Generation of different data streams based on temporal relevance

Abstract

An integrated circuit is described that performs multiple distinct types of measurements. This integrated circuit may include a measurement circuit. Furthermore, the integrated circuit may include, or be electrically coupled to, at least one sensor. During operation, the integrated circuit may perform distinct types of measurements of, or associated with, an object in an environment of poor or obscured information within the visible frequency band. For example, an environment of poor or obscured information may include fog or clouds. It should be noted that performing distinct types of measurements may include filtering measurements based at least partially on velocity relative to the ground and providing data streams having different spatial frequencies and sampling rates based at least partially on the filtering.

Description

Technical Field

[0001] The present disclosure relates to techniques for increasing the amount of information obtained from one or more sensors by using virtual sensors to generate different data streams.

Background Art

[0002] To provide improved safety and highly convenient means of transportation, many automobile manufacturers have equipped vehicles with additional sensors and / or functions. For example, autonomous vehicles typically include a variety of sensors such as acoustic and / or electromagnetic sensors that monitor the surrounding environment to detect other vehicles, people, animals, or obstacles. However, using multiple sensors (e.g., different types of sensors) typically increases the complexity and cost of the vehicle.

[0003] In principle, the cost and complexity of a vehicle can be reduced by eliminating sensors and / or using a single type of sensor. However, in practice, such rationalization and simplification can cause problems. For example, the performance of an autonomous vehicle, and hence its safety, can degrade in difficult cases such as when driving with reduced or obscured vision.

Summary of the Invention

Means for Solving the Problems

[0004] Integrated circuit embodiments are described. The integrated circuit includes a measurement circuit that uses one sensor (more generally, a reduced number of sensors) to perform multiple distinct types of measurements of an object within, or related to, an environment with poor or obscured information at frequencies in the visible light band. However, in other embodiments, the integrated circuit may perform distinct types of measurements in a variety of environments.

[0005] In particular, distinct types of measurements may include different types of measurements. Furthermore, distinct types of measurements may include time-of-arrival (TOA) measurements. Furthermore, distinct types of measurements may include radar measurements (such as pulsed echo measurements) and / or LiDAR measurements.

[0006] In addition, environments with limited or unclear information may include fog or clouds (such as visible clumps of condensed water vapor on or near the ground).

[0007] In some embodiments, a distinct type of measurement may be performed using multiple paths with different path lengths within the sensor and / or integrated circuit.

[0008] It should be noted that different types of measurements may provide multiple methods for determining one or more of the following: the position of an object, the movement of an object, the size of an object, the angle of incidence of the received signal, or the material properties of an object (such as reflectance).

[0009] Furthermore, a separate type of measurement may include a temporally related measurement in which the spatial measurement has a spatial frequency greater than a first predefined value and the temporal measurement has a sampling rate as a function of time less than a second predefined value.

[0010] Alternatively, a separate type of measurement may include spatially related measurements in which the spatial measurement has a spatial frequency smaller than a third predefined value.

[0011] Furthermore, the integrated circuit may provide a measurement in a distinct type of measurement having a latency less than a fourth predefined value before providing a second measurement in a distinct type of measurement having a latency greater than a fifth predefined value.

[0012] It should be noted that different types of measurements may have a confidence level (or level of certainty that the data is correct) that exceeds the sixth predefined value (such as 90, 95, or 99%).

[0013] In addition, a separate type of measurement may correspond to the field of view, which is a subset of the sensor's scanning area.

[0014] In some embodiments, performing a distinct type of measurement may involve filtering the measurements based at least partially on the velocity relative to the ground and providing data streams having different spatial frequencies and sampling rates based at least partially on the filtering. For example, the data streams may include a first data stream and a second data stream, the first data stream having a higher spatial frequency (or resolution) and a lower sampling rate (frame rate) than the second data stream.

[0015] It should be noted that integrated circuits can dynamically adapt separate types of measurements based at least partially on one or more properties of an object (such as the object's size, velocity, visibility of the object in the visible frequency band, angle of incidence of the received signal, material properties of the object, e.g., reflectivity) and / or the presence of the environment.

[0016] In some embodiments, the integrated circuit performs a distinct type of measurement by executing firmware or software. The integrated circuit may provide a sensor system that includes a processor and firmware or software, and includes or is coupled to at least a sensor.

[0017] Another embodiment provides an electronic device including an integrated circuit.

[0018] Another embodiment provides a system including an integrated circuit.

[0019] Another embodiment provides a method for performing a distinct type of measurement. This method includes at least some of the operations performed by an integrated circuit.

[0020] The summary of the invention is presented for the purpose of illustrating some exemplary embodiments to assist in a basic understanding of some aspects of the subject matter described herein. Thus, it will be understood that the features described above are examples and should not be construed in any way as narrowing the scope or spirit of the subject matter described herein. Other features, aspects, and advantages of the subject matter described herein will become apparent from the following detailed description of the invention, the drawings, and the claims.

[0021] Note that like reference numerals indicate corresponding parts throughout the drawings. Further, multiple instances of the same part are designated by a common prefix separated from the instance number by a dash.

Brief Description of the Drawings

[0022] [Figure 1] A drawing illustrating an example of a vehicle equipped with a radar sensor according to some embodiments of the present disclosure. [Figure 2] A block diagram illustrating an example of a driver assistance system according to some embodiments of the present disclosure. [Figure 3] A block diagram illustrating an example of a radar system according to some embodiments of the present disclosure. [Figure 4] A block diagram illustrating an example of a radar system according to some embodiments of the present disclosure. [Figure 5] A block diagram illustrating an example of the operation of a measurement system according to some embodiments of the present disclosure. [Figure 6] A drawing illustrating an example of a scanning pattern according to some embodiments of the present disclosure. [Figure 7] A drawing illustrating an example of a high-resolution scene and a low-resolution scene according to some embodiments of the present disclosure. [Figure 8] A flowchart illustrating an example of a method for performing a separate type of measurement according to some embodiments of the present disclosure.

Best Mode for Carrying Out the Invention

[0023] Integrated circuits that perform multiple distinct types of measurements are described. The integrated circuit may include measurement circuitry. Additionally, the integrated circuit may include or be electrically coupled to at least one sensor (and more generally, fewer sensors with respect to other integrated circuits that do not use the disclosed analysis techniques). During operation, the integrated circuit may perform distinct types of measurements of an object or associated with an object, such as in an environment with poor or unclear information within the visible band of frequencies. For example, an environment with poor or unclear information may include fog or clouds. Performing distinct types of measurements may include filtering measurements based at least in part on the speed with respect to the ground and providing data streams with different spatial frequencies and sampling rates based at least in part on the filtering.

[0024] By performing distinct types of measurements, these analysis techniques may enable or smooth improved measurements of an object even when the environment has poor or unclear information in the visible band of frequencies. Additionally, these analysis techniques may enable improved measurements (such as improved measurements of the object's placement, movement, size, angle of incidence of the received signal, and / or material properties of the object, such as reflectivity) to be performed with fewer, and in some embodiments, at least one sensor. As a result, these analysis techniques may reduce the cost and / or complexity of an electronic device (such as a vehicle) or system that includes the integrated circuit while enhancing or improving performance.

[0025] In the following description, a vehicle may include an automobile, a sport utility vehicle, a truck, a motorcycle, a train, an airplane, a boat, or other types of transportation vehicles. However, in the following description, an automobile is used as an exemplary example of a vehicle.

[0026] Furthermore, in the following description, a vehicle may use one or more types of sensors to perform measurements related to objects in the surrounding environment. A wide variety of sensor types may be used, but in the following description, radar sensors and / or LiDAR sensors are used as exemplary examples. Radar sensors may perform measurements using at least one of various operating modes (such as pulsed waves or continuous waves) and may involve the use of one or more types of modulation (such as amplitude, frequency, and / or phase modulation). In the following description, frequency-modulated continuous-wave (FMCW) radar is used as an example. Furthermore, the transmitted and received radar signals (for example, having a carrier frequency within the radar frequency band, such as between 3 MHz and 100 GHz) may be generated and / or processed in the analog and / or digital domains.

[0027] Next, we describe embodiments of the analysis technique. Figure 1 shows an example of a vehicle 110 equipped with a radar antenna array, which includes an antenna 112 for short-range sensing (e.g., for parking assistance), an antenna 114 for medium-range sensing (e.g., for monitoring traffic congestion and interruption events), and an antenna 116 for long-range sensing (e.g., for adaptive cruise control and collision warning), each of which may be located behind the front bumper cover. An antenna 118 for short-range sensing (e.g., for backup assist) and an antenna 120 for medium-range sensing (e.g., for rear collision warning) may be located behind the rear bumper cover. Furthermore, an antenna 122 for short-range sensing (e.g., for blind spot monitoring and side obstacle detection) may be located behind the car fender. Each antenna and each set of antennas may be grouped into one or more arrays. Furthermore, each array may be controlled by a radar array controller 205 (Figure 2). In some embodiments, a given set of antennas can perform multi-input multiple-output (MIMO) radar sensing. The type, number, and configuration of sensors in sensor placement configurations for vehicles with driver assistance and autonomous driving functions vary. A vehicle may employ sensor placement configurations to detect and measure the distance / direction to objects in various detection zones, enabling the vehicle to navigate while avoiding other vehicles and obstacles. While the above description exemplifies a vehicle 110 equipped with radar sensors, in other embodiments, the vehicle 110 may be equipped with one or more different (alternative to radar sensors) or one or more additional types of sensors, such as LiDAR, ultrasonic sensors, and cameras.

[0028] Figure 2 shows a block diagram illustrating an example of a driver assistance system. This driver assistance system may include an electronic control unit (ECU) 210 coupled to various sensors 212, a radar array controller 214, and a LiDAR 226 as the center of a star topology. However, other topologies may include series, parallel, and hierarchical (tree) topologies. The radar array controller 214 may be coupled to transmitting and receiving antennas (e.g., in antenna 114) to transmit electromagnetic waves, receive reflections, and determine the spatial relationship between the vehicle and its surroundings. Furthermore, the radar array controller 214 may be coupled to a carrier signal generator. In some embodiments, the radar array controller 214 may control the timing and sequence of operation of multiple carrier signal generators.

[0029] To provide automatic parking assistance, the ECU 210 may be coupled to a set of actuators such as a turn signal actuator 216, a steering actuator 218, a braking actuator 220, and / or a throttle actuator 222. Furthermore, the ECU 210 may be coupled to an interactive user interface 224 for receiving user input and displaying various measurements and system status.

[0030] Using the user interface 224, sensors, and actuators, the ECU 210 can provide automatic parking, parking assist, lane change assist, obstacle and blind spot detection, autonomous driving, and / or other desirable features. During operation of the vehicle 110 (Figure 1), sensor measurements may be acquired by the ECU 210 and used by the ECU 210 to determine the status of the vehicle 110. Furthermore, the ECU 210 may operate based on the status and input information to activate signal transmission and control transducers to coordinate and maintain the operation of the vehicle 110. For example, operations that may be provided by the ECU 210 include driver assistance functions such as automatic parking, lane following, automatic braking, and autonomous driving.

[0031] Furthermore, to obtain measurements, the ECU210 may employ a MIMO radar system. The radar system operates by emitting electromagnetic waves that propagate outward from a transmitting antenna, and then being reflected back toward a receiving antenna. The reflector may be any moderately reflective object in the path of the emitted electromagnetic waves. By measuring the propagation time of the electromagnetic waves from the transmitting antenna to the reflector and back toward the receiving antenna, the radar system may determine the distance to the reflector. In addition, by measuring the Doppler shift of the electromagnetic waves, the radar system may determine the velocity of the reflector relative to the vehicle 110 (Figure 1). When multiple transmitting or receiving antennas are used, or when multiple measurements are taken at different locations, the radar system may determine the direction toward the reflector, thereby tracking the position of the reflector relative to the vehicle 110 (Figure 1). With more advanced processing, multiple reflectors may be tracked. In some embodiments, the radar system may employ array processing to "scan" the directional beam of electromagnetic waves and construct an image of the environment around the vehicle 110 (Figure 1). Generally, pulsed and / or continuous wave implementations of radar systems can be implemented.

[0032] Figure 3 presents a block diagram illustrating an example of a radar system 310 having a MIMO configuration, where J transmitters are collectively coupled to M transmitting antennas 312 to transmit transmit signals 316, where J and M are non-zero integers. The M possible transmit signals 316 may also be reflected from one or more reflectors or targets 314 and received as received signals 318 via N receiving antennas 320 coupled to P receivers, where N and P are non-zero integers. Each receiver may extract the amplitude and phase or propagation delay associated with each of the M transmit signals 316, thereby enabling the system to acquire N·M measurements (however, only J·P of the measurements can be acquired simultaneously). The processing requirements associated with each receiver extracting J measurements can be reduced by using time-division multiplexing and / or orthogonal coding. Furthermore, the available antennas can be systematically multiplexed to the available transmitters and receivers to collect a complete set of measurements for radar imaging.

[0033] Figure 4 presents a block diagram showing an example of a radar transceiver circuit 410 (for example, within the radar system 310 in Figure 3). In some embodiments, the radar transceiver circuit 410 is implemented as an integrated circuit in a packaged chip. The radar transceiver circuit 410 may include a carrier signal (chirp) generator 412, a phase shifter 414, an amplifier 416, and / or a transmitting antenna 312 capable of transmitting a signal 316 at least partially based on the output of the carrier signal generator 412. Furthermore, the radar transceiver circuit 410 may include a receiving antenna 320, a low-noise amplifier (LNA) 418, and / or a mixer 420. The mixer 420 may mix the received signal 318 detected by the receiving antenna 312 with the signal from the carrier signal generator 412. Furthermore, the low-noise amplifier 418 may be used to amplify the received signal 318 detected by the receiving antenna 320. In some embodiments, the radar transceiver circuit 410 may include a sensitivity-time controller and equalizer (not shown), a broadband (BB) filter 422, an analog-to-digital converter (ADC) 424, and / or a processor 426 (e.g., the ECU 210 and / or radar array controller 214 in Figure 2) that can perform further processing of the received signal (e.g., Fourier transform). In some embodiments, the processor 426 and a low-noise amplifier 418 may be coupled for bidirectional communication.

[0034] In addition, in some embodiments, the carrier signal generator 412 may be coupled to a radar array controller 214 (Figure 2). The carrier signal generator 412 may include a chirp generator for generating the FMCW signal. The chip rate of the carrier signal generator 412 may be controlled by the radar array controller 214 (Figure 2). In some embodiments, the carrier signal generator 412 may be deactivated by the radar array controller 214 (Figure 2) to provide an unmodulated carrier signal. Furthermore, the carrier signal generator 412 may be implemented as a local oscillator (LO) signal generator, a fractional-N phase-locked loop (PLL) with a ΣΔ controller, or a direct digital synthesizer generator.

[0035] Furthermore, the carrier signal generator 412 may be coupled to the transmitting antenna 312 via a phase shifter 414 and an amplifier 416. The carrier signal generator 412 may also be coupled to the receiving antenna 312 via a mixer 420 and a low-noise amplifier 418. In addition, the carrier signal generator 412 may generate a transmit signal (e.g., a chirp signal). The amplifier 416 may receive the transmit signal from the carrier signal generator 412, and the transmit signal 316 corresponding to the transmit signal from the carrier signal generator 412 may be transmitted using the transmitting antenna 312.

[0036] In some embodiments, the radar transmitter may include a phase rotator, a two-phase phase modulator, a variable gain amplifier, a switch, a power amplifier driver, a power amplifier, and / or a digital signal processor (DSP). Furthermore, in some embodiments, the radar transmitter may include a digital controller, which may be included in the DSP or be a separate component. In addition, the phase rotator may be used for digital phase modulation. Moreover, the radar transmitter may use a wave-modulated power amplifier in digital envelope modulation techniques.

[0037] As mentioned earlier, in some environments, such as those with fog or clouds, accurately detecting one or more objects can be difficult. Furthermore, detecting one or more objects can be costly and / or complex. In the following description, LiDAR is used as an exemplary example of the analysis technique.

[0038] In the disclosed analysis techniques, a distinct type of measurement may be performed using at least one sensor. The resulting scene (or scan of the environment) may selectively have high resolution and / or selectively have a high frame rate, thereby enabling the accurate detection or determination of one or more objects (and / or one or more characteristics of one or more objects) in the environment.

[0039] For example, with LiDAR, high-resolution scanning is 0.05 per pixel. o and / or may have 300,000 pixels per image. Alternatively, low-resolution scanning may have 0.3 pixels per image. o and / or may have 50,000 pixels per image. Furthermore, high frame rate scanning may have 20 frames per second, while low frame rate scanning may have 7 frames per second.

[0040] It should be noted that point cloud sensors generally consume "frames," which represent a set of points on the surface of one or more objects in three-dimensional (3D) space, representing a consistent physical relationship over time. When used with off-the-shelf perception engines, the density of points that can be measured by a single sensor, and the timeliness of the delivery of those points, can be limited by the sensor's point calculation throughput, the required field of view, and the temporal stability of the most variable points (e.g., the cloud-based computer system that may be used to perform the calculations). Thus, sensors may have trade-offs between the timeliness and smoothness of the information provided to higher-layer processing (e.g., by the cloud-based computer system) and the density of the information.

[0041] To address these issues, at least one single sensor (or integrated circuit or electronic device performing the measurement) can function as two or more "virtual" sensors, each generating an independent data stream. The information contained in each of these data streams can be carefully constructed to coincide physically and temporally with one another over the longest possible period of time. This careful construction of data streams may require information that exists only within that sensor and may not be part of an upstream perceptual engine, such as a pre-trained artificial neural network that can be implemented in an electronic device including at least one sensor and / or a cloud-based computer system. (Therefore, in general, analytical techniques can be implemented locally in electronic devices, such as vehicles, and / or remotely in electronic devices that communicate with a cloud-based computer system that can implement at least part of the processing.)

[0042] It should be noted that in some embodiments, the sensor may generate independent data streams. However, in other embodiments, the sensor may output a single data stream that can be filtered and / or sampled in a way that generates two distinct streams. In these embodiments, the two data streams may contain highly correlated data (since the data streams may originate from a common signal). The output data streams from or associated with the sensor may represent more "raw" and / or more "processed" data, even if the originating measurement process (e.g., transmitting a modulated laser and receiving the reflected light) is common. As a result, packets within a class may be prioritized relative to one another, and different packet classes may also be prioritized relative to one another. For example, if several points are detected at a relatively short distance and high speed, the sensor's evaluation of what type of object or feature those points are may not be very important. Instead, the sensor may simply transfer the data to a processor, and therefore reducing the sensor's latency may be a high priority. More generally, the sensor may be analogous to a server, and the upstream host analogous to a client. In a typical automotive system, there is only one client (e.g., a domain controller or central computer). However, this may or may not be true for other systems. When there are multiple clients, the same information may be broadcast everywhere, or it may be more specific to the needs (or anticipated needs) of a particular client. This typically results in prioritization among clients.

[0043] In this way, a single sensor can provide the perception engine with both high frame rates and high point density without increasing the hardware or software requirements of the sensor itself.

[0044] Figure 5 presents a block diagram illustrating an example of the operation of the measurement system 500. In the measurement system 500, X is the number of horizontal lines per scanner pass (e.g., 16), N is the number of interleaved scanner passes (e.g., 4), Vth is the velocity magnitude threshold (e.g., 0.1 m / s), FPSlow is the number of end-of-frame markers per second in the low-resolution data stream (may have a value of N·FPSlow), FPShi is the number of end-of-frame markers per second in the high-resolution data stream (e.g., 1), and PPS is the sum of points per second in both the high-resolution and low-resolution data streams (e.g., 500,000). Furthermore, in the measurement system 500, integrated circuits within electronic devices (such as vehicles) may perform LiDAR measurements using odd and even scan lines with virtual sensors (such as high-resolution and low-resolution sensors). Furthermore, the integrated circuit may output corresponding high-resolution and low-resolution data streams within a frame using data packets (such as data packets having a user datagram protocol or UDP format).

[0045] In general, for LiDAR, the scanning pattern can be a zigzag pattern of horizontal X-rays. As shown in Figure 6, which illustrates an example of a scanning pattern, the scanner may change its vertical orientation and add a vertical offset each time the X-rays are scanned. Note that, in contrast to cameras, not all pixels can be acquired simultaneously during a scan. Instead, pixels may be temporally distributed as frames are acquired during the scan.

[0046] Furthermore, sorting or filtering may be performed in the disclosed analysis techniques. In particular, for non-moving vehicles, points may be sorted at least partially based on velocity measured directly from, for example, Doppler shift. Points with a component of radial velocity in the vehicle direction that is zero (or close to it, e.g., within 1%) have no lateral velocity, are close to the vehicle's velocity, and may be reported in a high-resolution data stream. Alternatively, points with non-zero velocity are moving and may be reported in a low-resolution data stream.

[0047] (Note that since the scanning angle and vehicle movement are known, a radial velocity component close to the vehicle's velocity can be calculated. Furthermore, the "radial velocity" vector may lie along the line connecting the sensor's origin and the target. This line may not necessarily align with the vehicle's motion vector due to reasons such as a small static origin difference between the sensor and the vehicle (e.g., in the case of a lateral LiDAR).)

[0048] In a system installed on a moving vehicle (such as an integrated circuit and at least one sensor), the radial velocity may be adjusted by the tangent of the measured azimuth angle relative to a point and the vehicle's velocity, thereby the motion may be relative to the ground rather than to at least one sensor. The vehicle's velocity may be estimated from vehicle network data (such as cellular data networks), the Global Positioning System (GPS), and / or statistical analysis of point data.

[0049] In some embodiments, measurements can be sorted or filtered based at least partially on their temporal relevance. For example, measurements having the same speed as the vehicle may be included in a first data stream having a high spatial frequency and a low frame rate. This may avoid aggregating measurements taken at completely different times that are not temporally coherent. In contrast, a second data stream containing temporally irrelevant measurements may have a lower spatial resolution and a higher frame rate. Measurements in the second data stream may be spatially sparse but temporally packed or dense within time.

[0050] It should be noted that sorting or filtering policies can be defined and implemented at least on the sensor and / or integrated circuit.

[0051] In the disclosed analysis techniques, the system may perform sorting or filtering to improve the timeliness of delivery, for example, sorting or filtering of points that are spatially close or approaching, have a short time to collision, and / or are within the vehicle's path of travel. For example, an example of the output stream in the analysis technique is shown in Table 1.

[0052] [Table 1]

[0053] In some embodiments, a system (for example, within an integrated circuit in an electronic device such as a vehicle) may have a visualizer configuration in which multiple virtual sensors can be enabled in a sensor configuration (for example, two virtual sensors may be enabled, one virtual sensor on port 2370 and the other virtual sensor on port 2368). A sensor corresponding to high resolution may color points based at least partially on range (this is sometimes referred to as "range coloring"), and a sensor corresponding to low resolution may color points based at least partially on velocity (this is sometimes referred to as "velocity coloring").

[0054] An integrated circuit can construct a scene of one or more objects in the environment around or near the vehicle containing the integrated circuit. In particular, the scene may correspond to high-resolution and low-resolution data streams. These two scenes may be constructed in parallel or simultaneously by the integrated circuit. Furthermore, a network (such as a communication network between an electronic device or vehicle and a cloud-based computer system) may prioritize packets that are part of the low-resolution data stream to reduce or minimize latency.

[0055] In some embodiments, the scope of prioritization may be broader than the sorting policy for point cloud attributes. This is because sensors (including LiDAR) typically generate qualitatively different classes of packets. For example, there may be at least five packet classes, which include point cloud data (the aforementioned sorting policy may be applied to any combination of point-level attributes sometimes referred to as “detection levels”), feature-level data (edges, or even just clusters of adjacent points that do not have complete semantic meaning), object-level data (e.g., road surface, objects on the road), telemetry or sensor health data (e.g., sensor temperature, scanner mechanical properties, and / or information about other self-checks), and / or response packets to field corrections from an upstream host. Note that the sorting policy may use one or a combination of attributes contained in the packet classes. In particular, the sorting policy may (sometimes or always) prioritize point cloud data within a specific spatial range of the detected road surface (not necessarily the center of the field of view for curved roads), and may require analysis by an upstream host regarding whether clusters of spatially adjacent points above the road surface are higher priority, for example, obstacles that can be driven over, thus requiring analysis by an upstream host, transmission of point cloud data within the detected area, not necessarily braking (this prioritization may be at least partially based on unsupervised analysis such as k-means clustering, density-based spatial clustering, or DBSCAN), updated type 1, 2, or 3 data in the field of view in response to field correction packet type 5, and / or may be at least partially based on updated type 1, 2, or 3 data in the field of view in response to field correction packet type 5 when the sensor detects that it is performing adequately only within a smaller or subset of the operating area due to a “wounded walking” type failure.

[0056] It should be noted that if the two data stream rate filters are mutually exclusive, these analysis techniques may not require additional processor overhead and / or additional UDP traffic overhead. These analysis techniques may require approximately 1.5kB of additional memory (such as SRAM memory). However, if the rate filters are not mutually exclusive, more system resources may be required.

[0057] In an exemplary use case, two upstream perception engines (which may be implemented locally and / or in the cloud) using the characteristics of different scanning systems may be serviced by at least one sensor and one scanning system. By using at least one sensor, the scanning system may behave as two virtual sensors, each sensor may have characteristics associated with its respective processing system. Figure 7 presents diagrams illustrating examples of high-resolution and low-resolution scenes.

[0058] Next, embodiments of the method will be described. Figure 8 presents a flowchart illustrating an example of method 800 for performing a distinct type of measurement, which may be performed by an integrated circuit and / or electronic device. During operation, the integrated circuit may perform a distinct type of measurement of or associated with an object in an environment where information in the visible frequency band is scarce or unclear (operation 810). Note that performing a distinct type of measurement (operation 810) may include filtering the measurements based at least in part on the velocity relative to the ground (operation 812) and providing data streams having different spatial frequencies and sampling rates, at least in part on the filtering (operation 814).

[0059] In some embodiments of Method 800, there may be additional or fewer operations. Furthermore, the order of operations may be changed, and / or two or more operations may be combined into a single operation.

[0060] The disclosed integrated circuits and analytical techniques may be (or may be included in) any electronic device or system. For example, an electronic device may include a mobile phone or smartphone, a tablet computer, a laptop computer, a notebook computer, a personal computer or desktop computer, a netbook computer, a media player device, an ebook device, a MiFi® device, a smartwatch, a wearable computing device, a portable computing device, a consumer electronic device, an access point, a router, a switch, communication equipment, test equipment, a vehicle, a ship, an aircraft, an automobile, a truck, a bus, a motorcycle, manufacturing equipment, agricultural machinery, construction machinery, or another type of electronic device.

[0061] While specific components are used to describe embodiments of integrated circuits and / or integrated circuits containing integrated circuits, in alternative embodiments, different components and / or subsystems may be present within the integrated circuits and / or integrated circuits containing integrated circuits. Thus, embodiments of integrated circuits and / or integrated circuits containing integrated circuits may include fewer components, additional components, different components, two or more components may be combined into a single component, a single component may be separated into two or more components, the positions of one or more components may be changed, and / or different types of components may be present.

[0062] Furthermore, the circuits and components in the integrated circuit and / or embodiments of the integrated circuit including the integrated circuit may be implemented using any combination of analog circuits and / or digital circuits, including bipolar, PMOS and / or NMOS gates or transistors. Furthermore, the signals in these embodiments may include digital signals with approximately discrete values ​​and / or analog signals with continuous values. In addition, the components and circuits may be single-ended or differential, and the power supply may be unipolar or bipolar. It should be noted that the electrical coupling or connection in the embodiments described above may be direct or indirect. In the embodiments described above, a single line corresponding to a route may represent one or more single lines or routes.

[0063] As mentioned above, integrated circuits and / or electronic devices may implement some or all of the functions of the analysis technique. These integrated circuits and / or electronic devices may include hardware and / or software mechanisms used to implement functions related to the analysis technique.

[0064] In some embodiments, the output of a process for designing an integrated circuit, or a part of an integrated circuit, including one or more of the circuits described herein, may be on a computer-readable medium, such as magnetic tape or optical or magnetic disk. The computer-readable medium may be encoded with data structures or other information describing the integrated circuit or circuits that can be physically instantiated as part of an integrated circuit. Various formats may be used for such encoding, but these data structures are generally described in Caltech Intermediate Format (CIF), Calma GDS II Stream Format (GDSII), Electronic Design Interchange Format (EDIF), OpenAccess (OA), or Open Artwork System Interchange Standard (OASIS). A person skilled in the art of integrated circuit design can develop such data structures from the types of wiring diagrams and corresponding descriptions detailed above and encode the data structures onto a computer-readable medium. A person skilled in the art of integrated circuit manufacturing can use such encoded data to manufacture an integrated circuit including one or more of the circuits described herein.

[0065] While some of the operations in the embodiments described above were implemented in hardware or software, in general, the operations in the embodiments described above can be implemented in a wide variety of configurations and architectures. Therefore, some or all of the operations in the embodiments described above can be performed in hardware, software, or both. For example, at least some of the operations in the analysis technique can be implemented using program instructions executed by a processor, or in firmware within an integrated circuit and / or another integrated circuit (such as a graphics processing unit i.e., a GPU).

[0066] Furthermore, while numerical examples are provided in the preceding description, different numerical values ​​may be used in other embodiments. Consequently, the numerical values ​​provided are not intended to be limiting.

[0067] In the explanation given earlier, we refer to “several embodiments.” Note that “several embodiments” describes a subset of all possible embodiments, but does not always specify the same subset of embodiments.

[0068] The foregoing description is intended to enable those skilled in the art to construct and use the disclosure and is provided in the context of a particular application and its requirements. Furthermore, the foregoing description of embodiments of the disclosure is presented for illustrative and illustrative purposes only. They are not intended to be exhaustive or to limit the disclosure to the forms disclosed. Accordingly, many modifications and variations will be obvious to those skilled in the art, and the general principles defined herein may also apply to other embodiments and applications without departing from the spirit or scope of the disclosure. In addition, the previously stated descriptions of embodiments are not intended to limit the disclosure. Accordingly, the disclosure is not intended to be limited to the illustrated embodiments, but rather to apply in the broadest scope consistent with the principles and features disclosed herein. [Explanation of Symbols]

[0069] 110 vehicles 112 Antenna 114 Antenna 116 Antenna 118 Antenna 120 Antenna 122 Antenna 205 Radar Array Controller 210 Electronic Control Unit (ECU) 212 sensors 214 Radar Array Controller 216 Turn signal actuator 218 Steering Actuator 220 Brake Actuator 222 Throttle Actuator 224 Interactive User Interfaces 310 Radar System 312 Transmitting Antenna 314 Reflector or target 316 Transmitted signal 318 Received signal 320 Receiving Antenna 410 Radar transceiver circuit 412 Carrier signal (chirp) generator 414 Phase shifter 416 Amplifier 418 Low-noise amplifier (LNA) 420 Mixer 422 Broadband (BB) Filter 424 Analog-to-Digital Converter (ADC) 426 processors 500 Measurement Systems 800 ways 810 operation 2368 ports Port 2370

Claims

1. It is an integrated circuit, An integrated circuit comprising a measuring circuit electrically coupled to at least one sensor and configured to perform multiple distinct types of measurements of or associated with an object in an environment where information within the visible frequency band is scarce or unclear.

2. The integrated circuit according to claim 1, wherein the aforementioned distinct types of measurements include different measurements.

3. The integrated circuit according to claim 1, wherein the aforementioned distinct type of measurement includes a time-of-arrival (TOA) measurement.

4. The integrated circuit according to claim 1, wherein the aforementioned distinct type of measurement includes radar measurement or LiDAR measurement.

5. The integrated circuit according to claim 1, wherein the environment in which information is scarce or unclear includes fog or clouds.

6. The integrated circuit according to claim 1, wherein the aforementioned distinct type of measurement is performed using a plurality of paths having different path lengths in the at least one sensor or the integrated circuit.

7. Performing the aforementioned separate type of measurement is Filtering measurements based at least partially on ground velocity, Based at least partially on the filtering described above, to provide data streams having different spatial frequencies and sampling rates. The integrated circuit according to claim 1, including the following:

8. The aforementioned data stream includes a first data stream and a second data stream, The integrated circuit according to claim 7, wherein the first data stream has a higher spatial frequency and a lower sampling rate than the second data stream.

9. The integrated circuit according to claim 1, wherein the distinct types of measurements include time-related measurements, the spatial measurement having a spatial frequency greater than a first predefined value and the temporal measurement having a sampling rate as a function of time less than a second predefined value.

10. The integrated circuit according to claim 1, wherein the aforementioned distinct type of measurement includes spatially related measurements in which the spatial measurement has a spatial frequency smaller than a predefined value.

11. The integrated circuit according to claim 1, wherein the integrated circuit is configured to provide a measurement in the distinct type of measurement having a latency less than a predefined value before providing a second measurement in the distinct type of measurement having a latency greater than a second predefined value.

12. The integrated circuit according to claim 1, wherein the aforementioned distinct types of measurement correspond to a field of view which is a subset of the scanning area of ​​the at least one sensor.

13. The integrated circuit according to claim 1, wherein the integrated circuit is configured to dynamically adapt the distinct type of measurement based at least partially on one or more properties of the object, the presence of the environment, or both.

14. It is an electronic device, An integrated circuit comprising a measuring circuit electrically coupled to at least one sensor and configured to perform a plurality of distinct types of measurements of or associated with an object in an environment where information within the visible frequency band is scarce or unclear. Electronic devices, including those mentioned above.

15. The electronic device according to claim 14, wherein the aforementioned distinct type of measurement includes time to arrival (TOA) measurement.

16. The electronic device according to claim 14, wherein the aforementioned distinct type of measurement is performed using a plurality of paths having different path lengths in the at least one sensor or the integrated circuit.

17. Performing the aforementioned separate type of measurement is Filtering measurements based at least partially on ground velocity, Based at least partially on the filtering described above, to provide data streams having different spatial frequencies and sampling rates. The electronic device according to claim 14, including the electronic device according to claim 14.

18. The electronic device according to claim 14, wherein the electronic device is configured to dynamically adapt the distinct type of measurement based at least partially on one or more properties of the object, the presence of the environment, or both.

19. A method for performing a different type of measurement, A step of performing the distinct type of measurement of an object or associated with an object in an environment where information within the visible frequency band is scarce or unclear, by an electronic device including at least one sensor or electrically coupled thereto, wherein the step of performing the distinct type of measurement is A step of filtering the measurements based at least in part on the velocity relative to the ground, The step of providing a data stream having different spatial frequencies and sampling rates, at least in part, based on the filtering described above, A method that includes the steps to be performed.

20. The method according to claim 18, wherein the aforementioned distinct type of measurement includes radar measurement or LiDAR measurement.

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