Adaptive quantization of video pipelines in automotive systems and applications

Abstract

The invention relates to adaptive quantization of video pipelines in automotive systems and applications. In various examples, a visual difference between video data and an encoded version of the video data is used to determine an update to a quantization parameter (QP) used to encode the video data to store or upload video clips corresponding to important events associated with a machine. The video data may correspond to an image applied to a machine learning model to perform a control operation on the machine. Metrics may be used to quantify visual discrepancies. To assess visual discrepancies, samples of the video data and encoded video data may be determined and analyzed, rather than the entire image or frame. To selectively enable updating of the QP, the system may detect that a deviation between a bit rate corresponding to the encoded data and a reference bit rate has exceeded a threshold.

Description

Background Art

[0001] Advanced driver assistance systems (ADAS) and autonomous vehicle (AV) systems can include high-definition cameras located around the vehicle to provide a comprehensive view of the vehicle's surroundings. Images from these cameras are integral to a variety of perception-related functions, including lane keeping assist, object detection, and trajectory planning. These images can capture notable events (such as accidents or near misses), and persistent storage of video from these cameras allows for review of events to improve the safety of ADAS and AV systems. However, managing the video generated using multiple high-definition cameras can present significant challenges, for example, due to storage size and bandwidth utilization.

[0002] Even if the video is encoded and video clips are selectively stored on the vehicle or uploaded to a server to capture important events, the challenges of managing video for ADAS and AV systems can persist. For example, conventional systems encode video using an encoder in fixed quantization parameter mode, which uses a fixed compression ratio. However, the bitrate of the resulting encoded video file can more than double when driving at night compared to daytime conditions. Other approaches may use encoders in constant or variable bitrate modes. However, these modes may not ensure that the encoded video file has sufficient fidelity for review. Summary of the invention

[0003] Embodiments of the present disclosure relate to adaptive quantization for a video or image processing pipeline. Systems and methods are disclosed that can be used to update quantization parameters used to encode video data obtained using one or more sensors of a machine. The disclosed methods can be used to reduce the bit rate of encoded video capturing the environment of a vehicle while maintaining the visual fidelity of video clips that are stored or uploaded for review of important events.

[0004] Compared to conventional methods, the disclosed method can determine one or more updates to one or more quantization parameters (QP) used to encode video data based at least on the visual difference between the video data obtained using one or more sensors of the machine and the encoded version of the video data to store or upload video clips corresponding to important events associated with the machine. The video data can correspond to an image applied to one or more machine learning models (MLMs) to perform one or more control operations on the machine. In at least one embodiment, a metric is used to quantize the visual difference between the video data obtained using one or more sensors of the machine and the encoded version of the video data. To evaluate the visual difference, the disclosed method can sample the video data and the encoded video data and analyze the samples instead of the entire image or frame. In at least one embodiment, to selectively enable the update of the QP, it is detected that the deviation between the bit rate corresponding to the encoded data and the reference bit rate has exceeded a threshold. At least based on the detection of the deviation, the visual difference between the video data and the encoded version of the video data can be analyzed, for example, to calculate one or more values ​​of the metric and the QP can be updated accordingly. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] The present system and method based on adaptive quantization of automotive pipelines are described in detail below with reference to the accompanying drawings, wherein:

[0006] Figure 1 is an example of a process for generating a video clip using video data obtained using one or more sensors of a machine according to some embodiments of the present disclosure;

[0007] Figure 2 is an example of a process for determining updates to one or more quantization parameters to generate a video clip using video data obtained using one or more sensors of a machine according to some embodiments of the present disclosure;

[0008] Figure 3 is a diagram illustrating high watermarks and low watermarks that may be used to selectively determine updates to one or more quantization parameters according to an embodiment of the present disclosure;

[0009] Figure 4 is a diagram illustrating regions of a video frame that may be sampled to determine one or more values ​​of one or more metrics according to an embodiment of the present disclosure;

[0010] Figure 5 is a flow chart illustrating a method for determining a video clip using at least one value of at least one quantization parameter determined based at least on detecting a deviation in a bit rate corresponding to an encoded video according to an embodiment of the present disclosure;

[0011] Figure 6is a flow chart illustrating a method for determining a video clip using updates to one or more quantization parameters based at least on one or more values ​​of one or more metrics according to an embodiment of the present disclosure;

[0012] Fig. 7A is an illustration of an example autonomous vehicle according to some embodiments of the present disclosure;

[0013] Figure 7B According to some embodiments of the present disclosure Fig. 7A Examples of autonomous vehicle camera positions and fields of view;

[0014] Figure 7C According to some embodiments of the present disclosure Fig. 7A a block diagram of an example system architecture for an example autonomous vehicle;

[0015] Fig.7D is a cloud-based server and Fig. 7A System diagram of communication between example autonomous vehicles;

[0016] Figure 8 is a block diagram of an example computing device suitable for implementing some embodiments of the present disclosure; and

[0017] Fig. 9 is a block diagram of an example data center suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0018] Systems and methods are disclosed that relate to adaptive quantization of video or image processing in automotive or robotic systems and applications. More specifically, the disclosure relates to techniques and methods for updating quantization parameters used to encode video data obtained using one or more sensors of a machine. The disclosed methods can be used to reduce the bit rate of encoded video capturing the environment of a vehicle (or other machine, such as a robot, a vessel, an aircraft, etc.) while maintaining the visual fidelity of video clips that are stored or uploaded for review of important events.

[0019] The disclosed method may determine one or more updates to one or more quantization parameters (QPs) used to encode video data based at least on visual differences between video data obtained using one or more sensors of the machine and an encoded version of the video data to store or upload video clips corresponding to significant events associated with the machine. The video data may correspond to images applied to one or more machine learning models (MLMs) to perform one or more control operations on the machine. Significant events may be captured using at least in part the video data, and video clips may be generated based at least on one or more indications or determinations of one or more significant events.

[0020] In at least one embodiment, a metric is used to quantize the visual difference between video data obtained using one or more sensors of the machine and an encoded version of the video data. The QP can be adjusted to reduce or minimize the difference between a reference value of the metric and a calculated value of the metric. The QP can include one or more of a B-frame quantization parameter, an I-frame quantization parameter, or a P-frame quantization parameter. One or more values ​​of any combination of these QPs can be set individually or in combination.

[0021] To evaluate the visual difference between video data obtained using one or more sensors of a machine and an encoded version of the video data, the disclosed method can sample the video data and the encoded video data and analyze these samples rather than the entire image or frame. A non-random spatial distribution algorithm, such as but not limited to Poisson disk sampling, can be used to define these samples. Using this method can provide well-distributed and consistently spaced samples, which can reduce the risk of missing key details or features and provide consistency and predictability for sampling.

[0022] A further aspect of the present disclosure provides for selectively enabling or disabling updates to the QP. In at least one embodiment, a deviation between a bit rate corresponding to the encoded data and a reference bit rate is detected to have exceeded a threshold. Based at least on detecting the deviation, the visual difference between the video data and the encoded version of the video data can be analyzed, for example, to calculate one or more values ​​of a metric. Thus, the frequency of analysis for updating the QP can be reduced to save computing resources. In at least one embodiment, a high watermark and a low watermark are used to detect the deviation. Updates or analysis can be enabled based at least on determining that the bit rate has exceeded the low watermark and / or the high watermark 304. Further, updates or analysis can be disabled based at least on determining that the bit rate has entered or is between the low watermark and the high watermark.

[0023] The systems and methods described herein may be used for a variety of purposes, including, by way of example and not limitation, for machine control, machine motion, machine driving, synthetic data generation, model training, perception, augmented reality, virtual reality, mixed reality, robotics, safety and surveillance, autonomous or semi-autonomous machine applications, deep learning, environment simulation, object or actor simulation and / or digital twins, data center processing, conversational AI, light transport simulation (e.g., ray tracing, path tracing, etc.), collaborative content creation for 3D assets, cloud computing, generative AI, (big) language models, and / or any other suitable application.

[0024] The disclosed embodiments may be included in a variety of different systems, such as automotive systems (e.g., control systems for autonomous or semi-autonomous machines, perception systems for autonomous or semi-autonomous machines), systems implemented using robots, aerial systems, medical systems, boating systems, smart area monitoring systems, systems for performing deep learning operations, systems for performing simulation operations, systems for performing digital twin operations, systems implemented using edge devices, systems including one or more virtual machines (VMs), systems for performing synthetic data generation operations, systems implemented at least in part in a data center, systems for performing conversational AI operations, systems for performing light transport simulations, systems for performing collaborative content creation of 3D assets, systems implemented at least in part using cloud computing resources, systems for performing generative AI operations, systems implementing operations using a large language model (LLM) or for performing operations, and / or other types of systems.

[0025] Reference Figure 1 , Figure 1 is an example of a process 100 for generating a video clip using video data obtained by one or more sensors of a machine according to some embodiments of the present disclosure. It should be understood that this and other arrangements described herein are set forth by way of example only. Other arrangements and elements (e.g., machines, interfaces, functions, orders, functional groupings, etc.) may be used in addition to or in place of the arrangements and elements shown, and some elements may be omitted entirely. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in combination with other components and implemented in any suitable combination and location. The various functions described herein as being performed by an entity may be performed by hardware, firmware, and / or software. For example, the various functions may be performed by a processor executing instructions stored in a memory. In some embodiments, the systems, methods, and processes described herein may be implemented using hardware, firmware, and / or software. Figure 7A-7D 700 vehicles, Figure 8 The example computing device 800 and / or Fig. 9 The example data center 900 may be implemented with similar components, features and / or functions.

[0026] Process 100 may be implemented using one or more video encoders 102, one or more quantization parameter (QP) managers 104, one or more machine learning models (MLMs) 106, one or more control components 108, or one or more servers 112, as well as additional or alternative components. Figure 1 As shown, the video encoder 102, QP manager 104, MLM 106, and control component 108 may be implemented at least in part on a machine such as Figure 7A-7D of vehicles 700.

[0027] As an overview, the sensor data 120 can be used to provide one or more inputs to the MLM 106, which can analyze the one or more inputs (e.g., images) to generate one or more outputs that the control component 108 uses to perform one or more control operations on the vehicle 700. The sensor data 120 can also provide video data for use by the video encoder 102 to generate encoded image data 124 (e.g., encoded video) using at least one value of one or more QPs. The video manager 110 can use the encoded image data 124 to generate one or more video clips, and can provide the one or more video clips to be stored in one or more files 122 on the vehicle 700 and / or uploaded to the server 112 via one or more networks 114. The QP manager 104 can use the encoded image data 124 and the image data corresponding to the sensor data 120 to determine whether to update one or more QPs and / or updated values ​​of one or more QPs. In at least one embodiment, process 100 operates repeatedly on a video stream corresponding to a stream of sensor data 120 obtained using one or more sensors of vehicle 700 , such as one or more cameras of vehicle 700 .

[0028] In one or more embodiments, the one or more sensors used to generate sensor data 120 may include at least one of one or more physical sensors in a physical environment or one or more virtual sensors in a simulated environment. For example, the one or more sensors may correspond to a physical version or a simulated version of a vehicle or machine 700, as described herein.

[0029] Sensor data 120 may include, but is not limited to, sensor data from any sensor of vehicle 700 (and / or other vehicles or objects, such as robotic devices, VR systems, AR systems, MR systems, etc., in some examples). For example, referring to Figure 7A-7C , the sensor data 120 may include data generated by or using, but not limited to, a global navigation satellite system (GNSS) sensor 758 (e.g., a global positioning system sensor, a differential GPS (DGPS), etc.), a RADAR sensor 760, an ultrasonic sensor 962, a LIDAR sensor 764, an inertial measurement unit (IMU) sensor 766 (e.g., an accelerometer, a gyroscope, a magnetic compass, a magnetometer, etc.), a microphone 796, a stereo camera 768, a wide-angle camera 770 (e.g., a fisheye camera), an infrared camera 772, a surround camera 774 (e.g., a 360-degree camera), a long-range and / or mid-range camera 798, a speed sensor 744 (e.g., for measuring the speed and / or travel distance of the vehicle 700), and / or other sensor types.

[0030] In some examples, the sensor data 120 may include sensor data generated using one or more forward-facing sensors, side-view sensors, and / or rear-view sensors. The sensor data 120 may be used to identify, detect, classify, and / or track the movement of objects around the vehicle 700 in the environment. In embodiments, any number of sensors may be used to combine multiple fields of view (e.g., Figure 7B The fields of view of the remote camera 798, the forward stereo camera 768, and / or the forward wide-angle camera 770) and / or the sensing field (e.g., of the LIDAR sensor 764, the RADAR sensor 760, etc.).

[0031] The sensor data 120 may include image data representing one or more images, image data representing a video (e.g., a snapshot of a video), data representing a sensing field of a sensor (e.g., a depth map of a LIDAR sensor, a value map of an ultrasonic sensor, etc.), and / or data representing measurements of a sensor. Where the sensor data 120 includes image data, any type of image data format may be used, such as, but not limited to, compressed images (e.g., Joint Photographic Experts Group (JPEG) or Luma / Chrominance (YUV) format), raw images (e.g., from Red-Transparent-Blue (RCCB), Red-Transparent-CC) or other types of image sensors and / or other formats. Furthermore, in some examples, the sensor data 120 may be used in the process 100 without any preprocessing (e.g., in a raw format or a captured format), while in other examples, the sensor data 120 may be subjected to preprocessing (e.g., noise balancing, demosaicing, scaling, cropping, enhancement, white balancing, tone curve adjustment, etc., such as using a sensor data preprocessor (not shown)). The sensor data 120 may refer to unprocessed sensor data, preprocessed sensor data, or a combination thereof.

[0032] The process 100 may include the MLM 106 receiving one or more inputs corresponding to the sensor data 120. The MLM 106 may use the one or more inputs to generate one or more outputs indicating one or more predictions made using the MLM 106. The one or more predictions may correspond to one or more tasks to facilitate performing one or more planning, control, or actuation operations on the vehicle 700. Non-limiting examples include, but are not limited to, object detection, lane detection, path or trajectory planning, semantic segmentation, optical flow analysis, traffic sign recognition, gesture recognition, anomaly detection, object tracking, pedestrian detection, vehicle classification, road surface condition assessment, lane change prediction, crosswalk detection, traffic light detection and recognition, speed limit recognition, road boundary or free space detection, obstacle avoidance, collision prediction, lane keeping assist, pedestrian behavior analysis, driver drowsiness detection, wrong way driving detection, weather condition assessment, parking space detection, road surface condition detection, bicycle detection and tracking, animal detection, traffic flow analysis, etc.

[0033] The process 100 may further include the control component 108 receiving one or more outputs from the MLM 106. The control component 108 may use the one or more outputs generated using the MLM 106 to perform any of a variety of control operations on the vehicle 700 (e.g., based at least on the predictions made using the MLM 106). Non-limiting examples include, but are not limited to, adaptive cruise control, lane keeping assist, lane departure warning, collision avoidance or warning, emergency braking, acceleration and deceleration control, adaptive headlight adjustment, traffic sign recognition and response, speed control and adjustment, automatic lane change, automatic lane keeping, obstacle avoidance and obstacle navigation, traffic merging and intersection management, pedestrian and cyclist detection and avoidance, automatic parking and docking, route planning and navigation, adaptive suspension and ride control, energy management, driver monitoring and assistance, emergency vehicle priority, traffic congestion relief, highway automated driving, automatic overtaking and merging, safe following distance maintenance, emergency lane clearing, hazardous road condition detection and response, target-specific control (e.g., turning to avoid a specific obstacle), etc.

[0034] The process 100 may also include the video encoder 102 receiving video data corresponding to the sensor data 120. The video encoder 102 may use the video data and one or more values ​​of one or more QPs (e.g., indicated by the QP update data 118) to generate encoded image data 124. The video encoder 102 may use any suitable method for implementing quantization to generate the encoded image data 124. For example, the video encoder 102 may generate compressed images as frames derived from a compressed video format, such as H.264 / Advanced Video Coding (AVC), H.265 / High Efficiency Video Coding (HEVC), VP8, VP9, ​​AV1, etc.

[0035] In at least one embodiment, one or more values ​​of one or more QPs may control and / or be used by the video encoder 102 to determine the size of the step size used by the video encoder 102 in quantization. Higher QP values ​​may correspond to larger step sizes, which may result in more details being discarded from the input video (e.g., YUV data) and higher compression. Conversely, lower QP values ​​may result in smaller step sizes, which may result in more details being included in the input video and less compression. For example, a higher QP value may allow a larger range of pixel values ​​to be mapped to the same quantization level, thereby reducing the amount of data encoded in the encoded image data 124. In at least one embodiment, the QP controls or defines the amount of compression for one or more blocks (e.g., each macroblock) in a frame.

[0036] In at least one embodiment, the one or more quantization parameters include one or more of a B-frame quantization parameter, an I-frame quantization parameter, or a P-frame quantization parameter. One or more values ​​of any combination of these QPs can be set individually or in combination. For example, a single value can control one or more of these QPs, or a corresponding value can be used to control each QP. Although QPs are described herein, various aspects of the present disclosure may be used to update or determine any encoding parameter or setting applied to a video encoder. Further, although video clips are described, in at least one embodiment, video data need not be used to generate or determine video clips. In at least one embodiment, the encoded video data is streamed (e.g., transmitted to a server 112 or elsewhere via a network 114) without having to generate or determine a video clip or file.

[0037] The video manager 110 may generate one or more video clips using the encoded image data 124, and may provide the one or more video clips to be stored in one or more files 122 on the vehicle 700 and / or uploaded to the server 112 via one or more networks 114. In at least one embodiment, the video manager 110 may selectively generate, store, and / or upload video clips based at least on receiving one or more indications or requests from a client (e.g., via an application programming interface) and / or a system. In at least one embodiment, the one or more indications may be based at least on detecting one or more significant events by one or more sensors and / or other components of the vehicle 700, wherein the video clip may capture the environment during a time period that at least partially overlaps and / or is associated with the significant event.

[0038] The vehicle 700 may detect one or more significant events using any suitable method, which may involve analyzing sensor data obtained using one or more sensors of the vehicle 700. The one or more sensors may include at least one camera for obtaining sensor data 120 and / or at least one other sensor of the vehicle 700. Examples of sensors include any combination of sensors described herein. Detecting one or more significant events may additionally or alternatively be based at least on one or more signals from software on the vehicle 700 and / or received from the vehicle 700 or received from another source external to the vehicle 700.

[0039] Examples of significant events include, but are not limited to, accidents or collisions, near misses or unsafe maneuvers, pedestrian or cyclist interactions, unusual road conditions, malfunctions or technical issues, traffic violations, unexpected obstacles, MLM prediction errors (e.g., perception errors), construction zones or road work, security threats, user or client requests, etc.

[0040] As an example and not limitation, the duration of a video clip can be less than one minute, such as between eight seconds and fifteen seconds. However, a video clip can have any suitable duration. In at least one embodiment, the length of the duration can correspond to the detected event.

[0041] Figure 1 The location of the video manager 110 in the process 100 is provided as an example. Various methods can be used to generate video clips. In at least one embodiment, the vehicle 700 continuously encodes (e.g., all) video data corresponding to the sensor data 120. The video data can be encoded in real time, regardless of the surrounding environment of the vehicle and / or the detection of important events. In an example, a budget and / or buffer can be used so that old video data can be overwritten by new video data when a budget (e.g., storage size, stored video duration, etc.) is exceeded or due to exceeding the budget. In such an example, the video manager 110 can generate a video clip from the encoded video data in the buffer based at least on receiving an indication of an important event.

[0042] As a further example, the vehicle 700 may perform event-based encoding on the video data. In at least one embodiment, the buffer and / or budget may still be available for unencoded video data. In such an example, the video manager 110 may generate a video clip from the unencoded video data (e.g., in the buffer) using the video encoder 102 based at least on receiving an indication of an important event. It is further contemplated that other or hybrid methods may be used to generate the video clip.

[0043] The video manager 110 may provide one or more video clips to be stored using one or more files 122 on the vehicle 700, such as in its file system. The file 122 may have any suitable format, such as MP4, AVI, or MOV. In at least one embodiment, the file 122 may include metadata and / or audio data in addition to the encoded video data. In at least one embodiment, the metadata may include one or more timestamps corresponding to when the footage was captured and / or when one or more important events were detected. In at least one embodiment, the metadata may include one or more attributes and / or types of one or more important events. One video clip may be stored in each file, or a file may store multiple video clips.

[0044] The video manager 110 may additionally or alternatively provide one or more video clips (e.g., files) for uploading to one or more servers 112 via one or more networks 114. Examples of suitable networks are described herein. Components of the vehicle 700 may be configured to transmit the video clips to the server 112 via the network 114. The network 114 may include a wide area network (WAN) (e.g., the Internet, a public switched telephone network (PSTN), etc.), a local area network (LAN) (e.g., Wi-Fi, ZigBee, Z-Wave, Bluetooth, Bluetooth low energy (BLE), Ethernet, etc.), a low power wide area network (LPWAN) (e.g., LoRaWAN, Sigfox, etc.), a global navigation satellite system (GNSS) network (e.g., a global positioning system (GPS)), and / or another network type.

[0045] Server 112 may include any suitable server for storing video clips. In at least one embodiment, server 112 is used as a central repository for storing, managing and / or viewing video clips. Server 112 may be cloud-based or locally located. In at least one embodiment, server 112 includes a remote server.

[0046] The bit rate of the encoded image data 124 may be greatly affected by the driving environment of the vehicle 700. For example, for the same encoding parameters, the encoded image data 124 may have a significantly higher bit rate under low light conditions, such as when the vehicle 700 enters a tunnel or drives at night. As a non-limiting example, when encoded using the same encoding parameters, the encoded image data 124 may have a bit rate of 60 mbps during the day and a bit rate of 300 mbps at night. Therefore, the bit rate of the encoded image data 124 may vary greatly and / or be greater than the necessary or desired bit rate, which may cause storage and / or bandwidth problems when storing video clips in files 122 on the vehicle 700 and / or uploading video clips to the server 112. These problems may be exacerbated by the number of cameras used by the vehicle 700 (e.g., eleven cameras) and the resolution of the cameras (e.g., high-definition 4K cameras). The disclosed method may use the QP manager 104 to provide a more consistent bitrate and / or a reduced bitrate for a video clip under different environmental conditions while maintaining the visual fidelity of the encoded image data 124 .

[0047] The QP manager 104 may use the encoded image data 124 and the image data corresponding to the sensor data 120 to determine whether to update one or more QPs and / or updated values ​​of the one or more QPs. The updated values ​​may be indicated to the video encoder 102 using the QP update data. In at least one embodiment, the QP manager 104 determines one or more values ​​of the one or more QPs based at least on calculating one or more values ​​of one or more metrics that quantify the visual difference between the video data encoded using the video encoder 102 and the corresponding encoded image data 124. For example, the QP manager 104 may compare one or more video frames encoded using the video encoder 102 with one or more encoded video frames corresponding to the one or more video frames to determine one or more metric values ​​or scores.

[0048] In at least one embodiment, the metric evaluated by the QP manager 104 may be based at least on a luma or chroma comparison between a reference frame and a coded frame. For example, the metric may indicate the degree to which luma information is preserved between the reference frame and the coded frame. In at least one embodiment, the comparison may be based at least on one or more measures of contrast (in terms of the dynamic range of pixel intensities), brightness, or dynamic range of the image.

[0049] In at least one embodiment, the metric evaluated by the QP manager 104 may be based at least on a local contrast comparison between a reference frame and a coded frame. For example, the metric may indicate the degree to which structural details, such as edges or textures, are preserved between the reference frame and the coded frame. In at least one embodiment, the comparison may be based at least on one or more measures of fine detail and local contrast variations.

[0050] In at least one embodiment, the metric evaluated by the QP manager 104 may be based at least on a comparison of structural elements between the reference frame and the coded frame. For example, the metric may indicate the degree to which structural elements or patterns are preserved between the reference frame and the coded frame. In at least one embodiment, the comparison may be based at least on one or more measurements of edges, textures, shapes, and other structural elements.

[0051] By way of example and not limitation, suitable metrics evaluated by the QP manager 104 include a structural similarity index (SSIM) metric. The SSIM metric value may indicate structural similarity between a reference frame and a coded frame. While the metric may be based on at least a peak signal-to-noise ratio (PSNR) or a mean square error (MSE), SSIM may more reliably indicate preservation of visual fidelity or clarity relative to human reviewers, while PSNR may be more suitable for analysis using MLM.

[0052] After calculating the one or more metric values, the QP manager 104 may use the one or more metric values ​​to update one or more QP values. Various methods may be used to determine the updated QP value. For example, the QP value may be adjusted based at least on a difference between the one or more calculated metric values ​​and one or more reference metric values ​​or a threshold metric value. In at least one embodiment, the one or more QP values ​​may be adjusted to reduce the difference between the calculated metric value and the threshold metric value. The threshold metric value may be fixed or may be adaptive or dynamic.

[0053] In at least one embodiment, the QP manager 104 calculates one or more updated QP values ​​using one or more mathematical formulas, for example, to map the calculated metric values ​​to the QP adjustments. In at least one embodiment, the QP manager 104 uses one or more lookup tables that map the calculated metric values ​​and / or ranges to corresponding QP adjustments. Interpolation may be used for values ​​that fall between table entries. In at least one embodiment, the QP manager 104 uses one or more trained MLMs (e.g., regression models or neural networks) to predict QP adjustments based at least on the calculated metric values. In at least one embodiment, the one or more metrics may be at least partially incorporated into one or more MLMs, and the MLMs may receive as inputs representations of one or more portions of at least one or more video frames and corresponding encoded video frames. The MLMs may be trained to output one or more metric values ​​and / or adjustments to one or more QPs and / or values ​​of one or more QPs. Any suitable combination of these methods may be used to determine the value of a QP.

[0054] As a non-limiting example, assume that the threshold metric value is set to 0.98. When the calculated metric value is above 0.98 (e.g., 0.99), one or more QP values ​​may be decreased to attempt to reach the threshold metric value. When the calculated metric value is below 0.98 (e.g., 0.97), one or more QP values ​​may be increased to attempt to reach the threshold metric value.

[0055] In at least one embodiment, the QP manager 104 determines one value or adjustment to use for all QPs, such as for each I-frame, B-frame, or P-frame. In at least one embodiment, the QP manager 104 determines different values ​​or adjustments to use for one or more QPs, such as for any combination of I-frames, B-frames, or P-frames. As an example, each of an I-frame, B-frame, or P-frame may have a range of possible values ​​(e.g., 0-51). The QP manager 104 may determine a single value to apply to each range or frame type (e.g., 22 for each range), or may determine different values ​​to apply to different ranges (e.g., 20 for I-frames, 22 for P-frames, and 25 for B-frames). In at least one embodiment, only the QP is adjusted, and other encoding parameters used by the video encoder 102 remain unchanged. In at least one embodiment, one or more other encoding parameters may be adjusted or determined in addition to one or more QPs.

[0056] By way of example and not limitation, P frames may have a more significant impact on bitrate than other frame types in lower light or nighttime conditions compared to higher light or daytime conditions (e.g., visual content may vary less during nighttime). Further, I frames may have a more significant impact on bitrate than other frame types in higher light or daytime conditions compared to lower light or nighttime conditions (e.g., visual content may vary more during daytime). Thus, in at least one embodiment, QP manager 104 may adjust one or more QPs based at least on one or more indications of lighting conditions, time of day, and / or other environmental characteristics associated with differences in relative impacts of frame types on bitrate.

[0057] By way of example and not limitation, the QP manager 104 may analyze sensor data to determine one or more indications. For example, the QP manager 104 may use one or more light sensors, brightness analysis of video data, and / or other techniques to determine or control relative magnitudes of adjustments to one or more QPs. By way of example, based at least on brightness detected in the environment and / or time of day (e.g., using a system clock), the QP manager 104 may select between a night mode or a day mode to control QP adjustments or dynamically change the adjustments. Thus, for example, the QP manager 104 may use greater adjustments for P frames at night than during the day and / or greater adjustments for I frames during the day than at night (in some examples, in addition to or in lieu of increasing the magnitude of the adjustments, for less impactful conditions, these values ​​may not be adjusted at all, or may be set to default values).

[0058] Reference now Figure 2 , Figure 2 is an example of a process 200 for determining updates to one or more quantization parameters to generate a video clip using video data obtained using one or more sensors of a machine according to some embodiments of the present disclosure. In particular, process 200 provides an example of how QP manager 104 uses sensor data 120 and encoded image data 124 to determine QP update data 118, e.g., in Figure 1 in the process of 100.

[0059] In process 200, QP manager 104 may be implemented using any combination of one or more image decoders 210, one or more image samplers 212, one or more sample analyzers 214, one or more parameter determiners 216, or one or more update managers 218, as well as additional or alternative components.

[0060] The image decoder 210 may be configured to decode one or more portions (e.g., frames) of the encoded image data 124. The image sampler 212 may be configured to generate and / or identify one or more samples of one or more portions (e.g., frames) of video data and one or more samples of decoded image data provided using the image decoder 210. The sample analyzer 214 may be configured to analyze samples of the video data and the decoded image data. For example, the sample analyzer 214 may analyze the samples to compare the video data corresponding to the sensor data 120 with the encoded image data 124 and / or determine one or more metric values, as described herein. The parameter determiner 216 may be configured to determine one or more values ​​of one or more QPs and / or adjustments to one or more QPs based at least on the analysis performed using the sample analyzer 214. For example, the parameter determination may determine the QP update data 118 based at least on the one or more metric values, as described herein. The update manager 218 may be configured to selectively enable or disable updates to the QP values.

[0061] In examples where the vehicle 700 includes multiple cameras or sensors that provide video streams or video feeds, process 100 and / or process 200 may be performed separately for each video feed using separate instantiations of the components, or process 100 and / or process 200 or at least some of its components may be shared for different video feeds or sensors (e.g., cameras). Thus, for example, the QP manager 104 and / or one or more of its components may be shared between one or more video feeds, or a separate QP manager may be used. Additionally or alternatively, one or more components of the QP manager 104 may operate on or account for multiple video feeds when processing image data, enable or disable updates to and / or update the QP, or may only account for individual video feeds.

[0062] In at least one embodiment, update manager 218 is configured to enable or disable updates to QP values ​​based at least on enabling or disabling one or more of image decoder 210, image sampler 212, sample analyzer 214, or parameter determiner 216. Thus, for example, when updates are disabled, QP manager 104 may suspend image decoding, image sample generation, sample analysis, and / or parameter determination until updates are enabled, thereby saving computational resources.

[0063] In at least one embodiment, update manager 218 determines whether to enable or disable updates to QP values ​​based at least on one or more bitrates and / or bandwidths corresponding to encoded image data 124. For example, update manager 218 may monitor the bitrate to determine whether to enable or disable updates to QP.

[0064] Reference now Figure 3 , Figure 3 is a diagram illustrating high and low watermarks that may be used to selectively determine updates to one or more quantization parameters according to an embodiment of the present disclosure. Figure 2 A high water mark 304 and a low water mark 306 are shown, which may be relative to a reference level 310 of a bit rate associated with the encoded image data 124. In at least one embodiment, the update manager 218 may be configured to enable updates based at least on a determination that the bit rate has exceeded the low water mark 306 and / or the high water mark 304. In at least one embodiment, the update manager 218 may be configured to disable updates based at least on a determination that the bit rate has entered the low water mark 306 and / or the high water mark 304. Thus, for example, when the bit rate is between the high water mark 304 and the low water mark 306, updates may be disabled, while when the bit rate is within the high water mark 304 and the low water mark 306, updates may be disabled.

[0065] In at least one embodiment, reference level 310 may be a default value and / or may be adapted based on one or more criteria. Similarly, high watermark 304 and low watermark 306 may be default values ​​and / or may be adapted based on one or more criteria. Using a high watermark or low watermark for bitrate may allow for less frequent updates to the QP. Further, enabling or disabling updates to the QP based on bitrate (e.g., using Figure 3 method or another method) can implicitly consider changes in environmental conditions of vehicle 700 to trigger updates to QP, such as vehicle 700 entering a tunnel or a shadow area.

[0066] In at least one embodiment, the bit rate evaluated by the update manager 218 can be calculated based on at least the number of frames, such as any number of frames in a group of pictures (GOP). For example, the update manager 218 can calculate a bit rate that corresponds at least in part to the GOP (e.g., the GOP size). For example, where the GOP includes 30 frames, the update manager 218 can calculate the bit rate of the 30 frames for comparison with the reference level 310. The GOP can specify the order in which intra-frames and inter-frames are arranged, and can correspond to a set of consecutive frames in an encoded video stream. The GOP can include one or more of an I frame, a P frame, a B frame, or a D frame. An I frame can indicate the beginning of a GOP, and can be followed by several P frames or B frames until another GOP begins at a subsequent I frame. By analyzing the GOP, the update manager 218 can calculate a more stable bit rate for evaluation. However, additional or alternative methods can be used to calculate the bit rate, such as based on at least calculating a moving average, using a filter, etc.

[0067] The update manager 218 may use additional or alternative information or techniques to enable or disable updates to the QP. Where multiple cameras or video feeds are available, the update manager 218 may take into account any number of cameras, or a separate update manager 218 may be used for each video feed. Thus, for example, when multiple video feeds are available, the bitrate monitored by the update manager 218 may correspond to a single video feed or to multiple video feeds.

[0068] In embodiments including the image sampler 212, the sample analyzer 214 need not analyze the entire image, thereby saving computational resources. The sample analyzer 214 may be configured to generate and / or identify one or more samples of one or more portions (e.g., frames) of video data and one or more samples of decoded image data provided using the image decoder 210 using any suitable method. In at least one embodiment, the sample analyzer 214 determines at least partially spatially overlapping samples from the reference image and the decoded image. For example, the sample analyzer 214 may determine one or more sample pairs corresponding to the same or substantially the same visual content between the reference image and the decoded image.

[0069] Reference now Figure 4 , Figure 4 4 is a diagram illustrating regions of a video frame 400 that may be sampled to determine one or more values ​​of one or more metrics, according to an embodiment of the present disclosure. In particular, these regions may correspond to Figure 4As an example and not limitation, a non-random spatial distribution algorithm can be used to determine these regions, such as Poisson disk sampling. Further examples of sampling algorithms include blue noise sampling, grid-based sampling, or stratified sampling.

[0070] In at least one embodiment, the image sampler 212 determines the locations of the regions and then generates corresponding samples at these locations in the reference image and the decoded image. In at least one embodiment, the image sampler 212 can determine the locations of the reference image and the decoded image separately, or different locations of these regions can be used for the reference image and the decoded image. In various embodiments, a region may include one or more pixels. As an example and not limitation, approximately 1000 regions may be sampled. Thus, for example, for a 4K image, the sample analyzer 214 may only need to analyze 1000 pixels instead of 3840*2160 pixels.

[0071] Reference now Figure 5 and Figure 6 , each block of method 500, method 600, and other methods described herein may include a computing process that can be performed using any combination of hardware, firmware, and / or software. For example, the various functions may be implemented by a processor executing instructions stored in a memory. The methods may also be embodied as computer-usable instructions stored on a computer storage medium. The methods may be provided by a stand-alone application, a service or a hosted service (standalone or in combination with another hosted service), or a plug-in for another product, to name a few. In addition, by way of example, with respect to Figure 1 The process of 100 or Figure 2 These methods are described in the process 200 of FIG. However, these methods may additionally or alternatively be performed by any one system or any combination of systems, including but not limited to those described herein.

[0072] Figure 5 1 is a flow chart showing a method 500 for determining a video clip using at least one value of at least one quantization parameter determined based at least on detecting a deviation from a bit rate corresponding to an encoded video according to an embodiment of the present disclosure. At block B502, the method 500 includes receiving video data. For example, the QP manager 104 may receive video data obtained using one or more sensors of the vehicle 700. The video data may correspond to an image that is applied to the MLM 106 to perform one or more control operations using the vehicle 700 and the control component 108.

[0073] At block B504, method 500 includes detecting a deviation between a bit rate and a reference bit rate. For example, update manager 218 may detect that a deviation between a bit rate corresponding to encoded image data 124 (e.g., a first portion of an encoded stream of video data) and a reference bit rate (e.g., reference level 310) has exceeded a threshold (e.g., high water mark 304 or low water mark 306).

[0074] At block B506, method 500 includes calculating one or more values ​​of one or more metrics. For example, based at least on the deviation exceeding the threshold, sample analyzer 214 may calculate one or more values ​​of one or more metrics quantifying visual differences between the first portion of the encoded stream and the video data.

[0075] At block B508, method 500 includes determining at least one value of one or more QPs based at least on the one or more values.For example, parameter determiner 216 may determine at least one value of one or more quantization parameters based at least on one or more values ​​of one or more metrics.

[0076] At block B510, the method 500 includes encoding the video data using at least one value of the one or more QPs to generate an encoded stream. For example, the video encoder 102 may encode the video data using at least one value of the one or more quantization parameters (e.g., based on the QP update data 118) to generate a second portion of the encoded stream (e.g., the encoded image data 124).

[0077] At block B512 , method 500 includes determining one or more video clips. For example, video manager 110 may determine one or more video clips corresponding to the encoded stream.

[0078] Figure 6 6 is a flow chart illustrating a method 600 for determining a video clip using an update to one or more quantization parameters based at least on one or more values ​​of one or more metrics, according to an embodiment of the present disclosure. At block B602, the method 600 includes encoding one or more first frames using one or more QPs. For example, the video encoder 102 may encode one or more first frames represented by video data using one or more quantization parameters to generate one or more first encoded video frames corresponding to the encoded image data 124 of the encoded video stream. The video data may be obtained using one or more sensors having one or more sensing fields that capture the environment of the vehicle 700.

[0079] At block B604, method 600 includes calculating one or more values ​​of one or more metrics. For example, sample analyzer 214 may calculate one or more values ​​of one or more metrics that quantify visual differences between one or more first video frames and one or more first encoded video frames. In at least one embodiment, the one or more values ​​of the one or more metrics are calculated based at least on a bit rate corresponding to the encoded video stream. For example, as described herein, the metric values ​​may be calculated based at least on a deviation between a bit rate corresponding to the detected and encoded video stream and a target bit rate.

[0080] At block B606, the method 600 includes determining one or more updates to one or more QPs based at least on the one or more values.For example, the parameter determiner 216 may determine one or more updates to one or more quantization parameters based at least on one or more values ​​of one or more metrics.

[0081] At block B608, the method 600 includes encoding one or more second video frames using the one or more updates. For example, the video encoder 102 may encode one or more second video frames represented by the video data using one or more updates to one or more quantization parameters (e.g., based on the QP update data 118) to generate one or more second encoded frames corresponding to the encoded image data 124.

[0082] At block B610, method 600 includes determining one or more video clips. For example, video manager 110 may determine one or more video clips corresponding to an encoded stream.

[0083] A system of one or more computers may be configured to perform specific operations or actions by installing software, firmware, hardware, or a combination thereof on the system, wherein the software, firmware, hardware, or a combination thereof causes the system to perform actions. One or more computer programs may be configured to perform specific operations or actions by including instructions that, when executed by a data processing device, cause the device to perform the actions. A general aspect includes one or more circuits that enable execution of operations including: receiving video data obtained using one or more sensors of a machine, the video data corresponding to an image applied to one or more machine learning models (MLMs) to perform one or more control operations of the machine; detecting that a deviation between a bit rate corresponding to a first portion of an encoded stream of the video data and a reference bit rate has exceeded a threshold; calculating one or more values ​​of one or more metrics that quantize a visual difference between the first portion of the encoded stream and the video data based at least on the deviation exceeding the threshold; determining at least one value of one or more quantization parameters based at least on the one or more values ​​of the one or more metrics; encoding the video data using the at least one value of the one or more quantization parameters to generate a second portion of the encoded stream; and determining one or more video clips corresponding to the encoded stream. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of these methods.

[0084] Implementations may include one or more of the following features. The one or more quantization parameters in the system include a B-frame quantization parameter, an I-frame quantization parameter, and a P-frame quantization parameter. Detecting a deviation is based at least on determining that the bit rate is outside a high water mark and a low water mark relative to a reference bit rate. Calculating one or more values ​​is based at least on: sampling an area of ​​one or more frames from a first portion of the encoded stream and the video data to generate samples of the areas; and comparing samples of the areas in the encoded stream and the video data. The samples are generated using a non-random spatial distribution algorithm. The operation may also include one or more of the following: saving one or more video clips to a file system of the machine; or uploading one or more video clips to a remote server. One or more video clips are desensitized to personally identifiable information. The determination of one or more video clips is based at least on detecting an event in an environment of the machine. The system may be included in at least one of the following: a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulations; a system for performing collaborative content creation of 3D assets; a system for performing deep learning operations; a system implementing one or more large language models (LLMs); a system implemented using an edge device; a system implemented using a machine; a system for performing conversational AI operations; a system for generating synthetic data; a system including one or more virtual machines (VMs); a system implemented at least in part in a data center; or a system implemented at least in part using cloud computing resources. Implementations of the described technology may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0085] A general aspect includes a method comprising: encoding one or more first video frames represented by video data obtained using one or more sensors having one or more sensing fields to generate one or more first encoded video frames using one or more quantization parameters; calculating one or more values ​​of one or more metrics quantizing visual differences between the one or more first video frames and the one or more first encoded video frames; determining one or more updates to the one or more quantization parameters based at least on the one or more values ​​of the one or more metrics; encoding one or more second video frames represented by the video data using the one or more updates to the one or more quantization parameters to generate one or more second encoded frames; and determining one or more video clips corresponding to the one or more second encoded frames. Other embodiments of this aspect include corresponding computer systems, apparatuses, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of these methods.

[0086] Implementations may include one or more of the following features. The method of calculating one or more values ​​of one or more metrics is based at least on detecting that a deviation between a bit rate corresponding to one or more first encoded video frames and a reference bit rate has exceeded a threshold. One or more first video frames are applied to one or more machine learning models (MLMs) to perform one or more control operations on a machine, and the one or more sensors are associated with the machine. One or more quantization parameters include B frame quantization parameters, I frame quantization parameters, and P frame quantization parameters. Calculating one or more values ​​of one or more metrics is based at least on detecting a change in lighting conditions in an environment of a machine including one or more sensors. Calculating the one or more values ​​is based at least on the following: sampling areas of one or more first video frames and one or more first encoded video frames to generate samples of these areas; and comparing samples of areas in one or more first video frames and one or more first encoded video frames. Implementations of the described techniques may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0087] A general aspect includes a processor including one or more circuits for determining one or more video clips corresponding to an encoded stream of video data obtained using one or more sensors of a machine, the encoded stream being generated using one or more quantization parameters corresponding to one or more values ​​of one or more metrics quantizing a visual difference between a portion of the encoded stream and the video data. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of these methods.

[0088] Implementations may include one or more of the following features. One or more values ​​of one or more metrics in the processor are calculated based at least on the deviation between the bit rate corresponding to the coded stream and the reference bit rate exceeding a threshold. Video data is applied to one or more machine learning models (MLMs) to perform one or more control operations on the machine. One or more quantization parameters include B frame quantization parameters, I frame quantization parameters, and P frame quantization parameters. Implementations of the described techniques may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0089] Example Autonomous Vehicle

[0090] Fig. 7Ais an illustration of an example autonomous vehicle 700 according to some embodiments of the present disclosure. Autonomous vehicle 700 (alternatively referred to herein as "vehicle 700") may include, but is not limited to, a passenger vehicle such as a car, a truck, a bus, an emergency vehicle, a shuttle, an electric or motorized bicycle, a motorcycle, a fire truck, a police car, an ambulance, a boat, an engineering vehicle, an underwater vessel, a robotic vehicle, a drone, an airplane, a vehicle coupled to a trailer (e.g., a semi-trailer for transporting cargo), and / or other types of vehicles (e.g., unmanned and / or capable of accommodating one or more passengers). Autonomous vehicles are generally described in terms of automation levels defined by the National Highway Traffic Safety Administration (NHTSA), a division of the U.S. Department of Transportation, and the Society of Automotive Engineers (SAE) “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles” (Standard No. J3016-201506, issued on June 15, 2018, Standard No. J3016-201609, issued on September 30, 2016, and previous and future versions of the standard). The vehicle 700 is capable of implementing one or more of Levels 3-5 of autonomous driving levels. The vehicle 700 is capable of implementing one or more of Levels 1-5 of autonomous driving levels. For example, depending on the embodiment, the vehicle 700 is capable of driver assistance (Level 1), partial automation (Level 2), conditional automation (Level 3), high automation (Level 4), and / or full automation (Level 5). The term "autonomy" as used herein may include any and / or all types of autonomy of 700 or other machines, such as full autonomy, high autonomy, conditional autonomy, partial autonomy, assisted autonomy, semi-autonomy, primary autonomy, or other names.

[0091] The vehicle 700 may include components such as a chassis, a body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and other components of the vehicle. The vehicle 700 may include a propulsion system 750, such as an internal combustion engine, a hybrid power plant, an all-electric engine, and / or another type of propulsion system. The propulsion system 750 may be connected to a drive train of the vehicle 700, which may include a transmission, to achieve propulsion of the vehicle 700. The propulsion system 750 may be controlled in response to receiving a signal from a throttle / accelerator 752.

[0092] A steering system 754, which may include a steering wheel, may be used to steer the vehicle 700 (e.g., along a desired path or route) when the propulsion system 750 is operating (e.g., when the vehicle is in motion). The steering system 754 may receive signals from a steering actuator 756. For fully automated (Level 5) functionality, a steering wheel may be optional.

[0093] Brake sensor system 746 may be used to operate vehicle brakes in response to receiving signals from brake actuator 748 and / or brake sensors.

[0094] May include one or more system on chip (SoC) 704 ( Figure 7C ) and / or one or more controllers 736 of one or more GPUs can provide signals (e.g., representing commands) to one or more components and / or systems of the vehicle 700. For example, the one or more controllers can send signals to operate vehicle brakes via one or more brake actuators 748, to operate the steering system 754 via one or more steering actuators 756, and / or to operate the propulsion system 750 via one or more throttles / accelerators 752. The one or more controllers 736 can include one or more onboard (e.g., integrated) computing devices (e.g., supercomputers) that process sensor signals and output operating commands (e.g., signals representing commands) to enable autonomous driving and / or assist a human driver in driving the vehicle 700. The one or more controllers 736 can include a first controller 736 for autonomous driving functions, a second controller 736 for functional safety functions, a third controller 736 for artificial intelligence functions (e.g., computer vision), a fourth controller 736 for infotainment functions, a fifth controller 736 for redundancy in emergency situations, and / or other controllers. In some examples, a single controller 736 may handle two or more of the above functions, two or more controllers 736 may handle a single function, and / or any combination thereof.

[0095] The one or more controllers 736 may provide signals for controlling one or more components and / or systems of the vehicle 700 in response to sensor data (e.g., sensor input) received from one or more sensors. The sensor data may be received from, for example and without limitation, a global navigation satellite system ("GNSS") sensor 758 (e.g., a global positioning system sensor), a RADAR sensor 760, an ultrasonic sensor 762, a LIDAR sensor 764, an inertial measurement unit (IMU) sensor 766 (e.g., an accelerometer, a gyroscope, a magnetic compass, a magnetometer, etc.), a microphone 796, a stereo camera 768, a wide angle camera 770 (e.g., a fisheye camera), an infrared camera 772, a surround camera 774 (e.g., a 360 degree camera), a long-range and / or mid-range camera 798, a speed sensor 744 (e.g., for measuring the velocity of the vehicle 700), a vibration sensor 742, a steering sensor 740, a brake sensor (e.g., as part of a brake sensor system 746), and / or other sensor types.

[0096] One or more of the controllers 736 may receive inputs (e.g., represented by input data) from the instrument cluster 732 of the vehicle 700 and provide outputs (e.g., represented by output data, display data, etc.) via a human machine interface (HMI) display 734, an audible annunciator, a speaker, and / or via other components of the vehicle 700. These outputs may include information such as vehicle speed, velocity, time, map data (e.g., Figure 7C The HMI display 734 may include information such as a high-definition (“HD”) map 722 of the vehicle 700 , location data (e.g., the location of the vehicle 700 on the map), directions, locations of other vehicles (e.g., occupancy grid), information about objects and states of objects as sensed by the controller 736 , and the like. For example, the HMI display 734 may display information about the presence of one or more objects (e.g., street signs, warning signs, traffic light changes, etc.) and / or information about driving maneuvers that the vehicle has made, is making, or will make (e.g., changing lanes now, leaving 34B in two miles, etc.).

[0097] The vehicle 700 further includes a network interface 724 that can communicate via one or more networks using one or more wireless antennas 726 and / or a modem. For example, the network interface 724 may be capable of communicating via Long Term Evolution (“LTE”), Wideband Code Division Multiple Access (“WCDMA”), Universal Mobile Telecommunications System (“UMTS”), Global System for Mobile Communications (“GSM”), IMT-CDMA Multi-Carrier (“CDMA2000”), etc. The one or more wireless antennas 726 may also enable communication between objects in the environment (e.g., vehicles, mobile devices, etc.) using one or more local area networks such as Bluetooth, Bluetooth Low Energy (“LE”), Z-wave, ZigBee, etc., and / or one or more low power wide area networks (“LPWAN”) such as LoRaWAN, SigFox, etc.

[0098] Figure 7B According to some embodiments of the present disclosure, Fig. 7A 700. The cameras and respective fields of view are an example embodiment and are not intended to be limiting. For example, additional and / or replaceable cameras may be included, and / or the cameras may be located at different locations on the vehicle 700.

[0099] The camera type for the camera may include, but is not limited to, a digital camera that may be suitable for use with components and / or systems of the vehicle 700. The camera may operate at an automotive safety integrity level (ASIL) B and / or at another ASIL. The camera type may have any image capture rate, such as 60 frames per second (fps), 120fps, 240fps, etc., depending on the embodiment. The camera may be capable of using a rolling shutter, a global shutter, another type of shutter, or a combination thereof. In some examples, the color filter array may include a red-white-white-white (RCCC) color filter array, a red-white-white-blue (RCCB) color filter array, a red-blue-green-white (RBGC) color filter array, a Foveon X3 color filter array, a Bayer sensor (RGGB) color filter array, a monochrome sensor color filter array, and / or another type of color filter array. In some embodiments, a clear pixel camera such as a camera with an RCCC, RCCB, and / or RBGC color filter array may be used in an effort to improve light sensitivity.

[0100] In some examples, one or more of the cameras can be used to perform advanced driver assistance system (ADAS) functions (e.g., as part of a redundant or fail-safe design). For example, a multi-function monocular camera can be installed to provide functions including lane departure warning, traffic sign assistance, and intelligent headlight control. One or more of the cameras (e.g., all cameras) can simultaneously record and provide image data (e.g., video).

[0101] One or more of the cameras may be mounted in a mounting assembly such as a custom designed (3D printed) assembly to cut off stray light and reflections from within the car (e.g., reflections from the dashboard reflected in the windshield mirror) that may interfere with the camera's image data capture capabilities. With respect to the wing mirror mounting assembly, the wing mirror assembly may be custom 3D printed so that the camera mounting plate matches the shape of the wing mirror. In some examples, one or more cameras may be integrated into the wing mirror. For side view cameras, one or more cameras may also be integrated into the four pillars at each corner of the cab.

[0102] A camera (e.g., a front-facing camera) having a field of view that includes a portion of the environment in front of the vehicle 700 can be used for surround view to help identify the forward path and obstacles, as well as assist in providing information critical to generating an occupancy grid and / or determining a preferred vehicle path with the help of one or more controllers 736 and / or control SoCs. The front-facing camera can be used to perform many of the same ADAS functions as LIDAR, including emergency braking, pedestrian detection, and collision avoidance. The front-facing camera can also be used for ADAS functions and systems, including lane departure warning ("LDW"), autonomous cruise control ("ACC"), and / or other functions such as traffic sign recognition.

[0103] A variety of cameras may be used in the front-facing configuration, including, for example, a monocular camera platform including a complementary metal oxide semiconductor ("CMOS") color imager. Another example may be a wide-angle camera 770, which may be used to sense objects (e.g., pedestrians, intersection traffic, or bicycles) entering the field of view from the periphery. Figure 7B Only one wide-angle camera is shown in the figure, but there may be any number (including zero) of wide-angle cameras 770 on the vehicle 700. In addition, any number of long-range cameras 798 (e.g., a long-view stereo camera pair) may be used for depth-based object detection, especially for objects for which a neural network has not been trained. Long-range cameras 798 may also be used for object detection and classification and basic object tracking.

[0104] Any number of stereo cameras 768 may also be included in the front configuration. In at least one embodiment, one or more stereo cameras 768 may include an integrated control unit including a scalable processing unit that may provide a multi-core microprocessor and programmable logic ("FPGA") with an integrated controller area network ("CAN") or Ethernet interface on a single chip. Such a unit can be used to generate a 3D map of the vehicle environment, including distance estimates for all points in the image. An alternative stereo camera 768 may include a compact stereo vision sensor that may include two camera lenses (one on the left and one on the right) and an image processing chip that may measure the distance from the vehicle to the target object and use the generated information (e.g., metadata) to activate autonomous emergency braking and lane departure warning functions. Other types of stereo cameras 768 may be used in addition to or alternatively to those described herein.

[0105] A camera having a field of view of a portion of the environment including the sides of the vehicle 700 (e.g., a side view camera) can be used for surround viewing, providing information used to create and update the occupancy grid and generate side impact collision warnings. Figure 7B The four surround cameras 774 shown in FIG. 7 can be placed on the vehicle 700. The surround cameras 774 can include wide-angle cameras 770, fisheye cameras, 360-degree cameras, and / or the like. For example, four fisheye cameras can be placed in front, back, and sides of the vehicle. In an alternative arrangement, the vehicle can use three surround cameras 774 (e.g., left, right, and back), and can utilize one or more other cameras (e.g., a forward-facing camera) as a fourth surround camera.

[0106] A camera having a field of view that includes a portion of the environment behind the vehicle 700 (e.g., a rear view camera) can be used to assist with parking, surround view, rear collision warning, and creating and updating occupancy grids. A variety of cameras can be used, including but not limited to cameras that are also suitable as front cameras as described herein (e.g., long-range and / or mid-range cameras 798, stereo cameras 768, infrared cameras 772, etc.).

[0107] Figure 7C According to some embodiments of the present disclosure, Fig. 7ABlock diagram of an example system architecture for an example autonomous vehicle 700. It should be understood that this arrangement and other arrangements described herein are set forth merely as examples. Other arrangements and elements (e.g., machines, interfaces, functions, sequences, functional groupings, etc.) may be used in addition to or in place of those shown, and some elements may be omitted entirely. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in combination with other components, and in any appropriate combination and location. The various functions described herein as being performed by an entity may be implemented by hardware, firmware, and / or software. For example, the various functions may be implemented by a processor executing instructions stored in a memory.

[0108] Figure 7C Each of the components, features, and systems of the vehicle 700 is illustrated as being connected via a bus 702. The bus 702 may include a controller area network (CAN) data interface (alternatively, referred to herein as a "CAN bus"). The CAN may be a network inside the vehicle 700 that assists in controlling various features and functions of the vehicle 700, such as the actuation of brakes, acceleration, braking, steering, windshield wipers, and the like. The CAN bus may be configured to have tens or even hundreds of nodes, each with its own unique identifier (e.g., CAN ID). The CAN bus may be read to find steering wheel angle, ground speed, engine speed per minute (RPM), button position, and / or other vehicle status indicators. The CAN bus may be ASIL B compatible.

[0109] Although bus 702 is described as a CAN bus here, this is not intended to be limiting. For example, in addition to or alternatively to the CAN bus, FlexRay and / or Ethernet can be used. In addition, although bus 702 is represented by a single line, this is not intended to be limiting. For example, there can be any number of buses 702, which may include one or more CAN buses, one or more FlexRay buses, one or more Ethernet buses, and / or one or more other types of buses using different protocols. In some examples, two or more buses 702 can be used to perform different functions, and / or can be used for redundancy. For example, a first bus 702 can be used for a collision avoidance function, and a second bus 702 can be used for drive control. In any example, each bus 702 can communicate with any component of the vehicle 700, and two or more buses 702 can communicate with the same component. In some examples, each SoC 704, each controller 736, and / or each computer in the vehicle can have access to the same input data (e.g., input from a sensor of the vehicle 700), and can be connected to a common bus such as a CAN bus.

[0110] The vehicle 700 may include one or more controllers 736, such as those described herein. Fig. 7A Those controllers described. Controller 736 can be used for a variety of functions. Controller 736 can be coupled to any other different components and systems of vehicle 700, and can be used for control of vehicle 700, artificial intelligence of vehicle 700, infotainment for vehicle 700, and / or the like.

[0111] The vehicle 700 may include one or more system on chip (SoC) 704. The SoC 704 may include a CPU 706, a GPU 708, a processor 710, a cache 712, an accelerator 714, a data store 716, and / or other components and features not shown. The SoC 704 may be used to control the vehicle 700 in a variety of platforms and systems. For example, one or more SoCs 704 may be combined with an HD map 722 in a system (e.g., a system of the vehicle 700), and the HD map may be downloaded from one or more servers (e.g., Fig.7D one or more servers 778) to obtain map refreshes and / or updates.

[0112] The CPU 706 may include a CPU cluster or CPU complex (alternatively, referred to herein as a "CCPLEX"). The CPU 706 may include multiple cores and / or L2 caches. For example, in some embodiments, the CPU 706 may include eight cores in a coherent multiprocessor configuration. In some embodiments, the CPU 706 may include four dual-core clusters, each of which has a dedicated L2 cache (e.g., a 2MB L2 cache). The CPU 706 (e.g., CCPLEX) may be configured to support simultaneous cluster operations, such that any combination of CPU 706 clusters can be active at any given time.

[0113] CPU 706 may implement power management capabilities including one or more of the following features: each hardware block may be automatically clock gated when idle to save dynamic power; each core clock may be gated when the core is not actively executing instructions due to the execution of WFI / WFE instructions; each core may be independently power gated; each core cluster may be independently clock gated when all cores are clock gated or power gated; and / or each core cluster may be independently power gated when all cores are power gated. CPU 706 may further implement an enhanced algorithm for managing power states, in which allowed power states and expected wake-up times are specified, and hardware / microcode determines the optimal power state to enter for the core, cluster, and CCPLEX. The processing core may support a simplified power state entry sequence in software, with the work being offloaded to the microcode.

[0114] The GPU 708 may include an integrated GPU (alternatively, referred to herein as an "iGPU"). The GPU 708 may be programmable and efficient for parallel workloads. In some examples, the GPU 708 may use an enhanced tensor instruction set. The GPU 708 may include one or more streaming microprocessors, each of which may include an L1 cache (e.g., an L1 cache with at least 96KB storage capacity), and two or more of these streaming microprocessors may share an L2 cache (e.g., an L2 cache with 512KB storage capacity). In some embodiments, the GPU 708 may include at least eight streaming microprocessors. The GPU 708 may use a computing application programming interface (API). In addition, the GPU 708 may use one or more parallel computing platforms and / or programming models (e.g., NVIDIA's CUDA).

[0115] In the case of automotive and embedded use, GPU 708 can be power optimized to achieve optimal performance. For example, GPU 708 can be manufactured on fin field effect transistors (FinFETs). However, this is not intended to be limiting, and GPU 708 can be manufactured using other semiconductor manufacturing processes. Each streaming microprocessor can merge several mixed precision processing cores divided into multiple blocks. For example and without limitation, 64 PF32 cores and 32 PF64 cores can be divided into four processing blocks. In such an example, each processing block can be allocated 16 FP32 cores, 8 FP64 cores, 16 INT32 cores, two mixed precision NVIDIA tensor cores for deep learning matrix arithmetic, L0 instruction cache, thread beam (warp) scheduler, dispatch unit and / or 64KB register file. In addition, the streaming microprocessor can include independent parallel integer and floating point data paths to provide efficient execution of workloads using a mix of calculations and addressing calculations. The streaming microprocessor may include independent thread scheduling capabilities to allow for finer-grained synchronization and collaboration between parallel threads. A streaming microprocessor may include a combined L1 data cache and shared memory unit to increase performance while simplifying programming.

[0116] GPU 708 can include high bandwidth memory (HBM) and / or a 16GB HBM2 memory subsystem that provides a peak memory bandwidth of approximately 900 GB / s in some examples. In some examples, synchronous graphics random access memory (SGRAM), such as fifth generation graphics double data rate synchronous random access memory (GDDR5), can be used in addition to or in lieu of HBM memory.

[0117] The GPU 708 may include unified memory technology that includes access counters to allow memory pages to be more accurately migrated to the processor that accesses them most frequently, thereby improving the efficiency of memory ranges shared between processors. In some examples, address translation service (ATS) support can be used to allow the GPU 708 to directly access the CPU 706 page table. In such an example, when the GPU 708 memory management unit (MMU) experiences a miss, an address translation request can be transmitted to the CPU 706. In response, the CPU 706 can look up the virtual-physical mapping for the address in its page table and transmit the translation back to the GPU 708. In this way, unified memory technology can allow a single unified virtual address space to be used for the memory of both the CPU 706 and the GPU 708, thereby simplifying GPU 708 programming and porting applications to the GPU 708.

[0118] In addition, GPU 708 may include access counters that can track how often GPU 708 accesses the memory of other processors. Access counters can help ensure that memory pages are moved to the physical memory of the processor that accesses them most frequently.

[0119] SoC 704 may include any number of caches 712, including those described herein. For example, cache 712 may include an L3 cache available to both CPU 706 and GPU 708 (e.g., connected to both CPU 706 and GPU 708). Cache 712 may include a write-back cache that may track the state of a line, for example, by using a cache coherence protocol (e.g., MEI, MESI, MSI, etc.). Depending on the embodiment, the L3 cache may include 4MB or more, although smaller cache sizes may also be used.

[0120] The SoC 704 may include one or more arithmetic logic units (ALUs) that may be used to perform processing for any of a variety of tasks or operations with respect to the vehicle 700, such as processing a DNN. Additionally, the SoC 704 may include a floating point unit (FPU) or other math coprocessor or digital coprocessor type for performing mathematical operations within the system. For example, the SoC 704 may include one or more FPUs integrated as execution units within the CPU 706 and / or GPU 708.

[0121] SoC 704 may include one or more accelerators 714 (e.g., hardware accelerators, software accelerators, or a combination thereof). For example, SoC 704 may include a hardware acceleration cluster, which may include optimized hardware accelerators and / or large on-chip memory. The large on-chip memory (e.g., 4MB SRAM) may enable the hardware acceleration cluster to accelerate neural networks and other calculations. The hardware acceleration cluster may be used to supplement GPU 708 and offload some tasks of GPU 708 (e.g., freeing up more cycles of GPU 708 for performing other tasks). As an example, accelerator 714 may be used for targeted workloads (e.g., perception, convolutional neural networks (CNNs), etc.) that are sufficiently stable to be easily controlled for acceleration. When used herein, the term "CNN" may include all types of CNNs, including region-based or regional convolutional neural networks (RCNNs) and fast RCNNs (e.g., for object detection).

[0122] The accelerator 714 (e.g., a hardware acceleration cluster) may include a deep learning accelerator (DLA). The DLA may include one or more tensor processing units (TPUs) that may be configured to provide an additional 10 trillion operations per second for deep learning applications and reasoning. The TPU may be an accelerator configured to perform image processing functions (e.g., for CNN, RCNN, etc.) and optimized for performing image processing functions. The DLA may be further optimized for a specific set of neural network types and floating point operations and reasoning. The design of the DLA may provide higher performance per millimeter than a general-purpose GPU, and far exceeds the performance of the CPU. The TPU may perform several functions, including a single instance convolution function, support, for example, INT8, INT16, and FP16 data types for both features and weights, and post-processor functions.

[0123] The DLA can quickly and efficiently execute neural networks, particularly CNNs, on processed or unprocessed data for any of a wide variety of functions, such as, but not limited to: a CNN for object recognition and detection using data from a camera sensor; a CNN for distance estimation using data from a camera sensor; a CNN for emergency vehicle detection and identification and detection using data from a microphone; a CNN for facial recognition and vehicle owner identification using data from a camera sensor; and / or a CNN for safety and / or security-related events.

[0124] The DLA can perform any function of the GPU 708, and by using an inference accelerator, for example, the designer can target any function to either the DLA or the GPU 708. For example, the designer can focus the processing of CNNs and floating point operations on the DLA, and leave other functions to the GPU 708 and / or other accelerators 714.

[0125] The accelerator 714 (e.g., a hardware acceleration cluster) may include a programmable vision accelerator (PVA), which may be alternatively referred to herein as a computer vision accelerator. The PVA may be designed and configured to accelerate computer vision algorithms for advanced driver assistance systems (ADAS), autonomous driving, and / or augmented reality (AR) and / or virtual reality (VR) applications. The PVA may provide a balance between performance and flexibility. For example, each PVA may include, for example and without limitation, any number of reduced instruction set computer (RISC) cores, direct memory access (DMA), and / or any number of vector processors.

[0126] The RISC core can interact with an image sensor (e.g., an image sensor of any camera described herein), an image signal processor, and / or the like. Each of these RISC cores can include any amount of memory. Depending on the embodiment, the RISC core can use any of a number of protocols. In some examples, the RISC core can execute a real-time operating system (RTOS). The RISC core can be implemented using one or more integrated circuit devices, application specific integrated circuits (ASICs), and / or storage devices. For example, the RISC core can include an instruction cache and / or a tightly coupled RAM.

[0127] The DMA may enable components of the PVA to access system memory independently of the CPU 706. The DMA may support any number of features used to provide optimizations for the PVA, including, but not limited to, support for multi-dimensional addressing and / or circular addressing. In some examples, the DMA may support addressing in up to six or more dimensions, which may include block width, block height, block depth, horizontal block stepping, vertical block stepping, and / or depth stepping.

[0128] The vector processor can be a programmable processor that can be designed to efficiently and flexibly perform programming for computer vision algorithms and provide signal processing capabilities. In some examples, the PVA can include a PVA core and two vector processing subsystem partitions. The PVA core can include a processor subsystem, one or more DMA engines (e.g., two DMA engines) and / or other peripherals. The vector processing subsystem can operate as the main processing engine of the PVA, and can include a vector processing unit (VPU), an instruction cache and / or a vector memory (e.g., VMEM). The VPU core can include a digital signal processor, such as, for example, a single instruction multiple data (SIMD), a very long instruction word (VLIW) digital signal processor. The combination of SIMD and VLIW can enhance throughput and rate.

[0129] Each of the vector processors may include an instruction cache and may be coupled to a dedicated memory. As a result, in some examples, each of the vector processors may be configured to execute independently of the other vector processors. In other examples, the vector processors included in a particular PVA may be configured to employ data parallelism. For example, in some embodiments, multiple vector processors included in a single PVA may execute the same computer vision algorithm, but on different regions of an image. In other examples, the vector processors included in a particular PVA may execute different computer vision algorithms simultaneously on the same image, or even execute different algorithms on sequence images or portions of an image. Among other things, any number of PVAs may be included in a hardware acceleration cluster, and any number of vector processors may be included in each of these PVAs. In addition, the PVA may include additional error correction code (ECC) memory to enhance overall system security.

[0130] The accelerator 714 (e.g., a hardware acceleration cluster) may include an on-chip computer vision network and SRAM to provide high bandwidth, low latency SRAM for the accelerator 714. In some examples, the on-chip memory may include at least 4MB of SRAM consisting of, for example and not limited to, eight field-configurable memory blocks, which may be accessed by both the PVA and the DLA. Each pair of memory blocks may include an advanced peripheral bus (APB) interface, a configuration circuit system, a controller, and a multiplexer. Any type of memory may be used. The PVA and DLA may access the memory via a backbone that provides high-speed memory access to the PVA and DLA. The backbone may include an on-chip computer vision network that interconnects the PVA and DLA to the memory (e.g., using APB).

[0131] The on-chip computer vision network may include an interface that determines that both the PVA and DLA provide ready and valid signals before transmitting any control signals / addresses / data. Such an interface may provide separate phases and separate channels for transmitting control signals / addresses / data, as well as burst communication for continuous data transmission. This type of interface may comply with ISO 26262 or IEC 61508 standards, but other standards and protocols may also be used.

[0132] In some examples, SoC 704 may include a real-time ray tracing hardware accelerator such as described in U.S. Patent Application No. 16 / 101,232 filed on August 10, 2018. The real-time ray tracing hardware accelerator may be used to quickly and efficiently determine the position and range of objects (e.g., within a world model) in order to generate real-time visualization simulations for RADAR signal interpretation, for sound propagation synthesis and / or analysis, for SONAR system simulations, for general wave propagation simulations, for comparison with LIDAR data for the purpose of positioning and / or other functions, and / or for other purposes. In some embodiments, one or more tree traversal units (TTUs) may be used to perform one or more ray tracing related operations.

[0133] The accelerator 714 (e.g., a hardware accelerator cluster) has a wide range of uses in autonomous driving. The PVA can be a programmable vision accelerator that can be used for key processing stages in ADAS and autonomous vehicles. The capabilities of the PVA are a good match for algorithmic domains that require predictable processing, low power, and low latency. In other words, the PVA performs well on semi-dense or dense rule computations, and even on small data sets that require predictable runtimes with low latency and low power. Therefore, in the context of a platform for autonomous vehicles, the PVA is designed to run classic computer vision algorithms because they are efficient at object detection and integer math.

[0134] For example, according to one embodiment of the technology, PVA is used to perform computer stereo vision. In some examples, algorithms based on semi-global matching can be used, but this is not intended to be limiting. Many applications for Level 3-5 autonomous driving require instant motion estimation / stereo matching (e.g., structure from motion, pedestrian recognition, lane detection, etc.). PVA can perform computer stereo vision functions on input from two monocular cameras.

[0135] In some examples, PVA can be used to perform dense optical flow. According to processing raw RADAR data (e.g., using 4D fast Fourier transform) to provide processed RADAR. In other examples, PVA is used for time-of-flight depth processing, which is for example by processing raw time-of-flight data to provide processed time-of-flight data.

[0136] DLA can be used to run any type of network to enhance control and driving safety, including, for example, a neural network that outputs a confidence measure for each object detection. Such a confidence value can be interpreted as a probability, or as providing a relative "weight" of each detection compared to other detections. This confidence value enables the system to make further decisions about which detections should be considered true positive detections rather than false positive detections. For example, the system can set a threshold for confidence and only consider detections that exceed the threshold as true positive detections. In an automatic emergency braking (AEB) system, a false positive detection will cause the vehicle to automatically perform emergency braking, which is obviously undesirable. Therefore, only the most confident detection should be considered a trigger for AEB. DLA can run a neural network for regressing confidence values. The neural network can take at least some subset of parameters as its input, such as bounding box dimensions, ground plane estimates obtained (e.g., from another subsystem), inertial measurement unit (IMU) sensor 766 output related to vehicle 700 orientation and distance, 3D position estimates of objects obtained from neural networks and / or other sensors (e.g., LIDAR sensor 764 or RADAR sensor 760), etc.

[0137] SoC 704 may include one or more data stores 716 (e.g., memory). Data store 716 may be on-chip memory of SoC 704 that may store neural networks to be executed on the GPU and / or DLA. In some examples, data store 716 may be large enough to store multiple instances of a neural network for redundancy and safety. Data store 716 may include an L2 or L3 cache 712. References to data store 716 may include references to memory associated with a PVA, DLA, and / or other accelerator 714 as described herein.

[0138] SoC 704 may include one or more processors 710 (e.g., embedded processors). Processor 710 may include a boot and power management processor, which may be a dedicated processor and subsystem for handling boot power and management functions and related safety implementations. The boot and power management processor may be part of the SoC 704 boot sequence and may provide runtime power management services. The boot power and management processor may provide clock and voltage programming, auxiliary system low power state transitions, SoC 704 thermal and temperature sensor management, and / or SoC 704 power state management. Each temperature sensor may be implemented as a ring oscillator whose output frequency is proportional to the temperature, and SoC 704 may use the ring oscillator to detect the temperature of CPU 706, GPU 708, and / or accelerator 714. If it is determined that the temperature exceeds a threshold, the boot and power management processor may enter a temperature fault routine and place SoC 704 in a lower power state and / or place the vehicle 700 in a driver safety parking mode (e.g., parking the vehicle 700 safely).

[0139] Processor 710 may further include a set of embedded processors that may be used as an audio processing engine. The audio processing engine may be an audio subsystem that allows for full hardware support for multi-channel audio through multiple interfaces and a wide range of flexible audio I / O interfaces. In some examples, the audio processing engine is a dedicated processor core having a digital signal processor with dedicated RAM.

[0140] The processor 710 may further include an always-on processor engine that may provide the necessary hardware features to support low-power sensor management and wake-up use cases. The always-on processor engine may include a processor core, tightly coupled RAM, supporting peripherals (e.g., timers and interrupt controllers), various I / O controller peripherals, and routing logic.

[0141] The processor 710 may further include a safety cluster engine, which includes a dedicated processor subsystem that handles safety management of automotive applications. The safety cluster engine may include two or more processor cores, tightly coupled RAM, supporting peripherals (e.g., timers, interrupt controllers, etc.), and / or routing logic. In safety mode, the two or more cores may operate in lockstep mode and act as a single core with comparison logic to detect any differences between their operations.

[0142] Processor 710 may further include a real-time camera engine, which may include a dedicated processor subsystem for handling real-time camera management.

[0143] Processor 710 may further include a high dynamic range signal processor, which may include an image signal processor, which is a hardware engine that is part of the camera processing pipeline.

[0144] The processor 710 may include a video image compositer, which may be a processing block (e.g., implemented on a microprocessor) that implements video post-processing functions required for a video playback application to produce a final image for a player window. The video image compositer may perform lens distortion correction for the wide-angle camera 770, the surround camera 774, and / or for an in-cab surveillance camera sensor. The in-cab surveillance camera sensor is preferably monitored by a neural network running on another instance of the advanced SoC, configured to recognize in-cab events and respond accordingly. The in-cab system may perform lip reading to activate mobile phone service and place a call, dictate an email, change a vehicle destination, activate or change the vehicle's infotainment system and settings, or provide voice-activated web surfing. Certain functions are only available to the driver when the vehicle is operating in autonomous mode and are disabled in other circumstances.

[0145] The video image compositer may include enhanced temporal noise reduction for both spatial and temporal noise reduction. For example, in the case of motion in the video, the noise reduction appropriately weights the spatial information and reduces the weight of information provided by adjacent frames. In the case where an image or portion of an image does not include motion, the temporal noise reduction performed by the video image compositer may use information from previous images to reduce noise in the current image.

[0146] The video image compositor may also be configured to perform stereoscopic rectification on the input stereoscopic footage frames. The video image compositor may further be used for user interface composition when the operating system desktop is in use and the GPU 708 does not need to continuously render new surfaces. Even when the GPU 708 is powered on and active for 3D rendering, the video image compositor may be used to offload the GPU 708 to improve performance and responsiveness.

[0147] The SoC 704 may further include a mobile industry processor interface (MIPI) camera serial interface for receiving video and input from a camera, a high-speed interface, and / or a video input block that may be used for camera and related pixel input functions. The SoC 704 may further include an input / output controller that may be controlled by software and may be used to receive I / O signals that are not committed to a specific role.

[0148] SoC 704 may further include a wide range of peripheral interfaces to enable communication with peripherals, audio codecs, power management, and / or other devices. SoC 704 may be used to process data from cameras (connected via Gigabit multimedia serial links and Ethernet), sensors (e.g., LIDAR sensor 764, RADAR sensor 760, etc., which may be connected via Ethernet), data from bus 702 (e.g., speed of vehicle 700, steering wheel position, etc.), data from GNSS sensor 758 (connected via Ethernet or CAN bus). SoC 704 may further include dedicated high-performance mass storage controllers, which may include their own DMA engines, and which may be used to free up CPU 706 from routine data management tasks.

[0149] SoC 704 can be an end-to-end platform with a flexible architecture that spans automation levels 3-5, providing a comprehensive functional safety architecture that leverages and efficiently uses computer vision and ADAS technologies to achieve diversity and redundancy, together with deep learning tools to provide a platform for a flexible and reliable driving software stack. SoC 704 can be faster, more reliable, and even more energy efficient and space efficient than conventional systems. For example, when combined with CPU 706, GPU 708, and data storage 716, accelerator 714 can provide a fast and efficient platform for level 3-5 autonomous vehicles.

[0150] The technology thus provides capabilities and functionality that cannot be achieved by conventional systems. For example, computer vision algorithms can be executed on CPUs, which can be configured using high-level programming languages ​​such as the C programming language to execute a wide variety of processing algorithms across a wide variety of visual data. However, CPUs often fail to meet the performance requirements of many computer vision applications, such as those related to, for example, execution time and power consumption. In particular, many CPUs are unable to execute complex object detection algorithms in real time, which is a requirement for in-vehicle ADAS applications and a requirement for practical Level 3-5 autonomous vehicles.

[0151] In contrast to conventional systems, by providing a CPU complex, a GPU complex, and a hardware acceleration cluster, the techniques described herein allow multiple neural networks to be executed simultaneously and / or sequentially, and the results to be combined together to achieve Level 3-5 autonomous driving functions. For example, a CNN executed on a DLA or dGPU (e.g., GPU 720) may include text and word recognition, allowing the supercomputer to read and understand traffic signs, including signs for which a neural network has not been specifically trained. The DLA may further include a neural network capable of recognizing, interpreting, and providing semantic understanding of the signs, and passing that semantic understanding to a path planning module running on the CPU complex.

[0152] As another example, as required for Level 3, 4, or 5 driving, multiple neural networks may be running simultaneously. For example, a warning sign consisting of "Caution: Flashing Lights Indicate Icing Conditions" along with electric lights may be interpreted by several neural networks independently or collectively. The sign itself may be recognized as a traffic sign by a deployed first neural network (e.g., a trained neural network), and the text "Flashing Lights Indicate Icing Conditions" may be interpreted by a deployed second neural network that informs the vehicle's path planning software (preferably executing on a CPU complex) that icing conditions exist when the flashing lights are detected. The flashing lights may be recognized by operating a deployed third neural network over multiple frames that informs the vehicle's path planning software of the presence (or absence) of the flashing lights. All three neural networks may be running simultaneously, for example, within the DLA and / or on the GPU 708.

[0153] In some examples, a CNN for facial recognition and owner recognition can use data from the camera sensor to identify the presence of an authorized driver and / or owner of the vehicle 700. The always-on sensor processing engine can be used to unlock the vehicle and turn on the lights when the owner approaches the driver's door, and in security mode, disable the vehicle when the owner leaves the vehicle. In this way, the SoC 704 provides security against theft and / or carjacking.

[0154] In another example, a CNN for emergency vehicle detection and identification can use data from microphone 796 to detect and identify emergency vehicle sirens. In contrast to conventional systems that use general classifiers to detect sirens and manually extract features, SoC 704 uses CNN to classify environmental and urban sounds and to classify visual data. In a preferred embodiment, the CNN running on the DLA is trained to identify the relative closing rate of emergency vehicles (e.g., by using the Doppler effect). The CNN can also be trained to identify emergency vehicles specific to the local area in which the vehicle is operating, as identified by the GNSS sensor 758. Thus, for example, when operating in Europe, the CNN will seek to detect European sirens, and when in the United States, the CNN will seek to identify only North American sirens. Once an emergency vehicle is detected, with the assistance of the ultrasonic sensor 762, the control program can be used to execute an emergency vehicle safety routine to slow the vehicle, drive to the side of the road, stop the vehicle, and / or idle the vehicle until the emergency vehicle passes.

[0155] The vehicle may include a CPU 718 (e.g., a discrete CPU or dCPU) that may be coupled to the SoC 704 via a high-speed interconnect (e.g., PCIe). The CPU 718 may include, for example, an X86 processor. The CPU 718 may be used to perform any of a variety of functions, including, for example, arbitrating potentially inconsistent results between ADAS sensors and the SoC 704, and / or monitoring the status and health of the controller 736 and / or the infotainment SoC 730.

[0156] The vehicle 700 may include a GPU 720 (e.g., a discrete GPU or dGPU) that may be coupled to the SoC 704 via a high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU 720 may provide additional artificial intelligence functionality, for example by executing redundant and / or different neural networks, and may be used to train and / or update the neural network based on inputs (e.g., sensor data) from sensors of the vehicle 700.

[0157] The vehicle 700 may further include a network interface 724, which may include one or more wireless antennas 726 (e.g., one or more wireless antennas for different communication protocols, such as cellular antennas, Bluetooth antennas, etc.). The network interface 724 can be used to enable wireless connections with the cloud (e.g., with the server 778 and / or other network devices), with other vehicles, and / or with computing devices (e.g., a passenger's client device) via the Internet. In order to communicate with other vehicles, a direct link can be established between the two vehicles, and / or an indirect link can be established (e.g., across a network and through the Internet). The direct link can be provided using a vehicle-to-vehicle communication link. The vehicle-to-vehicle communication link can provide the vehicle 700 with information about vehicles approaching the vehicle 700 (e.g., vehicles in front of, to the side of, and / or behind the vehicle 700). This functionality can be part of the collaborative adaptive cruise control functionality of the vehicle 700.

[0158] The network interface 724 may include a SoC that provides modulation and demodulation functions and enables the controller 736 to communicate over a wireless network. The network interface 724 may include a radio frequency front end for up-conversion from baseband to radio frequency and down-conversion from radio frequency to baseband. The frequency conversion may be performed by a known process and / or may be performed using a super-heterodyne process. In some examples, the radio frequency front end function may be provided by a separate chip. The network interface may include a wireless function for communicating via LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth, Bluetooth LE, Wi-Fi, Z-wave, ZigBee, LoRaWAN, and / or other wireless protocols.

[0159] The vehicle 700 may further include a data store 728, which may include off-chip (e.g., outside the SoC 704) storage. The data store 728 may include one or more storage elements, including RAM, SRAM, DRAM, VRAM, flash memory, a hard disk, and / or other components and / or devices that can store at least one bit of data.

[0160] The vehicle 700 may further include a GNSS sensor 758. The GNSS sensor 758 (e.g., GPS, assisted GPS sensor, differential GPS (DGPS) sensor, etc.) is used to assist in mapping, perception, occupancy grid generation, and / or path planning functions. Any number of GNSS sensors 758 may be used, including, for example and without limitation, GPS using a USB connector with an Ethernet to serial (RS-232) bridge.

[0161] The vehicle 700 may further include a RADAR sensor 760. The RADAR sensor 760 may be used by the vehicle 700 for remote vehicle detection even in darkness and / or inclement weather conditions. The RADAR functional safety level may be ASIL B. The RADAR sensor 760 may use CAN and / or bus 702 (e.g., to transmit data generated by the RADAR sensor 760) for control and access to object tracking data, accessing Ethernet in some examples to access raw data. A variety of RADAR sensor types may be used. For example and without limitation, the RADAR sensor 760 may be suitable for front, rear, and side RADAR use. In some examples, a pulse Doppler RADAR sensor is used.

[0162] The RADAR sensor 760 may include different configurations, such as long-range with a narrow field of view, short-range with a wide field of view, short-range side coverage, and the like. In some examples, the long-range RADAR may be used for adaptive cruise control functions. The long-range RADAR system may provide a wide field of view (e.g., within a range of 250m) achieved through two or more independent browsing. The RADAR sensor 760 may help distinguish between static objects and moving objects, and may be used by the ADAS system for emergency braking assistance and forward collision warnings. The long-range RADAR sensor may include a single-station multimode RADAR with multiple (e.g., six or more) fixed RADAR antennas and high-speed CAN and FlexRay interfaces. In an example with six antennas, the central four antennas may create a focused beam pattern designed to record the surroundings of the vehicle 700 at a higher rate with minimal traffic interference from adjacent lanes. The other two antennas may expand the field of view, making it possible to quickly detect vehicles entering or leaving the lane of the vehicle 700.

[0163] As an example, a medium-range RADAR system may include a range of up to 760m (front) or 80m (rear) and a field of view of up to 42 degrees (front) or 750 degrees (rear). A short-range RADAR system may include, but is not limited to, a RADAR sensor designed to be mounted on both ends of a rear bumper. When mounted on both ends of a rear bumper, such a RADAR sensor system may create two beams that continuously monitor the blind spots behind and beside the vehicle.

[0164] Short-range RADAR systems can be used in ADAS systems for blind spot detection and / or lane change assistance.

[0165] The vehicle 700 may further include an ultrasonic sensor 762. The ultrasonic sensor 762, which may be placed on the front, rear, and / or sides of the vehicle 700, may be used for parking assistance and / or creating and updating an occupancy grid. A variety of ultrasonic sensors 762 may be used, and different ultrasonic sensors 762 may be used for different detection ranges (e.g., 2.5 m, 4 m). The ultrasonic sensor 762 may operate at a functional safety level of ASIL B.

[0166] The vehicle 700 may include a LIDAR sensor 764. The LIDAR sensor 764 may be used for object and pedestrian detection, emergency braking, collision avoidance, and / or other functions. The LIDAR sensor 764 may be ASIL B for functional safety level. In some examples, the vehicle 700 may include multiple LIDAR sensors 764 (e.g., two, four, six, etc.) that may use Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

[0167] In some examples, the LIDAR sensor 764 may be able to provide a list of objects and their distances for a 360-degree field of view. Commercially available LIDAR sensors 764 may have, for example, an advertised range of approximately 700m, an accuracy of 2cm-3cm, and support for 700Mbps Ethernet connections. In some examples, one or more non-protruding LIDAR sensors 764 may be used. In such examples, the LIDAR sensor 764 may be implemented as a small device that can be embedded in the front, back, side, and / or corner of the vehicle 700. In such examples, the LIDAR sensor 764 may provide a field of view of up to 120 degrees horizontally and 35 degrees vertically, with a range of 200m, even for low reflectivity objects. The front-mounted LIDAR sensor 764 may be configured for a horizontal field of view between 45 and 135 degrees.

[0168] In some examples, LIDAR technologies such as 3D flash LIDAR may also be used. 3D flash LIDAR uses flashes of laser as an emission source to illuminate the vehicle's surroundings up to about 200m. The flash LIDAR unit includes a receiver that records the laser pulse transmission time and reflected light on each pixel, which in turn corresponds to the range from the vehicle to the object. Flash LIDAR can allow highly accurate and distortion-free images of the surrounding environment to be generated with each laser flash. In some examples, four flash LIDAR sensors can be deployed, one on each side of the vehicle 700. Available 3D flash LIDAR systems include solid-state 3D staring array LIDAR cameras (e.g., non-browsing LIDAR devices) with no moving parts other than fans. The flash LIDAR device can use 5 nanosecond Class I (eye-safe) laser pulses per frame, and can capture the reflected laser light in the form of a 3D range point cloud and co-registered intensity data. By using flash LIDAR, and because flash LIDAR is a solid-state device with no moving parts, the LIDAR sensor 764 may be less susceptible to motion blur, vibration, and / or shock.

[0169] The vehicle may further include an IMU sensor 766. In some examples, the IMU sensor 766 may be located at the center of the rear axle of the vehicle 700. The IMU sensor 766 may include, for example and without limitation, an accelerometer, a magnetometer, a gyroscope, a magnetic compass, and / or other sensor types. In some examples, for example, in a six-axis application, the IMU sensor 766 may include an accelerometer and a gyroscope, and in a nine-axis application, the IMU sensor 766 may include an accelerometer, a gyroscope, and a magnetometer.

[0170] However, various aspects of the present disclosure in some embodiments, the IMU sensor 766 can be implemented as a miniature high-performance GPS-assisted inertial navigation system (GPS / INS) that combines micro-electromechanical system (MEMS) inertial sensors, high-sensitivity GPS receivers, and advanced Kalman filter algorithms to provide estimates of position, velocity, and attitude. Thus, in some examples, the IMU sensor 766 can enable the vehicle 700 to estimate heading by directly observing and correlating velocity changes from the GPS to the IMU sensor 766 without the need for input from a magnetic sensor. In some examples, the IMU sensor 766 and the GNSS sensor 758 can be combined into a single integrated unit.

[0171] The vehicle may include a microphone 796 positioned in and / or around the vehicle 700. The microphone 796 may be used for, among other things, emergency vehicle detection and identification.

[0172] The vehicle may further include any number of camera types, including a stereo camera 768, a wide angle camera 770, an infrared camera 772, a surround camera 774, a long-range and / or mid-range camera 798, and / or other camera types. These cameras may be used to capture image data around the entire periphery of the vehicle 700. The type of camera used depends on the embodiment and the requirements of the vehicle 700, and any combination of camera types may be used to provide the necessary coverage around the vehicle 700. In addition, the number of cameras may vary depending on the embodiment. For example, the vehicle may include six cameras, seven cameras, ten cameras, twelve cameras, and / or another number of cameras. As an example and not limitation, the cameras may support Gigabit Multimedia Serial Link (GMSL) and / or Gigabit Ethernet. Each of the cameras described herein may include a 10GbE ... Fig. 7A and Figure 7B Described in more detail.

[0173] The vehicle 700 may further include a vibration sensor 742. The vibration sensor 742 may measure vibrations of components of the vehicle, such as axles. For example, changes in vibration may indicate changes in the road surface. In another example, when two or more vibration sensors 742 are used, the difference between the vibrations may be used to determine friction or slip of the road surface (e.g., when there is a vibration difference between a powered drive shaft and a free-spinning shaft).

[0174] The vehicle 700 may include an ADAS system 738. In some examples, the ADAS system 738 may include a SoC. The ADAS system 738 may include autonomous / adaptive / automatic cruise control (ACC), cooperative adaptive cruise control (CACC), forward collision warning (FCW), automatic emergency braking (AEB), lane departure warning (LDW), lane keeping assist (LKA), blind spot warning (BSW), rear cross traffic warning (RCTW), collision warning system (CWS), lane centering (LC), and / or other features and functions.

[0175] The ACC system may use a RADAR sensor 760, a LIDAR sensor 764, and / or a camera. The ACC system may include a longitudinal ACC and / or a lateral ACC. The longitudinal ACC monitors and controls the distance to the vehicle immediately in front of the vehicle 700, and automatically adjusts the vehicle speed to maintain a safe distance from the vehicle in front. The lateral ACC performs distance keeping and suggests that the vehicle 700 change lanes when necessary. The lateral ACC is related to other ADAS applications such as LCA and CWS.

[0176] CACC uses information from other vehicles, which can be received indirectly from other vehicles via a wireless link or through a network connection (e.g., through the Internet) via a network interface 724 and / or a wireless antenna 726. A direct link can be provided by a vehicle-to-vehicle (V2V) communication link, while an indirect link can be an infrastructure-to-vehicle (I2V) communication link. Typically, the V2V communication concept provides information about the vehicle immediately ahead (e.g., the vehicle immediately ahead of the vehicle 700 and in the same lane as it), while the I2V communication concept provides information about traffic farther ahead. The CACC system may include either or both of the I2V and V2V information sources. Given information about the vehicle ahead of the vehicle 700, CACC can be more reliable, and it is possible to improve the smoothness of traffic flow and reduce road congestion.

[0177] The FCW system is designed to alert the driver to hazards so that the driver can take corrective action. The FCW system uses a front camera and / or RADAR sensor 760 coupled to a dedicated processor, DSP, FPGA and / or ASIC, which is electrically coupled to driver feedback such as a display, speaker and / or vibrating components. The FCW system can provide warnings in the form of, for example, sound, visual warnings, vibrations and / or rapid brake pulses.

[0178] The AEB system detects an impending front collision with another vehicle or other object and can automatically apply the brakes if the driver does not take corrective action within specified time or distance parameters. The AEB system can use a front camera and / or RADAR sensor 760 coupled to a dedicated processor, DSP, FPGA and / or ASIC. When the AEB system detects a hazard, it typically first alerts the driver to take corrective action to avoid the collision, and if the driver does not take corrective action, the AEB system can automatically apply the brakes in an effort to prevent or at least mitigate the effects of the predicted collision. The AEB system may include technologies such as dynamic brake support and / or collision approach braking.

[0179] The LDW system provides visual, audible, and / or tactile warnings, such as steering wheel or seat vibrations, to alert the driver when the vehicle 700 crosses a lane marking. When the driver indicates an intention to leave the lane, by activating a turn signal, the LDW system is not activated. The LDW system may use a front-facing camera coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibrating component.

[0180] The LKA system is a variation of the LDW system. If the vehicle 700 begins to leave the lane, the LKA system provides steering input or braking to correct the vehicle 700.

[0181] The BSW system detects and warns the driver of vehicles in the car's blind spot. The BSW system can provide visual, audible and / or tactile alerts to indicate that it is unsafe to merge or change lanes. The system can provide additional warnings when the driver uses a turn signal. The BSW system can use one or more rear-facing cameras and / or one or more RADAR sensors 760, which are coupled to a dedicated processor, DSP, FPGA and / or ASIC, which are electrically coupled to drive feedback, such as a display, speaker and / or vibration component.

[0182] The RCTW system can provide visual, audible, and / or tactile notifications when an object is detected outside the range of the rear camera while the vehicle 700 is in reverse. Some RCTW systems include AEB to ensure that the vehicle brakes are applied to avoid a crash. The RCTW system can use one or more rear RADAR sensors 760 coupled to a dedicated processor, DSP, FPGA, and / or ASIC that is electrically coupled to driver feedback such as a display, speaker, and / or vibrating component.

[0183] Conventional ADAS systems may be prone to false positive results, which may annoy and distract the driver, but are typically not catastrophic because the ADAS system alerts the driver and allows the driver to decide whether the safety condition really exists and take action accordingly. However, in the autonomous vehicle 700, in the case of conflicting results, the vehicle 700 itself must decide whether to pay attention to the results from the main computer or the auxiliary computer (e.g., the first controller 736 or the second controller 736). For example, in some embodiments, the ADAS system 738 can be a backup and / or auxiliary computer for providing perception information to the backup computer rationality module. The backup computer rationality monitor can run redundant and diverse software on hardware components to detect faults in perception and dynamic driving tasks. The output from the ADAS system 738 can be provided to the supervisory MCU. If the outputs from the main computer and the auxiliary computer conflict, the supervisory MCU must determine how to coordinate the conflict to ensure safe operation.

[0184] In some examples, the master computer can be configured to provide a confidence score to the supervisory MCU, indicating the master computer's confidence in the selected result. If the confidence score exceeds a threshold, the supervisory MCU can follow the direction of the master computer, regardless of whether the auxiliary computer provides conflicting or inconsistent results. In the case where the confidence score does not meet the threshold and the master computer and the auxiliary computer indicate different results (e.g., conflict), the supervisory MCU can arbitrate between these computers to determine the appropriate result.

[0185] The supervisory MCU may be configured to run a neural network that is trained and configured to determine conditions under which the auxiliary computer provides a false alarm based on outputs from the primary computer and the auxiliary computer. Thus, the neural network in the supervisory MCU may learn when the output of the auxiliary computer may be trusted and when it may not. For example, when the auxiliary computer is a RADAR-based FCW system, the neural network in the supervisory MCU may learn when the FCW system is identifying a metal object that is not actually dangerous, such as a drainage grate or manhole cover that triggers an alarm. Similarly, when the auxiliary computer is a camera-based LDW system, the neural network in the supervisory MCU may learn to ignore the LDW when a cyclist or pedestrian is present and lane departure is actually the safest strategy. In embodiments that include a neural network running on the supervisory MCU, the supervisory MCU may include at least one of a DLA or GPU suitable for running the neural network with associated memory. In a preferred embodiment, the supervisory MCU may include and / or be included as a component of the SoC 704.

[0186] In other examples, the ADAS system 738 may include an auxiliary computer that uses traditional computer vision rules to perform ADAS functions. In this way, the auxiliary computer can use classic computer vision rules (if-then), and the presence of a neural network in the supervisory MCU can improve reliability, safety, and performance. For example, diverse implementations and intentional non-identity make the entire system more fault-tolerant, especially for failures caused by software (or software-hardware interface) functions. For example, if there is a software vulnerability or error in the software running on the main computer and the non-identical software code running on the auxiliary computer provides the same overall result, the supervisory MCU can be more confident that the overall result is correct and that the vulnerability in the software or hardware on the main computer does not cause a substantial error.

[0187] In some examples, the output of the ADAS system 738 can be fed to the perception block of the main computer and / or the dynamic driving task block of the main computer. For example, if the ADAS system 738 indicates a forward collision warning due to an object immediately ahead, the perception block can use this information in identifying the object. In other examples, the auxiliary computer can have its own neural network that is trained and thus reduces the risk of false positives as described herein.

[0188] The vehicle 700 may further include an infotainment SoC 730 (e.g., an in-vehicle infotainment system (IVI)). Although illustrated and described as an SoC, the infotainment system may not be an SoC and may include two or more discrete components. The infotainment SoC 730 may include a combination of hardware and software that may be used to provide audio (e.g., music, personal digital assistant, navigation instructions, news, radio, etc.), video (e.g., TV, movies, streaming media, etc.), phone (e.g., hands-free calling), network connectivity (e.g., LTE, Wi-Fi, etc.), and / or information services (e.g., navigation system, rear parking assistance, radio data system, vehicle-related information such as fuel level, total distance covered, brake fuel level, oil level, door open / closed, air filter information, etc.) to the vehicle 700. For example, the infotainment SoC 730 may include a radio, a disc player, a navigation system, a video player, USB and Bluetooth connectivity, an onboard computer, onboard entertainment, Wi-Fi, steering wheel audio controls, hands-free voice controls, a head-up display (HUD), an HMI display 734, a telematics device, a control panel (e.g., for controlling and / or interacting with various components, features, and / or systems), and / or other components. The infotainment SoC 730 may further be used to provide information (e.g., visual and / or auditory) to a user of the vehicle, such as information from an ADAS system 738, autonomous driving information such as planned vehicle maneuvers, trajectories, surrounding environment information (e.g., intersection information, vehicle information, road information, etc.), and / or other information.

[0189] The infotainment SoC 730 may include GPU functionality. The infotainment SoC 730 may communicate with other devices, systems, and / or components of the vehicle 700 via a bus 702 (e.g., a CAN bus, Ethernet, etc.). In some examples, the infotainment SoC 730 may be coupled to a supervisory MCU so that in the event of a failure of a master controller 736 (e.g., a main and / or backup computer of the vehicle 700), the GPU of the infotainment system may perform some self-driving functions. In such an example, the infotainment SoC 730 may place the vehicle 700 in a driver-safe parking mode as described herein.

[0190] The vehicle 700 may further include an instrument cluster 732 (e.g., a digital instrument panel, an electronic instrument cluster, a digital instrument panel, etc.). The instrument cluster 732 may include a controller and / or a supercomputer (e.g., a separate controller or a supercomputer). The instrument cluster 732 may include a set of instruments, such as a speedometer, a fuel level, an oil pressure, a tachometer, an odometer, a turn indicator, a shift position indicator, a seat belt warning light, a parking brake warning light, an engine fault light, an airbag (SRS) system information, lighting controls, safety system controls, navigation information, and the like. In some examples, information may be displayed and / or shared between the infotainment SoC 730 and the instrument cluster 732. In other words, the instrument cluster 732 may be included as part of the infotainment SoC 730, or vice versa.

[0191] Fig.7D For a cloud-based server and Fig. 7A Schematic diagram of a system for communicating between an example autonomous vehicle 700. System 776 may include a server 778, a network 790, and a vehicle including vehicle 700. Server 778 may include multiple GPUs 784 (A)-784 (H) (collectively referred to as GPUs 784 here), PCIe switches 782 (A)-782 (H) (collectively referred to as PCIe switches 782 here), and / or CPUs 780 (A)-780 (B) (collectively referred to as CPUs 780 here). GPUs 784, CPUs 780, and PCIe switches may be interconnected with high-speed interconnects and / or PCIe connections 786 such as, for example and without limitation, NVLink interfaces 788 developed by NVIDIA. In some examples, GPUs 784 are connected via NVLink and / or NVSwitch SoCs, and GPUs 784 and PCIe switches 782 are connected via PCIe interconnects. Although eight GPUs 784, two CPUs 780, and two PCIe switches are illustrated, this is not intended to be limiting. Depending on the embodiment, each of the servers 778 may include any number of GPUs 784, CPUs 780, and / or PCIe switches. For example, each of the servers 778 may include eight, sixteen, thirty-two, and / or more GPUs 784.

[0192] Server 778 may receive image data over network 790 and from a vehicle, the image data representing images showing unexpected or changed road conditions, such as recently begun road work. Server 778 may transmit neural network 792, updated neural network 792, and / or map information 794, including information about traffic and road conditions, over network 790 and to the vehicle. Updates to map information 794 may include updates to HD map 722, such as information about construction sites, potholes, curves, flooding, or other obstacles. In some examples, neural network 792, updated neural network 792, and / or map information 794 may have been generated from new training and / or data received from any number of vehicles in the environment and / or based on experience from training performed at a data center (e.g., using server 778 and / or other servers).

[0193] Server 778 can be used to train a machine learning model (e.g., a neural network) based on training data. The training data can be generated by the vehicle, and / or can be generated in simulation (e.g., using a game engine). In some examples, the training data is labeled (e.g., in the case where the neural network benefits from supervised learning) and / or undergoes other preprocessing, while in other examples, the training data is not labeled and / or preprocessed (e.g., in the case where the neural network does not require supervised learning). Training can be performed according to any one or more classes of machine learning techniques, including but not limited to the following classes: supervised training, semi-supervised training, unsupervised training, self-learning, reinforcement learning, joint learning, transfer learning, feature learning (including principal component and cluster analysis), multilinear subspace learning, manifold learning, representation learning (including alternate dictionary learning), rule-based machine learning, anomaly detection, and any variants or combinations thereof. Once the machine learning model is trained, the machine learning model can be used by the vehicle (e.g., transmitted to the vehicle via network 790), and / or the machine learning model can be used by server 778 to remotely monitor the vehicle.

[0194] In some examples, server 778 can receive data from the vehicle and apply the data to the latest real-time neural network for real-time intelligent reasoning. Server 778 may include a deep learning supercomputer and / or a dedicated AI computer powered by GPU 784, such as DGX and DGX Station machines developed by NVIDIA. However, in some examples, server 778 may include a deep learning infrastructure of a data center powered only by CPUs.

[0195] The deep learning infrastructure of server 778 may be capable of rapid real-time inference, and may use this capability to assess and verify the health of the processors, software, and / or associated hardware in vehicle 700. For example, the deep learning infrastructure may receive periodic updates from vehicle 700, such as a sequence of images and / or objects located in the sequence of images that vehicle 700 has located (e.g., via computer vision and / or other machine learning object classification techniques). The deep learning infrastructure may run its own neural network to identify objects and compare them to the objects identified by vehicle 700, and if the results do not match and the infrastructure concludes that the AI ​​in vehicle 700 has failed, server 778 may transmit a signal to vehicle 700 instructing the fail-safe computer of vehicle 700 to take control, notify passengers, and complete a safe parking maneuver.

[0196] For reasoning, the server 778 may include a GPU 784 and one or more programmable reasoning accelerators (e.g., NVIDIA's TensorRT). The combination of GPU-powered servers and reasoning acceleration can make real-time responses possible. In other examples, such as when performance is not so important, CPU, FPGA, and other processor-powered servers can be used for reasoning.

[0197] Example computing device

[0198] Figure 8 800 is a block diagram of an example computing device 800 suitable for implementing some embodiments of the present disclosure. The computing device 800 may include an interconnect system 802 that directly or indirectly couples the following devices: a memory 804, one or more central processing units (CPUs) 806, one or more graphics processing units (GPUs) 808, a communication interface 810, an input / output (I / O) port 812, an input / output component 814, a power supply 816, one or more presentation components 818 (e.g., a display), and one or more logic units 820. In at least one embodiment, the computing device 800 may include one or more virtual machines (VMs), and / or any of its components may include virtual components (e.g., virtual hardware components). For non-limiting examples, the one or more GPUs 808 may include one or more vGPUs, the one or more CPUs 806 may include one or more vCPUs, and / or the one or more logic units 820 may include one or more virtual logic units. Thus, computing device 800 may include discrete components (eg, a complete GPU dedicated to computing device 800 ), virtual components (eg, a portion of a GPU dedicated to computing device 800 ), or a combination thereof.

[0199] although Figure 8The various blocks of are shown as being connected via an interconnect system 802 having wires, but this is not intended to be limiting and is merely for clarity. For example, in some embodiments, a presentation component 818 such as a display device may be considered an I / O component 814 (e.g., if the display is a touch screen). As another example, the CPU 806 and / or the GPU 808 may include memory (e.g., the memory 804 may represent a storage device in addition to the memory of the GPU 808, the CPU 806, and / or other components). In other words, Figure 8 The computing devices described herein are illustrative only. No distinction is made between categories such as "workstations," "servers," "laptops," "desktops," "tablets," "client devices," "mobile devices," "handheld devices," "game consoles," "electronic control units (ECUs)," "virtual reality systems," and / or other device or system types, as all are considered Figure 8 within the range of computing devices.

[0200] The interconnection system 802 may represent one or more links or buses, such as an address bus, a data bus, a control bus, or a combination thereof. The interconnection system 802 may include one or more links or bus types, such as an industry standard architecture (ISA) bus, an extended industry standard architecture (EISA) bus, a video electronics standard association (VESA) bus, a peripheral component interconnect (PCI) bus, a peripheral component interconnect express (PCIe) bus, and / or another type of bus or link. In some embodiments, there is a direct connection between components. As an example, the CPU 806 may be directly connected to the memory 804. In addition, the CPU 806 may be directly connected to the GPU 808. In the case where there is a direct or point-to-point connection between components, the interconnection system 802 may include a PCIe link to perform the connection. In these examples, the computing device 800 does not need to include a PCI bus.

[0201] Memory 804 may include any of a variety of computer-readable media. Computer-readable media may be any available media that can be accessed by computing device 800. Computer-readable media may include volatile and nonvolatile media and removable and non-removable media. By way of example and not limitation, computer-readable media may include computer storage media and communication media.

[0202] Computer storage media may include volatile and nonvolatile media and / or removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, and / or other data types. For example, memory 804 may store computer-readable instructions (e.g., representing programs and / or program elements, such as an operating system). Computer storage media may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other storage technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by computing device 800. As used herein, computer storage media does not include the signals themselves.

[0203] Computer storage media may contain computer readable instructions, data structures, program modules, and / or other data types in a modulated data signal such as a carrier wave or other transmission mechanism, and include any information delivery media. The term "modulated data signal" may refer to a signal that has one or more of its characteristics set or changed in such a way that information is encoded into the signal. By way of example and not limitation, computer storage media may include wired media such as a wired network or a direct wired connection, and wireless media such as sound, RF, infrared, and other wireless media. Any combination of the above should also be included within the scope of computer readable media.

[0204] The CPU 806 may be configured to execute at least some of the computer-readable instructions to control one or more components of the computing device 800 to perform one or more of the methods and / or processes described herein. Each of the CPUs 806 may include one or more cores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.) capable of simultaneously processing a large number of software threads. The CPU 806 may include any type of processor, and may include different types of processors, depending on the type of computing device 800 implemented (e.g., a processor with fewer cores for mobile devices and a processor with more cores for servers). For example, depending on the type of computing device 800, the processor may be an Advanced RISC Mechanism (ARM) processor implemented using Reduced Instruction Set Computing (RISC) or an x86 processor implemented using Complex Instruction Set Computing (CISC). The computing device 800 may also include one or more CPUs 806 in addition to one or more microprocessors or supplementary coprocessors such as math coprocessors.

[0205] In addition to or in place of the CPU 806, the GPU 808 may also be configured to execute at least some computer-readable instructions to control one or more components of the computing device 800 to perform one or more methods and / or processes described herein. One or more GPUs 808 may be integrated GPUs (e.g., with one or more CPUs 806) and / or one or more GPUs 808 may be discrete GPUs. In an embodiment, one or more GPUs 808 may be coprocessors of one or more CPUs 806. The computing device 800 may use the GPU 808 to render graphics (e.g., 3D graphics) or perform general-purpose computing. For example, the GPU 808 may be used for general-purpose computing on the GPU (GPGPU). The GPU 808 may include hundreds or thousands of cores capable of processing hundreds or thousands of software threads simultaneously. The GPU 808 may generate pixel data for outputting an image in response to a rendering command (e.g., a rendering command from the CPU 806 received via a host interface). The GPU 808 may include a graphics memory such as a display memory for storing pixel data or any other suitable data (e.g., GPGPU data). Display memory may be included as part of memory 804. GPU 808 may include two or more GPUs operating in parallel (e.g., via a link). The link may connect the GPUs directly (e.g., using NVLINK) or through a switch (e.g., using NVSwitch). When combined together, each GPU 808 may generate pixel data or GPGPU data for different portions of output or for different outputs (e.g., a first GPU for a first image and a second GPU for a second image). Each GPU may include its own memory or may share memory with other GPUs.

[0206] In addition to or in lieu of the CPU 806 and / or the GPU 808, the logic unit 820 may be configured to execute at least some computer-readable instructions to control one or more components of the computing device 800 to perform one or more methods and / or processes described herein. In an embodiment, the CPU 806, the GPU 808, and / or the logic unit 820 may discretely or jointly perform any combination of methods, processes, and / or portions thereof. The one or more logic units 820 may be a part of and / or integrated into one or more CPUs 806 and / or one or more GPUs 808 and / or the one or more logic units 820 may be discrete components of the CPU 806 and / or GPU 808 or otherwise external thereto. In an embodiment, the one or more logic units 820 may be a processor of one or more CPUs 806 and / or one or more GPUs 808.

[0207] Examples of logic unit 820 include one or more processing cores and / or components thereof, such as a data processing unit (DPU), a tensor core (TC), a tensor processing unit (TPU), a pixel vision core (PVC), a vision processing unit (VPU), a graphics processing cluster (GPC), a texture processing cluster (TPC), a streaming multiprocessor (SM), a tree traversal unit (TTU), an artificial intelligence accelerator (AIA), a deep learning accelerator (DLA), an arithmetic logic unit (ALU), an application specific integrated circuit (ASIC), a floating point unit (FPU), an input / output (I / O) element, a peripheral component interconnect (PCI) or a peripheral component interconnect express (PCIe) element, etc.

[0208] The communication interface 810 may include one or more receivers, transmitters, and / or transceivers that enable the computing device 800 to communicate with other computing devices via an electronic communication network, including wired and / or wireless communications. The communication interface 810 may include components and functions that enable communication over any of a number of different networks, such as wireless networks (e.g., Wi-Fi, Z-wave, Bluetooth, Bluetooth LE, ZigBee, etc.), wired networks (e.g., communicating over Ethernet or InfiniBand), low power wide area networks (e.g., LoRaWAN, SigFox, etc.), and / or the Internet. In one or more embodiments, the logic unit 820 and / or the communication interface 810 may include one or more data processing units (DPUs) to transmit data received over a network and / or over the interconnect system 802 directly to one or more GPUs 808 (e.g., memory in a GPU 808).

[0209] The I / O port 812 can enable the computing device 800 to be logically coupled to other devices including I / O components 814, presentation components 818, and / or other components, some of which can be built into (e.g., integrated into) the computing device 800. Illustrative I / O components 814 include microphones, mice, keyboards, joysticks, game pads, game controllers, satellite dishes, browsers, printers, wireless devices, and the like. The I / O components 814 can provide a natural user interface (NUI) that processes air gestures, voice, or other physiological inputs generated by the user. In some instances, the input can be transmitted to an appropriate network element for further processing. The NUI can implement any combination of voice recognition, stylus recognition, facial recognition, biometric recognition, gesture recognition on and adjacent to the screen, air gestures, head and eye tracking, and touch recognition associated with the display of the computing device 800 (as described in more detail below). The computing device 800 may include a depth camera such as a stereo camera system, an infrared camera system, an RGB camera system, touch screen technology, and a combination of these for gesture detection and recognition. Additionally, computing device 800 may include an accelerometer or gyroscope to enable motion detection (e.g., as part of an inertial measurement unit (IMU)). In some examples, the output of the accelerometer or gyroscope may be used by computing device 800 to render immersive augmented reality or virtual reality.

[0210] The power supply 816 may include a hardwired power supply, a battery power supply, or a combination thereof. The power supply 816 may provide power to the computing device 800 to enable the components of the computing device 800 to operate.

[0211] The presentation component 818 may include a display (e.g., a monitor, a touch screen, a television screen, a head-up display (HUD), other display types, or a combination thereof), a speaker, and / or other presentation components. The presentation component 818 may receive data from other components (e.g., GPU 808, CPU 806, DPU, etc.) and output the data (e.g., as an image, video, sound, etc.).

[0212] Sample Data Center

[0213] Fig. 9 An example data center 900 is shown, which may be used in at least one embodiment of the present disclosure. The data center 900 may include a data center infrastructure layer 910, a framework layer 920, a software layer 930, and an application layer 940.

[0214] like Fig. 9As shown, the data center infrastructure layer 910 may include a resource coordinator 912, grouped computing resources 914, and node computing resources ("node CRs") 916 (1)-916 (N), where "N" represents any complete positive integer. In at least one embodiment, the node CRs 916 (1)-916 (N) may include, but are not limited to, any number of central processing units (CPUs) or other processors (including DPUs, accelerators, field programmable gate arrays (FPGAs), graphics processors or graphics processing units (GPUs), etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid-state drives or disk drives), network input / output (NW I / O) devices, network switches, virtual machines (VMs), power modules and cooling modules, etc. In some embodiments, one or more of the node CRs 916 (1)-916 (N) may correspond to a server having one or more of the above-mentioned computing resources. Furthermore, in some embodiments, nodes CR 916 ( 1 )- 916 (N) may include one or more virtual components, such as vGPUs, vCPUs, etc., and / or one or more of nodes CR 916 ( 1 )- 916 (N) may correspond to virtual machines (VMs).

[0215] In at least one embodiment, the computing resources 914 of the grouping can include the separate grouping (not shown) of the node CR916 contained in one or more racks, or many racks (not shown) contained in the data center of each geographical location. The separate grouping of the node CR916 in the computing resources 914 of the grouping can include the computing, network, memory or storage resources that can be configured or assigned to support the grouping of one or more workloads. In at least one embodiment, several node CR916 including CPU, GPU, DPU and / or other processors can be grouped in one or more racks to provide computing resources to support one or more workloads. One or more racks can also include any number of power modules, cooling modules and / or network switches in any combination.

[0216] Resource coordinator 912 may configure or otherwise control one or more nodes CR 916(1)-916(N) and / or grouped computing resources 914. In at least one embodiment, resource coordinator 912 may include a software design infrastructure (SDI) management entity for data center 900. Resource coordinator 912 may include hardware, software, or some combination thereof.

[0217] In at least one embodiment, Fig. 9As shown, the framework layer 920 may include a job scheduler 933, a configuration manager 934, a resource manager 936, and a distributed file system 938. The framework layer 920 may include a framework that supports the software 932 of the software layer 930 and / or one or more applications 942 of the application layer 940. The software 932 or the application 942 may include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud, and Microsoft Azure. The framework layer 920 may be, but is not limited to, a free and open source software network application framework, such as Apache Spark, which may utilize the distributed file system 938 for large-scale data processing (e.g., "big data"). TM (hereinafter referred to as "Spark"). In at least one embodiment, the job scheduler 933 may include a Spark driver to facilitate scheduling of workloads supported by various layers of the data center 900. In at least one embodiment, the configuration manager 934 may be able to configure different layers, such as the software layer 930 and the framework layer 920 including Spark and a distributed file system 938 for supporting large-scale data processing. The resource manager 936 can manage cluster or group computing resources mapped to or allocated to support the distributed file system 938 and the job scheduler 933. In at least one embodiment, the cluster or group computing resources may include grouped computing resources 914 at the data center infrastructure layer 910. The resource manager 936 can coordinate with the resource coordinator 912 to manage these mapped or allocated computing resources.

[0218] In at least one embodiment, software 932 included in software layer 930 may include software used by at least a portion of node CRs 916(1)-916(N), grouped computing resources 914, and / or distributed file system 938 of framework layer 920. One or more types of software may include, but are not limited to, Internet web page search software, email virus scanning software, database software, and streaming video content software.

[0219] In at least one embodiment, one or more applications 942 included in the application layer 940 may include one or more types of applications used by at least a portion of the node CRs 916(1)-916(N), the grouped computing resources 914, and / or the distributed file system 938 of the framework layer 920. The one or more types of applications may include, but are not limited to, any number of genomics applications, cognitive computing, and machine learning applications, including training or inference software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.), and / or other machine learning applications used in conjunction with one or more embodiments.

[0220] In at least one embodiment, any of configuration manager 934, resource manager 936, and resource coordinator 912 can implement any number and type of self-modification actions based on any number and type of data acquired in any technically feasible manner. The self-modification actions can relieve the data center operator of data center 900 from making potentially bad configuration decisions and can avoid underutilized and / or underperforming portions of the data center.

[0221] The data center 900 may include tools, services, software, or other resources for training one or more machine learning models or using one or more machine learning models to predict or infer information according to one or more embodiments described herein. For example, a machine learning model may be trained by calculating weight parameters according to a neural network architecture using the software and computing resources described above with respect to the data center 900. In at least one embodiment, by using weight parameters calculated by one or more training techniques, the resources described above with respect to the data center 900 may be used to infer or predict information using a trained machine learning model corresponding to one or more neural networks, such as but not limited to those described herein.

[0222] In at least one embodiment, the data center 900 can use a CPU, an application-specific integrated circuit (ASIC), a GPU, an FPGA, and / or other hardware (or virtual computing resources corresponding thereto) to use the above resources to perform training and / or reasoning. In addition, one or more of the above software and / or hardware resources can be configured as a service to allow users to train or perform information reasoning, such as image recognition, speech recognition, or other artificial intelligence services.

[0223] Example network environment

[0224] A network environment suitable for implementing embodiments of the present disclosure may include one or more client devices, servers, network attached storage (NAS), other backend devices, and / or other device types. The client devices, servers, and / or other device types (e.g., each device) may be configured to communicate with the client device, server, or other device types. Figure 8 The backend device 900 may be implemented on one or more instances of the computing device 800 of the present invention—for example, each device may include similar components, features, and / or functions of the computing device 800. In addition, in the case of implementing a backend device (e.g., a server, NAS, etc.), the backend device may be included as part of the data center 900, examples of which are described herein with respect to Fig. 9 Describe in more detail.

[0225] The components of the network environment can communicate with each other through the network, which can be wired, wireless, or both. The network can include multiple networks, or a network of multiple networks. For example, the network can include one or more wide area networks (WANs), one or more local area networks (LANs), one or more public networks (e.g., the Internet and / or a public switched telephone network (PSTN)), and / or one or more private networks. In the case where the network includes a wireless telecommunications network, components such as base stations, communication towers, or even access points (and other components) can provide wireless connections.

[0226] Compatible network environments may include one or more peer-to-peer network environments (in which case the server may not be included in the network environment), and one or more client-server network environments (in which case one or more servers may be included in the network environment). In a peer-to-peer network environment, the functionality described herein with respect to the server may be implemented on any number of client devices.

[0227] In at least one embodiment, the network environment may include one or more cloud-based network environments, distributed computing environments, combinations thereof, and the like. A cloud-based network environment may include a framework layer, a job scheduler, a resource manager, and a distributed file system implemented on one or more servers, which may include one or more core network servers and / or edge servers. The framework layer may include a framework for supporting software at the software layer and / or one or more applications at the application layer. The software or application may include network-based service software or application programs, respectively. In an embodiment, one or more client devices may use network-based service software or application programs (e.g., by accessing the service software and / or application programs via one or more application programming interfaces (APIs)). The framework layer may be, but is not limited to, a type of free and open source software network application framework, such as a distributed file system that may be used for large-scale data processing (e.g., "big data").

[0228] A cloud-based network environment can provide cloud computing and / or cloud storage that performs any combination of the computing and / or data storage functions (or one or more portions thereof) described herein. Any of these various functions can be distributed across multiple locations from a central or core server (e.g., one or more data centers that can be distributed across states, regions, countries, the world, etc.). If the connection to the user (e.g., client device) is relatively close to an edge server, the core server can assign at least a portion of the functionality to the edge server. A cloud-based network environment can be private (e.g., limited to a single organization), public (e.g., available to many organizations), and / or a combination thereof (e.g., a hybrid cloud environment).

[0229] The client device may include Figure 8 At least some of the components, features, and functionality of the described example computing device 800. By way of example and not limitation, the client device may be embodied as a personal computer (PC), a laptop computer, a mobile device, a smartphone, a tablet computer, a smart watch, a wearable computer, a personal digital assistant (PDA), an MP3 player, a virtual reality head mounted display, a global positioning system (GPS) or device, a video player, a camera, a surveillance device or system, a vehicle, a vessel, an aircraft, a virtual machine, a drone, a robot, a handheld communication device, a hospital device, a gaming device or system, an entertainment system, an in-vehicle computer system, an embedded system controller, a remote control, an appliance, a consumer electronic device, a workstation, an edge device, any combination of these described devices, or any other suitable device.

[0230] The present disclosure may be described in the general context of machine-usable instructions or computer codes executed by a computer or other machine such as a personal digital assistant or other handheld device, including computer-executable instructions such as program modules. Typically, program modules including routines, programs, objects, components, data structures, etc. refer to codes that perform specific tasks or implement specific abstract data types. The present disclosure may be practiced in a variety of system configurations, including handheld devices, consumer electronics, general-purpose computers, more specialized computing devices, etc. The present disclosure may also be practiced in a distributed computing environment in which tasks are performed by remote processing devices linked through a communication network.

[0231] As used herein, the description of "and / or" about two or more elements should be interpreted as referring to only one element or a combination of elements. For example, "element A, element B and / or element C" may include only element A, only element B, only element C, element A and element B, element A and element C, element B and element C, or elements A, B and C. In addition, "at least one of element A or element B" may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B. Further, "at least one of element A and element B" may include at least one of element A, at least one of element B, or at least one of element A and at least one of element B.

[0232] The subject matter of the present disclosure is described in detail herein to meet statutory requirements. However, the description itself is not intended to limit the scope of the present disclosure. On the contrary, the inventors have contemplated that the claimed subject matter may also be embodied in other ways to include steps different from the steps described herein in conjunction with other current or future technologies or combinations of similar steps. Moreover, although the terms "step" and / or "block" may be used herein to imply different elements of the method employed, these terms should not be interpreted as implying any particular order among or between the various steps disclosed herein, unless the order of the steps is explicitly described.

Claims

1. A system comprising: One or more circuits configured to cause operations to be performed, the operations comprising: receiving video data obtained using one or more sensors of a machine, the video data corresponding to images applied to one or more machine learning models MLM to perform one or more control operations on the machine; detecting that a deviation between a bit rate corresponding to a first portion of the encoded stream of the video data and a reference bit rate has exceeded a threshold; calculating, based at least on the deviation exceeding the threshold, one or more values ​​of one or more metrics quantifying a visual difference between the first portion of the encoded stream and the video data; determining at least one value of one or more quantization parameters based at least on the one or more values ​​of the one or more metrics; encoding the video data using the at least one value of the one or more quantization parameters to generate a second portion of the encoded stream; and One or more video clips corresponding to the encoded stream are determined.

2. The system of claim 1, wherein the one or more quantization parameters include: B frame quantization parameter, I frame quantization parameter and P frame quantization parameter.

3. The system of claim 1, wherein detecting the deviation is based at least on determining that the bit rate is outside a high water mark and a low water mark relative to a reference bit rate.

4. The system of claim 1 , wherein calculating the one or more values ​​is based at least on: sampling a region of one or more frames from the first portion of the encoded stream and the video data to generate samples of the region; and The samples of the region in the encoded stream and the video data are compared.

5. The system of claim 4, wherein the samples are generated using a non-random spatial distribution algorithm.

6. The system of claim 1, wherein the operations further comprise one or more of: saving the one or more video clips to a file system of the machine; or The one or more video clips are uploaded to a remote server.

7. The system of claim 1, wherein the one or more video clips are masked from personally identifiable information.

8. The system of claim 1, wherein the determination of the one or more video clips is based at least on detecting an event in an environment of the machine.

9. The system of claim 1, wherein the system comprises at least one of: control systems for autonomous or semi-autonomous machines; Perception systems for autonomous or semi-autonomous machines; A system for performing simulation operations; Systems for performing digital twin operations; A system for performing light transport simulations; A system for performing collaborative content creation of 3D assets; Systems for performing deep learning operations; A system implementing one or more large language models (LLMs); Systems implemented using edge devices; Systems implemented using machines; Systems for performing conversational AI operations; Systems for generating synthetic data; A system comprising one or more virtual machines VM; A system implemented at least in part in a data center; or A system implemented at least in part using cloud computing resources.

10. A method comprising: encoding one or more first video frames represented by video data obtained using one or more sensors having one or more sensing fields using one or more quantization parameters to generate one or more first encoded video frames; calculating one or more values ​​of one or more metrics quantifying visual differences between the one or more first video frames and the one or more first encoded video frames; determining one or more updates to the one or more quantization parameters based at least on the one or more values ​​of one or more metrics; encoding one or more second video frames represented by the video data using the one or more updates to the one or more quantization parameters to generate one or more second encoded frames; as well as One or more video clips corresponding to the one or more second encoded frames are determined.

11. The method of claim 10, wherein calculating the one or more values ​​of the one or more metrics is based at least on detecting that a deviation between a bit rate corresponding to the one or more first encoded video frames and a reference bit rate has exceeded a threshold.

12. The method of claim 10, wherein the one or more first video frames are applied to one or more machine learning models (MLM) to perform one or more control operations on a machine, and the one or more sensors are associated with the machine.

13. The method of claim 10, wherein the one or more quantization parameters include: B frame quantization parameter, I frame quantization parameter and P frame quantization parameter.

14. The method of claim 10, wherein calculating the one or more values ​​of the one or more metrics is based at least on detecting changes in lighting conditions in an environment of a machine including the one or more sensors.

15. The method of claim 10, wherein calculating the one or more values ​​is based at least on: Sampling a region of the one or more first video frames and the one or more first encoded video frames to generate samples of the region; and The samples of the region in the one or more first video frames and the one or more first encoded video frames are compared.

16. A processor, comprising: One or more circuits for determining one or more video clips corresponding to an encoded stream of video data obtained using one or more sensors of a machine, the encoded stream being generated using one or more quantization parameters corresponding to one or more values ​​of one or more metrics quantizing a visual difference between a portion of the encoded stream and the video data.

17. The processor of claim 16, wherein the one or more values ​​of the one or more metrics are calculated based at least on the fact that a deviation between a bit rate corresponding to the encoded stream and a reference bit rate has exceeded a threshold.

18. The processor of claim 16, wherein the video data is applied to one or more machine learning models (MLM) to perform one or more control operations on the machine.

19. The processor of claim 16, wherein the one or more quantization parameters include: B frame quantization parameter, I frame quantization parameter and P frame quantization parameter.

20. The processor of claim 16, wherein the processor comprises at least one of: control systems for autonomous or semi-autonomous machines; Perception systems for autonomous or semi-autonomous machines; A system for performing simulation operations; Systems for performing digital twin operations; A system for performing light transport simulations; A system for performing collaborative content creation of 3D assets; Systems for performing deep learning operations; A system implementing one or more large language models (LLMs); Systems implemented using edge devices; Systems implemented using machines; Systems for performing conversational AI operations; Systems for generating synthetic data; A system comprising one or more virtual machines VM; A system implemented at least in part in a data center; or A system implemented at least in part using cloud computing resources.

Images