be considered illustrative rather than restrictive. The sole and exclusive indication of the scope of this invention, and what the applicant expects to be the scope of this invention, is the literal and equivalent scope of the claims published from this application in the specific form of the published claims, including any subsequent amendments. Any definitions of terms expressly set forth herein for inclusion in such claims should be taken as meaning as such terms are used in the claims. Furthermore, when the term “comprising” is used in the preceding specification or appended claims, what follows that phrase may be an additional step or entity, or a sub-step / sub-entity of a previously stated step or entity.

Claims

1. A sensor device, comprising: A plurality of pixels, wherein the plurality of pixels are connected in parallel, wherein each pixel includes one or more regions of a Geiger pattern micro-element (GM micro-element), a photodiode, an optical filter, and a deep trench pattern, and wherein at least one of the plurality of pixels includes: A first type of photodiode, wherein the first type of photodiode captures reflected light from the environment and outputs three-dimensional position information, namely 3D position information and reflectivity information; and A second type of photodiode with a filter, wherein the second type of photodiode captures filtered reflected light from the environment, wherein a color filter separates data from the filtered reflected light into color information output by the second type of photodiode, and wherein the first type of photodiode and the second type of photodiode are juxtaposed on a common substrate; wherein the 3D position information, reflectivity information, and color information of at least one pixel among the plurality of pixels are fused and output by the sensor.

2. The sensor device according to claim 1, wherein, The first type of photodiode is a single-photon avalanche diode (SPAD) with internal gain, consisting of one or more photons.

3. The sensor device according to claim 1, wherein, The second type of photodiode is a photodiode with a second internal gain.

4. The sensor device according to claim 1, wherein, Applying the deep trench pattern to the common substrate reduces crosstalk between the second type of photodiode and the first type of photodiode.

5. The sensor device according to claim 1, wherein, The position information, reflectivity information, and image data captured by the first type of photodiode and the second type of photodiode are fused together before being processed by external circuitry.

6. The sensor device according to claim 1, wherein, The sensor device is part of an autonomous vehicle system.

7. The sensor device according to claim 1, wherein, The sensor device outputs one or both of the first type of photodiode and the second type of photodiode to generate a continuous output signal.

8. The sensor device according to claim 1, wherein, The color filter is a red / green / blue filter, i.e., an RGB filter, which typically has a visible light range between 400 nm and 700 nm, or between 400 nm and 700 nm, or a near-infrared filter, i.e., a NIR filter, which typically has a visible light range between 700 nm and 2 μm, or between 700 nm and 2 μm.

9. The sensor device according to claim 8, wherein, The NIR filter has optical characteristics that are the same as or different from those of the SPAD region, wherein the optical characteristics include at least transmission bandwidth and attenuation factor.

10. A method for information fusion, comprising: Using a first-type photodiode, the reflected light from the emitted light in the environment is captured to obtain position information and reflectivity information; Color information is captured using a second type of photodiode, wherein the second type of photodiode is coupled to a color filter to obtain the color information, and wherein the first type of photodiode and the second type of photodiode are juxtaposed on a common substrate, the common substrate comprising a plurality of pixels connected in parallel, wherein each pixel comprises one or more regions of a Geiger pattern micro-element (GM micro-element), a photodiode, an optical filter, and a deep trench pattern; and The position information and reflectivity information are fused with the color information on a pixel-by-pixel basis.

11. The method according to claim 10, wherein, The first type of photodiode is a single-photon avalanche diode (SPAD) with internal gain, consisting of one or more photons.

12. The method according to claim 10, wherein, The second type of photodiode is a photodiode with a second internal gain.

13. The method of claim 10, further comprising: Applying the deep trench pattern to the common substrate reduces crosstalk between the second type of photodiode and the first type of photodiode.

14. The method of claim 10, further comprising: The location information and reflectivity information captured using the first type of photodiode and the second type of photodiode, along with the image data, are fused together before being processed by external circuitry.

15. The method of claim 10, further comprising: The fused location, reflectivity, and color information are provided to the control system of the autonomous vehicle.

16. The method of claim 10, further comprising: Output one or both of the first type of photodiode and the second type of photodiode to generate a continuous output signal.

17. A non-transitory computer-readable storage medium having instructions stored thereon, the instructions, when executed by one or more processors, causing the one or more processors to perform an operation, the operation comprising: Using a first-type photodiode, the reflected light from the emitted light in the environment is captured to obtain position information and reflectivity information; Color information is captured using a second type of photodiode, wherein the second type of photodiode is coupled to a color filter to obtain the color information, and wherein the first type of photodiode and the second type of photodiode are juxtaposed on a common substrate, the common substrate comprising a plurality of pixels connected in parallel, wherein each pixel comprises one or more regions of a Geiger pattern micro-element (GM micro-element), a photodiode, an optical filter, and a deep trench pattern; and The position information and reflectivity information are fused with the color information on a pixel-by-pixel basis.

18. The non-transitory computer-readable storage medium according to claim 17, wherein, The first type of photodiode is a single-photon avalanche diode (SPAD) with internal gain, consisting of one or more photons.

19. The non-transitory computer-readable storage medium according to claim 17, wherein, The second type of photodiode is a photodiode with a second internal gain.

20. The non-transitory computer-readable storage medium of claim 17, further comprising: Applying the deep trench pattern to the common substrate reduces crosstalk between the second type of photodiode and the first type of photodiode.

21. A sensor device, comprising: A plurality of pixels, wherein the plurality of pixels are connected in parallel, wherein each pixel includes one or more regions of a Geiger pattern micro-element (GM micro-element), a photodiode, an optical filter, and a deep trench pattern, and wherein at least one of the plurality of pixels includes: A first type of photodiode, wherein the first type of photodiode captures reflected light from the environment and outputs three-dimensional position information, namely 3D position information and reflectivity information; and A second type of photodiode with a filter, wherein the second type of photodiode captures filtered reflected light from the environment, wherein a color filter separates data from the filtered reflected light into color information output by the second type of photodiode, and wherein the first type of photodiode and the second type of photodiode are juxtaposed on a common substrate; wherein the 3D position information, reflectivity information, and color information of at least one pixel among the plurality of pixels are fused and output by the sensor, and The first type of photodiode and the second type of photodiode generate dual output signals that can distinguish and count individual pulses.

22. A computer program product storing instructions that, when executed by one or more processors, cause the one or more processors to perform the method according to any one of claims 10-16.

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