Camera triggering based on gptp time synchronization

Abstract

Some methods for triggering cameras in an autonomous vehicle (AV). The methods include receiving, by a programmable circuit, Generalized Precision Time Protocol (gPTP) information for synchronizing the programmable circuit with a gPTP Grand Master (GM); generating, by the programmable circuit, a pulse per second (PPS) signal having a peri-od of one second based on the gPTP time information; generating, by the programmable circuit, a synchronizing signal having a period less than the period of the PPS signal; generating, by the programmable circuit, a triggering signal based on the PPS signal, wherein a period of the triggering signal is the same as the period of the synchronizing signal; and sending, by the programmable circuit and to one or more cameras of an AV, the triggering signal for triggering the one or more cameras to capture one or more images. Systems and computer program products are also provided.

Description

[0001] CAMERA TRIGGERING BASED ON GPTP TIME SYNCHRONIZATION

[0002] CROSS-REFERENCE TO RELATED APPLICATION

[0003] [1] This application claims the benefit of U.S. Provisional Application No. 63 / 533,872 filed

[0004] August 21 , 2023, the disclosure of which is incorporated herein by reference in its entirety.

[0005] BACKGROUND

[0006] [2] In an autonomous vehicle (AV), cameras and Light Detection and Ranging (LiDAR) sensors capture data associated with the environment. The captured data is used to perceive the environment and enable the vehicle to make driving decisions.

[0007] BRIEF DESCRIPTION OF THE FIGURES

[0008] [3] FIG. 1 is an example environment in which a vehicle including one or more components of an autonomous system can be implemented;

[0009] [4] FIG. 2 is a diagram of one or more systems of a vehicle including an autonomous system;

[0010] [5] FIG . 3 is a diagram of components of one or more devices and / or one or more systems of

[0011] FIGS. 1 and 2;

[0012] [6] FIG, 4 is a diagram of certain components of an autonomous system;

[0013] [7] FIG. 5 is a diagram of art implementation of an architecture for AV compute;

[0014] [8] FIG. 6 is a diagram of an example system for synchronizing cameras with a LiDAR sensor;

[0015] [9] FIG. 7 A illustrates an example time sequence where a PPS signal pulse arrives earlier than the 20th pulse among a previous set of synchronizing signal pulses;

[0016]

[0010] FIG. 7B illustrates an example time sequence where a PPS signal pulse arrives later than the 20th pulse among a previous set of synchronizing signal pulses;

[0017]

[0011] FIG. 8 illustrates an ex ample state machine of the synchronizing signal generator;

[0018]

[0012] FIG. 9 illustrates an example flow chart of a process for synchronizing cameras with a

[0019] LiDAR sensor;

[0020]

[0013] FIG. 10 illustrates example time sequences of gPTP, a PPS signal, a synchronizing signal, a triggering signal, and a LiDAR triggering signal. DETAILED DESCRIPTION

[0021]

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

[0022]

[0015] Specific arrangements or orderings of schematic elements, such as those representing systems, devices, modules, instruction blocks, data elements, and / or the like, are illustrated in the drawings for ease of description. However, it w ill be understood by those skilled in the art that the specific ordering or arrangement of the schematic elements in the drawings is not meant to imply that a particular order or sequence of processing, or separation of processes, is required unless explicitly described as such. Further, the inclusion of a schematic element in a drawing is not meant to imply that such element is required in all embodiments or that the features represented by such element may not be included in or combined with other elements in some implementations unless explicitly described as such.

[0023]

[0016] Further, where connecting elements such as solid or dashed lines or arrows are used in the drawings to illustrate a connection, relationship, or association between or among two or more other schematic elements, the absence of any such connecting elements is not meant to imply that no connection, relationship, or association can exist. In other words, some connections, relationships, or associations between elements are not illustrated in the drawings so as not to obscure the disclosure. In addition, for ease of' illustration, a single connecting element can be used to represent multiple connections, relationships, or associations between elements. For example, where a connecting element represents communication of signals, data, or instructions (e.g., “software instructions”), it should be understood by those skilled in the art that such element can represent one or multiple signal paths (e.g., a bus), as may be needed, to affect the communication.

[0024]

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

[0025]

[0018] The terminology used in the description of the various described embodiments 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 described embodiments and the appended claims, the singular forms “a,” an.” and "the” are intended to include the plural forms as well and can 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 encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and / or “comprising.” when used in this description specify the presence of 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.

[0026]

[0019] As used herein, the terms “communication” and “communicate” refer to at least one of the reception, receipt, transmission, transfer, provision, and / or the like of information (or information represented by, for example, data, signals, messages, instructions, commands, and / or the like). For one unit (e.g., a device, a system, a component of a device or system, combinations thereof, and / or the like) to be in communication with another unit means that the one unit is able to directly or indirectly receive information from and / or send (e.g., transmit) information to the other unit. This may refer to a direct or indirect connection that is wired and / or wireless in nature. Additionally, two units may be in communication with each other even though the information transmitted may be modified, processed, relayed, and / or routed between the first and second units. For example, a first unit may be in communication with a second unit even though the first unit passively receives information and does not actively transmit information to the second unit. As another example, a first unit may be in communication with a second uni t if at least one intermediary unit (e.g., a third unit located between the first unit, and the second unit) processes information received from the first unit and transmits the processed information to the second unit. In some implementations, a message may refer to a network packet (e.g., a data packet and / or the like) that includes data.

[0027]

[0020] As used herein, the term “if’ is, optionally, construed to mean “when,” “upon,” “in response to determining,” “in response to detecting,” and / or the like, depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining,” “in response to determining?’ “upon detecting [the stated condition or event],” “in response to detecting [the stated condition or event],” and / or the like, depending on the context. Also, as used herein, the terms “has,” “have,” “having,” or the like are intended to be open-ended terms. Further, the phrase “based on” is intended to mean “based at least partially on” unless explicitly stated otherwise.

[0028]

[0021] Reference will now be made in detail to 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 can 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 th e embodiments.

[0029]

[0022] General Overview

[0030]

[0023] This disclosure provides methods and systems for triggering cameras in an autonomous vehicle (AV). Triggering sensors to capture information at predetermined times enables synchronization between multiple sensors, such as cameras and Light Detection and Ranging (LiDAR.) sensors (e.g., an over-head 360-degree LiDAR sensor) of the AV. In some implementations, the methods and systems for synchronization are performed by a Field Programmable Gate Array (FPGA), or performed by a System-on-a-Chip (SOC) (e.g., a Xilins Multi-Processor System on a Chip (MPSOC)) that includes a Centra] Processing Unit (CPU Core) and an FPGA. The FPGA, SOC, and multiple sensors are communicatively coupled by a network. The FPGA, SOC, and multiple sensors exchange data and share resources though network connections. In some implementations, the cameras and the LiDAR sensors are synchronized using a Generalized Precision Time Protocol (gPTP) on the network. Synchronization enables information from multiple sensors, including cameras and LiDAR sensors, to be fused into an integrated data represetatfon. The resulting fused information produces more consistent, accurate, and useful information when compared with the individual information captured by respective cameras or LiDAR sensors. In examples, lower level fusion is implemented by causing contemporaneous data capture by a LiDAR sensor and a camera. For example, when a LiDAR beam scans an object in the environment, a camera observes the object within its field of view (e.g., pointing toward the object) and exposes its sensor element at the precise moment of the LiDAR scan.

[0031]

[0024] To achieve this fusion, hardware components of the network are clocked in order to coordinate data capture by multiple sensors. In an implementation, a network switch (e.g., an Ethernet switch) executing on the FPGA is a gPTP slave node on the network, while a General Purpose Virtual Machine (GPVM) serves as a gPTP Grand Master (GM ) GPVM, according to the gPTP (IEEE 802.1) protocol. The GPVM disciplines clocks of the systems on the network including the FPGA and the LIDAR sensor. Using gPTP, the times kepi by slave nodes, such as the FPGA, are synchronized to a master clock of the GVPM. In examples, a network includes multiple gPTP time domains, where nodes synchronize .multiple clocks to multiple master clocks for redundancy. Each time domain includes one master clock, and any number of slave clocks. The protocol synchronizes the slave clocks to the master clock by sending sync messages from the master clock nodes to the slave clock nodes. In some implementations, cameras are communicatively coupled with the FPGA (e.g,, gPTP slave node) which triggers the cameras for data capture synchronized with at least one LIDAR sensor.

[0032]

[0025] In some implementations, a network switch (e.g., Ethernet switch) in the FPGA converts a. hardware clock signal from an oscillator to a pulse per second (PPS) signal based on gPTP time information. In some implementations, the hardware clock signal has a frequency of 100 MHz. A person having ordinary skill in the art understands that the frequency of the hardware clock signal can be a different value and is design specific. The PPS signal has a frequency of 1 Hz and a period of I second. The FPGA then generates a synchronizing signal with multiple pulses within 1 second. The synchronizing signal has a particular frequency (e.g,, 20 Hz) that is substantially the same as a rotating frequency (e.g., 20 Hz) of the LiDAR sensor. The desired frame rate of the cameras is substantially equal to the rotating frequency of the LiDAR sensor. Each pulse of the PPS signal corresponds to a set of pulses (e.g., 20 pulses) of the synchronizing signal. In some implementations, the rising edge of a respective pulse of the PPS signal (e.g., PPS signal pulse) is synchronized with the rising edge of a first pulse among each set of pulses (e.g., 1st pulse among 20 pulses) of the synchronizing signal (e.g., synchronizing signal pulse). The FPGA causes a respective camera to capture data that coincides with LiDAR data capture according to the synchronizing signal and a time offset. In examples, the synchronizing signal is generated using a state machine where the state (e.g., idle, deferred, waiting, triggered) is set based on various parameters (e.g.:cam trigger, pps in, ticks since trigger). The parameters are derived from the arrival of pulses associated with the PPS signal. The time offset is used to correct clock drifts or breaks in the synchronization between the PPS signal and the synchronization signal.

[0033]

[0026] In some implementations, the hardware cloc k from the oscillator of the FPGA has a clock drift relative to the clock of the GVPM. In case of clock drift, the hardware clock is disciplined according to the gP TP protocol, and the rising edge of a PPS signal pulse will shift. This would temporarily break the synchronization of the PPS signal pulse with the rising edge of the first pulse among a set of synchronizing signal pul ses (e.g ., 1st pulse among 20 pulses ). For example, clock drift causes the PPS signal pulse to arrive earlier than or later than the 20th pulse among a previous set of synchronizing signal pulses. When a PPS signal pulse arrives in close proximity to a previously generated synchronizing signal pulse, a deadzone (see FIG. 8) is introduced to decide whether to drop one synchronizing signal pulse or to generate a new synchronizing signal pulse that is synchronous with the rising edge of the PPS. The deadzone’s width (e.g., 45 ms) depends on the camera’s specifications.

[0034]

[0027] The synchronizing signal is not directly used to trigger the cameras. In some implementations, a time offset (mathematically calculated given the geometry of the vehicle and positions of the cameras around the vehicle) is applied to the synchronizing signal, so that the cameras are triggered when die LiDAR scans over the center of the field of view. The triggering signal is obtained from a combination of the synchronizing signal, and the time offset. The time offset is a fixed time period (e.g., 70 milliseconds, 100 milliseconds, etc.), depending on positions of cameras and vehicle size. The time offset is obtained by calibrating the cameras 202a of an AV.

[0028] By virtue of the implementation of systems, methods, and computer program products described herein, some of the advantages of these techniques include the generation of camera triggering signals by an FPGA, which is hardware and thus generates a more reliable signal that is capable of adequately smoothing out temporary synchronization breaks due to clock drifts by taking into account a time offset. FPGA is hardware and thus would not be impacted by any software applications (e.g., kernel software, or application software based on camera data) executing on a processor, resulting in a more stable camera triggering signal, with a synchronization accuracy of tens of nanoseconds.

[0035]

[0029] Referring now to FIG. 1 , illustrated is example environment 100 in which vehicles that, include autonomous systems, as well as vehicles that do not, are operated. As illustrated, environment 100 includes vehicles 1023“102n, objects 104a- 104n, routes I06a-106n, area 108, vehicle-to-infrastructure (V2I) device 110, network 112, remote autonomous vehicle (AV) system 1 14, fleet management system 1 1.6, and V2I system 1.18, Vehicles 102a -102n, vehicle-to- intrastructure (V2I) device 110, network 112, autonomous vehicle (AV) system 1 14, fleet management system 1 16, and V21 system 118 interconnect (e.g., establish a connection to communicate and / or the like) via wired connections, wireless connections, or a combination of wired or wireless connections. In some implementations, objects 104a~104n interconnect with at least one of vehicles l02a-102n, vehicle-to-mfrasimcture (V2I) device 11.0, network 112, autonomous vehicle (AV) system 114, fleet management system 116, and V21 system 118 via wired connections, wireless connections, or a combination, of wired or wireless connections.

[0036]

[0030] Vehicles 102a™102n (referred to individually as vehicle 102 and collectively as vehicles 102) include at least one device configured to transport goods and / or people. In some implementations, vehicles 102 are configured to be in communication with V2I device 110, remote AV system 114, fleet management system 116, and / or V2I system 1 18 via network 1 12. In some implementations, vehicles 102 include cars, buses, trucks, trains, and / or the like. In some implementations, vehicles 102 are the same as, or similar to, vehicles 200, described herein (see FIG. 2). In some implementations, a vehicle 200 of a set of vehicles 200 is associated with an autonomous fleet manager. In some implementations, vehicles 102 travel along respective routes l06a~I06n (referred to individually as route 106 and collectively as routes 106), as described herein. In some implementations, 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).

[0037]

[0031] Objects 104a -104n (referred to individually as object 104 and collectively as objects 104) include, for example, at least one vehicle, at least one pedestrian, at least one cyclist, at least one structure (e.g., a building, a sign, a fire hydrant, etc.), and / or the like. Each object 104 is stationary (e.g., located at a fixed location for a period of time) or mobile (e.g., having a velocity and associated with at least one trajectory') In some implementations, objects 104 are associated with corresponding locations in area 108.

[0038]

[0032] Routes 106a - 106n (referred to individually as route 106 and collectively as routes 106) are each associated with (e.g., prescribe) a sequence of actions (also known as a trajectory) connecting states along which an AV can navigate. Each route 106 starts at an initial state (e.g , a state that corresponds to a first spatiotemporal location, velocity, and / or the like) and ends at a final goal state (e.g., a state that corresponds to a second spatiotemporal location that is different from the first spatiotemporal location) or goal region (e.g., a subspace of acceptable states (e.g., terminal states)). In some implementations, the first state includes a location at which an individual or individuals are to be picked-up by the AV and the second state or region includes a location or locations at which the individual or individuals picked-up by the AV are to be dropped-off. In some implementations, routes 106 include a plurality of acceptable state sequences (e.g., a plurality of spatiotemporal location sequences), the plurality of state sequences associated with (e.g., defining) a plurality of trajectories. In an example, routes 106 include only high level actions or imprecise state locations, such as a series of connected roads dictating turning directions at roadway intersections. Additionally, or al tentatively, routes 106 may include more precise actions or states such as, for example, specific target lanes or precise locations within the lane areas and targeted speed at those positions. In an example, routes 106 include a plurality of precise state sequences along the at least one high level action sequence with a limited lookahead horizon to reach intermediate goals, where the combination of successive iterations of limited horizon state sequences cumulatively correspond to a plurality' of trajectories that collectively form the high level route to terminate at the final goal state or region.

[0039]

[0033] Area 108 includes a physical area (e.g., a geographic region) within which vehicles 102 can navigate. In an example, area 108 includes at least one state (e.g., a country, a province, an individual state of a plurality of states included in a country, etc.), at least one portion, of a state, at least one city, at least one portion of a city, etc. In some implementations, area 108 includes at least one named thoroughfare (referred to herein as a “road”) such as a highway, an interstate highway, a parkway, a city street, etc. Additionally, or alternatively, in some examples area 108 includes at least one unnamed road such as a driveway, a Section of a parking lot, a section of a vacant and / or undeveloped lot, a dirt path, etc. In some implementations, a road includes at least one lane (e.g., a portion of the road that can be traversed by vehicles 102). In an example, a road includes at least one lane associated with (e.g., identified based on) at least one lane marking.

[0040]

[0034] Vehicle-to-infrastructure (V21) device 110 (sometimes referred to as a Vehicle-to- Infrastructure or Vehicle-to-Everything (V2X) device) includes at least one device configured to be in communication with vehicles 102 and / or V2I infrastructure system 1.18. In some implementations. V2I device 110 is configured to be in communication with vehicles 102, remote AV system 114, fleet management system 116, and / or V2I system 1 18 via network 112. In some implementations, V2I device 110 includes a radio frequency identification ( RFID) device, signage, cameras (e.g., two-dimensional (2D) and / or three-dimensional (3D) cameras), lane markers, streetlights, parking meters, etc. In some implementations, V2I device 1 10 is configured to communicate directly with vehicles 102 Additionally, or alternatively, in some implementations V2I device 110 is configured to communicate with vehicles 102, remote AV system 1 14, and / or fleet management system 1 16 via V21 system 118. In some implementations, V2I device 110 is configured to communicate with V21 system 1 18 via network 112

[0041]

[0035] Network 1 12 includes one or more wired and / or wireless networks. In an example, network 1 12 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., the public switched telephone network (PSTN), a private network, an ad hoc network, an intranet, the Internet, a fiber optic-based network, a cloud computing network, etc., a combination of some or all of these networks, and / or the like.

[0042]

[0036] Remote AV system 1 14 includes at least one device configured to be in communication with vehicles 102, V2I device .110, network 1 12, fleet management system 116, and / or V2I system 118 via network 112. In an example, remote AV system 114 includes a server, a group of servers, and / or other like devices. In some implementations, remote AV system 11.4 is co-located with the fleet management system 116. In some implementations, remote AV system 1 14 is involved in the installation of some or all of the components of a vehicle, including an autonomous system , an autonomous vehicle compute, software implemented by an autonomous vehicle compute, and / or the like. In some implementations, remote AV system 1 14 maintains (e.g., updates and / or replaces) such components and / or softw are during the lifetime of the vehicle.

[0043]

[0037] Fleet management system 116 includes at least one device configured to be in communication with vehicles 102, V2I device 110, remote AV system 114, and / or V21 infrastructure system 118. In an example, fleet management system 116 includes a server, a group of servers, and / or oilier like devices. In some implementations, fleet management system 116 is associated with a ridesharing company (e.g., an organization that controls operation of multiple vehicles (e.g., vehicles that include autonomous systems and / or vehicles that do not include autonomous systems) and / or the like).

[0038] In some implementations, V2I system 118 includes at least one device configured to be in communication with vehicles 102, V2I device 110, remote AV system 114, and / or fleet management system 116 via network 1 12. In some examples, V2I system 1 18 is configured to be in communication with V2I device 110 via a connection different from network 112. In some implementations, V2I system 1 18 includes a server, a group of servers, and / or other like devices In some implementations, V2I system 1 18 is associated with a municipality or a private institution (eg, a private institution that maintains V2I device 1.10 and / or the like),

[0044]

[0039] The number and arrangement of elements illustrated m MG 1 are provided as an example. There can be additional elements, fewer elements, different elements, and / or differently arranged elements, than those illustrated in FIG. 1. Additionally, or alternatively, at least one element of environment 100 can perform one or more functions described as being performed by at least one different element of FIG. 1. Additionally, or alternatively, at least one set of elements of environment 100 can perform one or more functions described as being performed by at least one different set of elements of environment 100.

[0045]

[0040] Referring now to FIG. 2, vehicle 200 (which may be the same as, or similar to vehicle. 102 of FIG . 1) .includes or is associated with autonomous system 202, powertrain control system 204, steering control system 206, and brake system 208. In some implementations, vehicle 200 is the same as or similar to vehicle 102 (see FIG. 1). In some implementations, autonomous system 202 is configured to confer vehicle 200 autonomous driving capability (e.g., implement at least one driving automation or maneuver-based function, feature, device, and / or the like that enable vehicle 200 to be partially or fully operated without human intervention including, without limitation, fully autonomous vehicles (e.g., vehicles that forego reliance on human intervention such as Level 5 ADS-operated vehicles), highly autonomous vehicles (e.g., vehicles that forego reliance on human intervention in certain situations such as Level 4 ADS-operated vehicles), conditional autonomous vehicles (e.g., vehicles that forego reliance on human intervention in limited situations such as Level 3 ADS-operated vehicles) and / or the like In one embodiment, autonomous system 202 includes operational or tactical functionality-' required to operate vehicle 200 in on-road traffic and per form part or all of Dynamic Driving Task (DDT) on a sustained bas is. In another embodiment, autonomous system 202 includes an Advanced Driver Assistance System (ADAS) that includes driver support features. 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's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, which is incorporated by reference in its entirety. In some implementations, vehicle 200 is associated with an autonomous fleet manager and / or a ridesharing company.

[0046]

[0041] Autonomous system 202 includes a sensor suite that includes one or more devices such as cameras 202a, LIDAR sensors 202b, radar sensors 202c, and microphones 202d. In some implementations, autonomous system 202 can include more or fewer devices and / or different devices (e.g., ultrasonic sensors, inertial sensors, GPS receivers (discussed below), odometry sensors that generate data associated with an indication of a distance that vehicle 200 has traveled, and / or the like). In some implementations, autonomous system 202 uses the one or more devices included in autonomous system 202 to generate data associated with environment 100, described herein. The data generated by the one or more devices of autonomous system 202 can be used by one or more systems described herein to observe the environment (e.g., environment 100) in which vehicle 200 is located. In some implementations, autonomous system 202 includes communication device 202e, autonomous vehicle compute 202 E drive-by-wire (DBW) system 202h, and safety controller 202g.

[0047]

[0042] Cameras 202a include at least one device configured, to be in communication with communication device 202e, autonomous vehicle compute 202f, and / or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bits 302 of FIG. 3). Cameras 202a include at least one camera (e.g., a digital camera using a light sensor such as a Charge-Coupled Device (CCD), a thermal camera, an infrared (IR) camera, an event camera, and or the like) to capture images including physical objects (e.g., cars, buses, curbs, people, and / or the like). In some implementations, camera 202a generates camera data as output, in some examples, camera 202a generates camera data that includes image data associated with an image. Tn this example, the image data may specify at least one parameter (e.g., image characteristics such as exposure, brightness, etc., an image timestamp, and / or the like) corresponding to the image. In such an example, the image may be in a format (e.g., RAW, JPEG, PNG, and / or the like). In some implementations, camera 202a includes a plurality of independent cameras configured on (e.g., positioned on) a vehicle to capture images for the purpose of stereopsis (stereo vision). In some examples, camera 202a includes a plurality of cameras that generate image data and transmit the image data to autonomous vehicle compute 202f and / or a fleet management system (e.g a fleet management system that is the same as or similar to fleet management system 116 of FIG. 1). In such an example, autonomous vehicle compute 202f determines depth to one or more objects in a field of vie w of at least two cameras of the plurality of cameras based on the image data from the at least two cameras. In some implementations, cameras 202a is configured to capture images of objects within a distance from cameras 202a (e.g.. up to 100 meters, up to a kilometer, and / or the like). Accordingly, cameras 202a include features such as sensors and lenses that are optimized for perceiving objects that are at. one or more distances from cameras 202a.

[0048]

[0043] In an embodiment, camera 202a includes at least one camera configured to capture one or more images associated with one or more traffic lights, street signs and / or other physical objects that provide visual navigation information. In some implementations, camera 202a generates traffic light data associated with one or more images. In some examples, camera 202a generates TLD (Traffic Light Detection) data associated with one or more images that include a format (e.g., RAW, JPEG, PNG, and / or the like). In some implementations, camera 202a that generates TLD data differs from other systems described herein incorporating cameras in that camera 202a can include one or more cameras with a wide field of view (e.g., a wide-angle lens, a fish-eye lens, a lens having a viewing angle of approximately 120 degrees or more, and / or the like) to generate images about as many physical objects as possible.

[0049]

[0044] Light Detection and Ranging (LiDAR) sensors 202b include at least one device configured to be in communication with cornmimicatiori device 202e, autonomous vehicle compute 202f. and / or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). LiDAR sensors 202b include a system configured to transmit light from a light emitter (e.g., a laser transmitter). Light emitted by LiDAR sensors 202b include light (e.g,, infrared light and / or the like) that is outside of the visible spectrum . In some implementations, during operation, light emitted by LiDAR sensors 202b encounters a physical object (e.g., a vehicle) and is reflected back to LiDAR sensors 202b. In some implementations, the light emitted by LiDAR sensors 202b does not penetrate the physical objects that the light encounters. LiDAR sensors 202b also include at least one light detector which detects the light that was emitted from the light emitter after the light encounters a physical object. In some implementations, at least one data processing system associated with LiDAR sensors 202b generates an image (e.g., a point cloud, a combined point cloud, and / or the like) representing the objects included in a field of view of LiDAR sensors 202b. In some examples, the at least one data processing system associated with LiDAR sensor 202b generates an image that represents the boundaries of a physical object, the surfaces (e.g., the topology of the surfaces) of the physical object, and / or the like. In such an example, the image is used to determine the boundaries of physical objects in the field of view of LiDAR sensors 202b.

[0050]

[0045] Radio Detection and Ranging (radar) sensors 202c include at least one device configured to be in communication with communication device 2O2e, autonomous vehicle compute 202f, and / or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG, .3). Radar sensors 202c include a system, configured to transmit radio waves (either pulsed or continuously). The radio waves transmitted by radar sensors 202c include radio waves that are within a predetermined spectrum. In some implementations, during operation, radio waves transmitted by radar sensors 202c encounter a physical object and are reflected back to radar sensors 202c. In some implementations, the radio waves transmitted by radar sensors 202c are not reflected by some objects. In some implementations, at least one data processing system associated with radar sensors 202c generates signals representing the objects included in a field of view of radar sensors 202c, For example, the at least one data processing system associated with radar sensor 202c generates an image that represents the boundaries of a physical object, the surfaces (e.g., the topology of the surfaces) of the physical object, and / or the like. In some examples, the image is used to determine the boundaries of physical objects in the field of view of radar sensors 202c,

[0051]

[0046] Microphones 202d includes at least one device configured to be in communication with communication device 202e, autonomous vehicle compute 202f, and / or safety controller 202g via a bus (e.g., a bus that is the same as or similar to bus 302 of FIG. 3). Microphones 202d include one or more microphones (e.g., array microphones, external, microphones, and / or the like) that capture audio signals and generate data associated with (e.g., representing) the audio signals. In some examples, microphones 202d include transducer devices and / or like devices. In some implementations, one or more systems described herein can receive the data generated by microphones 202d and determine a position of an object relative to vehicle 200 (e.g., a distance and / or the like) based on the audio signals associated with the data.

[0052]

[0047] Communication device 202e includes at least one device configured to be in communication with cameras 202a, Li DAR sensors 202b, radar sensors 202c, microphones 202d, autonomous vehicle compute 202L safety controller 202g, and / or DBW (Drive-By-Wire) system 202h. For example, communication device 2O2e may include a device that is the same as or similar to communication interface 314 of FIG. 3. In some implementations, communication device 2O2e includes a vehicle-to-vehicle (V2V) communication device (e.g., a device that enables wireless communication of data between vehicles).

[0053]

[0048] Autonomous vehicle compute 2O2f include at least one device configured to be in communication with cameras 202a, LiDAR sensors 202b, radar sensors 202c, microphones 202d, communication device 202e, safety controller 202g, and / or DBW system 202h. In some examples, autonomous vehicle compute 202f includes a device such as a client device, a mobile device (e.g., a cellular telephone, a tablet, and / or the like), a server (e.g., a computing device including one or more central processing units, graphical processing units, and / or the like), and / or the like. In some implementations, autonomous vehicle compute 202f is the same as or similar to autonomous vehicle (AV) compute 400, described herein. Additionally, or alternatively, in some implementations, autonomous vehicle compute 202fis configured to be in communication with an autonomous vehicle system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system 114 of FIG. I), a fleet management system (e.g. , a fleet management system that is the same as or similar to fleet management system 116 of FIG . I k a V2.L device (e.g... a V2I device that is the same as or similar to V2J device 110 of FIG 1), and / or a V2I system (e. g., a V2I system that is the same as or similar to V21 system 1 18 of FIG. 1 ).

[0054]

[0049] Safety controller 202g includes at least one device configured to be in communication with cameras 202a, LiDAR sensors 202b, radar sensors 202c, microphones 202d, communication device 202e, autonomous vehicle computer 202£ and / or DBW system 2O2h. In some examples, safety controller 202g includes one or more controllers (electrical controllers, electromechanical controllers, and / or the like) that are configured to generate and / or transmit control signals to operate one or more devices of vehicle 200 (e.g., powertrain control system 204, steering control system 206, brake system 208, and / or the like). In some implementations, safety controller 202g is configured to generate control signals that take precedence over (e.g., overrides) control signals generated and / or transmitted by autonomous vehicle compute 202f.

[0055]

[0050] DBW system 202h includes at least one device configured to be in communication with communication device 202e and / or autonomous vehicle compute 202f. In some examples, DBW system 202b includes one or more controllers (e.g., electrical controllers, electromechanical controllers, and / or the like) that are configured to generate and / or transmit control signals to operate one or more devices of vehicle 200 (e.g., powertrain control system 204, steering control system 206, brake system 208, and / or the like). Additionally, or alternatively, the one or more control to of DBW sy stem 202h are configured to generate and / or transmit control signals to operate at least one different device (e.g., a turn signal, headlights, door locks, windshield wipers, and / or the like) of vehicle 200.

[0056]

[0051] Powertrain control system 204 includes at least one device configured to be in communication with DBW system 202h. In some examples, powertrain control system 204 includes at least one controller, actuator, and / or the like. In some implementations, powertrain control system 204 receives control signals from DBW system 202h, and powertrain control system 204 causes vehicle 200 to make longitudinal vehicle motion, such as start moving forward, stop moving forward, start moving backward, stop moving backward, accelerate in a direction, decelerate in a direction or to make lateral vehicle motion such as performing a left turn, performing a right turn, and / or the like. In an example, powertrain control system 204 causes the energy (e.g,, fuel, electricity, and / or the like) provided to a motor of the vehicle to increase, remain the same, or decrease, thereby causing at least one wheel of vehicle 200 to rotate or not rotate.

[0057]

[0052] Steering control system 206 includes at least one device configured to rotate one or more wheels of vehicle 200. In some examples, steering control system 206 includes at least one controller, actuator, and / or the like. In some implementations, steering control system 206 causes the front two wheels and / or the rear two wheels of vehicle 200 to rotate to the left or right to cause vehicle 200 to turn to the left or right. In other words, steering control system 206 causes activities necessary for the regulation of the y-axis component of vehicle motion.

[0058]

[0053] Brake system 208 includes at least one device configured to actuate one or more brakes to cause vehicle 200 to reduce speed and / or remain stationary. In some examples, brake system 208 includes at least one controller and / or actuator that is configured to cause one or more calipers associated with one or more wheels of vehicle 200 to close on a corresponding rotor of vehicle 200. Additionally., or alternatively, in some examples brake system 208 includes an automatic emergency braking (AEB) system, a regenerative braking system, and / or the like.

[0059]

[0054] In some implementations, vehicle 200 includes at least one platform sensor (not explicitly illustrated) that measures or infers properties of a state or a condition of vehicle 200. In some examples, vehicle 200 includes platform sensors such as a global positioning system (OPS) receiver, an inertial measurement unit (IMU ), a wheel speed sensor, a wheel brake pressure sensor, a wheel torque sensor, an engine torque sensor, a steering angle sensor, and / or the like. Although brake system 208 is illustrated to be located on the near side of vehicle 200 in FIG, 2, brake system 208 may be located anywhere in vehicle 200.

[0060]

[0055] Referring now to FIG. 3, illustrated is a schematic diagram of a device 300. As illustrated, device 300 includes processor 304, memory 306, storage component 308, input interface 310, output interface 312, communication interface 314, and bus 302. In some implementations, device 300 corresponds to at least one device of vehicles 102 (e.g., at least one device of a system of vehicles 102), and / or one or more devices of network 112 (e.g., one or more devices of a system of network 112). In some implementations, one or more devices of vehicles 102 (e.g., one or more devices of a system of vehicles 102), and / or one or more devices of network 112 (e.g., one or more devices of a sy stem of network 1 12) include at least one device 300 and / or at least one component of device 300. As shown in FIG. 3, device 300 includes bus 302, processor 304, memory 306, storage component 308, input interface 310, output interface 312, and communication interface 314.

[0061]

[0056] Bus 302 includes a component that pennits communication among the components of device 300. In some cases, processor 304 includes a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), an accelerated processing unit (APU), and / or the like), a microphone, a digital signal processor (DSP), and / or any processing component (e.g., a field- programmable gate array (FPGA), an application specific integrated circuit (ASIC), and / or the like) that can be programmed to perform at least one function. Memory 306 includes random access memory (RAM), read-only tnemory (ROM), and / or another type of dynamic and / or static storage device (e.g., flash memory, magnetic memory, optical memory, and / or the like) that stores data and / or instructions for use by processor 304.

[0062]

[0057] Storage component 308 stores data and / or software related to the operation and use of device 300. In some examples, storage component 308 includes a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, a solid state disk, and / or the like), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, a CD-ROM, RAM, PROM, EPROM, FLAS11-EPROM, NV»RAM, and / or another type of computer readable medium, along with a corresponding drive.

[0063]

[0058] Input interface 310 includes a component that permits device 300 to receive information, such as via user input (e.g., a touchscreen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, a camera, and / or the like). Additionally or alternatively, in some implementations input interface 310 includes a sensor that senses information (e.g., a global positioning system (GPS) receiver, an accelerometer, a gyroscope, an actuator, and / or the like). Output interface 312 includes a component that provides output information from device 300 (e.g., a display, a speaker, one or more light-emitting diodes (LEDs), and / or the like).

[0064]

[0059] In some implementations, communication interface 314 includes a transceiver-like component (e.g., a transceiver, a separate receiver and transmitter, and / or the like) that permits 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, communication interface 314 permits device 300 to receive information from another device and / or provide information to another device. In some examples, communication interface 314 includes an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (R.F) interface, a universal serial bus (USB) interface, a Wi-Fi* interface, a cellular network interface, and / or the like.

[0065]

[0060] In some implementations, device 300 performs one or more processes described herein. Device 300 performs these processes based on processor 304 executing software instructions stored by a computer-readable medium, such as memory 305 and / or 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 memory space located inside a single physical storage device or memory space spread across multiple physical storage devices.

[0066]

[0061] In some implementations, software instructions are read into memory 306 and / or storage component 308 from another computer-readable medium or from a]other device via communication interface 314. When executed, software instructions stored in memory 306 and / or storage component 308 cause processor 304 to perform one or more processes described herein. Additionally or alternatively, hardwired circuitry is used in place of or in combination with software instructions to perform one or more processes described herein. Thus, embodiments described herein are not limited to any specific combination of hardware circuitry and software unless explicitly stated otherwise.

[0067]

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

[0068]

[0063] In some implementations, device 300 is configured to execute software instructions that are either stored in memory 306 and / or in 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 memory 306 and / or in the memory of another device that, when executed by processor 304 and / or by a processor of another device (e.g., another device that is the same as or similar to device 300) cause device 300 (e.g., at least one component of device 300) to perform one or more processes described herein. In some implementations, a module is implemented in software, firmware, hardware, and / or the like.

[0069]

[0064] The number and arrangement of components illustrated in FIG. 3 are provided as an example. In some Implementations, device 300 can include additional components, fewer components, different components, or differently arranged components than those illustrated in FIG, 3, Additionally or alternatively, a set of components (e.g., one or more components) of device 300 can perform one or more functions described as being performed by another component or anot her set of com ponents of device 300.

[0070]

[0065] Referring now to FIG. 4, illustrated is an example block diagram of an autonomous vehicle compute 400 (sometimes referred to as an “AV stack”). As illustrated, autonomous vehicle compute 400 includes perception system 402 (sometimes referred to as a perception module), planning system 404 (sometimes referred to as a planning module), localization system 406 (sometimes referred to as a localization module), control system 408 (sometimes referred to as a control module), and database 410. In some implementations, perception system 402, planning system 404, localization system 406, control system 408, and database 410 are included and / or implemented in an autonomous navigation system of a vehicle (e.g., autonomous vehicle compute 202f of vehicle 200). Additionally, or alternatively, in some implementations, perception system 402, planning system 404, localization system 406, control system 408, and database 410 are included in one or more standalone systems (e.g., one or more systems that are the same as or similar to autonomous vehicle compute 400 and / or the like). In some examples, perception system 402, planning system 404, localization system 406, control system 408, and database 410 are included in one or mote standalone systems that are located in a vehicle and / or at least one remote system as described herein. In some implementations, any and / or all of die systems included in autonomous vehicle compute 400 are implemented in software (e.g., in software instructions stored in memory), computer hardware (e.g., by microprocessors, microcontrollers, application-specific integrated circuits (ASICs), Field Programmable Gate Arrays (FPGAs), and / or the like), or combinations of computer software and computer hardware. It will also be understood that, in some implementations, autonomous vehicle compute 400 is configured to be in communication with a remote system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system 114, a fleet management system 116 that is the same as or similar to fleet management system 1 16, a V2I system that is the same as or similar to V2I system 118, and / or the like).

[0071]

[0066] In some implementations, perception system 402 receives data associated with at least one physical object (e.g., data that is used by perception system 402 to detect the at least one physical object) in an environment and classifies the at least one physical object. In some examples, perception system 402 receives image data captured by at least one camera (e g., cameras 202a), the image associated with (e.g., representing) one or more physical objects within a field of view of the at least one camera. In such an example, perception system 402 classifies at least one physical object based on one or more groupings of physical objects (e.g., bicycles, vehicles, traffic signs, pedestrians, and / or the like). In some implementations, perception system 402 transmits data associated with the classification of the physical objects to planning system 404 based on perception system 402 classifying the physical objects.

[0072]

[0067] In some implementations, planning system 404 receives data associated with a destination and generates data associated with at least one route (e.g., routes 106 of FIG. I) along which a vehicle (e.g., vehicles 102) can travel along toward a destination. In some implementations, planning system 404 periodically or continuously receives data from perception system 402 (e.g., data associated with the classification of physical objects, described above) and planning system 404 updates the at least one trajectory or generates at least one different trajectory based on the data generated by perception system 402. In other words, planning system 404 may perform tactical function-related tasks that are required to operate vehicle 102 in on-road traffic. Tactical efforts involve maneuvering the vehicle in traffic during a trip, including but not limited to deciding whether and when to overtake another vehicle, change lanes, or selecting an appropriate speed, acceleration, deacceleration, etc. In some implementations, planning system 404 receives data associated with an updated position of a vehicle (e.g... vehicles 102) from localization system 406 and planning system 404 updates the at least one trajectory or generates at least one different trajectory based on the data generated by localization system 406.

[0073]

[0068] In some implementations, localization system 406 receives data associated with (e.g., representing) a location of a vehicle (e.g., vehicles 102) in an area. In some examples, localization system 406 receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensors 202b). In certain examples, localization system 406 receives data associated with at least one point cloud from multiple LiDAR sensors and localization system 406 generates a combined point cloud based on each of the point clouds. In these examples, localization system 406 compares the at least one point cloud or the combined point cloud to two- dimensional (2D) and / or a three-dimensional (3D) map of the area stored in database 410. Localization system 406 then determines the position of the vehicle in the area based on localization system 406 comparing the at least one point cloud or the combined point cloud to the map. In. some implementations, the map includes a combined point cloud of the area generated prior to navigation of the vehicle. In some implementations, maps include., without limitation, high-precision maps of the roadway geometric properties, maps describing road network connectivity properties, maps describing roadway physical properties (such as traffic speed, traffic volume, the number of vehicular and cyclist traffic lanes, lane width, lane traffic directions, or lane marker types and locations, or combinations thereof), and maps describing the spatial locations of road features such as crosswalks, traffic signs or other travel signals of various types. In some implementations, the map is generated in real-time based on the data received by the perception system.

[0074]

[0069] In another example, localization system 406 receives Global Navigation. Satellite System (GNSS) data generated by a global positioning system (GPS) receiver. In some examples, localization system 406 receives GNSS data associated with the location of the vehicle in the area and localization system 406 determines a latitude and longitude of the vehicle in the area. In such an example, localization system 406 determines the position of the vehicle in the area based on the latitude and longitude of the vehicle. In some implementations, localization system 406 generates data associated with the position of the vehicle. In some examples, localization system 406 generates data associated with the position of the vehicle based on localization system 406 determining the position of the vehicle. In such an example, the data associated with the position of the vehicle includes data associated with one or more semantic properties corresponding to the position of the vehicle.

[0075]

[0070] In some implementations, control system 408 receives data associated with at least one trajectory from planning system 404 and control system 408 controls operation of the vehicle. In some examples, control system 408 receives data associated with at least one trajectory from planning system 404 and control system 408 controls operation of the vehicle by generating and transmitting control signals to cause a powertrain control system (e.g., DBW system 202h, powertrain control system 204, and / or the like), a steering control system (e.g., steering control system 206), and / or a brake system (e.g., brake system 208) to operate. For example, control system 408 is configured to perform operational functions such as a lateral vehicle motion control or a longitudinal vehicle motion control. The lateral vehicle motion control causes activities necessary for the regulation of the y-axis component of vehicle motion. The longitudinal vehicle motion control causes activities necessary for the regulation of the x-axis component of vehicle motion. In an example, where a trajectory includes a left turn, control system 408 transmits a control signal to cause steering control system 206 to adjust a steering angle of vehicle 200, thereby causing vehicle 200 to turn left. Additionally, or alternatively, control system 408 generates and transmits control signals to cause other devices (e g. , headlights, turn signal, door locks, windshield wipers, and / or the like) of vehicle 200 to change states.

[0076]

[0071] In some implementations, perception system 402, planning system 404, localization 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, at least one transformer, andfor the like). In some examples, perception system 402, planning system 404, localization system 406, and / or control system 408 implement at least one machine learning model alone or in combination with one or more of the above-noted systems. In some examples, perception system 402, planning system 404. localization 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 an environment and / or the like).

[0077]

[0072] Database 410 stores data that is transmitted to, recei ved from, and / or updated by perception system 402, planning system 404, localization system 406, and / or control system 408. In some examples, database 410 includes a storage component (e.g., a storage component that is the same as or similar to storage component 308 of FIG. 3) that stores data and / or software related to the operation and uses at least one system of autonomous vehicle compute 400. In some implementations, 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 portion of a city, multiple portions of multiple cities, malttple cities, a county, a state, a State (e.g, ., a country), and / or the like). In such an example, a vehicle (e.g., a vehicle that is the same as or similar to vehicles 102 and / or vehicle 200) can drive along one or more drivable regions (e.g., single-lane roads, multi-lane roads, highways, back roads, off road trails, and / or the like) and cause at least one LiDAR sensor (e.g., a LiDAR sensor that is the same as or similar to LiDAR sensors 202b) to generate data associated with an image representing the objects included in a field of view of the at least one LiDAR sensor.

[0078]

[0073] In some implementations, database 410 can be implemented across a plurality of devices. In some examples, database 410 is included in a vehicle (e.g., a vehicle that is the same as or similar to vehicles 102 and / or vehicle 200), an autonomous vehicle system (e.g., an autonomous vehicle system that is the same as or similar to remote AV system 114, a fleet management system (e.g., a fleet management system that is the same as or similar to fleet management system 116 of FIG. 1 , a V2I system (e.g., a V2I system that is the same as or similar to V2I system 118 of FIG. 1 ) and / or the like.

[0079]

[0074] FIG. 5 is a diagram of .implementation 500 of an architecture for AV compute 502. In some implementations, camera images captured by cameras 202a and point clouds generated by LiDAR sensor 202b are input into perception system 402. The perception system 402 detects and classifies physical objects around the AV and outputs classified objects 504 to planning system 404. The planning system 404 generates trajectories 506 for the AV. The control system 408 controls the AV to drive along at least one of trajectories 506. The perception system 402 enables fusion of data captured by the sensors including cameras 202a and LiDAR sensors 202b. The cameras 202a and the LiDAR sensor 202b are synchronized with each other, where image capture of an area within a field of view of a camera coincides with the capture of LiDAR data from the area. In examples, when a LiDAR beam output by LiDAR sensors 202b scans an object in the environment, each camera 202a, of which the object is located in a respective field of view, exposes its sensor element to observe the same precise moment the LiDAR scan of the object occurs.

[0075] For example, the LID AR sensor 202b is a scanning LiDAR with a frequency 01'20 Flz and an angular range of three hundred sixty degrees. In this example, the LiDAR sensor 202b completes 20 rotations per second. Accordingly, the LiDAR sensor 202b completes one full scan or rotation every 1 / 20th of a second, resulting in multiple scans (in the present example, 20 scans ) of the surrounding environment per second. Each scan captures a set of data points that represent the reflectivity associated with multiple locations in the environment. At least one scan is used to generate a point cloud. To synchronize with the LiDAR sensor 202b, the cameras 202a capture images every 1 / 20th of a second (at a rate of 20 frames per second). Each image represents a snapshot of the environment corresponding to a LiDAR scan of respective camera field of view at a particular moment in time.

[0080]

[0076] FIG. 6 is a diagram of an example system 600 for synchronizing cameras with a LiDAR sensor. The system 600 triggers cameras 202a so that, the cameras are synchronized with a LiDAR sensor 202b (e.g., an overhead 360-degree LiDAR sensor), e.g., using a Generalized Precision Time Protocol (gPTP). In some implementations, the system 600 inchides a Field Programmable Gate Array (FPG A) 602 and a Central Processing Unit ( CPU Core) 604. The FPG A 602 and the CPU 604 can be implemented on System-on-a-Chip (SOC) (e.g., a Xilinx Multi-Processor System on a Chip (MPSOC)). In some implementations, the FPGA 602 and the CPU 604 are integrated with each other. For example, the CPU 604 includes one or more reprogrammable execution units programmed to execute different types of customized instructions. When the CPU 604 loads a program for execution a bitfile associated with the program is also loaded. The CPU 604 programs a reprogrammable execution unit with the bitfile so that the reprogrammable execution unit is capable of executing specialized instructions associated with the program. During execution, a dispatch unit dispatches the specialized instructions to the reprogrammable execution unit for execution. The results of other instructions, such as integer and floating point instructions, are available immediately to instructions executing on the reprogrammable execution unit since the reprogrammable execution unit shares the processor registers with the integer and floating point execution units.

[0081]

[0077] The FPGA 602 includes oscillator 606, a first network switch (e.g., an Ethernet switch) 608, synchronizing signal generator 610, and offset applicator 612. The oscillator 606 generates a hardware clock 614. The first network switch 608 is connected to a General Purpose Virtual Machine (GPVM) 616 through a second network switch 618. The first network switch 608 executing on the FPGA 602 is a gPTP slave node on the network, while the GPVM 616 serves as a gPTP Grand Master (GM) GPVM via the gPTP (IEEE 802,1) protocol. The GPVM 616 disciplines clocks of the systems on the network including the FPGA 602 and the LIDAR sensor 202b that is connected to the second network switch 618. The first network switch 608 converts the hardware dock 614 from the oscillator 606 to a pulse per second (PPS) signal 620 based on the gPTP time information. The PPS signal 620 has a frequency of 1 Hz and a period of 1 second.

[0082]

[0078] The synchronizing signal generator 610 generates a. synchronizing signal 622 having a particular frequency (e.g., 20 Hz) that is substantially the same as the rotating frequency (e.g., 20 Hz) of the LiDAR sensor 202b and the desired frame rate of the cameras 202a. Each pulse of the PPS signal 620 corresponds to a set of pulses (e.g., 20 pulses) of the synchronizing signal 622. In some implementations, the rising edge of the respective pulse of the PPS signal 620 (e.g., PPS signal pulse) is synchronized with a rising edge of the first pulse among each set of pulses (e.g., 1st pulse among 20 pulses) of the synchronizing signal 622 (e.g., synchronizing signal pulse).

[0083]

[0079] In some implementations, the hardware clock 614 from the oscillator 606 has a clock drift relative to the clock o f the GPVM 616. In case of clock drift, the hardware clock 614 is disciplined or corrected by gPTP protocol, and the rising edge of the PPS signal pulse (e.g., PPS 620) will shift relative to the synchronizing signal. This shift breaks the synchronization of the PPS signal pulse with the rising edge of the first pulse among a set of synchronizing signal pulses (e.g., 1st pulse among 20 pulses). For example, AV drives into a tunnel and may lose Global Positioning System (GPS) signal associated with GPVM 616 within the tunnel, which results in a large dock drift. In a clock drift, the PPS signal pulse arrives earlier than or later than the 20th pulse among a previous set of synchronizing signal pulses. Clock drift is shown in FIGS. 7 A and 7B.

[0084]

[0080] FIG. 7 A illustrates a. time sequence where a PPS signal pulse arrives earlier than the 20th pulse among a previous set of synchronizing signal pulses. For example, if the clock of the FPGA 602 is ticking slower than that of the gPTP grand master GPVM 606, GPVM 606 will correct the clock of the FPGA 602 by speeding i t up. As a consequence, a PPS signal pulse arrives early.

[0085]

[0081] Referring to FIG. 7A, two one-second intervals 701 and 702 are shown. The PPS signal 704A includes a single pulse occurring once per second, while the -synchronizing signal 706A generates approximately 20 pulses per second. As shown at the dashed line 708 marking the end of the first interval 701 and the beginning of the second interval 702, the next pulse of the PPS signal 704A occurs at the start of the second i nterval 702, prior to the rising edge of the 20thpulse of the synchronizing signal 706A. The difference 703 between the start of the second interval 702 and the rising edge of the 19* synchronizing signal pulse is less than the “deadzone” period (e.g., 45 ms). The 20thsynchronizing signal pulse in second interval 702 is skipped or dropped, so that the subsequent synchronizing signal pulses in the second interval 702 can align with the FPS signal 704A.

[0086]

[0082] FIG. 7B Illustrates a time sequence where a PPS signal pulse arrives more than 50ms after the 20 th pulse among a previous set of synchronizing signal pulses. If the PPS signal pulse arrives more than 50ms, 21.s;synchronizing signal pulse is generated and the IT synchronizing signal pulse in the second interval 712 is skipped. For example, if the clock of the FPGA 602 is ticking faster than that of the gPTP grand master GPVM 606, GPVM 606 will correct the clock of the FPGA 602 by slowing it down. As a consequence, a PPS signal pulse arrives late.

[0087]

[0083] Referring to FIG. 7B, two one-second intervals 711 and 712 are shown. The PPS signal 704B includes a single pulse occurring once per second, while the synchronizing signal 706B generates approximately 20 pulses per second. As shown at the dashed line 718 marking the real end of the first interval 711 and the beginning of the second interval 712, the next pulse of the PPS signal occurs at the start. of the second interval 712, after the rising edge of the 20!hpulse of the synchronizing signal 706B. While the difference 705 between the start of the second interval 712 and the rising edge of the 20thsynchronizing signal pulse is more than the “deadzone” period (e.g., 45 ms), the second interval 712 starts later than a rising edge of a new 21wsynchronizing signal pulse and the difference between the start of the 712 interval and the rising edge of the 21stsignal is less than the “deadzone” period. Thus, the 2sdsynchronizing signal pulse within the interval 712 is aligned with the PPS signal 704B.

[0088]

[0084] Referring again to Figure 6, to “smooth out” the temporary break such that the rising edge of the PPS signal occurs simultaneously with the rising edge of the first pulse of the synchronizing signal, the offset applicator 612 applies a time offset to the synchronizing signal 622 and generates a triggering signal (Finggw) 624 to trigger image capture by the cameras 202a. The triggering signal 624 results from a combination of the synchronizing signal 622 and. the time offset. The time offset, is a fixed time period (e.g., 70 milliseconds, 100 milliseconds, etc.), depending on camera position on a vehicle and vehicle size. Each camera corresponds to a time offset based on its position on a vehicle. To synchronize with a LiDAR sensor, each camera is triggered by the triggering signal 624 at its corresponding time offset with respect to a reference time point (e.g., a rising edge of a PPS signal pulse or the top of a second). For example, a LiDAR sensor is at 0 degrees at the rising edge of a second, and a front left camera is triggered when LiDAR sensor rotates io 20 degrees. The LiDAR sensor rotates at 20Hz, the time offset from the rising edge of the second is 20 / 360 x 50ms = 2.78 ms. The front left camera is triggered at 2.78ms with respect to the rising edge of the second. In some implementations, different, vehicle sizes may result in different camera positions, which leads to different time offsets. For example, the front left camera on a vehicle having a larger dimension may not be triggered at 20 degrees rotated by the LiDAR sensor. The time offset can be obtained when calibrating cameras 202a. The triggering signal 624 is transmitted to cameras 202a to trigger image capture by respective cameras 202a, so that the cameras 202a are synchronized with the LiDAR sensor 202b.

[0089]

[0085] The CPU 604 includes software stack 626 for gPTP, network interface 628, and offset, configuration unit 630. The software stack 626 for gPTP implements and manages the functionality of gPTP in a networked system. The network interface 628 provides the physical and data link layer functionality that establishes and. maintains network connections. The offset configuration unit 630 controls the offset applicator 612 and causes the offset applicator 612 to apply a time offset to the synchronizing signal 622 when there is a break in synchronization.

[0090]

[0086] The point cloud output from the LiDAR sensor 202b is transmitted to a LiDAR perception system 632, and the images captured by the cameras 202a are transmitted to a vision perception system 634. The LiDAR perception system 632 and the vision perception system 634 are a part of perception system 402 as shown in FIGS. 4 and 5.

[0091]

[0087] In some ini piementations, the synchronizing signal generator 610 generates the synchronizing signal 622 using a state machine. FIG. 8 illustrates a state machine 800 of the synchronizing signal generator 610. The state machine 800 includes four states: “idle” state 802, “deferred” state 804, “waiting” state 806, and “triggered” state 808. Referring to FIGS. 6 and 8, in the beginning, a synchronizing signal generator 610 is in an idle state 802, and a camera trigger parameter (cam trigger) is set equal to zero (e.g., cam trigger:::O), which indicates that no synchronizing signal 622 is generated in the idle state 802. When a PPS signal pulse arrives, a PPS incoming parameter (ppsjn) is set equal to one (e.g., ppsj.n::::l), and the state of the synchronizing signal generator 610 is switched from idle state 802 into triggered state 808. In the triggered state 808, the camera trigger parameter is set equal to one (e.g., cam trigger::::I), indicating that a synchronizing signal 622 is generated in the triggered state 808 in order to trigger data capture by a a r reessppeeccttiivvee ccaammeerraa.. A Addddiititioonnaallllyy,, a ticks since trigger parameter (ticks, since trigger) is set equal to zero (e.g., ticks since trigger - 0), indicating initialization of the ticks since trigger parameter upon generation of the synchronizing signal 622. After 1. tick (e.g., 10 nanoseconds), the state of the synchronizing signal generator 610 is switched from the triggered state 808 into a waiting state 806. In some implementations, the ticks since trigger parameter is updated every .1 tick (e.g., 10 nanoseconds), because the state machine 800 is evaluated by the FPGA 602 every 1. tick.

[0092] When (i) the PPS incoming parameter is set equal to one (e.g., pps in ™ 1) and the ticks_since_trigger parameter is greater than or equal to a predetermined time period or (ii) the ticks_since„trigger parameter is greater than or equal to live million ticks (e.g. , ticksjsince__trigger >™ 5,000,000 ticks or 50 milliseconds), the state of the synchronizing signal generator 610 is switched from the waiting state 806 into the triggered state 808. With respect to (i), a PPS incoming parameter set equal to one (e.g., pps_i.fi ~ 1 ) indicates arrival of a PPS signal pulse. Additionally, the predetermined time period compared to the ticks, since trigger is a deadzone, where the ticks_since_trigger greater than or equal to the deadzone indicates that the time since generatton of the most recent synchronizing signal 622 is more than the predetermined time period (e.g., deadzone). The deadzone refers to a predetermined minimum amount of time (for example, 25 milliseconds) between any two synchronizing signals 622, subject to the exposure time of cameras 202a and processing capabilities of the downstream systems (e.g., perception system 402, planning system 404, localization system 406, control system 408 of FIG. 4).

[0093]

[0089] When the PPS incoming parameter is set equal to one (e.g., pps in - 1) indicating the arrival of a PPS signal pulse, and the ticks since trigger parameter is less than the predetermined time period (e.g., “ticks ...since trigger” < deadzone) indicating the time since the generation of the most recent synchronizing signal 622 is less than a predetermined time period (e.g., deadzone), the state of the synchronizing signal generator 610 is switched from the waiting state 806 into a deferred state 804. In the deferred state 804, the camera trigger parameter is set equal to zero (e.g., camjrigger - 0), indicating that no synchronizing signal 622 is generated in the deferred state 804, while the ticks_since_higger parameter is set equal to zero (e.g., ticks^sincejrigger:::0), indicating initialization of the ticks_since_trigger parameter when switching to the deferred state 804. After 1 tick (e.g., 10 nanoseconds), the state of the synchronizing signal generator 610 is switched from the deferred state 804 back into the waiting state 806.

[0090] FIG. 9 illustrates an example flow chart of a process 900 for synchronizing cameras with a LiDAR sensor. In some implementations, process 900 is implemented (e.g... completely, partially., and / or the like) using at least one processor (e.g., processor 304 of FIG. 3) or a programmable circuit (e.g., FPGA 602 of FIG. 6, Complex Programmable Logic Device (CPLD), Programmable Array Logic (PAL), Programmable Logic Array (PLA), Complex Programmable Logic Array (CPLA), Antifuse-based Programmable Circuit, etc.).

[0094]

[0091] At block 902, the programmable circuit receives gPTP information for synchronizing the programmable circuit with a gPTP GM. The GPVM 616 serves as a gPTP GM and is connected to the world time source.

[0095]

[0092] At block 904, the programmable circuit generates a PPS signal having a period of one second based on the gPTP time information. The network switch 608 converts the hard ware dock 614 to a PPS signal 620. The PPS signal 620 has a frequency of I Hz and a period of 1 second.

[0096]

[0093] At block 906, the programmable circuit generates a synchronizing signal having a period less than the period of the PPS signal. Each PPS signal pulse corresponds to a set of synchronizing signal pulses. The synchronizing signal generator 610 generates a synchronizing signal 622 having a particular frequency (e.g., 20 Hz) that is substantially the same as the rotating frequency (e.g., 20 Hz) of the LiDAR. sensor 202b and the desired frame rate of the cameras 202a. For example, the synchronizing signal 622 has a frequency of 20 Hz and a period of 50 milliseconds. Each PPS signal pulse corresponds to a set of synchronizing signal pulses... For example, each PPS signal pulse corresponds to 20 synchronizing signal pulses.

[0097]

[0094] At block 908, the programmable circuit generates a triggering signal based on the PPS signal. The period of the triggering signal is the same as the period of the synchronizing signal. When the oscillator 606 has a clock drift relative to the clock of the GPVM 616, the offset applicator 612 applies a time offset to the synchronizing signal 622 and generates triggering signal 624. When the oscillator 606 does not have a clock drift relative to the clock of the GPVM 616, the time offset is zero, and the triggering signal 624 is the same as the synchronizing signal 622.

[0098]

[0095] At block 910, the programmable circuit sends the triggering signal for triggering the one or more cameras to capture one or more images. The triggering signal 624 is used for triggering the one or more cameras 202a that synchronize with the LiDAR sensor 202b. The cameras 202a are triggered at the same time as the LiDAR sensor 202b starts a full scan.

[0096] FIG. 10 illustrates example time sequences of gPTP, a PPS signal, a synchronizing signal, a triggering signal, and a LiDAR triggering signal. As shown in FIG. 10, a LiDAR sensor starts to scan at 0 az. (0 degree or 0 azimuth). In some implementations, the LiDAR sensor starts to scan at a rising edge of a PPS signal pulse (the top of the second). The scanning frequency of the LiDAR sensor is 20 Hz. A camera is triggered at its corresponding time offset (indicated by 1002) with respect to a rising edge of a PPS signal pulse (indicated by 1004). The time offset corresponds to the center of the field of view of this camera.

[0099]

[0097] In some implementations, the example process 900 shown in FIG, 9 can be modified or reconfigured to include additional, fewer, or different operations, which can be performed in the order shown or in a different order. In some instances, one or more of the operations can be repeated or iterated, for example, until a terminating condition is reached. In some implementations, one or more of the individual operations shown in FIG. 9 can be executed as multiple separate operations, or one or more subsets of the operations shown in FIG. 9 can be combined and executed as a single operation,

[0100]

[0098] According to some non-limiting embodiments or examples, provided is a method, including: receiving, by a programmable circuit, Generalized Precision Time Protocol (gPTP) information for synchronizing the programmable circuit with a gPTP Grand Master (GM);- generating, by the programmable circuit, a pulse per second (PPS) signal having a period of one second based on the gPTP time information; generating, by the programmable circuit, a synchronizing signal having a period less than the period of the PPS signal, wherein respective PPS signal pulses correspond to a set of synchronizing signal pulses: generating, by the programmable circuit, a triggering signal based on the PPS signal, wherein a period of the triggering signal is the same as the period of the synchronizing signal; and sending, by the programmable circuit and to one or more cameras of an autonomous vehicle (AV), the triggering signal for triggering the one or more cameras to capture one or more images.

[0101]

[0099] According to some non-limiting embodiments or examples, provided is a system, including at least one programmable circuit, configured to perform the above method.

[0102]

[0100] According to some non-limiting embodiments or examples, provided is a non-transitory, computer-readable storage medium having instructions stored thereon, which when executed by at least one programmable circuit; cause the at least one programmable circuit to perform the above method. Clause It A method, including: receiving, by a programmable circuit, Generalized Precisian Time Protocol (gPTP) information for synchronizing the programmable circuit with a gPTP Grand Master (GM); generating, by the programmable circuit, a pulse per second (PPS) signal having a period of one second based on the gPTP time information; generating, by the programmable circuit, a synchronizing signal having a period less than the period of the PPS signal, wherein respective PPS signal pulses correspond to a set of synchronizing signal pulses; generating, by the programmable circuit, a triggering signal based on the PPS signal, wherein a period of the triggering signal is the same as the period of the synchronizing signal; and sending, by the programmable circuit and to one or more cameras of an autonomous vehicle (AV), the triggering signal for triggering the one or more cameras to capture one or more images.

[0103]

[0102] Clause 2: The method of clause 1, wherein the programmable circuit is a field programmable gate array (PPG A).

[0104]

[0103] Clause 3: The method of clause 1 , wherein a rising edge of respective triggering signal pulses is synchronized with a start of each sweep of one or more LiDAR sensors of the AV.

[0105]

[0104] Clause 4: The method of clause 3, wherein the period of the PPS signal is X times greater than the period of the synchronizing signal, wherein X is the number of revolutions of the one or more LiDAR sensors per second, wherein the respective PPS signal pulses correspond to X synchronizing signal pulses.

[0106]

[0105] Clause 5 : The method of clause 4, wherein the period of the PPS signal is at least 20 times greater than the period of the synchronizing signal, and the respective PPS signal pulses correspond to at least 20 synchronizing signal pulses.

[0107]

[0106] Clause 6: The method of clause 1 , wherein a rising edge of the respective PPS signal pulses is synchronized with a rising edge of a first pulse among the set of synchronizing signal pulses.

[0108]

[0107] Clause 7; I'he method of clause 1, wherein the synchronizing signal is generated using a state machine including an idle state, a deferred state, a waiting state, and a triggered state.

[0109]

[0108] Clause 8: The method of clause 7, wherein the idle state is switched to the triggered state when the PPS signal pulse arrives.

[0110]

[0109] Clause 9: The method erf clause 8, wherein the triggered state is switched to the waiting state after a first predetermined time period.

[0111]

[0110] Clause 10: The method of clause 9, wherein the first predetermined time period is at least 10 nanoseconds.

[0111] Clause 1 1 : The method of clause 9, wherein the waiting state is switched to the triggered state when (i) the PPS signal pulse arrives and the time since generation of a most recent synchronizing signal is more than or equal to a second predetermined time period, or (i.i) the lime since generation of the most recent synchronizing signal is more than or equal to a third predetermined time period, wherein the second predetermined time period is less than the third predetermined time period.

[0112]

[0112] Clause 12: The method of clause 1 1 , wherein the second predetermined time period is at least 25 milliseconds, and the third predetermined time period is at least 50 milliseconds.

[0113]

[0113] Clause 13: The method of clause 9, wherein the waiting state is switched to the deferred state when the PPS signal pulse arrives and the time since generation of a most recent synchronizing signal is less than a second predetermined time period.

[0114]

[0114] Clause 14: The method of clause 13, wherein the deferred state is switched to the waiting state after the first predetermined period of time.

[0115] [U5j Clause 15: The method of clause 13, wherein the first predetermined period of time is at least 10 nanoseconds, and the second predetermined time period is at least 25 milliseconds.

[0116]

[0116] Clause 16: The method of clause 1, further including applying a time offset to the synchronizing signal to generate the triggering signal, wherein the time offset is a fixed period of time.

[0117]

[0117] Clause 17: A system including: at least one programmable circuit, configured to perform operations, including: receiving, by a programmable circuit; Generalized Precision Time Protocol (gPTP) information for synchronizing the programmable circuit with a gPTP Grand Master (GM); generating, by the programmable circuit, a pulse per second (PPS) signal having a period of one second based on the gPTP time information; generating, by the programmable circuit, a synchronizing signal, having a period less than the period of the PPS signal, wherein respective PPS signal pulses correspond to a set of synchronizing signal pulses; generating, by the programmable circuit, a triggering signal based on the PPS signal, wherein a period of the triggering signal is the same as the period of the synchronizing signal; and sending, by the programmable circuit and to one or more cameras of an autonomous vehicle ( AV), the triggering signal for triggering the one or more cameras to capture one or more images.

[0118]

[0118] Clause 18: A non-transitory, computer-readable storage medium having instructions stored thereon, that when executed by a programmable circuit, cause the programmable circuit to perform operations, including: receiving, by a programmable circuit, Generalized Precision Time Protocol (gPTP) information for synchronizing the programmable circuit with a gPTP Grand Master (GM); generating, by the programmable circuit, a pulse per second (PPS) signal having a. period of one second based on the gPTP time information; generating, by the programmable circuit, a synchronizing signal having a period less than the period of the PPS signal, wherein respective PPS signal pulses correspond to a set of synchronizing signal pulses; generating, by the programmable circuit, a triggering signal based on the PPS signal, wherein a period of the triggering signal is the same as the period of the synchronizing signal; and sending, by the programmable circuit and to one or more cameras of an autonomous vehicle (AV), the triggering signal for triggering the one or more cameras to capture one or more images.

[0119]

[0119] In the foregoing description, embodiments of the invention have been described with reference to numerous specific details that may vary from implementation to implementation. The description and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the set of c laims that issue from this application, in the specific form in which such claims issue, including any subsequent correction. Any definitions expressly set forth herein for terms contained in such claims shall govern the meaning of such terms as used in the claims. In addition, the term “further comprising’'’ is used in the foregoing description or following claims; what follows this phrase can be an additional step or entity, or a sub-step / sub-entity of a previously-recited step or entity.

Claims

WHAT IS CLAIMED IS:1 . A method, comprising: receiving, by a programmable circuit, Generalized Precision Time Protocol (gPTP) information for synchronizing the programmable circuit with a gPTP Grand Master (GM); generating, by the programmable circuit, a pulse per second (PPS) signal having a period of one second based on the gPTP time information; generating, by the programmable circuit, a synchronizing signal having a period less than the period of the PPS signal, wherein respective PPS signal pulses correspond to a set of synchronizing signal pulses; generating, by the programmable circuit, a triggering signal based on the PPS signal, wherein a period of the triggering signal is the same as the period of the synchronizing signal; and sending, by the programmable circuit and to one or more cameras of an autonomous vehicle (AV), the triggering signal for triggering the one or more cameras to capture one or more images.

2. The method of claim 1, wherein the programmable circuit is a field programmable gate array (FPGA).

3. The method of claim 1, wherein a rising edge of respective triggering signal pulses is synchronized with a start of each sweep of one or more LiDAR sensors of the AV.

4. The method of clai m 3, wherein the period of the PPS signal is X times greater than the period of the synchronizing signal, wherein X is the number of revolutions of the one or more LiDAR sensors per second, wherein the respective PPS signal pulses correspond to X synchronizing signal pulses.

5. The method of claim 4, wherein the period of the PPS signal is at least 20 times greater than the period of the synchronizing signal, and the respective PPS signal pulses correspond to at least 20 synchronizing signal pulses.

6. The method of claim 1 , wherein a rising edge of the respective PPS signal pulses is synchronized with a rising edge of a first pulse among the set of synchronizing signal pulses .

7. The method of claim 1, wherein the synchronizing signal is generated using a state machine including an idle state, a deferred state, a waiting state, and a triggered state.

8. The method of claim 7, wherein the idle state is switched to the triggered state when the PPS signal pulse arrives.

9. The method of claim 8, wherein the triggered state is switched to the waiting state after a first predetermined time period.

10. The method of claim 9, wherein the first predetennined time period is at least 10 nanoseconds.

11. The method of claim 9, wherein the waiting state is switched to the triggered state when (i) the PPS signal pulse arrives and the time since generation of a most recent synchronizing signal is more than or equal to a second predetermined time period, or (if) the time since generation of the most recent synchronizing signal is more than or equal to a third predetermined time period, wherein the second predetermined time period is less than the third predetennined time period.

12. I'he method of claim 1 1 , wherein the second predetermined time period is at least 25 milliseconds, and the third predetermined time period is at least 50 milliseconds.

13. The method of claim 9, wherein the waiting state is switched to the deferred state when the PPS signal pulse arrives and the time since generation of a most recent synchronizing signal is less than a second predetennined time period.

14. The method of claim 13, wherein the deferred stale is switched to the waiting state after the first predetermined period of time.

15. The method of claim 13, wherein the first predetermined period of time is at least 10 nanoseconds, and the second predetermined time period is at least 25 milliseconds.

16. The method of claim L further comprising applying a time offset to the synchronizing signal to generate the triggering signal, wherein the time offset is a fixed period of time.1.

7. A. system. comprising: at least one programmable circuit, configured to perform operations, comprising: receiving, by a programmable circuit. Generalized Precision Time Protocol (gPTP) information for synchronizing the programmable circuit with a gPTP Grand Master (GM); generating, by the programmable circuit, a pulse per second (PPS) signal having a period of one second based on the gPTP time information; generating, by the programmable circuit, a synchronizing signal having a period less than the period of the PPS signal, wherein respective PPS signal pulses correspond to a sei of synchronizing signal pulses: generating, by the programmable circuit, a triggering signal based on the PPS signal, wherein a period of the triggering signal is the same as the period of the synchronizing signal; and sending, by the programmable circuit and to one or more cameras of an autonomous vehicle (AV), the triggering signal for triggering the one or more cameras to capture one or more images.

18. A non- transitory, computer-readable storage medium having instructions stored thereon, that when executed by a programmable circuit, cause the programmable circuit to perform operations, comprising: receiving, by a programmable circuit. Generalized Precision Time Protocol (gPTP) information for synchronizing the programmable circuit with a gPTP Grand Master (GM);generating, by the programmable circuit, a pulse per second (PPS) signal having a period of one second based on the gPTP time information: generating, by the programmable circuit; a synchronizing signal having a period less than the period of the PPS signal, wherein respective PPS signal pulses correspond to a set of synchronizing signal pulses: generating, by the programmable circuit, a triggering signal based on the PPS signal, wherein a period of the triggering signal is the same as the period of the synchronizing signal; and sending, by the programmable circuit and to one or more cameras of an autonomous vehicle (AV), the triggering signal for triggering the one or more cameras to capture one or more images.

Images