Method and system for transmit beam-independent radar calibration

Abstract

To provide techniques and systems for transmit beam agnostic radar calibration.SOLUTION: A system transmits each transmit antenna element to individually transmit electromagnetic energy according to a first transmit beam pattern, and generates data representing a collection pattern based on reflections of the electromagnetic energy transmitted according to the first transmit beam pattern. The system synthesizes, using the data representing the collection pattern, a second transmit beam pattern that differs from the first transmit beam pattern, and estimates a mutual coupling matrix for processing reflections of electromagnetic energy transmitted according to the second transmit beam pattern. The system generates, based on the mutual coupling matrix, a model for operating the radar onboard a vehicle. The model enables a vehicle radar system having one or more radars that match the radar to transmit and receive electromagnetic energy according to the first transmit beam pattern and the second transmit beam pattern.SELECTED DRAWING: Figure 7

Description

[Background technology]

[0001] Automotive radar uses radio waves to detect the presence, distance, direction, and speed of objects in the vehicle's surrounding environment. A vehicle radar system transmits radio signals from a transmitter, which then bounce back from nearby objects and return to a receiver. By analyzing the characteristics of the returned signals (also referred to herein as radar echoes), the vehicle radar system can determine the location, speed, and direction of objects in the environment, such as other vehicles, pedestrians, road boundaries, and obstacles. In some cases, radar data is used by the vehicle's advanced driver-assistance systems (ADAS) or automated driving systems (ADS) to provide warnings to the driver or to take autonomous action to avoid collisions. In other cases, vehicle control systems use radar data when determining control strategies for autonomous navigation by the vehicle.

[0002] Some vehicle radar systems use radar beamforming to shape and direct radar signals in a specific direction, allowing individual radars on the vehicle to concentrate energy on a particular region of interest. Beamforming can provide high-resolution data with improved detection capabilities compared to conventional radar systems. To perform beamforming, the radar electronically guides the direction and shape of the radar beam by adjusting the phase of the signals transmitted from each antenna element in the antenna array, without requiring any physical movement of the antennas. By carefully controlling the phase and amplitude of these signals, the radar system can create the desired beam direction and shape using constructive and destructive interference. [Overview of the Initiative]

[0003] Exemplary embodiments relate to techniques and systems for transmit beam-independent radar calibration. Such techniques can be used during radar testing and calibration to separate transmit beamforming from receive calibration, enabling efficient processing and scaling of the radar. A system can implement the disclosed calibration techniques to generate models that allow the radar to transmit electromagnetic energy according to a number of beam shapes and directions while operating on a vehicle. In some cases, a vehicle system may implement the disclosed techniques to adjust radar performance during vehicle navigation.

[0004] One embodiment describes a method. The method involves a computing system triggering each transmitting antenna element of a radar to individually transmit electromagnetic energy according to a first transmitting beam pattern; the computing system generating data representing a collection pattern based on the reflection of the electromagnetic energy transmitted according to the first transmitting beam pattern; the method also involves using the data representing the collection pattern to synthesize a second transmitting beam pattern different from the first transmitting beam pattern; estimating an interconnection matrix for processing the reflection of the electromagnetic energy transmitted according to the second transmitting beam pattern; and the computing system generating a model for operating a vehicle-mounted radar based on the interconnection matrix. The model enables a vehicle radar system having one or more radars matching the radar to transmit and receive electromagnetic energy according to the first and second transmitting beam patterns.

[0005] In another embodiment, a system is described. The system includes a radar and a computing system. The computing system is configured to trigger each transmitting antenna element of the radar to individually transmit electromagnetic energy according to a first transmitting beam pattern, generate data representing a collection pattern based on the reflection of the electromagnetic energy transmitted according to the first transmitting beam pattern, and use the data representing the collection pattern to synthesize a second transmitting beam pattern different from the first transmitting beam pattern. The computing system is also configured to estimate an interconnection matrix for processing the reflection of the electromagnetic energy transmitted according to the second transmitting beam pattern, and to generate a model for operating a vehicle-mounted radar based on the interconnection matrix. The model enables a vehicle radar system having one or more radars matching the radar to transmit and receive electromagnetic energy according to the first and second transmitting beam patterns.

[0006] In yet another embodiment, a non-temporary computer-readable medium is described. The non-temporary computer-readable medium is configured to store instructions, which, when executed by a computing system comprising one or more processors, cause the computing system to perform an action. The action involves triggering each transmitting antenna element of a radar to individually transmit electromagnetic energy according to a first transmitting beam pattern; generating data representing a collection pattern based on the reflection of the electromagnetic energy transmitted according to the first transmitting beam pattern; and using the data representing the collection pattern to synthesize a second transmitting beam pattern different from the first transmitting beam pattern. The action also involves estimating an interconnection matrix for processing the reflection of the electromagnetic energy transmitted according to the second transmitting beam pattern; and generating a model for operating a vehicle-mounted radar based on the interconnection matrix. The model enables a vehicle radar system having one or more radars matching the radar to transmit and receive electromagnetic energy according to the first and second transmitting beam patterns.

[0007] These, as well as other embodiments, advantages, and alternatives, will become apparent to those skilled in the art by reading the following detailed description with appropriate reference to the accompanying drawings. [Brief explanation of the drawing]

[0008] [Figure 1] This is a functional block diagram illustrating a vehicle according to an exemplary embodiment. [Figure 2A] This is an illustrative diagram of the physical configuration of a vehicle according to an exemplary embodiment. [Figure 2B] This is an illustrative diagram of the physical configuration of a vehicle according to an exemplary embodiment. [Figure 2C] This is an illustrative diagram of the physical configuration of a vehicle according to an exemplary embodiment. [Figure 2D] This is an illustrative diagram of the physical configuration of a vehicle according to an exemplary embodiment. [Figure 2E] This is an illustrative diagram of the physical configuration of a vehicle according to an exemplary embodiment. [Figure 2F] This is an illustrative diagram of the physical configuration of a vehicle according to an exemplary embodiment. [Figure 2G] This is an illustrative diagram of the physical configuration of a vehicle according to an exemplary embodiment. [Figure 2H] This is an illustrative diagram of the physical configuration of a vehicle according to an exemplary embodiment. [Figure 2I] This is an illustrative diagram of the physical configuration of a vehicle according to an exemplary embodiment. [Figure 2J] This is an illustrative diagram of the field of view of various sensors according to an exemplary embodiment. [Figure 2K] This is an illustrative diagram of beam steering for a sensor according to an exemplary embodiment. [Figure 3] This is a conceptual diagram illustrating wireless communication between various computing systems related to autonomous or semi-autonomous vehicles, according to an exemplary embodiment. [Figure 4] This is a block diagram of a system including a radar unit according to an exemplary embodiment. [Figure 5]This is a flowchart illustrating a method for radar calibration and testing according to an exemplary embodiment. [Figure 6] This is a conceptual diagram illustrating the generation of radar data representing a calibration collection pattern, according to an exemplary embodiment. [Figure 7] This is a flowchart illustrating another method for radar calibration and testing, according to an exemplary embodiment. [Modes for carrying out the invention]

[0009] Exemplary methods and systems are intended herein. Any exemplary embodiment or feature described herein should not necessarily be construed as being preferable or advantageous to other embodiments or features. Furthermore, the exemplary embodiments described herein are not intended to be limiting. Certain aspects of the disclosed systems and methods can be arranged and combined in a wide variety of different configurations, and it will be readily apparent that all of these configurations are intended herein. In addition, specific arrangements shown in the figures should not be considered limiting. It should be understood that other embodiments may include more or fewer of each element shown in a given figure. Furthermore, some of the illustrated elements may be combined or omitted. Moreover, exemplary embodiments may include elements not illustrated in the figures.

[0010] Vehicle radar systems utilize beamforming for several advantages, including increased sensing range, higher resolution, improved object tracking, and reduced interference. Beamforming allows vehicle radar to focus transmitted energy in a specific direction, extending the effective sensing range of the radar system. This allows for the detection of objects, pedestrians, and vehicles at longer distances, creating more time for decision-making and response by the vehicle control system. The narrowed beam also increases resolution, enabling vehicle systems to distinguish between nearby and distant objects and accurately determine their position relative to the vehicle. Beamforming can also be used to reduce the effects of radio frequency interference (RFI) by directing the radar beam towards specific target areas, while allowing for the filtering out of undesirable signals originating outside these areas. Thus, beamforming enables vehicle radar systems to generate cleaner and more accurate data, which can help them safely navigate the dynamic environments they encounter during navigation.

[0011] However, testing and calibrating vehicle radar for beamforming presents significant challenges, potentially stemming from the complex nature of radar systems and the need for precise calibration. In some cases, beamforming performance can be negatively affected by variations between antenna elements within the radar array, which in turn impact the radiation patterns and sensitivity of individual elements. In addition, in some instances, mutual coupling between adjacent antenna elements can also contribute to undesirable effects affecting beam patterns and calibration accuracy.

[0012] To enable proper beam steering and shaping, it can be difficult to accurately control the phase and amplitude of each antenna element. Environmental factors (such as temperature variations and vibrations experienced during vehicle navigation) can also affect the physical alignment of the antenna elements, and calibration techniques may need to be used periodically to maintain the accuracy of beamforming over a long period. In some cases, the dynamic nature of the driving scenario may require real-time adaptive calibration, increasing the complexity of calibration algorithms and hardware requirements.

[0013] Therefore, to address these challenges, careful attention may need to be paid to the manufacturing process, calibration techniques, and reliable test procedures so that vehicle radars with beamforming capabilities can provide the levels of accuracy and reliability necessary for safe autonomous driving. Testing the different capabilities of each radar for various beam patterns is time-consuming and can slow down the overall testing and calibration of the radar.

[0014] The exemplary techniques and systems presented herein relate to techniques and systems for transmit beam-independent radar calibration, which enables acceleration of radar testing and calibration by separating transmit beamforming from receive calibration. Instead of separately collecting data for each transmit beam, the disclosed techniques provide a channelized approach where different transmit beams can be individually synthesized from data collected from channelized waveforms. Channelized waveforms can be generated by each transmit antenna of the radar individually transmitting electromagnetic energy at different times for subsequent collection by the receiver, resulting in channelized waveforms. Each transmit antenna can transmit over a range of azimuth angles and then use the generated data to simulate and test the performance of the radar for different beam patterns. Phase calibration (e.g., mutual coupling matrix) and propagation delays can then be calculated based on the synthesized transmit beams and used to generate a model (or models) that enables the radar to transmit different transmit beam patterns. The model can then be distributed as an over-the-air update for use by vehicle radar systems on various types of vehicles.

[0015] Various technologies can be used to facilitate the seamless delivery of vehicle updates with one or more models to vehicles. For example, high-speed cellular networks (e.g., LTE and 5G) can be used to distribute updates to vehicles. Wi-Fi connectivity enables updates in environments with wireless networks, such as dealerships, businesses, and home garages. Satellite communications can be used to deliver models to remote areas and ensure updates are delivered to vehicles in transit. In addition, short-range technologies (e.g., Bluetooth) may be used for local updates, while dedicated short-range communications (DSR) can support vehicle-to-vehicle and infrastructure communications. Mesh networks, cloud services, and software-defined networking (SDN) may contribute to a versatile and dynamic approach that enables vehicles to access updates efficiently and securely. Updates can be delivered periodically or continuously as they become available within the implementation.

[0016] The disclosed techniques and systems offer several advantages over existing calibration options. In particular, the exemplary techniques eliminate the need to collect data for each individual transmit beam available to the radar and avoid custom spatial support collection patterns. Rather, the disclosed techniques replace each transmit beam with a single collection pattern that is independent of the location of the transmit beam lobes and nulls. This strategy frees up the flexibility to prepare calibrations for all variations of the transmit beam on demand in a vehicle-mounted state. Such techniques can be implemented during the manufacturing of the radar system. In practice, calibration tests may occur after the radar hardware components have been assembled and before the final product is shipped or deployed on a vehicle.

[0017] In some embodiments, manifold matching is used to minimize the difference when calculating phase calibration with a non-uniform waveform compared to the current practice of calibrating with a homogeneous waveform. In practice, the equivalence between free space and composite beamforming is not precise. Due to various factors (e.g., near-field effects, radome, snapshot time, and transmit coupling), the composite beam may exhibit differences that make the composite manifold unsuitable for calibrating the free-space manifold. Thus, manifold matching can be used to harmonize the free-space and composite waveforms to minimize the discrepancy between the composite beam and the free-space beam. As an exemplary result, a model for operating a vehicle-mounted radar can be generated based on information created from the manifold matching process.

[0018] The disclosed techniques can be implemented during radar manufacturing, testing, and / or calibration. For example, individual radars can be tested using the disclosed techniques to generate one or more models. The models can be provided to a vehicle system via over-the-air updates or other means (e.g., wired connections), which allows the vehicle radar system to use one or more models for subsequent radar operations. In particular, each model may allow the radar to implement different beamforming patterns during navigation.

[0019] In some embodiments, the data acquisition process using a collection pattern is performed before the radar is installed in the factory. The radar software can then utilize the data and undergo the necessary estimation calibration process to form a new transmit beam pattern. Once the radar is deployed (e.g., positioned on a vehicle implementing a route), the radar software can be augmented, such as through over-the-air updates, to form other calibrated transmit beam patterns that were not programmed during the factory process.

[0020] In some cases, the disclosed techniques are implemented on a vehicle. For example, a vehicle radar system can generate new radar models or modify existing models using the disclosed techniques, which enable the vehicle radar to transmit and receive electromagnetic energy according to different types of beams. The generation process may involve using raw measurements made with acquired patterns during radar manufacturing, while other parts of the process are implemented in-vehicle during vehicle navigation, such as determining transport delays and interconnection matrices. As an exemplary result, a vehicle radar system can transmit and receive electromagnetic energy according to different transmit beam patterns.

[0021] The following description and accompanying drawings illustrate the features of various exemplary embodiments. The embodiments provided are illustrative and not intended to be limiting. Accordingly, the dimensions in the drawings are not necessarily to scale.

[0022] Herein, exemplary systems within the scope of this disclosure will be described in more detail. Exemplary systems may be implemented in or take the form of automobiles. Additionally, exemplary systems may also be implemented in or take the form of various vehicles, such as automobiles, trucks (e.g., pickup trucks, vans, tractors, and tractor-trailers), motorcycles, buses, airplanes, helicopters, drones, lawnmowers, bulldozers, boats, submarines, all-terrain vehicles, snowmobiles, aircraft, recreational vehicles, amusement park vehicles, agricultural machinery or agricultural vehicles, construction machinery or construction vehicles, warehouse equipment or warehouse vehicles, factory equipment or factory vehicles, trams, golf carts, electric trains, trolleys, pedestrian transport vehicles, and robotic devices. Other vehicles are similarly possible. Furthermore, in some embodiments, exemplary systems may not include a vehicle.

[0023] Referring here to the figure, Figure 1 is a functional block diagram illustrating an exemplary vehicle 100, which may be configured to operate fully or partially in autonomous mode. More specifically, vehicle 100 may operate in autonomous mode without human interaction by receiving control commands from a computing system. As part of its operation in autonomous mode, vehicle 100 may use sensors to detect, and possibly detect, objects in its surrounding environment to enable safe navigation. Additionally, vehicle 100 may operate in a partially autonomous (i.e., semi-autonomous) mode, in which some functions of vehicle 100 are controlled by a human driver of vehicle 100, and some functions of vehicle 100 are controlled by a computing system. For example, vehicle 100 may also include subsystems that allow a driver to control the operation of vehicle 100, such as steering, acceleration, and braking, while the computing system implements assistance functions, such as lane departure warning / lane keeping assist or adaptive cruise control, based on other objects in the surrounding environment (e.g., other vehicles).

[0024] As described herein, in a partially autonomous driving mode, the vehicle assists with one or more driving actions (e.g., steering, braking, and / or acceleration for lane centering, adaptive cruise control, advanced driver-assistance systems (ADAS), and emergency braking), but the human driver is expected to situationally perceive the vehicle's surroundings and supervise the assisted driving actions. Here, the vehicle may perform all driving tasks in certain situations, but the human driver is expected to take control as needed.

[0025] For the sake of simplification and brevity, various systems and methods are described below in conjunction with autonomous vehicles, but these or similar systems and methods may be used in various driver assistance systems that do not reach the level of fully autonomous driving systems (i.e., partially autonomous driving systems). In the United States, the Society of Automotive Engineers (SAE) defines different levels of automated driving behavior to indicate how much or how little a vehicle controls the driving, but different organizations in the United States or other countries may classify the levels differently. More specifically, the systems and methods of this disclosure may be used in SAE Level 2 driver assistance systems that implement steering, braking, acceleration, lane centering, adaptive cruise control, and other driver assistance. The disclosed systems and methods may be used in SAE Level 3 driver assistance systems that enable autonomous driving under limited conditions (e.g., highways). Similarly, the disclosed systems and methods may be used in vehicles using SAE Level 4 automated driving systems that operate autonomously under most normal driving conditions and require only occasional attention from a human operator. In all such systems, accurate lane estimation is performed automatically without driver input or control (e.g., while the vehicle is moving), resulting in improved reliability of vehicle positioning and navigation, as well as overall safety of autonomous, semi-autonomous, and other driver assistance systems. As stated above, in addition to the way the SAE classifies levels of autonomous driving operations, other organizations in the United States or other countries may classify levels of autonomous driving operations differently. The systems and methods disclosed herein may be used in driver assistance systems defined by the levels of autonomous driving operations of these other organizations, but are not limited to these.

[0026] As shown in Figure 1, the vehicle 100 may include various subsystems, such as a propulsion system 102, a sensor system 104, a control system 106, one or more peripheral devices 108, a power supply 110, a computer system 112 (which may also be called a computing system) having data storage 114, and a user interface 116. In other examples, the vehicle 100 may include more or fewer subsystems, each containing multiple elements. The subsystems and components of the vehicle 100 may be interconnected in various ways. In addition, the functions of the vehicle 100 described herein may be divided into additional functional or physical components, or combined into fewer functional or physical components within an embodiment. For example, the control system 106 and the computer system 112 may be combined into a single system that operates the vehicle 100 according to various operations.

[0027] The propulsion system 102 may include one or more components that are operable to provide powered motion to the vehicle 100, and among other possible components, it may include an engine / motor 118, an energy source 119, a transmission 120, and wheels / tires 121. For example, the engine / motor 118 may be configured to convert the energy source 119 into mechanical energy, and among other possible options, it may correspond to one or a combination of an internal combustion engine, an electric motor, a steam engine, or a Stirling engine. For example, in some embodiments, the propulsion system 102 may include a number of types of engines and / or motors, such as a gasoline engine and an electric motor.

[0028] The energy source 119 represents an energy source that can, all or partly, power one or more systems of the vehicle 100 (e.g., engine / motor 118). For example, the energy source 119 may be gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and / or other power sources. In some embodiments, the energy source 119 may include a combination of a fuel tank, batteries, a capacitor, and / or a flywheel.

[0029] The transmission 120 may transmit mechanical power from the engine / motor 118 to the wheels / tires 121 and / or other possible systems of the vehicle 100. Thus, the transmission 120 may include, among other possible components, a gearbox, a clutch, a differential, and a drive shaft. The drive shaft may include an axle connected to one or more wheels / tires 121.

[0030] The wheels / tires 121 of the vehicle 100 may have various configurations within the exemplary embodiment. For example, the vehicle 100 may exist in the form of a unicycle, bicycle / motorcycle, tricycle, or four-wheeled automobile / truck, among other possible configurations. Thus, the wheels / tires 121 may be attached to the vehicle 100 in various ways and may exist in different materials such as metal and rubber.

[0031] The sensor system 104 may include various types of sensors, among other possible sensors, such as a Global Positioning System (GPS) 122, an Inertial Measurement Unit (IMU) 124, a radar 126, a rider 128, a camera 130, a steering sensor 123, and a throttle / brake sensor 125. In some embodiments, the sensor system 104 may also include sensors (e.g., an O2 monitor, a fuel gauge, engine oil temperature, and brake wear) configured to monitor the internal systems of the vehicle 100.

[0032] The GPS 122 may include a transceiver capable of operating to provide information regarding the position of the vehicle 100 relative to the Earth. The IMU 124 may have a configuration using one or more accelerometers and / or gyroscopes, and may sense changes in the position and orientation of the vehicle 100 based on inertial acceleration. For example, the IMU 124 may detect the pitch and yaw of the vehicle 100 while the vehicle 100 is stationary or in motion.

[0033] The radar 126 may represent one or more systems configured to sense objects in the surrounding environment of the vehicle 100, including their speed and orientation, using radio signals. Therefore, the radar 126 may include antennas configured to transmit and receive radio signals. In some embodiments, the radar 126 may correspond to a mountable radar configured to obtain measurements of the surrounding environment of the vehicle 100.

[0034] The lidar 128 may include, among other system components, one or more laser sources, a laser scanner, and one or more detectors, and may operate in coherent mode (e.g., using heterodyne detection) or incoherent detection mode (i.e., time-of-flight mode). In some embodiments, one or more detectors of the lidar 128 may include one or more photodetectors, which may be particularly sensitive detectors (e.g., avalanche photodiodes). In some embodiments, such photodetectors may be capable of detecting single photons (e.g., single-photon avalanche diodes (SPADs)). Furthermore, such photodetectors may be arranged in an array (e.g., like silicon photomultiplier tubes (SiPMs)) (e.g., through series electrical connections). In some embodiments, one or more photodetectors are devices operating in Geiger mode, and the lidar includes subcomponents designed for such Geiger mode operation.

[0035] The camera 130 may include one or more devices (e.g., a still camera, a video camera, a thermal imaging camera, a stereo camera, and a night vision camera) configured to capture images of the surrounding environment of the vehicle 100.

[0036] The steering sensor 123 may sense the steering angle of the vehicle 100, which may include measuring the angle of the steering wheel or measuring an electrical signal representing the angle of the steering wheel. In some embodiments, the steering sensor 123 may measure the angle of the wheels of the vehicle 100, such as detecting the angle of the wheels relative to the front axis of the vehicle 100. The steering sensor 123 may also be configured to measure a combination (or subset) of the steering wheel angle, an electrical signal representing the angle of the steering wheel, and the angle of the wheels of the vehicle 100.

[0037] The throttle / brake sensor 125 may detect either the throttle position or the brake position of the vehicle 100. For example, the throttle / brake sensor 125 may measure the angles of both the accelerator pedal (throttle) and the brake pedal, or it may measure an electrical signal representing, for example, the angle of the accelerator pedal (throttle) and / or the angle of the brake pedal. The throttle / brake sensor 125 may also measure the angle of the throttle body of the vehicle 100, which may include part of a physical mechanism that provides modulation of the energy source 119 to the engine / motor 118 (e.g., a butterfly valve and a carburetor). In addition, the throttle / brake sensor 125 may measure the pressure of one or more brake pads on the rotor of the vehicle 100, or a combination (or subset) of the angles of the accelerator pedal (throttle) and the brake pedal, an electrical signal representing the angles of the accelerator pedal (throttle) and the brake pedal, the angle of the throttle body, and the pressure applied by at least one brake pad to the rotor of the vehicle 100. In other embodiments, the throttle / brake sensor 125 may be configured to measure the pressure applied to a vehicle pedal, such as the throttle or brake pedal.

[0038] The control system 106 may include components configured to assist in the navigation of the vehicle 100, such as a steering unit 132, a throttle 134, a brake unit 136, a sensor fusion algorithm 138, a computer vision system 140, a navigation / route search system 142, and an obstacle avoidance system 144. More specifically, the steering unit 132 may be operable to adjust the direction of the vehicle 100, and the throttle 134 may control the acceleration of the vehicle 100 by controlling the operating speed of the engine / motor 118. The brake unit 136 can decelerate the vehicle 100, which may involve using friction to decelerate the wheels / tires 121. In some embodiments, the brake unit 136 may convert the kinetic energy of the wheels / tires 121 into an electric current for subsequent use by the vehicle 100's system or multiple systems.

[0039] The sensor fusion algorithm 138 may include a Kalman filter, a Bayesian network, or other algorithms capable of processing data from the sensor system 104. In some embodiments, the sensor fusion algorithm 138 may provide evaluations based on the received sensor data, such as evaluations of individual objects and / or features, evaluations of specific situations, and / or evaluations of possible effects within a given situation.

[0040] The computer vision system 140 may include hardware and software (e.g., a general-purpose processor such as a central processing unit (CPU), a dedicated processor such as a graphics processing unit (GPU) or tensor processing unit (TPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), volatile memory, non-volatile memory, or one or more machine learning models) capable of processing and analyzing images to determine objects that are in motion (e.g., other vehicles, pedestrians, cyclists, or animals) and objects that are not in motion (e.g., streetlights, road boundaries, speed bumps, or potholes). Thus, the computer vision system 140 can use object recognition, structure from motion (SFM), video tracking, and other algorithms used in computer vision, such as recognizing objects, mapping environments, tracking objects, and estimating the speed of objects.

[0041] The navigation / route search system 142 can determine the driving path of the vehicle 100, which may involve dynamically adjusting the navigation during operation. Thus, the navigation / route search system 142 may navigate the vehicle 100 using data from the sensor fusion algorithm 138, GPS 122, and maps, among other sources. The obstacle avoidance system 144 can evaluate potential obstacles based on sensor data and cause the vehicle 100's system to avoid or otherwise navigate around potential obstacles.

[0042] As shown in Figure 1, the vehicle 100 may also include peripherals 108, such as a wireless communication system 146, a touchscreen 148, a microphone 150 (e.g., one or more internal and / or external microphones), and / or a speaker 152. The peripherals 108 may provide control or other elements for the user to interact with the user interface 116. For example, the touchscreen 148 may provide information to the user of the vehicle 100. The user interface 116 may also accept input from the user via the touchscreen 148. The peripherals 108 may also enable the vehicle 100 to communicate with devices, such as devices in other vehicles.

[0043] The wireless communication system 146 may communicate wirelessly with one or more devices, either directly or via a communication network. For example, the wireless communication system 146 may use 3G cellular communication such as Code Division Multiple Access (CDMA), Evolution Data Optimization (EVDO), Global System for Mobile Communications (GSM) / General-Purpose Packet Radio Service (GPRS), or cellular communication such as 4G Worldwide Interoperability for Microwave Access (WiMAX) or Long-Term Evolution (LTE), or 5G. Alternatively, the wireless communication system 146 may communicate with a wireless local area network (WLAN) using Wi-Fi® or other possible connections. The wireless communication system 146 may also communicate directly with devices using, for example, an infrared link, Bluetooth, or ZigBee. Other wireless protocols, such as various vehicle communication systems, are possible within the context of this disclosure. For example, the wireless communication system 146 may include one or more dedicated narrow-area communication (DSRC) devices, which may include public and / or private data communications between vehicles and / or roadside stations.

[0044] The vehicle 100 may include a power supply 110 for supplying power to its components. In some embodiments, the power supply 110 may include a rechargeable lithium-ion or lead-acid battery. For example, the power supply 110 may include one or more batteries configured to provide power. The vehicle 100 may also use other types of power supplies. In an exemplary embodiment, the power supply 110 and the energy source 119 may be integrated to form a single energy source.

[0045] Vehicle 100 may also include a computer system 112 for performing operations such as those described therein. Thus, the computer system 112 may include a processor 113 (which may include at least one microprocessor) capable of executing instructions 115, stored in a non-temporary computer-readable medium such as data storage 114. In this way, the processor 113 can represent one or more processors. In some embodiments, the computer system 112 may represent a plurality of computing devices capable of functioning to control individual components or subsystems of vehicle 100 in a distributed manner.

[0046] In some embodiments, the data storage 114 may include instructions 115 (e.g., program logic) executable by the processor 113 for performing various functions of the vehicle 100, including those described above in relation to Figure 1. The data storage 114 may also include additional instructions, including instructions for transmitting, receiving, interacting with, and / or controlling one or more of the propulsion system 102, sensor system 104, control system 106, and peripheral devices 108.

[0047] In addition to instruction 115, data storage 114 may store data such as road maps and route information, among other information. Such information may be used by the vehicle 100 and the computer system 112 during the operation of the vehicle 100 in autonomous mode, semi-autonomous mode, and / or manual mode.

[0048] Vehicle 100 may include a user interface 116 for providing information to or receiving input from the user of vehicle 100. The user interface 116 may control or enable control of the layout of content and / or interactive images that may be displayed on the touchscreen 148. Furthermore, the user interface 116 may include one or more input / output devices within a set of peripherals 108, such as a wireless communication system 146, a touchscreen 148, a microphone 150, and a speaker 152.

[0049] The computer system 112 can control the functions of the vehicle 100 based on inputs received from various subsystems (e.g., the propulsion system 102, the sensor system 104, or the control system 106) and from the user interface 116. For example, the computer system 112 may utilize inputs from the sensor system 104 to estimate the outputs generated by the propulsion system 102 and the control system 106. Depending on the embodiment, the computer system 112 may be operable to monitor many aspects of the vehicle 100 and its subsystems. In some embodiments, the computer system 112 may disable some or all of the functions of the vehicle 100 based on signals received from the sensor system 104.

[0050] The components of the vehicle 100 may be configured to function in a manner that interconnects with other components, either internally or externally, to each of their respective systems. For example, in an exemplary embodiment, the camera 130 may capture multiple images that can represent information about the state of the surrounding environment of the vehicle 100 operating in autonomous or semi-autonomous mode. The state of the surrounding environment may include parameters of the road on which the vehicle is operating. For example, the computer vision system 140 may be able to recognize the slope (gradient) or other features based on multiple images of the road. Additionally, the combination of the GPS 122 and the features recognized by the computer vision system 140 may be used together with map data stored in the data storage 114 to determine specific road parameters. Furthermore, radar 126, and / or LiDAR 128, and / or several other environmental mapping, range, and / or positioning sensor systems may also provide information about the vehicle's surroundings.

[0051] In other words, a combination of various sensors (which can be called input indicator sensors and output indicator sensors) and the computer system 112 can interact to provide input indicators or indicators of the vehicle's surroundings that are provided for controlling the vehicle.

[0052] In some embodiments, the computer system 112 may make decisions about various objects based on data provided by systems other than the wireless system. For example, the vehicle 100 may have lasers or other optical sensors configured to sense objects within the vehicle's field of view. The computer system 112 may use the outputs from the various sensors to determine information about objects within the vehicle's field of view, and may determine distance and directional information to various objects. The computer system 112 may also determine whether an object is desirable or undesirable based on the outputs from the various sensors.

[0053] Figure 1 shows various components of vehicle 100 (i.e., wireless communication system 146, computer system 112, data storage 114, and user interface 116) integrated into vehicle 100, but one or more of these components may be mounted separately from or associated with vehicle 100. For example, data storage 114 may exist partially or completely separately from vehicle 100. Thus, vehicle 100 may be provided in the form of device elements that may be located separately or together. The device elements constituting vehicle 100 may be coupled together in a wired and / or wireless manner so as to be able to communicate together.

[0054] Figures 2A to 2E show an exemplary vehicle 200 (e.g., a fully autonomous vehicle or a semi-autonomous vehicle) which may include some or all of the functions described in relation to vehicle 100 with reference to Figure 1. For illustrative purposes, vehicle 200 is illustrated in Figures 2A to 2E as a van with side mirrors, but the disclosure is not limited thereto. For example, vehicle 200 could represent a truck, passenger car, semi-trailer truck, motorcycle, golf cart, off-road vehicle, agricultural vehicle, or any other vehicle described elsewhere in this specification (e.g., bus, boat, airplane, helicopter, drone, lawnmower, bulldozer, submarine, all-terrain vehicle, snowmobile, aircraft, recreation vehicle, amusement park vehicle, farm equipment, construction machinery or construction vehicle, warehouse equipment or warehouse vehicle, factory equipment or factory vehicle, tram, train, trolley, pedestrian transport vehicle, and robotic device).

[0055] The vehicle 200 may include one or more sensor systems 202, 204, 206, 208, 210, 212, 214, and 218. In some embodiments, sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218 may represent one or more optical systems (e.g., cameras), one or more lidars, one or more radars, one or more inertial sensors, one or more humidity sensors, one or more acoustic sensors (e.g., microphones and sonar devices), or one or more other sensors configured to sense information about the environment surrounding the vehicle 200. In other words, any sensor system currently known or to be created in the future may be coupled to the vehicle 200 and / or used in conjunction with various operations of the vehicle 200. As an example, a lidar may be used for autonomous driving or other types of navigation, planning, perception, and / or mapping operations of the vehicle 200. In addition, sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218 may represent combinations of sensors described herein (e.g., one or more lidars and radars, one or more lidars and cameras, one or more cameras and radars, or one or more lidars, cameras, and radars).

[0056] It should be noted that the number, location, and type of sensor systems (e.g., 202 and 204) depicted in Figures 2A-E are intended as non-limiting embodiments of the location, number, and type of such sensor systems in autonomous or semi-autonomous vehicles. Alternative numbers, locations, types, and configurations of such sensors are possible (e.g., to reduce vehicle size, shape, aerodynamics, fuel economy, aesthetics, or costs, or to adapt to other conditions for special environments or application situations). For example, the sensor systems (e.g., 202 and 204) may be installed in various other locations on the vehicle (e.g., in location 216) and may have a field of view corresponding to the interior and / or surrounding environment of the vehicle 200.

[0057] The sensor system 202 may include one or more sensors mounted on top of the vehicle 200 and configured to detect information about the environment surrounding the vehicle 200 and output instructions for that information. For example, the sensor system 202 may include any combination of cameras, radar, lidar, inertial sensors, humidity sensors, and acoustic sensors (e.g., microphones and sonar devices). The sensor system 202 may include one or more movable mounts that can operate to adjust the orientation of one or more sensors within the sensor system 202. In one embodiment, the movable mount may include a rotating platform that can scan the sensors to acquire information from each direction around the vehicle 200. In another embodiment, the movable mount of the sensor system 202 may be movable in a scanning manner within a specific range of angles, and / or azimuth angles, and / or elevation angles. The sensor system 202 may be mounted on the roof of the vehicle, although other mounting locations are also possible.

[0058] Additionally, the sensors of sensor system 202 may be distributed in different locations and do not need to be placed together in a single location. Furthermore, each sensor of sensor system 202 may be configured to move or scan independently of the other sensors of sensor system 202. Additionally or alternatively, multiple sensors may be mounted in one or more of sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218. For example, there may be two LiDAR devices mounted at the sensor locations, and / or one LiDAR device and one radar mounted at the sensor locations.

[0059] One or more sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218 may include one or more lidar devices. For example, a lidar device may include a plurality of light emitter devices arranged over a range of angles with respect to a given plane (e.g., the xy-plane). For example, one or more of the sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218 may be configured to rotate or swirl about an axis perpendicular to a given plane (e.g., the z-axis) to illuminate the environment surrounding the vehicle 200 with light pulses. Information about the surrounding environment can be determined based on the detection of various aspects of the reflected light pulses (e.g., elapsed time of flight, polarization, and intensity).

[0060] In exemplary embodiments, sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218 may be configured to provide point cloud information that may relate to physical objects in the surrounding environment of the vehicle 200. While the vehicle 200 and sensor systems 202, 204, 206, 208, 210, 212, 214, and 218 are illustrated to include certain features, it will be understood that other types of sensor systems are contemplated within the scope of this disclosure. Furthermore, the vehicle 200 may include any of the components described in relation to the vehicle 100 in Figure 1.

[0061] In exemplary configurations, one or more radars may be located on the vehicle 200. Similar to radar 126 described above, one or more radars may include antennas configured to transmit and receive radio waves (e.g., electromagnetic waves having frequencies between 30 Hz and 300 GHz). Such radio waves may be used to determine the distance and / or speed to one or more objects in the environment surrounding the vehicle 200. For example, one or more sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218 may include one or more radars. In some embodiments, one or more radars may be located near the rear of the vehicle 200 (e.g., sensor systems 208 and 210) to actively scan the environment near the rear of the vehicle 200 for the presence of radio wave reflecting objects. Similarly, one or more radars may be located near the front of the vehicle 200 (e.g., sensor systems 212 or 214) to actively scan the environment near the front of the vehicle 200. The radar can be installed in a location suitable for illuminating an area including the forward travel path of the vehicle 200 without being obstructed by other features of the vehicle 200. For example, the radar can be embedded in and / or mounted on or near the front bumper, front headlights, cowl, and / or hood. Furthermore, one or more additional radars can be positioned to actively scan the sides and / or rear of the vehicle 200 to detect the presence of radar-reflective objects, such as by including such devices in or near the rear bumper, side panels, rocker panels, and / or chassis.

[0062] Vehicle 200 may include one or more cameras. For example, one or more sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218 may include one or more cameras. The cameras may be photosensitive devices such as still cameras, video cameras, thermal imaging cameras, stereo cameras, and night vision cameras, configured to capture multiple images of the surrounding environment of vehicle 200. For this purpose, the cameras may be configured to detect visible light and, additionally or alternatively, to detect light from other parts of the spectrum, such as infrared or ultraviolet light. The cameras may be two-dimensional detectors and, optionally, may have a sensitivity range in three-dimensional space. In some embodiments, the cameras may include range detectors configured to produce a two-dimensional image showing the distance from the camera to several points in the surrounding environment, for example. For this purpose, the cameras may use one or more range detection techniques. For example, the camera can provide range information by using structured light techniques, in which the vehicle 200 illuminates objects in the surrounding environment with a predetermined light pattern, such as a grid or checkerboard pattern, and the camera is used to detect the reflection of the predetermined light pattern from the surrounding environment. Based on the distortion aberration of the reflected light pattern, the vehicle 200 can determine the distance to a point on the object. The predetermined light pattern may include infrared light or radiation of other wavelengths suitable for such measurements. In some embodiments, the camera can be mounted inside the windshield of the vehicle 200. Specifically, the camera can be positioned to capture images from a forward view relative to the orientation of the vehicle 200. Other mounting locations and field of view of the camera can also be used either inside or outside the vehicle 200. Furthermore, the camera may have associated optical elements that are operable to provide an adjustable field of view. Moreover, the camera can be mounted on the vehicle 200 using a movable mount to change the camera's directional angle via a pan / tilt mechanism, etc.

[0063] Vehicle 200 may also include one or more acoustic sensors used to sense the surrounding environment of Vehicle 200 (for example, one or more of sensor systems 202, 204, 206, 208, 210, 212, 214, 216, 218 may include one or more acoustic sensors). The acoustic sensors may include microphones (e.g., piezoelectric microphones, condenser microphones, ribbon microphones, or microelectromechanical system (MEMS) microphones) used to sense acoustic waves (i.e., pressure differences) in the fluid (e.g., air) of the environment surrounding Vehicle 200. Such acoustic sensors may be used to identify sounds in the surrounding environment (e.g., sirens, human speech, animal sounds, or alarms) on which the control strategy of Vehicle 200 may be based. For example, if an acoustic sensor detects a siren (e.g., an ambulance siren or a fire truck siren), Vehicle 200 may slow down and / or navigate to the edge of the road.

[0064] Although not shown in Figures 2A to 2E, the vehicle 200 may include a wireless communication system (for example, similar to and / or in addition to the wireless communication system 146 in Figure 1). The wireless communication system may include a radio transmitter and a radio receiver that can be configured to communicate with devices outside or inside the vehicle 200. Specifically, the wireless communication system may include, for example, a transceiver configured to communicate with other vehicles and / or computing devices in a vehicle communication system or road station. Examples of such vehicle communication systems include DSRC, radio frequency identification (RFID), and other communication standards proposed for intelligent transport systems.

[0065] Vehicle 200 may include, in addition to or instead of, these indicated components. These additional components may include electrical or mechanical functions.

[0066] The control system of the vehicle 200 may be configured to control the vehicle 200 according to a control strategy selected from a plurality of possible control strategies. The control system may be configured to receive information from sensors coupled to the vehicle 200 (on or outside the vehicle 200), modify the control strategy (and associated driving behavior) based on that information, and control the vehicle 200 according to the modified control strategy. The control system may further be configured to monitor the information received from the sensors and continuously evaluate the driving conditions, and may also be configured to modify the control strategy and driving behavior based on changes in the driving conditions. For example, the route taken by the vehicle from one destination to another may be modified based on the driving conditions. Additionally or alternatively, speed, acceleration, turning angle, follow distance (i.e., the distance of the current vehicle to the vehicle in front), lane selection, etc., may all be modified in response to changes in the driving conditions.

[0067] As described above, in some embodiments, the vehicle 200 may take the form of a van, but alternative forms are also possible and intended herein. Accordingly, Figures 2F to 2I illustrate embodiments in which the vehicle 250 takes the form of a semi-truck. For example, Figure 2F illustrates a front view of the vehicle 250, and Figure 2G illustrates an isometric view of the vehicle 250. In embodiments in which the vehicle 250 is a semi-truck, the vehicle 250 may include a tractor portion 260 and a trailer portion 270 (illustrated in Figure 2G). Figures 2H and 2I provide a side view and a top view of the tractor portion 260, respectively. Similar to the vehicle 200 illustrated above, the vehicle 250 illustrated in Figures 2F to 2I may also include various sensor systems (for example, sensor systems 202, 206, 208, 210, 212, and 214 shown and described with reference to Figures 2A to 2E). In some embodiments, the vehicle 200 in Figures 2A to 2E may include only a single copy of several sensor systems (e.g., sensor system 204), while the vehicle 250 illustrated in Figures 2F to 2I may include multiple copies of its sensor system (e.g., sensor systems 204A and 204B as illustrated).

[0068] While the drawings and overall description may refer to a given vehicle configuration (e.g., a semi-truck vehicle 250 or a vehicle 200 shown as a van), it should be understood that the embodiments described herein are equally applicable in the context of various vehicles (e.g., with modifications adopted to take into account the vehicle's form factor). For example, sensors and / or other components described or illustrated as part of vehicle 200 may also be used in a semi-truck vehicle 250 (e.g., for navigation and / or obstacle detection and avoidance).

[0069] Figure 2J illustrates various sensor fields of view (for example, associated with the vehicle 250 described above). As described above, the vehicle 250 may contain multiple sensors / sensor units. Various sensor locations may correspond to the sensor locations disclosed in Figures 2F to 2I, for example. However, in some cases, sensors may have other locations. For the sake of simplicity in the drawings, sensor location reference numbers are omitted from Figure 2J. For each sensor unit of the vehicle 250, Figure 2J illustrates typical fields of view (for example, fields of view labeled as 252A, 252B, 252C, 252D, 254A, 254B, 256, 258A, 258B, and 258C). The sensor field of view may include angular regions (e.g., azimuth and / or elevation regions) in which the sensor can detect objects.

[0070] Figure 2K illustrates beam steering for a sensor in a vehicle (e.g., vehicle 250 shown and described with reference to Figures 2F-2J) according to an exemplary embodiment. In various embodiments, the sensor unit of vehicle 250 may be radar, lidar, sonar, etc. Furthermore, in some embodiments, while the sensor is operating, the sensor may scan within the sensor's field of view. Various different scanning angles for the exemplary sensor are shown as regions 272, each indicating the angular region in which the sensor is operating. The sensor may periodically or iteratively change the region in which it is operating. In some embodiments, multiple sensors may be used by vehicle 250 to measure the regions 272. In addition, other regions may be included in other embodiments. For example, one or more sensors may measure the aspect of the trailer 270 of vehicle 250 and / or the region in front of vehicle 250.

[0071] At certain angles, the sensor's operating area 275 may include the rear wheels 276A and 276B of the trailer 270. Therefore, the sensor may measure the rear wheels 276A and / or 276B during operation. For example, the rear wheels 276A and 276B may reflect the rider or radar signal transmitted by the sensor. The sensor may receive the signal reflected from the rear wheels 276A and 276. Thus, the data collected by the sensor may include data from reflections from the wheels.

[0072] In some cases, such as when the sensor is radar, reflections from rear wheels 276A and 276B may appear as noise in the received radar signal. As a result, the radar may operate with an enhanced signal-to-noise ratio in cases where rear wheels 276A and 276B direct the radar signal away from the sensor.

[0073] Figure 3 is a conceptual illustration of wireless communication between various computing systems related to autonomous or semi-autonomous vehicles, according to an exemplary embodiment. In particular, wireless communication may occur between a remote computing system 302 and a vehicle 200 via a network 304. Wireless communication may also occur between a server computing system 306 and a remote computing system 302, and between the server computing system 306 and a vehicle 200.

[0074] Vehicle 200 can accommodate various types of vehicles capable of transporting passengers or objects between locations, and can take any one or more forms of the vehicles considered above. In some cases, vehicle 200 may operate in autonomous or semi-autonomous mode, where a control system uses sensor measurements to enable safe navigation of vehicle 200 between destinations. When operating in autonomous or semi-autonomous mode, vehicle 200 can navigate with or without passengers. As a result, vehicle 200 can pick up and drop off passengers between desired destinations.

[0075] The remote computing system 302 may represent any type of device relating to remote assistance technology, including but not limited to those described herein. In examples, the remote computing system 302 may represent any type of device configured to (i) receive information relating to the vehicle 200, (ii) provide an interface through which a human operator can then become aware of the information and input a response relating to the information, and (iii) transmit the response to the vehicle 200 or to another device. The remote computing system 302 can take various forms, such as a workstation, desktop computer, laptop, tablet, mobile phone (e.g., smartphone), and / or server. In some examples, the remote computing system 302 may include multiple computing devices operating together in a network configuration.

[0076] The remote computing system 302 may include one or more subsystems and components similar to, or identical to, those of the vehicle 200. At a minimum, the remote computing system 302 may include a processor configured to perform the various operations described herein. In some embodiments, the remote computing system 302 may also include a user interface, including input / output devices such as a touchscreen and a speaker. Other embodiments are equally possible.

[0077] Network 304 represents the infrastructure that enables wireless communication between the remote computing system 302 and the vehicle 200. Network 304 also enables wireless communication between the server computing system 306 and the remote computing system 302, and between the server computing system 306 and the vehicle 200.

[0078] The location of the remote computing system 302 can vary within the scope of the embodiment. For example, the remote computing system 302 may be located remotely from the vehicle 200, which has wireless communication via the network 304. In another embodiment, the remote computing system 302 may correspond to a computing device within the vehicle 200, separate from the vehicle 200, which a human operator can use to interact with the passengers or driver of the vehicle 200. In some embodiments, the remote computing system 302 may be a computing device equipped with a touchscreen that can be operated by the passengers of the vehicle 200.

[0079] In some embodiments, the operations described herein, which are performed by the remote computing system 302, may be additionally or alternatively performed by the vehicle 200 (i.e., by any system or subsystem of the vehicle 200). In other words, the vehicle 200 may be configured to provide a remote assistance mechanism that can be interacted with by the vehicle's driver or passengers.

[0080] The server computing system 306 may be configured to communicate wirelessly with the remote computing system 302 and the vehicle 200 (or, optionally, directly with the remote computing system 302 and / or the vehicle 200) via the network 304. The server computing system 306 may represent any computing device configured to receive, store, determine, and / or transmit information about the vehicle 200 and its remote assistance. Thus, the server computing system 306 may be configured to perform any operation or part of such operation described herein, as performed by the remote computing system 302 and / or the vehicle 200. The server computing system 306 may be available in some embodiments of the wireless communication related to remote assistance, but not in other embodiments.

[0081] The server computing system 306 may include one or more subsystems and components that are similar to or identical to those of the remote computing system 302 and / or the vehicle 200, such as a processor configured to perform the various operations described herein, and a wireless communication interface for receiving and providing information to the remote computing system 302 and the vehicle 200.

[0082] The various systems described above can perform a variety of operations. These operations and their associated characteristics are described below.

[0083] In line with the above considerations, a computing system (e.g., a remote computing system 302, a server computing system 306, or a computing system local to the vehicle 200) may operate to capture images of the surrounding environment of the autonomous or semi-autonomous vehicle using a camera. Generally, at least one computing system can analyze the images and, if possible, control the autonomous or semi-autonomous vehicle.

[0084] In some embodiments, to facilitate autonomous or semi-autonomous operation, a vehicle (e.g., vehicle 200) may receive data (also referred to herein as “environmental data”) representing objects in the environment surrounding the vehicle in various ways. The vehicle’s sensor system may provide environmental data representing objects in the surrounding environment. For example, the vehicle may have a variety of sensors, including cameras, radar, lidar, microphones, wireless units, and other sensors. Each of these sensors may communicate the environmental data about the information it receives to a processor in the vehicle.

[0085] In one embodiment, the camera may be configured to capture still images and / or video. In some embodiments, the vehicle may have two or more cameras positioned in different orientations. Also in some embodiments, the cameras may be movable to capture images and / or video in different orientations. The cameras may be configured to store the captured images and video in memory for subsequent processing by the vehicle's processing system. The captured images and / or video may be environmental data. Furthermore, the camera may include an image sensor, as described herein.

[0086] In another embodiment, the radar may be configured to transmit electromagnetic signals reflected by various objects near the vehicle and then capture the electromagnetic signals reflected from the objects. The captured reflected electromagnetic signals may enable the radar (or processing system) to make various decisions about the objects that reflected the electromagnetic signals. For example, the distance and position to various reflective objects may be determined. In some embodiments, the vehicle may have two or more radars oriented in different directions. The radar may be configured to store the captured information in memory for subsequent processing by the vehicle's processing system. The information captured by the radar may be environmental data.

[0087] In another embodiment, the lidar may be configured to transmit electromagnetic signals (e.g., infrared light, such as from a gas or diode laser, or other possible light source) reflected by a target object near the vehicle. The lidar may be capable of capturing the reflected electromagnetic (e.g., infrared) signals. The captured reflected electromagnetic signals may enable a ranging system (or processing system) to determine the distance to various objects. The lidar may also determine the velocity or speed of the target object and store it as environmental data.

[0088] Additionally, in one embodiment, the microphone may be configured to capture audio of the vehicle's surrounding environment. The sounds captured by the microphone may include emergency vehicle sirens and sounds of other vehicles. For example, the microphone may capture the sounds of sirens from ambulances, fire engines, or police vehicles. The processing system may be able to detect that the captured audio signal indicates an emergency vehicle. In another embodiment, the microphone may capture the sounds of exhaust from another vehicle, such as exhaust from a motorcycle. The processing system may be able to detect that the captured audio signal indicates a motorcycle. The data captured by the microphone may form part of the environmental data.

[0089] In yet another embodiment, the radio unit may be configured to transmit an electromagnetic signal, which may take the form of a Bluetooth signal, an 802.11 signal, and / or other radio technology signal. The first electromagnetic radiation signal may be transmitted via one or more antennas located on the radio unit. Furthermore, the first electromagnetic radiation signal may be transmitted in one of many different radio signal modes. However, in some embodiments, it is desirable to transmit the first electromagnetic radiation signal in a signal mode that requests a response from a device located near an autonomous or semi-autonomous vehicle. The processing system may detect nearby devices based on the response returned to the radio unit and may use this communicated information as part of the environmental data.

[0090] In some embodiments, the processing system may be able to combine information from various sensors to further determine the vehicle's surrounding environment. For example, the processing system may combine data from both radar information and captured images to determine whether another vehicle or pedestrian is in front of the autonomous or semi-autonomous vehicle. In other embodiments, other combinations of sensor data may be used by the processing system to make decisions about the surrounding environment.

[0091] While operating in autonomous (or semi-autonomous) mode, a vehicle may control its movement with little or no human input. For example, if a human operator enters an address into the vehicle, the vehicle may be able to drive to the designated destination without further human input (e.g., without the human needing to operate or touch the brake / accelerator pedals). Furthermore, while the vehicle is operating autonomously or semi-autonomously, the sensor system may receive environmental data. The vehicle's processing system may modify the vehicle's control based on the environmental data received from various sensors. In some embodiments, the vehicle may change its speed in response to environmental data from various sensors. The vehicle may change its speed to avoid obstacles, comply with traffic laws, etc. When the processing system in the vehicle detects an object near the vehicle, the vehicle may change its speed or otherwise alter its movement.

[0092] If a vehicle detects an object but lacks sufficient confidence in its detection, the vehicle may request a human operator (or a more powerful computer) to perform one or more remote assistance tasks, such as (i) verifying whether the object is actually present in the surrounding environment (e.g., whether there is actually a stop sign or not), (ii) verifying whether the vehicle's object detection is correct, (iii) correcting the detection if it was incorrect, and / or (iv) providing supplementary instructions for the autonomous or semi-autonomous vehicle (or modifying current instructions). Remote assistance tasks may also include providing instructions for a human operator to control the vehicle's actions (e.g., if the human operator determines that the object is a stop sign, instructing the vehicle to stop at the stop sign), although in some scenarios the vehicle itself may control its actions based on human operator feedback related to object detection.

[0093] To facilitate this, the vehicle may analyze environmental data representing objects in the surrounding environment to determine at least one object with a detection confidence level below a threshold. The vehicle's processor may be configured to detect various objects in the surrounding environment based on environmental data from various sensors. For example, in one embodiment, the processor may be configured to detect objects that may be important for the vehicle to recognize. Such objects may include pedestrians, cyclists, street signs, other vehicles, indicator signals of other vehicles, and various other objects detected in the captured environmental data.

[0094] Detection confidence can indicate the likelihood that a determined object is correctly detected or present in its surrounding environment. For example, a processor may perform object detection on objects in image data within the received environmental data and determine that an object has a detection confidence below a threshold if it cannot be detected if at least one object has a detection confidence above a threshold. If the result of object detection or recognition is inconclusive, the detection confidence may be low or below a set threshold.

[0095] Depending on the source of the environmental data, the vehicle may detect objects in its surroundings in various ways. In some embodiments, the environmental data may be image or video data coming from a camera. In other embodiments, the environmental data may come from a lidar. The vehicle may analyze the captured image or video data to detect objects in the image or video data. The method and apparatus may be configured to monitor the image and / or video data for the presence of objects in the surrounding environment. In other embodiments, the environmental data may be radar, audio, or other data. The vehicle may be configured to identify objects in the surrounding environment based on radar, audio, or other data.

[0096] In some embodiments, the technique used by the vehicle to detect objects may be based on a set of known data. For example, data related to environmental objects may be stored in memory located in the vehicle. The vehicle may determine an object by comparing the received data with the stored data. In other embodiments, the vehicle may be configured to determine an object based on the context of the data. For example, a road sign related to construction may generally have an orange color. Therefore, the vehicle may be configured to detect an orange object located near the side of the road as a road sign related to construction. Additionally, when the vehicle's processing system detects an object in the captured data, it may also calculate the confidence level of each object.

[0097] Furthermore, the vehicle may also have a confidence threshold. The confidence threshold may vary depending on the type of object being detected. For example, the confidence threshold may be lower for objects that may require a quick response action from the vehicle, such as the brake lights of another vehicle. However, in other embodiments, the confidence threshold may be the same for all detected objects. If the confidence associated with a detected object is higher than the confidence threshold, the vehicle may assume that the object has been correctly recognized and, based on that assumption, responsively adjust the vehicle's controls.

[0098] If the confidence level associated with a detected object is lower than the confidence threshold, the action taken by the vehicle may change. In some embodiments, the vehicle may react as if the detected object exists, despite the low confidence level. In other embodiments, the vehicle may react as if the detected object does not exist.

[0099] When a vehicle detects an object in its surrounding environment, it can also calculate a confidence level to associate it with a specific detected object. The confidence level can be calculated in various ways depending on the embodiment. In one embodiment, when an object is detected in the surrounding environment, the vehicle may compare environmental data with predetermined data associated with a known object. The closer the match between the environmental data and the predetermined data, the higher the confidence level. In other embodiments, the vehicle may use a mathematical analysis of the environmental data to determine the confidence level associated with the object.

[0100] In response to a determination that an object has a detection confidence level below a threshold, the vehicle may send a request for remote assistance to the remote computing system along with the detection of the object. As considered above, the remote computing system can take various forms. For example, the remote computing system may be a computing device located in a vehicle separate from the vehicle itself, but which may be a touchscreen interface for displaying remote assistance information, thereby allowing a human operator to interact with the vehicle's passengers or driver. Additionally or alternatively, in another embodiment, the remote computing system may be a remote computer terminal or other device located not near the vehicle.

[0101] A request for remote assistance may include environmental data, such as image data and audio data, including objects. The vehicle may transmit the environmental data to the remote computing system via a network (e.g., network 304) and, in some embodiments, via a server (e.g., server computing system 306). A human operator of the remote computing system may then use the environmental data as a basis for responding to the request.

[0102] In some embodiments, when an object is detected as having a confidence level below a confidence threshold, the object may be given a preliminary identification, and the vehicle may be configured to adjust its operation in response to the preliminary identification. Such adjustments to operation may take the form of stopping the vehicle, switching the vehicle to a human-controlled mode, or changing the vehicle's speed (e.g., speed and / or direction), among other possible adjustments.

[0103] In other embodiments, even if the vehicle detects an object with a confidence level that meets or exceeds a threshold, the vehicle may act according to the detected object (for example, stop if the object is detected with high confidence as a stop sign), but the vehicle may be configured to request remote assistance at the same time as (or after) acting according to the detected object.

[0104] Figure 4 is a block diagram of a system according to an exemplary embodiment. In particular, Figure 4 shows a system 400 including a system controller 402, a radar system 410, a sensor 412, and a controllable component 414. The system controller 402 includes a processor 404, a memory 406, and instructions 408 stored on the memory 406 and executable by the processor 404 to perform functions such as operations disclosed herein.

[0105] The processor 404 may include one or more processors, such as one or more general-purpose microprocessors (e.g., having single-core or multi-core) and / or one or more dedicated microprocessors. The one or more processors may include, for example, one or more central processing units (CPUs), one or more microcontrollers, one or more graphics processing units (GPUs), one or more tensor processing units (TPUs), one or more ASICs, and / or one or more field-programmable gate arrays (FPGAs). Other types of processors, computers, or devices configured to execute software instructions are also construed herein.

[0106] Memory 406 may include, but is not limited to, computer-readable media such as non-temporary computer-readable media, including read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), non-volatile random access memory (e.g., flash memory), solid-state drives (SSDs), hard disk drives (HDDs), compact discs (CDs), digital video discs (DVDs), digital tapes, read / write (R / W) CDs, R / W DVDs, etc.

[0107] The radar system 410 can be used in autonomous or semi-autonomous vehicles for navigation and object detection by using radio waves to detect and measure the distance, speed, and direction of objects in the surrounding environment. The radar system 410 may include one or more radar units, each consisting of a radar transmitter that emits radio waves and a radar receiver that captures waves reflected from objects. By analyzing the time it takes for the waves to return and their frequency shift (Doppler effect), the radar system 410 can determine the presence, location, and movement of objects.

[0108] In the context of autonomous or semi-autonomous vehicles, the radar system 410 provides measurements that can assist in navigation and collision avoidance. The radar unit is typically mounted on the exterior of the vehicle, such as the front, rear, and sides. During navigation, the radar system 410 may continuously emit radio waves in various directions, scanning the environment around the vehicle. When the waves encounter an object, they bounce back to the radar receiver, allowing the radar system 410 to analyze the reflected waves and calculate the object's distance, relative speed, and angle. This information can be used by the vehicle's control system to make decisions, adjust the vehicle's trajectory accordingly, and detect and react to obstacles, pedestrians, vehicles, and other potential hazards in its path. By providing real-time data about the surrounding environment, the radar system 410 can enhance the vehicle's perception capabilities and contribute to safer and more reliable navigation.

[0109] The radar system 410 offers operational advantages over other types of sensors in several embodiments, such as cameras and lidars. Radar can function well even in adverse weather conditions such as rain, fog, or dust, where other sensors have limitations. In particular, the radio waves emitted by the radar system 410 can penetrate such adverse conditions and perform reliable object detection. For this reason, radar is especially useful for improving the robustness and safety of autonomous or semi-autonomous vehicles in various weather scenarios. In addition, radar is also good at detecting the speed and relative speed of nearby objects, which is useful for evaluating the movement of surrounding vehicles, pedestrians, and other obstacles. By providing accurate speed information, the radar system 410 enables the vehicle (or the driver of the vehicle) to make informed decisions about potential collision risks and adjust its behavior accordingly. In some cases, the radar system 410 can also provide a longer measurement range and a wider field of view compared to other sensors coupled to the vehicle.

[0110] Similarly, the system controller 402 can use the outputs from the radar system 410 and the sensors 412 to determine the characteristics of the system 400 and / or the surrounding environment. For example, the sensors 412 may include one or more of the following: GPS, IMU, image capture device (e.g., camera), optical sensor, thermal sensor, one or more LiDAR devices, and other sensors that indicate parameters related to the system 400 and / or the surrounding environment. For illustrative purposes, the radar system 410 is depicted as separate from the sensors 412 and, in some embodiments, may be considered as part of or as part of the sensors 412.

[0111] Based on the characteristics of the system 400 and / or the surrounding environment determined by the system controller 402 based on the outputs from the radar system 410 and the sensor 412, the system controller 402 may control the controllable components 414 to perform one or more actions. For example, the system 400 may correspond to a vehicle, in which case the controllable components 414 may include the vehicle's braking system, turning system, and / or acceleration system, and the system controller 402 may change the configuration of these controllable components based on the characteristics determined from the radar device 410 and / or the sensor 412 (for example, when the system controller 402 controls the vehicle in autonomous or semi-autonomous mode). In the embodiment, the radar device 410 and the sensor 412 are also controllable by the system controller 402.

[0112] Calibrating radar antenna arrays for beamforming enables accurate and reliable radar operation. Vehicle radar systems often utilize beamforming to improve target detection and tracking accuracy by concentrating energy in specific directions for individual radars positioned on the vehicle. For example, a vehicle radar system can use beamforming to have a forward-facing radar measure areas in front of and to the sides of the vehicle.

[0113] Generally, radar calibration and testing may involve physically aligning and positioning antenna elements during radar manufacturing to ensure that the antenna elements are precisely spaced and oriented according to the desired design specifications. Array elements are precisely positioned to produce the desired radiation pattern during radar transmission. In particular, each antenna element in the array is used to transmit or receive signals with the correct phase relationship to achieve accurate beamforming.

[0114] Radar calibration involves both phase calibration and amplitude calibration. Phase calibration is performed to adjust the phase of the signal from each element to create constructive interference in a desired direction and destructive interference in other directions during signal transmission. A computing system may use a reference signal or measure the phase of a signal received from a known target for phase calibration. The computing system can then use advanced calibration algorithms and hardware to adjust the phase settings and ensure that the radar forms a focused beam in the intended direction. In addition to phase calibration, amplitude calibration may be performed to allow all antenna elements of the radar to contribute equally to the beamforming process. The computing system can adjust the amplitude (signal intensity) of each element until a uniform signal contribution across the antenna array is achieved. Without amplitude calibration, the radar may experience amplitude fluctuations during signal transmission, which can lead to an uneven beam pattern that negatively impacts the overall performance of the radar. Once the radar antenna array is calibrated for beamforming, the radar can then be used to precisely direct the beam pattern in different directions to capture accurate measurements of the environment.

[0115] Radar calibration processes can be performed during radar testing to check the radar's performance against known standards, which may exist before or after the radar is installed in a vehicle. In some cases, the calibration process is performed as part of vehicle maintenance to account for any environmental factors or changes that may affect radar performance over time. During calibration, various parameters of the radar system are adjusted to match desired specifications and performance standards, such as frequency, gain, phase, and sensitivity. Thus, accurate calibration enables the radar to operate effectively in a variety of applications.

[0116] However, testing the different capabilities of each radar for various beam patterns is time-consuming, which can delay the overall installation of the radar on the vehicle, or the testing of the radar after it has been installed on the vehicle. Therefore, the disclosed technique can reduce the time required for radar testing and calibrate the radar for subsequent use, such as vehicle installation. In addition, the disclosed technique can also be implemented after installation on a vehicle so that the vehicle radar system can transmit and receive electromagnetic energy according to different beamform patterns and shapes that were not originally tested during the pre-calibration.

[0117] The disclosed technique involves using a calibration processing chain that eliminates the need for dedicated transmit calibration measurements and unified transmit and receive calibration acquisitions. The computing system can implement the calibration processing chain to eliminate the need for custom spatial support acquisition patterns specific to the transmit beam by using synthetic transmit beamforming. In particular, the computing system can accelerate the calibration process by synthesizing the transmit beam patterns based on a single acquisition pattern, thereby reducing overall acquisition time. Thus, the calibration processing chain can be used to improve transmit calibration by averaging calibration across the entire field of view, and may also involve coarsely compensating for manifold mismatches between free space and the synthesized manifold.

[0118] Figure 5 is a flowchart of a radar calibration method. Method 500 may include one or more operations, functions, or actions, as illustrated by one or more of blocks 502, 504, 506, 508, 510, 512, 514, 516, 517, 518, and 520. Although the blocks are illustrated in a sequential order, these blocks may, in some cases, be performed in parallel and / or in an order different from the order described herein. Also, various blocks may be combined into fewer blocks, divided into additional blocks, and / or removed based on the desired implementation.

[0119] In addition, for the methods 500 disclosed herein and other processes and methods, flowcharts illustrate the functionality and operation of one possible implementation of these embodiments. In this regard, each block may represent a module, segment, or portion of program code containing one or more instructions executable by a processor for performing a specific logical function or step in a process. The program code may be stored on any type of computer-readable medium or memory, such as a storage device including a disk or hard drive.

[0120] In block 502, method 500 involves collecting a multiplexed waveform. During a free-space beamforming event, the radar's transmitting array produces an array response by transmitting simultaneously with all transmitting elements (also referred to herein as transmitting antennas or antennas). This is different from the generation of a multiplexed waveform, also referred to herein as a channeled waveform.

[0121] In some cases, multiplexed waveforms are created by temporally dividing the transmission time for each element. Computing systems can use time-domain multiplexing (TDM), which involves transmitting radar pulses at discrete time intervals or slots. This allows the radar system to alternate between transmitting and receiving reflections. To generate a multiplexed waveform pattern, the system can trigger the radar's transmitting elements to transmit in a sequential order at different points in time. By dividing the transmission time for each transmitting element on the radar, the receiving array can record the response for each transmitting element separately, thereby creating data representing the collected pattern of the multiplexed waveform. This data representing the collected pattern can then be used for composite transmit beamforming.

[0122] In some embodiments, the radar's transmitting array can be controlled in other ways to generate multiplexed waveforms. For example, the system can use frequency domain multiplexing (FDM), code domain multiplexing (CDM), TDM, or a combination of these techniques. For FDM, each transmitting antenna element can transmit electromagnetic energy on a separate frequency channel, allowing the receiving antenna array to distinguish transmissions from different transmitting antenna elements. For CDM, the transmitting antenna array can be individually controlled to transmit electromagnetic energy according to different codes, thereby allowing subsequent distinction of reflections received by the radar's receiving antenna array.

[0123] The generated multiplexed waveform represents the acquisition pattern that determines the spatial sampling grid for acquiring the bidirectional response. To determine the acquisition pattern used to generate data for subsequent calibration, the cost function can be optimized across different acquisition patterns that can reduce the spatial peak sidelobe level. Acquisition pattern I(Φ) is defined by the discrete angle {Φ}. k This can be explained using the comb function of} as follows:

number

number

number

number

[0124] A computing system can generate acquisition patterns by acquiring different angles for calibration measurements. In free-space beamforming for multiple transmit beams, each transmit beam requires a different angle (i.e., acquisition pattern) to characterize the main lobe of the transmit beam. Generating a different acquisition pattern for each transmit beam pattern increases the cost and time required as the total number of beams desired for later use increases. When synthesizing transmit beams, a computing system can use a single acquisition pattern, minimizing the number of angles required. By using a single acquisition pattern, the computing system can synthesize various transmit beam patterns in less time and cost.

[0125] In some embodiments, the generation of multiplexed waveforms may involve transmitting waveforms in a manner that distinguishes transmissions from each transmitting element (e.g., TDM, FDM, CDM), receiving reflections of the transmissions, and then processing the received data to extract information. For example, a radar can be used to generate and transmit radar signals with known characteristics toward a calibration target, such as pulsed waveforms, continuous wave (CW) signals, or frequency-modulated continuous wave (FMCW) signals. Different channels in the signal may be assigned specific frequency ranges, time, and / or modulation characteristics. A radar receiving array captures radar reflections from the calibration target, containing a mixture of signals from all channels. Signal processing techniques (e.g., Fast Fourier Transform (FFT) or digital filtering) can be used to separate the received signals into individual channels, each corresponding to a specific frequency range or function within the radar system.

[0126] In block 504, method 500 involves zero-Doppler averaging. Generally, zero-Doppler averaging is used in radar testing and signal processing to improve the quality of radar data by reducing the effects of Doppler shift, which occurs when radar signals are reflected from a moving target, causing a change in the frequency of the reflected signal. Thus, a computing system can perform zero-Doppler averaging by applying a filter that removes or minimizes the Doppler shift.

[0127] In some embodiments, the computing system measures the time delay and Doppler shift of the received signal when collecting raw radar data for calibration. The raw data may include information about the range and Doppler frequency of the detected target. To average out zero Doppler, the computing system may apply a Doppler filter to the raw radar data to attenuate or remove undesirable Doppler frequencies. The filtered data may be averaged over multiple radar pulses or sweeps.

[0128] In general, averaging Doppler levels can help reduce noise and improve the signal-to-noise ratio of radar data. Once the data is averaged to zero Doppler, computing systems can perform further signal processing steps.

[0129] In block 506, method 500 involves performing range compression, which can improve the accuracy and precision of range measurement. During radar calibration, the computing system may analyze whether the radar can accurately determine the distance to a calibration target or multiple calibration targets. The computing system may perform range compression during the radar pulse transmission and reception process using special waveforms such as chirps or coded sequences to effectively sharpen the radar's range resolution. By transmitting a waveform modulated with a known, precisely controlled pattern, the computing system can use the radar to accurately measure the time delay between the transmitted pulse and the received signal to determine the precise range to the calibration target or reflector.

[0130] In addition, range compression can also help reduce range ambiguity. Without compression, radar pulses may overlap, making it difficult to distinguish targets at different distances. By using range compression techniques such as frequency modulation or encoded waveforms, radar systems can resolve calibration targets that are close together or far apart, reducing range ambiguity, which can lead to more accurate calibration results. Thus, range compression can improve the reliability and accuracy of range measurements during radar calibration.

[0131] In block 508, method 500 involves estimating transmit calibration parameters. The computing system can estimate transmit calibration parameters to ensure that the radar operates accurately and effectively on the vehicle as part of a vehicle radar system.

[0132] Transmit calibration parameters can represent specific settings and characteristics that can be adjusted during the calibration process to ensure that the transmitted signal meets desired performance criteria. Transmit calibration parameters are used to maintain the accuracy and reliability of vehicle radar systems where precise target detection, tracking, and measurement are critical. Thus, transmit calibration parameters and their values ​​may vary depending on the radar's design, frequency band, and intended application. Some exemplary transmit calibration parameters include transmit power, frequency bandwidth, pulse width, pulse repetition frequency, antenna and modulation characteristics, waveform shape and phase, alignment and synchronization, frequency accuracy, and signal-to-noise ratio (SNR). A computing system may calibrate transmit parameters using a combination of hardware adjustments, software settings, and performance measurements. In some cases, one or more aspects of the calibration process for transmit calibration parameters may be performed periodically on the vehicle to maintain radar accuracy and ensure compliance with operating requirements.

[0133] In block 510, method 500 involves calibrating a transmission phase shifter, which may involve adjusting and aligning phase shifters in the radar transmission chain to ensure that the radar system can accurately and precisely control the phase of the transmission signals.

[0134] Phase shifters are used within radar antenna arrays to electronically guide the radar beam. By adjusting the phase of the signals transmitted to different antenna elements, the radar can control the direction in which the radar beam is focused. A computing system can perform transmit phase shifter calibration by precisely setting the phase delay for each antenna element to achieve the desired beamforming characteristics. This makes it possible to concentrate the transmitted energy in a specific direction, improving target detection and tracking capabilities.

[0135] In reality, electronic components such as phase shifters can exhibit slight variations in performance due to manufacturing tolerances, temperature changes, or aging. Computing systems can take these variations into account, compensate for any hardware inconsistencies, and ensure that all phase shifters within the radar system operate consistently to maintain beamforming accuracy.

[0136] In block 512, method 500 involves synthesizing transmit beamformers. Synthesizing radar signals means the process of generating or creating radar waveforms, which can then be used to generate models for use by vehicle radar systems. The waveform of the transmitted radar signal determines various characteristics of the radar signal, including its frequency, modulation, pulse width, amplitude, and phase. By synthesizing radar signals, custom waveforms can be created using specific parameters to simulate various radar scenarios or test radar systems under controlled conditions. For example, a single acquisition pattern can be used to synthesize transmit beam patterns with different shapes and directions.

[0137] In some cases, a computing system can use synthesized radar signals to test the performance of a radar. By generating controlled radar signals, testers can evaluate how a radar system responds to different scenarios, such as detecting different types of targets, interference, jamming, or environmental conditions. This helps assess the radar's capabilities and identify potential problems or areas for improvement.

[0138] In block 514, method 500 involves estimating a transport delay, which represents the time delay introduced into the radar signal path as electromagnetic energy moves from the radar transmitter to the antenna element, or from the antenna element to the radar receiver. The transport delay may arise due to the physical separation between the transmitter and receiver components of the radar system.

[0139] In radar systems that use phase arrays for beamforming, transport delay occurs due to the physical separation of each antenna element from others. When transmitting a signal, the radar can ensure that the transmitted signal reaches the target region simultaneously and forms a coherent radar beam by taking into account the difference in propagation time from the transmitter to each antenna element. Similarly, during signal reception, the radar compensates for the transport delay to align the signals received from different antenna elements. Alignment enables coherent signal processing and accurate beamforming. Therefore, to perform beamforming and calibration optimally, the radar system can use accurate measurements of transport delay to properly align and synchronize the transmitted and received signals. Calibration algorithms can adjust the phase and time delays of the signals, taking into account the distance between antenna elements, thereby enabling positively precise beam steering and shaping for optimal radar performance.

[0140] In block 516, Method 500 involves performing waveform matching, also referred to herein as manifold matching. Generally, waveform matching involves comparing a transmitted or received radar waveform generated by the radar with a predicted or reference waveform. The comparison allows a computing system to assess the performance and accuracy of the radar system. As shown, block 517 of Method 500 involves collecting an onboard waveform, which can be used as part of the waveform matching process.

[0141] The reference waveform used for waveform matching can be a predefined or ideal radar signal representing how the radar transmits and receives. In some cases, the reference waveform is generated for specific operating conditions, such as the radar's calibration environment. Thus, a computing system or another computing device may generate the reference waveform based on the radar's design specifications and intended use case. The reference waveform may include information about the waveform's frequency, pulse width, modulation, amplitude, and other relevant parameters.

[0142] A computing system can use waveform matching to evaluate the received radar reflection relative to a reference signal. The received radar reflection may vary due to environmental conditions, hardware defects, and other factors. The computing system uses comparisons to determine how closely the radar performance matches expected behavior. The main aspects of the comparison may include checking deviations in signal frequency, phase, amplitude, and timing.

[0143] A computing system can use the results of waveform comparison to assess the performance of a radar. When the actual radar signal closely matches the reference waveform, the comparison results indicate that the radar is operating as expected and is likely to perform well in its intended task. When the comparison shows a deviation between the actual radar signal and the reference waveform, this may indicate a radar problem such as a calibration error or hardware failure. If a discrepancy between the actual waveform and the reference waveform is detected during testing, the computing system can use information from the waveform matching process to identify and address the difference. Waveform matching can be used to verify and validate radars to ensure that each radar can reliably and accurately detect and track its target.

[0144] The computing system may perform manifold matching after the transmit beams have been synthesized and before the interconnection matrix is ​​estimated. For each transmit beam synthesized by the computing system based on the acquired pattern, the computing system can model the free-space array response in the main lobe of the transmit beam. This model can be based on acquired data, ray tracing techniques, numerical electromagnetic codes (NECs), or other methods. Using the model, the free-space response near the main lobe of the synthesized transmit beam pattern can be roughly characterized.

[0145] Next, the computing system can calculate the mean complex multiplier of the synthesized array response and the free-space array response, and then fold the difference back into the synthesized array response across the entire collection pattern. The computing system can then compute complex corrections using least-squares techniques or simple inversion methods. As an exemplary result, the synthesized collection pattern can mimic its free-space counterpart and provide a coupled matrix estimate that closely matches the estimates baked into a "natural" free-space manifold.

[0146] In block 518, method 500 involves estimating the inter-coupling matrix. The inter-coupling matrix (also known as the inter-coupling matrix or inter-coupling coefficient matrix) is a mathematical representation that describes the electromagnetic coupling or interaction between individual antenna elements in an antenna array. This matrix characterizes how the presence of one antenna element affects the radiation patterns and performance of other antenna elements in the array. A computing system can use an understanding of inter-coupling to enable accurate calibration of a radar antenna array.

[0147] Radar systems often use antenna arrays consisting of multiple individual antenna elements. These arrays may be linear, planar, or three-dimensional and are designed to transmit and receive radar signals, control beamforming, and work together to improve overall system performance. When antenna elements are close to each other within an array, they can influence the performance of other antenna elements in the array through electromagnetic coupling effects, which can cause variations in the antenna's radiation pattern, impedance, and other characteristics.

[0148] The coupling matrix can differ for different angles. When radar transmits or receives electromagnetic waves, the waves can interact with nearby antennas based on various factors, such as the angle at which the incoming or outgoing waves are incident. In particular, different angles can result in different spatial relationships between antennas, which can lead to variations in coupling. Since radar uses constructive and destructive interference to guide the radar beam in a specific direction, the coupling matrix can be used to determine the phase relationships between antenna elements to achieve desired beamforming. Thus, computing systems can estimate the coupling matrix for a radar to compensate for coupling effects and ensure accurate target detection and tracking at various angles. In some embodiments, the coupling matrix is ​​a square matrix where each element represents the coupling coefficient between a pair of antenna elements in an array. Since the coupling effect is reciprocal (i.e., the effect of antenna "A" on antenna "B" is the same as the effect of antenna "B" on antenna "A"), the matrix is ​​usually symmetric.

[0149] The interconnection matrix is ​​used to account for these interactions during beamforming calculations, ensuring that the radar beam is precisely formed and directed. The computing system analyzes the interconnections to compensate for the effects of coupling within the radar antenna array, and utilizes calibration techniques to adjust the phase and amplitude of the signals transmitted to each element to mitigate the effects of coupling, allowing the radar to maintain accurate beamforming and target detection.

[0150] In block 520, method 500 involves determining a receiver beamformer index, which represents an objective measurement that can be used to evaluate the performance of a receiver beamforming system. The receiver beamformer index can provide a quantifiable indicator of how effectively the radar is functioning.

[0151] In some embodiments, the received beamformer indices can encompass various aspects of beamformer performance, such as beamwidth, sidelobe level, beam steering accuracy, and sensitivity. Generally, beamwidth measures the angular width of the main lobe of the beamforming pattern, which affects the angular resolution. Sidelobe level quantifies the intensity of radiation in directions other than the main beam and can affect interference rejection. Beam steering accuracy represents the precision with which the beam is directed towards a desired target or source, and sensitivity measures the radar system's ability to detect weak signals.

[0152] In addition, other metrics such as directivity, SNR, and dynamic range can be used to assess aspects related to signal strength and noise elasticity. Computing systems can use beamformer metrics to provide a comprehensive view of the radar's ability to enhance signal reception and reduce interference. In some cases, the system can use the receiving beamformer for system optimization and performance evaluation.

[0153] Figure 6 is a conceptual diagram of radar data generation and analysis, illustrating a comparison between free-space beamforming and composite beamforming. Free-space beamforming is represented by the simultaneous transmission of electromagnetic energy by transmitting antennas 602, 604, and 606 to form a free-space beam received by receiving antenna 608. Conversely, composite beamforming is represented by having each transmitting antenna 602-606 individually transmit electromagnetic energy based on variations within the domain. In the embodiment shown in Figure 6, transmitting antennas 602-606 are shown to transmit at different times (TDM), causing receiving antenna 608 to receive reflections from each transmitting antenna 602-606 at different times. In other embodiments, different frequencies or codes can be used to distinguish the transmissions of transmitting antennas 602-606.

[0154] In exemplary embodiments, time is used to distinguish the transmissions of transmitting antennas 602-606 for composite beamforming. Specifically, transmitting antenna 602 is shown to transmit a signal at time 1, transmitting antenna 604 is shown to transmit a signal at time 2, and transmitting antenna 606 is shown to transmit a signal at time 3. Time 1 is before time 2, and time 2 is before time 3. The variance between different times may vary within the embodiment. For example, time 2 may occur 5 milliseconds after time 1, and time 3 may occur 5 milliseconds after time 2. The difference between times may vary within the embodiment.

[0155] As shown in Figure 6, the receiving antenna 608 is shown receiving electromagnetic energy formed based on the free-space transmit beam pattern and the combined transmit beam pattern. However, for combined beamforming, the receiving antenna 608 is shown receiving signals transmitted at different times, which are transmitted by the transmitting antennas 602-606 individually at different times. In some embodiments, each transmitting antenna 602-606 transmits a signal across different angles for reception by the receiving antenna 608. The transmitting antennas 602-606 can transmit according to a specific collection pattern that is independent of the location of lobes and nulls in the combined transmit beam. For the free-space transmit beam, separate collection patterns may be required to mitigate the location of lobes and nulls.

[0156] To further explain free-space beamforming and composite beamforming, Equation 2 shows the desired transmit weight (w) that can be used by the transmit antennas 602-606. Φ Equation 3 is provided below to represent the transmit calibration phase offset (t), while Equation 4 is given for the transmit steering vector a(Φ).

number

[0157] The transmission of a signal pulse at time (n) "x(n)" is represented by Equation 5, the reception of a steering vector in direction Φ can be represented by Equation 6, and the reception calibration term can be represented by Equation 7.

number

number

[0158] In general, the concepts of array response and array manifold can be used synonymously to describe wavefield sampling of antenna arrays in space. For a conventional transmitting phase array with isotropic elements, the desired unidirectional transmitting response in direction φ at time (n) can be described as follows:

number

number

number

[0159] For far-field vision, the composite transmission weighting function is as follows:

number

[0160] Assuming the transmitting elements of the linear array are isotropic, the above weighting function describes the transmitting array manifold. Neglecting range losses, the bidirectional array response of the entire receiving array for an isotropic reflector can be described by the following vector.

number

[0161] This allows us to treat the pulse train x(n) as identical pulses across pulse indices, thereby eliminating the dependence on n from x. Next, the free-space response is as follows:

number

[0162] Using Equation 12, the response received by the receiving antenna 608, which is the free-space transmit beam pattern, can be represented. Conceptually similar to synthetic aperture radar (SAR), the free-space transmit beam can be synthesized in a computing system using bidirectional responses from individual transmitting elements. Mapping

number

number

[0163] This mapping symbolizes activating only the transmitting element p in the array while turning off all other elements, as shown in Figure 6. By activating each transmitting element 602-606 individually, the following received array response vector for the p-th pulse is obtained.

number

[0164] The transmitted beamform synthesized upon reception will look like this:

number

[0165] Therefore, equations 14 and 15 can represent the composite beam pattern formed by the computing system using the disclosed technique. Regarding superposition, it can be asserted that:

number

[0166] However, in reality, the synthesized response (Γ pThe reception calibration factor in (Φ) is different from the free space response (Γ(Φ)). This may be due to various factors such as the short - range wireless interaction between the antenna element and the radome, and the difference in coupling interaction due to the difference in the transmission beam.

[0167] Free space (s RX (φ)) and the synthesis (s’ RX To account for this discrepancy between (φ)) array manifold, the computing system can estimate the angle - dependent term γ(φ) that can be used to roughly correct between the two by performing manifold alignment. The angle - dependent term γ(φ) is estimated at low - density points in space from both the (s RX (φ)) array and the (s’ RX (φ) array and can be sampled in the transmission steering direction of φ. This step manifold matches between free space and the synthetic array response. w Φ Considering that w dominates the transmission array response, the flexible and retroactive generation of the received array response for different w Φ is made possible by synthesizing s’ RX (φ) using consecutive transmission elements.

[0168] FIG. 7 is a flowchart of another method for radar calibration. Method 700 may include one or more operations, functions, or actions as illustrated by one or more of blocks 702, 704, 706, and 708. The blocks are illustrated in a sequential order, but these blocks may, in some cases, be performed in parallel and / or in an order different from the order described herein. Also, various blocks may be combined into fewer blocks, divided into additional blocks, and / or removed based on the desired implementation.

[0169] In addition, for Method 700 disclosed herein and other processes and methods, flowcharts illustrate the functionality and operation of one possible implementation of these embodiments. In this regard, each block may represent a module, segment, or portion of program code containing one or more instructions executable by a processor for performing a specific logical function or step in a process. The program code may be stored on any type of computer-readable medium or memory, such as a storage device including a disk or hard drive.

[0170] In block 702, method 700 involves triggering each transmitting antenna element of a radar to individually transmit electromagnetic energy according to a first transmitting beam pattern. The radar includes multiple transmitting antenna elements. For example, a computing system can trigger each transmitting antenna element of the radar to individually transmit electromagnetic energy in a sequential order. In addition, each transmitting antenna element of the radar may be triggered to individually transmit electromagnetic energy across multiple azimuth angles.

[0171] As an example, the computing system can trigger a first transmitting antenna element to transmit a first electromagnetic energy in a first time, then trigger a second transmitting antenna element to transmit a second electromagnetic energy in a second time, the second time following the first time. The computing system can then trigger a third transmitting antenna element to transmit a third electromagnetic energy in a third time, the third time following the second time, and so on. This allows the receiving antenna array (and therefore the computing system) to collect radiation independently from each transmitting antenna. In this way, the computing system can generate data representing the collection pattern based on the reflections corresponding to the first, second, and third electromagnetic energies.

[0172] In block 704, method 700 involves generating data representing a collection pattern by a computing system based on the reflection of electromagnetic energy transmitted according to a first transmission beam pattern. The data representing the collection pattern can be generated such that the collection pattern does not depend on the locations of lobes and nulls corresponding to the first transmission beam pattern.

[0173] In block 706, method 700 involves synthesizing a second transmit beam pattern different from a first transmit beam pattern using data representing an acquired pattern. The shape, orientation, size, and / or other aspects of the beam pattern may vary. In some cases, a computing system can synthesize multiple transmit beam patterns different from the first transmit beam pattern. Various transmit beam patterns can be synthesized based on data representing a single acquired pattern.

[0174] In block 706, method 700 involves estimating a coupling matrix for processing the reflection of electromagnetic energy transmitted according to a second transmit beam pattern. For example, a computing system may estimate the coupling matrix based on a reference array response matrix. The reference array response matrix is ​​dependent on the radar environment. The computing system may use different reference array response matrices based on triggering the radar in different calibration environments. The reference array response matrix can convey how a specific array of antennas responds to a signal that can be transmitted in terms of amplitude, phase, and other relevant parameters. The reference array response matrix may be determined as a function of the acquisition geometry and may be used to describe the phase front measured by each element due to the turret at a point from the acquisition geometry. In some embodiments, the reference array response matrix is ​​determined within a laboratory setting, such as testing the radar configuration. The reference array response matrix can be determined through simulation, transmit and receive data analysis, or a combination thereof.

[0175] In some embodiments, computing systems use a radar steering matrix as a reference array response matrix. A radar steering matrix is ​​a mathematical representation of how radar can guide its beam in different directions. A radar antenna array can be electronically or mechanically guided to focus transmitted or received signals in specific directions. The radar steering matrix is ​​used to provide a way to understand and quantify the guidance capability of radar. In addition, the radar steering matrix can transmit the phase setting of each element in the array in the direction to which the radar beam is guided. The steering matrix allows the radar system to orient its beam in a desired direction, track moving targets, and adapt to changing operating requirements. Thus, a computing system can use a radar steering matrix as a reference array response matrix when estimating an interconnection matrix for processing reflections of electromagnetic energy transmitted according to different transmitted beam patterns.

[0176] In some embodiments, the computing system estimates the transport delay corresponding to the second transmit beam pattern and uses the transport delay to perform waveform matching based on the synthesis of the second transmit beam pattern. The computing system can then estimate the interconnection matrix based on the waveform matching. The estimation can factorize the reference array response matrix.

[0177] In some embodiments, the computing system may perform a manifold matching process to model the free-space array response for the main lobe of the second transmit beam pattern. The computing system may also determine the difference between the free-space array response and the composite array response for the main lobe of the second transmit beam pattern and calculate a correction based on the difference. The computing system can then estimate the interconnection matrix based on the correction.

[0178] In block 708, method 700 involves generating a model for operating a vehicle-mounted radar based on an interconnection matrix. The model enables a vehicle radar system having one or more radars matching the radar to transmit and receive electromagnetic energy according to a first transmit beam pattern and a second transmit beam pattern. The second transmit beam pattern has a different shape or orientation from the first transmit beam pattern. In some cases, the computing system also performs a manifold matching process (waveform matching) and generates the model further based on angular dependency terms determined based on performing the manifold matching process.

[0179] In some embodiments, a computing system provides a model to one or more vehicles as an over-the-air update via wireless communication. The computing system may also provide the vehicles with data representing acquired patterns along with the model as part of the over-the-air update. The combination of the model and the data representing acquired patterns enables each vehicle radar system to synthesize multiple transmit beam patterns and estimate an interconnection matrix corresponding to the multiple transmit beam patterns for use during vehicle navigation.

[0180] In some embodiments, Method 700 involves receiving reflections of electromagnetic energy transmitted according to a first transmit beam pattern from the radar's receiving antenna element, the reflections of electromagnetic energy transmitted according to the first transmit beam pattern being reflected from calibration targets located within the radar environment before reaching the radar's receiving antenna element at a first plurality of angles. Thus, the computing system can then estimate the interconnection matrix based on the reflections of electromagnetic energy transmitted according to a second transmit beam pattern reaching the radar's receiving antenna element at a second plurality of angles.

[0181] The computing system can similarly synthesize a third transmit beam pattern, distinct from a first transmit beam pattern and a second transmit beam pattern, based on data representing the collected patterns, and estimate a second interconnection matrix for processing the reflection of electromagnetic energy transmitted according to the third transmit beam pattern. The computing system can estimate the second interconnection matrix based on the reflection of electromagnetic energy transmitted according to the third transmit beam pattern reaching the radar's receiving antenna elements at a third set of angles distinct from the first set of angles and the second set of angles. The computing system may generate a model based on both the interconnection matrix (e.g., the first interconnection matrix) and the second interconnection matrix. The model can then enable the radar to transmit and receive electromagnetic energy according to the first transmit beam pattern, the second transmit beam pattern, and the third transmit beam pattern.

[0182] In some embodiments, Method 700 also involves applying a range compression filter and a zero-Doppler filter to data representing the collection pattern, estimating transmit calibration parameters based on the data representing the collection pattern after applying the range compression filter and the zero-Doppler filter, and calibrating the transmit phase shifter based on the transmit calibration parameters. The computing system can then estimate the interconnection matrix based on the transmit phase shifter.

[0183] In some embodiments, a computing system may estimate the transport delay corresponding to a second transmit beam pattern, and use the transport delay to perform waveform matching to determine the difference between the second transmit beam pattern and the corresponding free-space beam pattern. The computing system may then further estimate the interconnection matrix based on the difference.

[0184] In some embodiments, vehicle radar can be used with control electronics that may include one or more field-programmable gate arrays (FPGAs), ASICs, CPUs, GPUs, and / or TPUs. For example, a radar unit can generate and receive complex signals that require significant processing. One or more control electronics can be programmed to implement various signal processing algorithms such as filtering, modulation / demodulation, noise reduction, and digital beamforming. These operations help extract relevant information from the received radar signal, improve signal quality, and enhance target detection and tracking. In addition, radar systems often involve the conversion of analog signals to digital formats for further processing. Control electronics may include analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) to facilitate these conversions. The control electronics can receive analog signals from radar sensors, digitize them, and process the digital data for analysis and interpretation.

[0185] In addition, control electronics can also provide the capability for real-time control and coordination of various radar system components. For example, control electronics can handle synchronization, timing generation, and system control, ensuring proper timing and sequencing of operations within the radar system. This real-time control is crucial for accurate and synchronized signal transmission and reception. Control electronics can efficiently handle the large amounts of data generated by the radar system. Control electronics can implement data storage, buffering, and data flow management techniques, enabling efficient data handling during signal transmission. This includes tasks such as data compression, data packetization, and data routing, ensuring smooth and reliable data transmission within the radar system. Control electronics can also integrate various interfaces and protocols necessary for radar signal transmission, such as processors, memory modules, communication modules, and display units. Control electronics can provide the necessary interface logic to facilitate seamless data exchange between these components, enabling efficient data flow and system integration. Control electronics can also be reconfigured and customized to meet specific radar system requirements and adapt to changing operational needs. This allows radar system designers to implement and optimize algorithms and functions specific to their application, resulting in improved performance and efficiency.

[0186] This disclosure is not limited to the specific embodiments described herein, and the specific embodiments are intended to be illustrative of various aspects. As will be apparent to those skilled in the art, many modifications and variations can be made without departing from the spirit and scope of this disclosure. In addition to the methods and apparatus enumerated herein, functionally equivalent methods and apparatus within the scope of this disclosure will be apparent to those skilled in the art from the foregoing description. Such modifications and variations are intended to fall within the scope of the appended claims.

[0187] The above detailed description, with reference to the accompanying drawings, describes various features and functions of the disclosed systems, devices, and methods. In the drawings, unless the context otherwise indicates, similar symbols typically represent similar components identically. The exemplary embodiments described herein and in the drawings are not intended to be limiting. Other embodiments may be utilized and other modifications may be made without departing from the scope of the subject matter presented herein. It will be readily apparent that the aspects of this disclosure generally described herein and illustrated in the drawings can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations, all of which are expressly intended.

[0188] With respect to any or all of the message flow diagrams, scenarios, and flowcharts in the figures and discussed herein, each step, block, action, and / or communication may represent the processing of information and / or the transmission of information according to the exemplary embodiments. Alternative embodiments are included within the scope of these exemplary embodiments. In these alternative embodiments, for example, actions described as steps, blocks, transmissions, communications, requests, responses, and / or messages may be performed in an order different from those shown or discussed, such as substantially simultaneously or in reverse order, depending on the relevant functions. Furthermore, more or fewer blocks and / or actions may be used in any of the message flow diagrams, scenarios, and flowcharts discussed herein, and these message flow diagrams, scenarios, and flowcharts may be combined with each other in part or as a whole.

[0189] Steps, blocks, or operations corresponding to the processing of information may correspond to a network of circuits, which may be configured to perform a specific logical function of the method or technique described herein. Alternatively or additionally, steps or blocks corresponding to the processing of information may correspond to a module, segment, or portion of program code (including associated data). The program code may include one or more instructions that are executable by a processor to perform a specific logical operation or action in the method or technique. The program code and / or associated data may be stored on any type of computer-readable medium, such as a storage device including RAM, a disk drive, a solid-state drive, or another storage medium.

[0190] Furthermore, one or more steps, blocks, or operations corresponding to information transmission may correspond to information transmission between software modules and / or hardware modules on the same physical device. However, other information transmissions may be between software modules and / or hardware modules on different physical devices.

[0191] The specific arrangements shown in the figures should not be considered limiting. It should be understood that other embodiments may include more or fewer of each element shown in the given figures. Furthermore, some of the illustrated elements may be combined or omitted. Moreover, exemplary embodiments may include elements not illustrated in the figures.

[0192] Various aspects and embodiments are disclosed herein, but other aspects and embodiments will be obvious to those skilled in the art. The various aspects and embodiments disclosed herein are for illustrative purposes only and are not intended to limit, and the true scope is indicated by the following claims.

Claims

1. It is a method, The computing system triggers each transmitting antenna element of the radar to individually transmit electromagnetic energy according to a first transmission beam pattern, The computing system generates data representing a collection pattern that is independent of the location of the lobes and nulls of the transmitted beam, based on the reflection of the electromagnetic energy transmitted according to the first transmitted beam pattern. Using the data representing the collection pattern, a plurality of additional transmission beam patterns different from the first transmission beam pattern are synthesized. To process the reflection of electromagnetic energy transmitted according to the aforementioned multiple additional transmission beam patterns, estimate multiple interconnection matrices as a function of the combined beam steering angle, The computing system generates a model for operating the radar mounted on the vehicle, based on the plurality of interconnection matrices, The aforementioned model has a vehicle radar system, (i) Synthesizing additional transmit beam patterns upon request, (ii) Estimating the corresponding interconnection matrix, (iii) A method that enables the synthetic guidance of radar beams to compensate for variations in mutual coupling across different spatial relationships between antennas.

2. Estimating the aforementioned multiple interconnection matrices is The method according to claim 1, comprising estimating the plurality of interconnection matrices based on a reference array response matrix, wherein the reference array response matrix depends on the radar environment.

3. The method according to claim 1, further comprising receiving a reflection of the electromagnetic energy transmitted according to the first transmit beam pattern from a receiving antenna element of the radar, wherein the reflection of the electromagnetic energy transmitted according to the first transmit beam pattern is reflected from a calibration target located in the radar environment before it reaches the receiving antenna element of the radar at a first plurality of angles.

4. Estimating the plurality of interconnection matrices for processing the reflection of electromagnetic energy transmitted according to the plurality of additional transmission beam patterns, The method of claim 3, comprising estimating the plurality of interconnection matrices based on the reflection of the electromagnetic energy transmitted according to the plurality of additional transmit beam patterns that reach the receiving antenna element of the radar at a second plurality of angles.

5. Based on the data representing the collection pattern, a third transmission beam pattern different from the first transmission beam pattern and the plurality of additional transmission beam patterns is synthesized. The method according to claim 4, further comprising estimating a second coupling matrix for processing the reflection of electromagnetic energy transmitted according to the third transmission beam pattern.

6. Estimating the second coupling matrix for processing the reflection of electromagnetic energy transmitted according to the third transmission beam pattern, The method of claim 5, comprising estimating the second coupling matrix based on the reflection of the electromagnetic energy transmitted according to the third transmit beam pattern reaching the receiving antenna element of the radar at a third plurality of angles, wherein the third plurality of angles is different from the first plurality of angles and the second plurality of angles.

7. To generate the model for operating the radar mounted on the vehicle, The method according to claim 6, comprising generating the model based on both the plurality of interconnection matrices and the second interconnection matrix, wherein the model enables the radar to transmit and receive electromagnetic energy according to the first transmit beam pattern, the plurality of additional transmit beam patterns, and the third transmit beam pattern.

8. The method according to claim 1, further comprising providing the model to the vehicle as an over-the-air update via wireless communication.

9. The method of claim 8, further comprising providing the vehicle with the model along with the data representing the acquisition pattern as part of the over-the-air update, the combination of the model and the data representing the acquisition pattern enabling each vehicle radar system to synthesize a plurality of transmit beam patterns and estimate an interconnection matrix corresponding to the plurality of transmit beam patterns for use during navigation by the vehicle.

10. The manifold matching process is performed to model the free-space array response for the main lobes of the multiple additional transmit beam patterns, Determining the difference between the free-space array response and the composite array response for the main lobe of the plurality of additional transmit beam patterns, This further includes calculating a correction based on the aforementioned difference, Estimating the aforementioned multiple interconnection matrices is The method according to claim 1, comprising estimating the plurality of interconnection matrices based on the correction.

11. To trigger each transmitting antenna element of the radar and transmit electromagnetic energy individually, Triggering a first transmitting antenna element to transmit a first electromagnetic energy in a first time period, Triggering a second transmitting antenna element to transmit a second electromagnetic energy at a second time, which is after the first time, The method according to claim 1, comprising triggering a third transmitting antenna element to transmit a third electromagnetic energy at a third time which is after the second time.

12. To trigger each transmitting antenna element of the radar and transmit electromagnetic energy individually, The method according to claim 11, comprising triggering each transmitting antenna element of the radar to individually transmit electromagnetic energy over a plurality of azimuth angles, wherein the plurality of azimuth angles depend on the first transmitting beam pattern.

13. Applying a range compression filter and a zero Doppler filter to the data representing the aforementioned collection pattern, Based on the data representing the collection pattern after applying the range compression filter and the zero Doppler filter, the transmission calibration parameters are estimated. The method according to claim 1, further comprising calibrating a transmit phase shifter based on the transmit calibration parameters.

14. Estimating the aforementioned multiple interconnection matrices is The method according to claim 13, further comprising estimating the plurality of interconnection matrices based on the transmission phase shifter.

15. It is a system, Radar and, A computing system comprising, the computing system, Each transmitting antenna element of the radar is triggered to individually transmit electromagnetic energy according to the first transmitting beam pattern. Based on the reflection of the electromagnetic energy transmitted according to the first transmission beam pattern, data representing a collection pattern independent of the location of the lobes and nulls of the transmission beam is generated. Using the data representing the collection pattern, a plurality of additional transmission beam patterns different from the first transmission beam pattern are synthesized. To process the reflection of electromagnetic energy transmitted according to the aforementioned multiple additional transmission beam patterns, multiple interconnection matrices are estimated as a function of the combined beam steering angle. Based on the aforementioned multiple interconnection matrices, it is configured to generate a model for operating the radar mounted on the vehicle. The aforementioned model has a vehicle radar system, (i) Synthesizing additional transmit beam patterns upon request, (ii) Estimating the corresponding interconnection matrix, (iii) A system that enables the synthetic guidance of radar beams to compensate for variations in mutual coupling across different spatial relationships between antennas.

16. The computing system, The transport delay corresponding to the aforementioned multiple additional transmission beam patterns is estimated, The system according to claim 15, further configured to use the transport delay to perform waveform matching to determine the difference between the plurality of additional transmit beam patterns and the corresponding free-space beam patterns.

17. The computing system, The system according to claim 16, further configured to estimate the plurality of interconnection matrices based on the aforementioned difference.

18. The computing system, Using the data representing the collection pattern, a plurality of transmission beam patterns different from the first transmission beam pattern and the plurality of additional transmission beam patterns are synthesized. The system according to claim 15, further configured to estimate a plurality of interconnection matrices for processing the reflection of electromagnetic energy transmitted according to the plurality of transmission beam patterns.

19. A non-temporary computer-readable medium configured to store instructions, wherein when the instructions are executed by a computing system comprising one or more processors, the computing system... Triggering each transmitting antenna element of the radar to individually transmit electromagnetic energy according to a first transmission beam pattern, Based on the reflection of the electromagnetic energy transmitted according to the first transmission beam pattern, data is generated that represents a collection pattern independent of the location of the lobes and nulls of the transmission beam. Using the data representing the collection pattern, a plurality of additional transmission beam patterns different from the first transmission beam pattern are synthesized. To process the reflection of electromagnetic energy transmitted according to the aforementioned multiple additional transmission beam patterns, estimate multiple interconnection matrices as a function of the combined beam steering angle, The operation includes generating a model for operating the radar mounted on the vehicle based on the aforementioned plurality of interconnection matrices, The aforementioned model has a vehicle radar system, (i) Synthesizing additional transmit beam patterns upon request, (ii) Estimating the corresponding interconnection matrix, (iii) A non-temporary computer-readable medium that enables the synthetic guidance of radar beams to compensate for variations in interconnection across different spatial relationships between antennas.

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