Methods and systems for transmit beam agnostic 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 Art

[0001] Automotive radar involves using radio waves to detect the presence, distance, direction, and speed of objects in the surrounding environment of a vehicle. A vehicle radar system transmits a wireless signal from a transmitter, and the wireless signal then bounces off nearby objects and returns to the receiver. By analyzing the characteristics of the returned signal (also referred to herein as a radar echo), the vehicle radar system can determine the location, speed, and direction of objects located in the environment, such as other vehicles, pedestrians, road boundaries, and obstacles. In some cases, the radar data is used by the vehicle's advanced driver assistance system (ADAS) or autonomous driving system (ADS) to provide warnings to the driver or to take autonomous actions to avoid collisions. In other instances, the vehicle control system uses the radar data when determining control strategies for autonomous navigation by the vehicle.

[0002] Some vehicle radar systems use radar beamforming to shape the radar signal in a specific direction, enabling directional focusing of the energy of individual radars on the vehicle to a particular region of interest. Beamforming can provide high-resolution data with improved detection capabilities compared to conventional radar systems. To implement beamforming, the radar adjusts the phase of the signals transmitted from each antenna element in the antenna array to electronically steer the direction and shape of the radar beam without the need for any physical movement of the antenna. By carefully controlling the phase and amplitude of these signals, the radar system can use constructive and destructive interference to create the desired beam direction and shape.

Summary of the Invention

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

[0004] In one aspect, a method is described. The method involves triggering, by a computing system, each transmit antenna element of a radar to individually transmit electromagnetic energy according to a first transmit beam pattern, and generating, by the computing system and based on reflections of the electromagnetic energy transmitted according to the first transmit beam pattern, data representing a collection pattern. The method also involves using the data representing the collection pattern to synthesize a second transmit beam pattern different from the first transmit beam pattern, estimating a mutual coupling matrix for processing reflections of electromagnetic energy transmitted according to the second transmit beam pattern, and generating, by the computing system and based on the mutual coupling matrix, a model for operating a radar mounted on a vehicle. 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 transmit beam pattern and the second transmit beam pattern.

[0005] In another aspect, a system is described. The system includes a radar and a computing system. The computing system triggers each transmission antenna element of the radar to individually transmit electromagnetic energy according to a first transmission beam pattern, generates data representing a collection pattern based on reflections of the electromagnetic energy transmitted according to the first transmission beam pattern, and is configured to synthesize a second transmission beam pattern different from the first transmission beam pattern using the data representing the collection pattern. The computing system is also configured to estimate a mutual coupling matrix for processing reflections of electromagnetic energy transmitted according to the second transmission beam pattern and generate a model for operating a radar mounted on a vehicle based on the mutual coupling matrix. 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 transmission beam pattern and the second transmission beam pattern.

[0006] In yet another aspect, a non-transitory computer-readable medium is described. The non-transitory computer-readable medium is configured to store instructions that, when executed by a computing system comprising one or more processors, cause the computing system to perform operations. The operations include triggering each transmission antenna element of the radar to individually transmit electromagnetic energy according to a first transmission beam pattern, generating data representing a collection pattern based on reflections of the electromagnetic energy transmitted according to the first transmission beam pattern, and synthesizing a second transmission beam pattern different from the first transmission beam pattern using the data representing the collection pattern. The operations also include estimating a mutual coupling matrix for processing reflections of electromagnetic energy transmitted according to the second transmission beam pattern and generating a model for operating a radar mounted on a vehicle based on the mutual coupling matrix. 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 transmission beam pattern and the second transmission beam pattern.

[0007] These, as well as other aspects, 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 Description of the Drawings

[0008]

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[0009] Exemplary methods and systems are contemplated herein. Any exemplary embodiment or feature described herein should not necessarily be construed as being more preferred or advantageous than other embodiments or features. Further, the exemplary embodiments described herein are not meant to be limiting. It will be readily understood that certain aspects of the disclosed systems and methods can be arranged and combined in a wide variety of different configurations, all of which are contemplated herein. Additionally, the specific arrangements shown in the figures should not be regarded as limiting. It should be understood that other embodiments can include more or fewer of each element shown in a given figure. Additionally, some of the illustrated elements can be combined or omitted. Still further, the exemplary embodiments can include elements not illustrated in the figures.

[0010] Vehicle radar systems use beamforming for several advantages, such as increased sensing range, higher resolution, improved object tracking, and reduced interference. By beamforming, a vehicle radar can focus transmitted energy in a specific direction, extending the effective sensing range of the radar system, which allows objects, pedestrians, and vehicles to be detected at longer distances, creating more time for decision-making and response by the vehicle control system. The narrowed beam also increases resolution, enabling the vehicle system to distinguish closely spaced objects and accurately determine their positions 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 unwanted signals originating outside these areas to be filtered out. Thus, beamforming enables the vehicle radar system to generate cleaner and more accurate data that can assist in safely navigating the dynamic environment encountered during navigation.

[0011] However, testing and calibrating vehicle radar for beamforming presents significant challenges that can arise due to the complex nature of the radar system and the need for accurate calibration. In some cases, the performance of beamforming can be negatively affected by variations between antenna elements within the radar array, variations in the radiation patterns and sensitivities of individual elements. Additionally, in some instances, mutual coupling that occurs between adjacent antenna elements can also contribute to unwanted effects that impact the beam pattern 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 (e.g., 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 required 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 accelerating radar testing and calibration by separating transmit beamforming from receive calibration. Instead of collecting data separately for each transmit beam, the disclosed techniques provide a channelized approach where various 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 a channelized waveform. Each transmit antenna can transmit over a range of azimuth angles and then the generated data can be used to simulate and test the radar's performance for different beam patterns. Phase calibration (e.g., mutual coupling matrix) and transport delay are then calculated based on the synthesized transmit beams and can be used to generate a model (or models) that enables the radar to transmit various 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] To facilitate seamless delivery of vehicle updates with one or more models to a vehicle, various techniques can be used. For example, high-speed cellular networks (e.g., LTE and 5G) can be used to distribute updates to the vehicle. Wi-Fi connections enable updates in environments with wireless networks, such as dealerships, enterprises, and home garages. Using satellite communication, models can be delivered to remote areas and updates to vehicles in transit can be ensured. Additionally, while short-range technologies (e.g., Bluetooth) may be used for local updates, dedicated short-range communication (DSR) can support vehicle-to-vehicle and infrastructure communication. 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 safely. Updates can be delivered periodically or continuously once they become available within an embodiment.

[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 transmission beam available to the radar and also avoid custom spatial support collection patterns. Instead, the disclosed techniques use one collection pattern that is independent of the location of the transmission beam lobes and nulls to replace each transmission beam. This strategy liberates the flexibility to prepare calibrations on-demand in-vehicle for any variation of the transmission beam. Such techniques can be implemented during the manufacture of the radar system. In practice, the calibration tests may occur after the radar hardware components are assembled and before the final product is shipped or deployed on a vehicle.

[0017] In some embodiments, manifold alignment is used to minimize the differences in calculating phase calibration with a non-uniform waveform, as opposed to calibrating with a uniform waveform. In practice, the equivalence between free space and synthetic beamforming is not exact. Due to various factors (e.g., near-field effects, radomes, snapshot time, and transmit coupling), the synthesized beam may exhibit differences that are not suitable for calibrating the synthesized manifold to the free-space manifold. Thus, manifold alignment can be used to reconcile the free-space and synthetic waveforms and minimize the mismatch between the synthetic beam and the free-space beam. As an illustrative result, a model for operating a radar mounted on a vehicle can be generated based on information created from the manifold alignment 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 enables the vehicle radar system to use the model or models for subsequent radar operations. In particular, each model can enable the radar to implement different beamforming patterns during navigation.

[0019] In some embodiments, the data collection process by the collection pattern is performed before the installation of the radar in the factory. Subsequently, the radar software can utilize the data and go through a process of estimated calibration necessary 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 enhanced, 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 use the disclosed techniques that enable a vehicle radar to transmit and receive electromagnetic energy according to different types of beams to generate a new radar model or modify an existing model. The generation process can involve using raw measurements taken using a collection pattern during radar manufacturing, while other parts of the process are implemented in-vehicle during vehicle navigation, such as determining transport delays and mutual coupling 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 the accompanying drawings, disclose the features of various exemplary embodiments. The embodiments provided are by way of example and are not intended to be limiting. Accordingly, the dimensions in the drawings are not necessarily to scale.

[0022] Here, an exemplary system within the scope of the present disclosure will be described in more detail. The exemplary system can be implemented on a motor vehicle or can take the form of a motor vehicle. Additionally, the exemplary system can 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, lawn mowers, bulldozers, boats, submarines, all-terrain vehicles, snowmobiles, aircraft, recreational vehicles, amusement park vehicles, agricultural implements or agricultural vehicles, construction machinery or construction vehicles, warehouse equipment or warehouse vehicles, factory equipment or factory vehicles, trams, golf carts, trains, trolleys, pedestrian conveyors, and robotic devices. Other vehicles are likewise possible. Further, in some embodiments, the exemplary system may not include a vehicle.

[0023] Referring now to the figures, FIG. 1 is a functional block diagram illustrating an exemplary vehicle 100 that may be configured to operate fully or partially in an autonomous mode. More specifically, vehicle 100 may operate in an autonomous mode without human interaction by receiving control instructions from a computing system. As part of the operation in the autonomous mode, vehicle 100 may use sensors to detect objects in the surrounding environment and, in some cases, detect and enable safe navigation. Additionally, vehicle 100 may operate in a partially autonomous (i.e., semi-autonomous) mode where 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 a subsystem that enables a driver to control operations of vehicle 100 such as steering, acceleration, and braking, while on the other hand, the computing system may perform assistance functions such as lane departure warning / lane keeping assist or adaptive cruise control based on other objects (e.g., vehicles) in the surrounding environment.

[0024] As described herein, in the partially autonomous driving mode, the vehicle assists with one or more driving operations (e.g., steering, braking, and / or accelerating to perform lane centering, adaptive cruise control, advanced driver assistance systems (ADAS), and emergency braking), while the human driver is expected to situationally perceive the surroundings of the vehicle and monitor the assisted driving operations. Here, while the vehicle may perform all driving tasks in a particular situation, the human driver is expected to take responsibility for control as needed.

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

[0026] As shown in FIG. 1, 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 source 110, a computer system 112 (which may also be referred to as a computing system) having a data storage 114, and a user interface 116. In other examples, vehicle 100 may include more, fewer, or different subsystems, each of which may include multiple elements. The subsystems and components of vehicle 100 may be interconnected in various ways. Additionally, the functions of vehicle 100 described herein may be divided among additional functional or physical components, or combined into fewer functional or physical components within an embodiment. For example, control system 106 and computer system 112 may be combined into a single system that operates vehicle 100 according to various operations.

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

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

[0029] Transmission 120 may transmit mechanical power from engine / motor 118 to wheel / tire 121 and / or other possible systems of vehicle 100. Thus, 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 that connects to one or more wheel / tire 121.

[0030] Wheel / tire 121 of vehicle 100 may have various configurations within the exemplary embodiments. For example, vehicle 100 may exist in the form of a unicycle, a bicycle / motorcycle, a tricycle, or a four-wheeled form of an automobile / truck, among other possible configurations. Thus, wheel / tire 121 may be connected to vehicle 100 in various ways and can exist in different materials such as metal and rubber.

[0031] Sensor system 104 can 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 lidar 128, a camera 130, a steering sensor 123, and a throttle / brake sensor 125. In some embodiments, sensor system 104 may also include sensors (e.g., O 2 monitor, fuel gauge, engine oil temperature, and brake wear) configured to monitor the internal systems of vehicle 100.

[0032] The GPS 122 may include a transceiver operable 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 based on inertial acceleration, may sense changes in the position and orientation of the vehicle 100. For example, the IMU 124 may detect the pitch and yaw of the vehicle 100 while the vehicle 100 is stationary or moving.

[0033] The radar 126 may represent one or more systems configured to sense objects in the surrounding environment of the vehicle 100, including the speed and orientation of the objects, using radio signals. Thus, the radar 126 may include an antenna configured to transmit and receive radio signals. In some embodiments, the radar 126 may correspond to an attachable 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 a coherent mode (e.g., using heterodyne detection) or an 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 examples, such photodetectors may be capable of detecting single photons (e.g., single photon avalanche diodes (SPADs)). Further, such photodetectors can be arranged in an array (e.g., like a silicon photomultiplier (SiPM)) (e.g., through series electrical connections). In some examples, one or more photodetectors are devices operating in Geiger mode, and the lidar includes sub-components designed for such Geiger mode operation.

[0035] The camera 130 may include one or more devices (e.g., a stationary camera, a video camera, a thermal imaging camera, a stereo camera, and a night vision camera) configured to capture an image 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 wheel of the vehicle 100, such as detecting the angle of the wheel relative to the front axle of the vehicle 100. The steering sensor 123 may also be configured to measure a combination (or subset) of the angle of the steering wheel, the electrical signal representing the angle of the steering wheel, and the angle of the wheel 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 may measure, for example, an electrical signal representing 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 a part of a physical mechanism that provides modulation of the energy source 119 to the engine / motor 118 (e.g., butterfly valve and carburetor). Additionally, 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 the 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 finding system 142, and an obstacle avoidance system 144. More specifically, the steering unit 132 may be operable to adjust the orientation of the vehicle 100, and the throttle 134 may control the operating speed of the engine / motor 118 to control the acceleration of the vehicle 100. The brake unit 136 may decelerate the vehicle 100, which may involve using friction to decelerate the wheel / tire 121. In some embodiments, the brake unit 136 may convert the kinetic energy of the wheel / tire 121 into an electric current for subsequent use by the system or systems of the vehicle 100.

[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 an evaluation based on the received sensor data, such as an evaluation of individual objects and / or features, an evaluation of a particular situation, and / or an evaluation 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 a 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) operable to process and analyze images to determine objects in motion (e.g., other vehicles, pedestrians, cyclists, or animals) and objects not in motion (e.g., road lighting fixtures, lane boundaries, speed bumps, or depressions). Accordingly, the computer vision system 140 can use object recognition, structure from motion (SFM), video tracking, and other algorithms used in computer vision, such as for recognizing objects, mapping the environment, tracking objects, and estimating the speed of objects.

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

[0042] As shown in FIG. 1, vehicle 100 may also include peripheral devices 108 such as a wireless communication system 146, a touch screen 148, a microphone 150 (e.g., one or more internal and / or external microphones), and / or a speaker 152. The peripheral devices 108 may provide control or other elements for a user to interact with the user interface 116. For example, the touch screen 148 may provide information to a user of the vehicle 100. The user interface 116 may also receive input from the user via the touch screen 148. The peripheral devices 108 may also enable the vehicle 100 to communicate with devices such as devices of 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 Optimized (EVDO), Global System for Mobile Communications (GSM) / General 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 (registered trademark) or other possible connections. The wireless communication system 146 may also communicate directly with devices using, for example, infrared links, Bluetooth, or ZigBee. Other wireless protocols such as various vehicle communication systems are possible within the context of the present disclosure. For example, the wireless communication system 146 may include one or more dedicated short range communication (DSRC) devices that may include public and / or private data communication between the vehicle and / or roadside stations.

[0044] Vehicle 100 may include a power source 110 for supplying power to components. In some embodiments, the power source 110 may include a rechargeable lithium ion or lead-acid battery. For example, the power source 110 may include one or more batteries configured to provide power. Vehicle 100 may also use other types of power sources. In an exemplary embodiment, the power source 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) operable to execute instructions 115 stored on a non-transitory computer-readable medium such as data storage 114. As such, the processor 113 may represent one or more processors. In some embodiments, the computer system 112 may represent a plurality of computing devices that may function to control the individual components or subsystems of the 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 connection with FIG. 1. The data storage 114 may also include additional instructions that include instructions for transmitting data, receiving data, interacting with, and / or controlling one or more of the propulsion system 102, the sensor system 104, the control system 106, and the peripheral devices 108.

[0047] In addition to the instructions 115, the data storage 114 may store data such as road maps, route information, among other information. Such information may be used by the vehicle 100 and the computer system 112 during 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 a user of the vehicle 100. The user interface 116 may control or enable the control of the layout of content and / or interactive images that may be displayed on the touch screen 148. Further, the user interface 116 may include one or more input / output devices within a set of peripheral devices 108, such as the wireless communication system 146, the touch screen 148, the microphone 150, and the speaker 152.

[0049] The computer system 112 may 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) as well as 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 vehicle 100 may be configured to function in a manner that interconnects with other components, either within or external to their respective systems. For example, in an exemplary embodiment, camera 130 can capture a plurality of images that can represent information regarding the state of the surrounding environment of 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, computer vision system 140 may be able to recognize inclinations (gradients), or other features, based on a plurality of images of the road. Additionally, the combination of GPS 122 and the features recognized by computer vision system 140 can be used with the map data stored in data storage 114 to determine specific road parameters. Further, radar 126, and / or lidar 128, and / or some other environmental mapping, ranging, and / or positioning sensor systems can also provide information about the surroundings of the vehicle.

[0051] In other words, the combination of various sensors (which can be referred to as input indicator sensors and output indicator sensors) and computer system 112 can interact to provide an indicator of the input provided to control the vehicle or an indicator of the surroundings of the vehicle.

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

[0053] FIG. 1 shows various components of vehicle 100 (i.e., wireless communication system 146, computer system 112, data storage 114, and user interface 116) as integrated into vehicle 100, although one or more of these components may be attached or associated separately from vehicle 100. For example, data storage 114 can exist partially or completely separate from vehicle 100. Thus, vehicle 100 may be provided in the form of device elements that can be located separately or together. The device elements that make up vehicle 100 can be communicatively coupled together in a wired and / or wireless manner.

[0054] FIGS. 2A - 2E show an exemplary vehicle 200 (e.g., a fully autonomous vehicle or a semi-autonomous vehicle) that may include some or all of the functions described in relation to vehicle 100 with reference to FIG. 1. Vehicle 200 is illustrated in FIGS. 2A - 2E as a van with side mirrors for illustrative purposes, but the present disclosure is not so limited. For example, vehicle 200 can represent a truck, a passenger vehicle, a semi-trailer truck, a motorcycle, a golf cart, an off-road vehicle, an agricultural vehicle, or any other vehicle described elsewhere in this specification (e.g., a bus, a boat, an airplane, a helicopter, a drone, a lawn mower, a bulldozer, a submarine, an all-terrain vehicle, a snowmobile, an aircraft, a recreational vehicle, an amusement park vehicle, an agricultural implement, a construction machine or construction vehicle, a warehouse facility or warehouse vehicle, a factory facility or factory vehicle, a tram, a train, a trolley, a pedestrian conveyance vehicle, and a robotic device).

[0055] 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 vehicle 200. In other words, any sensor system now known or hereafter created may be coupled to vehicle 200 and / or utilized in conjunction with the various operations of vehicle 200. As an example, a lidar may be utilized for autonomous driving or other types of navigation, planning, perception, and / or mapping operations of vehicle 200. Additionally, 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] Note that the number, location, and type of sensor systems (e.g., 202 and 204) depicted in FIGS. 2A - E are intended as non - limiting examples of the location, number, and type of such sensor systems for 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 cost, or to conform to other conditions for special environments or application scenarios). For example, sensor systems (e.g., 202 and 204) may be disposed at various other locations on the vehicle (e.g., at location 216) and may have a field of view corresponding to the interior and / or surrounding environment of vehicle 200.

[0057] The sensor system 202 can be attached to the upper part of the vehicle 200 and can include one or more sensors configured to detect information about the environment surrounding the vehicle 200 and output an indication of that information. For example, the sensor system 202 can include any combination of cameras, radars, lidars, inertial sensors, humidity sensors, and acoustic sensors (e.g., microphones and sonar devices). The sensor system 202 can include one or more movable mounts that can be operable to adjust the orientation of one or more sensors within the sensor system 202. In one embodiment, the movable mount can 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 can be movable in a scanning pattern within a specific range of angles, and / or azimuth, and / or elevation. The sensor system 202 can be attached on the roof of the vehicle, although other attachment locations are also possible.

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

[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 an angular range with respect to a given plane (e.g., the x-y 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 pivot about an axis (e.g., the z-axis) perpendicular to a given plane so as to illuminate an environment surrounding the vehicle 200 with light pulses. Information about the surrounding environment may be determined based on detecting various aspects of the reflected light pulses (e.g., elapsed time of flight, polarization, and intensity).

[0060] In an exemplary embodiment, the sensor systems 202, 204, 206, 208, 210, 212, 214, and / or 218 may be configured to provide respective point cloud information that may be associated with physical objects within the surrounding environment of the vehicle 200. The vehicle 200, as well as the sensor systems 202, 204, 206, 208, 210, 212, 214, and 218, are illustrated as including certain features, but it will be understood that other types of sensor systems are contemplated within the scope of the present disclosure. Further, the vehicle 200 may include any of the components described in connection with the vehicle 100 of FIG. 1.

[0061] In an exemplary configuration, one or more radars may be located on vehicle 200. Similar to the radar 126 described above, one or more radars may include an antenna configured to transmit and receive radio waves (e.g., electromagnetic waves having frequencies in the range of 30 Hz to 300 GHz). Such radio waves can be used to determine the distance and / or speed to one or more objects in the surrounding environment of 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 are located near the rear of vehicle 200 (e.g., sensor systems 208 and 210) and can actively scan the environment near the rear of vehicle 200 for the presence of radio wave reflecting objects. Similarly, one or more radars are located near the front of vehicle 200 (e.g., sensor system 212 or 214) and can actively scan the environment near the front of vehicle 200. The radar can be installed in a suitable location to illuminate an area including the forward movement path of vehicle 200 without being blocked, for example, by other features of vehicle 200. For example, the radar can be embedded in and / or attached to or near the front bumper, front headlight, cowl, and / or hood. Additionally, one or more additional radars can be positioned to actively scan the sides and / or rear of vehicle 200 to confirm the presence of radio wave reflecting objects, such as by including such devices within or near the rear bumper, side panel, rocker panel, and / or underbody of vehicle 200.

[0062] Vehicle 200 can 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 camera can be a photosensitive device such as a stationary camera, a video camera, a thermal imaging camera, a stereo camera, a night vision camera, etc., configured to capture multiple images of the surrounding environment of vehicle 200. For this purpose, the camera can be configured to detect visible light, and additionally or alternatively, can be configured to detect light from other parts of the spectrum such as infrared or ultraviolet light. The camera can be a two-dimensional detector and optionally can have a sensitivity range in three-dimensional space. In some embodiments, the camera can include a range detector configured to generate a two-dimensional image indicating, for example, the distance from the camera to several points within the surrounding environment. For this purpose, the camera can use one or more range detection techniques. For example, the camera can provide range information by using structured light techniques, in which vehicle 200 illuminates objects within 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 of the reflected light pattern, vehicle 200 can determine the distance to points on the object. The predetermined light pattern may include infrared light or radiation of other wavelengths suitable for such measurements. In some examples, the camera can be mounted inside the front windshield of vehicle 200. Specifically, the camera can be installed to capture images from a forward view with respect to the orientation of vehicle 200. Other mounting locations of the camera and field of view angles can also be used either inside or outside vehicle 200. Further, the camera can have associated optical elements operable to provide an adjustable field of view. Additionally, the camera can be mounted to vehicle 200 using a movable mount to change the pointing angle of the camera via a pan / tilt mechanism or the like.

[0063] Vehicle 200 may also include one or more acoustic sensors used to sense the surrounding environment of vehicle 200 (e.g., 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 a fluid (e.g., air) in 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 the acoustic sensors detect a siren (e.g., an ambulance siren or a fire truck siren), vehicle 200 may decelerate and / or navigate to the edge of the road.

[0064] Although not shown in FIGS. 2A-2E, vehicle 200 can include a wireless communication system (e.g., similar to and / or in addition to wireless communication system 146 of FIG. 1). The wireless communication system may include a wireless transmitter and a wireless receiver configured to communicate with devices external or internal to 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 a road station. Examples of such vehicle communication systems include DSRC, radio frequency identification (RFID), and other communication standards proposed for intelligent transportation systems.

[0065] In addition to or instead of those shown, vehicle 200 may include one or more other components. The additional components may include electrical or mechanical functions.

[0066] The control system of vehicle 200 may be configured to control vehicle 200 according to a control strategy selected from a plurality of possible control strategies. The control system may receive information from sensors coupled to vehicle 200 (on or outside vehicle 200), modify the control strategy (and associated driving behavior) based on that information, and be configured to control vehicle 200 according to the modified control strategy. The control system may be further 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, following distance (i.e., the distance to the vehicle ahead of the current vehicle), lane selection, etc. may all be modified in response to changes in the driving conditions.

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

[0068] The drawings and the overall description may refer to a given vehicle form (e.g., vehicle 200 shown as a semi - truck vehicle 250 or a van), but it is understood that the embodiments described herein are equally applicable in the context of various vehicles (e.g., using modifications employed to account for 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 semi - truck vehicle 250 (e.g., for navigation and / or obstacle detection and avoidance).

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

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

[0071] At some angles, the operating region 275 of the sensor can include the rear wheels 276A, 276B of trailer 270. Thus, the sensor can measure rear wheels 276A and / or rear wheels 276B during operation. For example, rear wheels 276A, 276B can reflect a lidar signal or a radar signal transmitted by the sensor. The sensor can receive the signal reflected from rear wheels 276A, 276. Accordingly, the data collected by the sensor can include data from reflections from the wheels.

[0072] In some cases, such as when the sensor is a radar, the reflections from rear wheels 276A, 276B can appear as noise in the received radar signal. As a result, the radar can operate with an enhanced signal - to - noise ratio in cases where rear wheels 276A, 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 can occur between the remote computing system 302 and the vehicle 200 via the network 304. Wireless communication may also occur between the server computing system 306 and the remote computing system 302, and between the server computing system 306 and the vehicle 200.

[0074] The vehicle 200 can correspond to various types of vehicles that can transport passengers or objects between locations and can take any one or more forms of the vehicles discussed above. In some cases, the vehicle 200 can operate in an autonomous or semi-autonomous mode that allows the control system to use sensor measurements to safely navigate the vehicle 200 between destinations. When operating in an autonomous or semi-autonomous mode, the vehicle 200 can navigate regardless of the presence of passengers. As a result, the vehicle 200 can pick up and drop off passengers between desired destinations.

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

[0076] The remote computing system 302 may include one or more subsystems and components that are the same as, 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 touch screen and speakers. Other embodiments are also possible.

[0077] The network 304 represents an infrastructure that enables wireless communication between the remote computing system 302 and the vehicle 200. The 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 examples. For example, the remote computing system 302 may be in a remote location from the vehicle 200 having wireless communication via the network 304. In another example, the remote computing system 302 may correspond to a computing device within the vehicle 200 that is separate from the vehicle 200 but with which a human operator can interact with a passenger or driver of the vehicle 200. In some embodiments, the remote computing system 302 may be a computing device having a touch screen operable by a passenger of the vehicle 200.

[0079] In some embodiments, the operations described herein that are performed by the remote computing system 302 may alternatively or additionally be 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 with which a driver or passenger of the vehicle can interact.

[0080] The server computing system 306 may be configured to wirelessly communicate with the remote computing system 302 and the vehicle 200 (or, in some cases, 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 regarding the vehicle 200 and its remote assistance. Thus, the server computing system 306 may be configured to perform any operation or portion of such operations described herein as being performed by the remote computing system 302 and / or the vehicle 200. In some embodiments of the wireless communication related to remote assistance, the server computing system 306 may be utilized, while in other embodiments it may not be.

[0081] The server computing system 306 may include one or more subsystems and components similar 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 information from and providing information to the remote computing system 302 and the vehicle 200.

[0082] The various systems described above may perform various operations. These operations and related features are described herein.

[0083] In accordance 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 vehicle 200) can operate to capture an image of the surrounding environment of an autonomous or semi-autonomous vehicle using a camera. Generally, at least one computing system can analyze the image 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) can receive data representing objects in the environment surrounding the vehicle (also referred to herein as "environmental data") in various ways. The vehicle's sensor system may provide environmental data representing objects in the surrounding environment. For example, the vehicle may have various sensors including cameras, radars, lidars, microphones, wireless units, and other sensors. Each of these sensors can communicate environmental data about the information received by each respective sensor to a processor within the vehicle.

[0085] In one example, a 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 camera may be capable of moving to capture images and / or video in different directions. The camera may be configured to store the captured images and video in memory for later processing by the vehicle's processing system. The captured images and / or video may be environmental data. Further, 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 the processing system) to make various determinations about the objects that reflected the electromagnetic signals. For example, the distances and positions to various reflecting objects can be determined. In some embodiments, the vehicle may have two or more radars in different orientations. The radar may be configured to store the captured information in a memory for later 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 (such as infrared light from a gas or diode laser, or from other possible light sources) reflected by target objects near the vehicle. The lidar may be capable of capturing the reflected electromagnetic (e.g., infrared light) signals. The captured reflected electromagnetic signals may enable the ranging system (or the processing system) to determine the distances to various objects. The lidar can also determine the speed or velocity of the target object and store it as environmental data.

[0088] Additionally, in one embodiment, the microphone may be configured to capture the audio of the vehicle's surrounding environment. The sound captured by the microphone may include the sirens of emergency vehicles and the sounds of other vehicles. For example, the microphone may capture the sound of the siren of an ambulance, a fire truck, or a police vehicle. 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 sound of the exhaust of another vehicle, such as that 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 wireless 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 wireless technology signals. The first electromagnetic radiation signal may be transmitted via one or more antennas located at the wireless unit. Further, the first electromagnetic radiation signal may be transmitted in one of many different wireless 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 in the vicinity of the autonomous or semi-autonomous vehicle. The processing system may be able to detect nearby devices based on the responses returned to the radio unit and 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 a 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 determinations about the surrounding environment.

[0091] While operating in autonomous mode (or semi-autonomous mode), the vehicle can control its operation 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 specified destination without further input from the human (e.g., without the human having to operate or touch the brake / accelerator pedals). Further, while the vehicle is operating autonomously or semi-autonomously, the sensor system can receive environmental data. The vehicle's processing system can change the control of the vehicle 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 can change its speed to avoid obstacles and comply with traffic laws. When the processing system in the vehicle detects an object near the vehicle, the vehicle may be able to change its speed or otherwise change its movement.

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

[0093] To facilitate this, the vehicle may analyze environmental data representing objects in the surrounding environment to determine at least one object having a detection reliability 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] The detection reliability may indicate the likelihood that the determined object is correctly detected or exists within the surrounding environment. For example, the processor may perform object detection of an object in the image data within the received environmental data and, based on the inability to detect that at least one object has a detection reliability exceeding a threshold, may determine that the object has a detection reliability below the threshold. When the result of object detection or object recognition of an object is not conclusive, the detection reliability may be low or below a set threshold.

[0095] The vehicle may detect objects in the surrounding environment in various ways depending on the source of the environmental data. 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 within 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 techniques used by a vehicle to detect an object may be based on a set of known data. For example, data related to environmental objects may be stored in a 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 street sign related to construction may generally have an orange color. Thus, the vehicle may be configured to detect an orange object located near the roadside as a construction-related street sign. Additionally, when the vehicle's processing system detects an object in the captured data, it can 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 the detected object. For example, for an object that may require a quick response action from the vehicle, such as the brake light of another vehicle, the confidence threshold may be low. However, in other embodiments, the confidence threshold may be the same for all detected objects. If the confidence associated with the detected object is higher than the confidence threshold, the vehicle may assume that the object is correctly recognized and, based on that assumption, adjust the vehicle's control respondently.

[0098] If the confidence associated with the detected object is lower than the confidence threshold, the action taken by the vehicle may vary. 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 the vehicle detects an object in the surrounding environment, it can also calculate a confidence level associated with the specific detected object. The confidence level can be calculated in various ways depending on the embodiment. In one example, when detecting an object in the surrounding environment, the vehicle may compare the environmental data with predetermined data associated with known objects. 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 a remote computing system, along with the detection of the object. As discussed above, the remote computing system can take various forms. For example, the remote computing system may be a computing device within a vehicle separate from the vehicle, with a touch screen interface or the like for displaying remote assistance information by which a human operator can interact with the passengers or driver of the vehicle. Additionally or alternatively, as another example, the remote computing system may be a remote computer terminal or other device located in a location not near the vehicle.

[0101] The request for remote assistance may include environmental data including the object, such as image data, audio data, etc. In some embodiments, the vehicle may send the environmental data to the remote computing system over a network (e.g., network 304), via a server (e.g., server computing system 306). The 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, a preliminary identification may be given to the object, and the vehicle may be configured to adjust the operation of the vehicle in response to the preliminary identification. Such adjustment of the operation may take the form of, among other possible adjustments, stopping the vehicle, switching the vehicle to a manual control mode, changing the speed (e.g., speed and / or direction) of the vehicle, etc.

[0103] In other embodiments, even when the vehicle detects an object having a confidence level that meets or exceeds the threshold, the vehicle may operate according to the detected object (e.g., stop when the object is detected as a stop sign with a high confidence level), but the vehicle may be configured to request remote assistance either simultaneously with (or after) operating according to the detected object.

[0104] FIG. 4 is a block diagram of a system according to an exemplary embodiment. In particular, FIG. 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 the operations disclosed herein.

[0105] The processor 404 can include one or more processors such as one or more general-purpose microprocessors (e.g., having a single core or multiple cores) and / or one or more dedicated microprocessors. The one or more processors can 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 contemplated herein.

[0106] Memory 406 may include computer-readable media such as non-transitory computer-readable media including, without limitation, 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 drive (SSD), hard disk drive (HDD), compact disc (CD), digital video disc (DVD), digital tape, read / write (R / W) CD, R / W DVD, etc.

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

[0108] In the context of autonomous or semi-autonomous vehicles, the radar system 410 provides measurements that can assist with navigation and collision avoidance. The radar unit is typically mounted outside the vehicle, such as at the front, rear, and sides. During navigation, the radar system 410 may continuously emit radio waves in various directions to scan the environment around the vehicle. When the waves encounter an object, they bounce back to the radar receiver, enabling the radar system 410 to analyze the reflected waves to calculate the distance, relative speed, and angle of the object. This information is used by the vehicle's control system to make decisions and accordingly adjust the vehicle's trajectory, detecting and reacting to obstacles, pedestrians, vehicles, and other potential hazards within 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] In some aspects, such as cameras and lidars, the radar system 410 offers operational advantages over other types of sensors. Radar can function well in adverse weather conditions such as rain, fog, or dust where other sensors may have limitations. In particular, the radio waves emitted by the radar system 410 can penetrate such adverse conditions and enable reliable object detection. For this reason, radar is particularly useful for improving the robustness and safety of autonomous or semi-autonomous vehicles in various weather scenarios. Additionally, radar is excellent 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 vehicle's driver) to make information-based decisions about potential collision risks and adjust its behavior accordingly. In some cases, the radar system 410 may also be able to provide a longer measurement range and a wider field of view compared to other sensors attached to the vehicle.

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

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

[0112] By calibrating the radar antenna array for beamforming, accurate and reliable radar operation can be enabled. Vehicle radar systems often utilize beamforming, where individual radars positioned on the vehicle concentrate energy in specific directions to enhance target detection and tracking accuracy. For example, a vehicle radar system can use beamforming to cause the forward-facing radar to measure the areas in front of and to the sides of the vehicle.

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

[0114] Radar calibration involves both phase calibration and amplitude calibration. Phase calibration is performed to adjust the phase of the signals of each element in order to create constructive interference in the desired direction and destructive interference in other directions during signal transmission. The computing system can use a reference signal or measure the phase of the signals received from a known target for phase calibration. The computing system can then use advanced calibration algorithms and hardware to adjust the phase settings to 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 the antenna elements of the radar to contribute equally to the beamforming process. The computing system can adjust the amplitude (signal strength) of each element until a uniform signal contribution across the antenna array is achieved. Without performing amplitude calibration, the radar may experience amplitude variations during signal transmission, which can lead to a non-uniform beam pattern that negatively affects the overall performance of the radar. Once the radar antenna array is calibrated for beamforming, the radar can then be used to accurately direct the beam pattern in different directions to capture accurate measurements of the environment.

[0115] The calibration process on the radar can be performed during the radar test to check the performance of the radar against known criteria that may be before or after the radar is installed in the 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 the performance of the radar over time. During calibration, various parameters of the radar system are adjusted to match the desired specifications and performance criteria such as frequency, gain, phase, and sensitivity. Therefore, accurate calibration enables the radar to operate effectively in various applications.

[0116] However, it takes time to test the different capabilities of each radar for various beam patterns, and the installation of the radar on the vehicle or the testing of the radar after installation on the vehicle may be overall delayed. Therefore, the disclosed techniques can shorten the time required for radar testing and calibrate the radar for subsequent use such as vehicle mounting. In addition, the disclosed technology can also be implemented after mounting on the 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 pre-calibration.

[0117] The disclosed techniques involve using a calibration processing chain that eliminates dedicated transmit calibration measurements and unified transmit and receive calibration collections. The computing system can implement the calibration processing chain by using synthetic transmit beamforming to eliminate the need for a custom spatial support collection pattern specialized for the transmit beam. In particular, the computing system can synthesize the transmit beam pattern based on a single collection pattern to shorten the overall collection time and accelerate the calibration process. In this way, the calibration process chain can be used to improve transmit calibration by averaging the calibration across the field of view, and may also involve roughly correcting the manifold mismatch between free space and the synthesized manifold.

[0118] FIG. 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 method 500 and other processes and methods disclosed herein, the flowchart illustrates the functionality and operations of one possible implementation of the present embodiment. In this regard, each block may represent a portion of a module, segment, or program code that includes one or more instructions executable by a processor to perform a specific logical function or step in the 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 multiplexed waveforms. During a free-space beamforming event, the radar transmit array creates an array response by transmitting simultaneously with all transmit elements (also referred to herein as transmit antennas or antennas). This is different from the generation of multiplexed waveforms, also referred to herein as channelized waveforms.

[0121] In some cases, the multiplexed waveform is created by temporally dividing the transmission time for each element. The computing system can use time-division multiplexing (TDM), which involves transmitting radar pulses at discrete time intervals or slots. This allows the radar system to alternately transmit and receive reflections. To generate the multiplexed waveform pattern, the system can trigger the radar's transmit elements to transmit in a sequential order at different times. By dividing the transmission time for each transmit element on the radar, the receive array can separately record the responses for each transmit element, thereby creating data representing the collection pattern of the multiplexed waveform. The data representing the collection pattern can then be used for synthetic transmit beamforming.

[0122] In some embodiments, the radar transmit array can be controlled in other ways to generate the multiplexed waveform. For example, the system can use frequency-division multiplexing (FDM), code-division multiplexing (CDM), TDM, or a combination of these techniques. For FDM, each transmit antenna element can transmit electromagnetic energy in a separate frequency channel that allows the receive antenna array to distinguish transmissions from different transmit antenna elements. For CDM, the transmit antenna array can be individually controlled for each transmitted electromagnetic energy according to different codes, thereby allowing the reflections received by the radar's receive antenna array to be subsequently distinguished.

[0123] The generated multiplexed waveform represents a collection pattern that determines a spatial sampling grid for collecting two-way responses. The cost function can be optimized over different collection patterns that can reduce the spatial peak sidelobe level to determine the collection pattern used to generate data for subsequent calibration. The collection pattern I(Φ) can be described as follows by the comb function of discrete angles {Φ k}.

Number

Number

Number

Number

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

[0125] In some embodiments, the generation of the multiplexed waveform can involve transmitting the waveform in a way that distinguishes the transmissions from each transmitting element (e.g., TDM, FDM, CDM), receiving the reflections of the transmissions, and then processing the received data to extract information. For example, a radar can be used to generate and transmit a radar signal with known characteristics towards a calibration target such as a pulse waveform, a continuous wave (CW) signal, or a frequency modulated continuous wave (FMCW) signal. Specific frequency ranges, times, and / or modulation characteristics can be assigned to different channels within the signal. The radar receiving array captures the radar reflections from the calibration target, which contain 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 signal into individual channels, where each channel corresponds 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 shifts, which occur when a radar signal is reflected from a moving target and causes a change in the frequency of the reflected signal. Thus, the 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 can include information about the range and Doppler frequency of the detected target. For zero Doppler averaging, the computing system can apply a Doppler filter to the raw radar data to attenuate or remove unwanted Doppler frequencies. The filtered data can be averaged over multiple radar pulses or sweeps.

[0128] Generally, averaging the Doppler level can help reduce noise and improve the signal-to-noise ratio of radar data. When the data is averaged with respect to zero Doppler, the computing system 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 measurements. During radar calibration, the computing system can analyze whether the radar accurately determines the distance to a calibration target or multiple calibration targets. The computing system can perform range compression using special waveforms such as chirps or coded sequences during the radar pulse transmission and reception process, effectively sharpening the range resolution of the radar. 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 and determine the accurate 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 coded waveforms, the radar system can resolve closely spaced calibration targets and reduce range ambiguity, which can lead to more accurate calibration results. Thus, range compression can enhance 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 a vehicle as part of a vehicle radar system.

[0132] Transmission calibration parameters can represent specific settings and characteristics that can be adjusted during a calibration process to ensure that transmitted signals meet desired performance criteria. Transmission calibration parameters are used to maintain the accuracy and reliability of vehicle radar systems where accurate target detection, tracking, and measurement are important. Thus, transmission calibration parameters and their values can vary depending on the design of the radar, the frequency band, and the intended use. Some exemplary transmission 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 can calibrate transmission parameters using a combination of hardware adjustments, software settings, and performance measurements. In some cases, one or more aspects of the calibration process for transmission calibration parameters can be performed periodically on the vehicle to maintain radar accuracy and ensure compliance with operating requirements.

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

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

[0135] In practice, electronic components such as phase shifters can have slight variations in their performance due to manufacturing tolerances, temperature changes, or aging. A computing system can take these variations into account to ensure that all phase shifters within a radar system operate consistently, compensating for any hardware discrepancies and maintaining the accuracy of beamforming.

[0136] In block 512, method 500 involves synthesizing a transmit beamformer. Synthesizing the radar signal means the process of generating or creating a radar waveform, which can then be used to generate a model for use by a vehicle radar system. 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 the radar signal, custom waveforms can be created using specific parameters to simulate various radar scenarios or test radar systems under controlled conditions. For example, a single collection pattern can be used to synthesize transmit beam patterns in different shapes and directions.

[0137] In some cases, the computing system can use the synthesized radar signal to test the performance of the radar. By generating a controlled radar signal, the tester can evaluate how the radar system responds to different scenarios such as the detection of different types of targets, interference, jamming, or environmental conditions. This helps to assess the capabilities of the radar and identify potential problems or improvements.

[0138] In block 514, method 500 involves estimating a transport delay that 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 can occur due to the physical separation between the transmitter components and the receiver components of the radar system.

[0139] In a radar that uses a phased array for beamforming, a propagation delay occurs due to each antenna element being physically separated from other antenna elements. When transmitting a signal, the radar can take into account the difference in propagation times from the transmitter to each antenna element, enabling the transmitted signals to reach the target area simultaneously and form a coherent radar beam. Similarly, during signal reception, the radar compensates for the propagation delay to align the signals received from different antenna elements. This alignment enables coherent signal processing and accurate beamforming. Therefore, to optimally perform beamforming and calibration, the radar system can use accurate measurements of the propagation delay to properly align and synchronize the transmitted and received signals. Calibration algorithms can take into account the distances between antenna elements to adjust the phase and time delay of the signals, thereby enabling precise beam steering and shaping for optimal radar performance.

[0140] In block 516, method 500 involves performing waveform alignment, also referred to herein as manifold alignment. Generally, waveform alignment involves comparing the transmitted or received radar waveform generated by the radar with a predicted or reference waveform. This comparison enables the computing system to assess the performance and accuracy of the radar system. As shown, block 517 of method 500 involves collecting on-board waveforms that can be used as part of the waveform alignment process.

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

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

[0143] A computing system can use the results of the waveform comparison to assess the radar's performance. When the actual radar signal exactly matches the reference waveform, the result of the comparison indicates that the radar is operating as expected and is likely to function well in its intended task. When the comparison shows a deviation between the actual radar signal and the reference waveform, this can indicate a problem with the radar, such as a calibration error or a hardware malfunction. If a mismatch between the actual and reference waveforms is detected during testing, the computing system can use the information from the waveform integration process to identify and address the difference. Waveform integration can be used to verify and validate the radar to ensure that each radar can reliably and accurately detect and track targets.

[0144] The computing system may perform manifold alignment after the transmission beams are synthesized and before estimating the mutual coupling matrix. For each transmission beam synthesized by the computing system based on the collection pattern, the computing system can model the free-space array response in the main lobe of the transmission beam. This model can be based on either the collected data, ray-tracing techniques, numerical electromagnetic code (NEC), or other methods. The model can be used to roughly characterize the free-space response near the main lobe of the synthesized transmission beam pattern.

[0145] Next, the computing system can calculate the average complex multiplier between the synthesized array response and the free-space array response and fold the difference back into the synthesized array response over the entire collection pattern. The computing system can use least-squares techniques or simple inversion methods to calculate the complex correction. As an exemplary result, the synthesized collection pattern can mimic the free-space counterpart and provide a mutual coupling matrix estimate that agrees well with the estimate imprinted on the "natural" free-space manifold.

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

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

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

[0149] The mutual coupling matrix is used to account for these interactions during beamforming calculations, ensuring that the radar beam is accurately formed and directed. The computing system analyzes the mutual coupling 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 reduce the effects of coupling and allow the radar to maintain accurate beamforming and target detection.

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

[0151] In some embodiments, the receive beamformer metric can include various aspects of beamformer performance such as beam width, sidelobe level, beam steering accuracy, and sensitivity. Generally, the beam width measures the angular width of the main lobe of the beamforming pattern, which affects angular resolution. The sidelobe level quantifies the strength of radiation in directions other than the main beam and can affect interference rejection. The accuracy of beam steering represents the accuracy of pointing the beam at a desired target or source, and sensitivity measures the ability of the radar system 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 resilience. The computing system can use the beamformer metric 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 receive beamformer for system optimization and performance evaluation.

[0153] FIG. 6 is a conceptual diagram of radar data generation and analysis, which shows a comparison between free-space beamforming and synthetic beamforming. Free-space beamforming is represented by all of the transmit antennas 602, 604, and 606 simultaneously transmitting electromagnetic energy to form a free-space beam that is received by the receive antenna 608. In contrast, synthetic beamforming is represented by causing each of the transmit antennas 602-606 to transmit electromagnetic energy individually based on variations within the domain. In the example shown in FIG. 6, the transmit antennas 602-606 are each shown to transmit at different times (TDM), which causes the receive antenna 608 to receive reflections from each of the transmit antennas 602-606 at different times. In other examples, different frequencies or codes can be used to distinguish the transmissions of the transmit antennas 602-606.

[0154] In an exemplary embodiment, time is used to distinguish the transmissions of the transmit antennas 602-606 for synthetic beamforming. In particular, transmit antenna 602 is shown to transmit a signal at time 1, transmit antenna 604 is shown to transmit a signal at time 2, and transmit 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 dispersion between different times can vary within the example. For example, time 2 could occur 5 milliseconds after time 1, and time 3 could occur 5 milliseconds after time 2. The difference between times can vary within the example.

[0155] As shown in FIG. 6, the receiving antenna 608 is shown receiving electromagnetic energy formed based on the free space transmission beam pattern and the combined transmission beam pattern. However, with respect to combined beamforming, the receiving antenna 608 has been shown to receive signals transmitted at different times, which are by the transmitting antennas 602-606 that transmit individually at different times. In some embodiments, each of the transmitting antennas 602-606 transmits signals over different angles for reception by the receiving antenna 608. The transmitting antennas 602-606 can transmit according to a specific collection pattern that does not depend on the locations of the lobes and nulls of the combined transmission beam. For the free space transmission beam, a separate collection pattern may be required to reduce the locations of the lobes and nulls.

[0156] To further illustrate free space beamforming and combined beamforming, Equation 2 is provided below to represent the desired transmission weights (w Φ ) that can be used by the transmitting antennas 602-606, while Equation 3 represents the transmission calibration phase offset (t), and the transmission steering vector a(Φ) is represented by Equation 4.

Number

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

Number

Number

[0158] Generally, the concepts of array response and array manifold can be used interchangeably to describe the wave field sampling of an antenna array in space. For a conventional transmit phased array with isotropic elements, the desired one-way transmit response in the direction φ at time (n) can be described as follows.

Number

Number

Number

[0159] In the far field, the composite transmit weighting function becomes as follows.

Number

[0160] Assuming that the transmit elements of the linear array are isotropic, the above weighting function describes the transmit array manifold. Ignoring the range loss, the two-way array response of the entire receive array for an isotropic reflector can be described by the following vector.

Number

[0161] Making the pulse train x(n) the same pulse over the pulse index and enabling the removal of the dependence on n from x. Next, the free space response becomes as follows.

Number

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

Number

Number

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

Number

[0164] The transmitted beamform synthesized at reception is as follows.

Number

[0165] Therefore, Equations 14 and 15 can represent the synthetic beam pattern formed by the computing system using the disclosed technique. Regarding the superposition, the following can be claimed.

Number

[0166] However, in practice, 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 and the synthesis (s’ RX (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’ Φ Considering that w dominates the transmit array response, the flexible and retroactive generation of the receive array response for different w Φ is made possible by synthesizing s’ RX (φ) using consecutive transmit 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 the method 700 and other processes and methods disclosed herein, the flowchart illustrates the functionality and operation of one possible implementation of this embodiment. In this regard, each block may represent a module, segment, or portion of program code that includes one or more instructions executable by a processor to perform a specific logical function or step in the 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] At block 702, method 700 involves triggering each transmit antenna element of the radar to individually transmit electromagnetic energy according to a first transmit beam pattern. The radar includes a plurality of transmit antenna elements. For example, a computing system can trigger each transmit antenna element of the radar to individually transmit electromagnetic energy in a sequential order. Additionally, each transmit antenna element of the radar may be triggered to individually transmit electromagnetic energy over a plurality of azimuth angles.

[0171] As an example, the computing system can trigger a first transmit antenna element to transmit a first electromagnetic energy at a first time, and then trigger a second transmit antenna element to transmit a second electromagnetic energy at a second time, where the second time follows the first time. Then, the computing system can trigger a third transmit antenna element to transmit a third electromagnetic energy at a third time, where the third time follows the second time, and so on. Thereby, the receive antenna array (and thus the computing system) can collect emissions independently from each transmit antenna. In this way, the computing system can generate data representing a collection pattern based on the reflections corresponding to the first electromagnetic energy, the second electromagnetic energy, and the third electromagnetic energy.

[0172] In block 704, method 700 involves generating data representing a collection pattern based on the reflection of electromagnetic energy transmitted by a computing system and 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 the lobes and nulls corresponding to the first transmission beam pattern.

[0173] In block 706, method 700 involves synthesizing a second transmission beam pattern different from the first transmission beam pattern using the data representing the collection pattern. The shape, direction, size, and / or other aspects of the beam pattern can be different. In some cases, the computing system can synthesize a plurality of transmission beam patterns different from the first transmission beam pattern. The various transmission beam patterns can be synthesized based on the data representing a single collection pattern.

[0174] In block 706, method 700 involves estimating a mutual coupling matrix for processing the reflection of electromagnetic energy transmitted according to the second transmission beam pattern. For example, the computing system can estimate the mutual coupling matrix based on a reference array response matrix. The reference array response matrix depends on the radar environment. The computing system can 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 signals that can be transmitted from the perspective of amplitude, phase, and other relevant parameters. The reference array response matrix is determined as a function of the collection geometry and can be used to describe the phase fronts measured by each element due to a target at a point from the collection geometry. In some embodiments, the reference array response matrix is determined within a laboratory setting, such as a test of the radar configuration. The reference array response matrix can be determined through simulation, transmission and reception data analysis, or a combination.

[0175] In some embodiments, the computing system uses a radar steering matrix as the reference array response matrix. The radar steering matrix is a mathematical representation that describes how a radar can direct its beam in different directions. The radar's antenna array can be steered electronically or mechanically to focus the transmitted or received signals in a specific direction. The radar steering matrix is used to provide a way to understand and quantify the radar's steering capabilities. In addition, the radar steering matrix can communicate the phase setting of each element within the array in the direction in which the radar beam is steered. The steering matrix allows the radar system to direct the beam in a desired direction, track moving targets, and adapt to changing operational requirements. Thus, the computing system can use the radar steering matrix as the reference array response matrix when estimating the mutual coupling matrix for processing the reflections of electromagnetic energy transmitted according to different transmit beam patterns.

[0176] In some embodiments, the computing system estimates the propagation delay corresponding to a second transmit beam pattern and uses the propagation delay to perform waveform alignment based on the synthesis of the second transmit beam pattern. The computing system can then estimate the mutual coupling matrix further based on the performance of the waveform alignment. The estimation can factorize the reference array response matrix.

[0177] In some embodiments, the computing system may perform a manifold alignment 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 synthetic 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 mutual coupling matrix based on the correction.

[0178] In block 708, method 700 involves generating a model for operating radar mounted on a vehicle based on a mutual coupling matrix. The model enables a vehicle radar system having one or more radars that match 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 direction than the first transmit beam pattern. Optionally, the computing system also performs a manifold alignment process (waveform alignment) and generates the model further based on an angle-dependent term determined based on performing the manifold alignment process.

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

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

[0181] A computing system can similarly synthesize a third transmission beam pattern different from the first and second transmission beam patterns based on data representing a collection pattern, and estimate a second mutual coupling matrix for processing reflections of electromagnetic energy transmitted according to the third transmission beam pattern. The computing system can estimate the second mutual coupling matrix based on reflections of electromagnetic energy transmitted according to the third transmission beam pattern that reach the radar receive antenna elements at a third plurality of angles different from the first plurality of angles and the second plurality of angles. The computing system may generate a model based on both the mutual coupling matrix (e.g., the first mutual coupling matrix) and the second mutual coupling matrix. The model can then enable the radar to transmit and receive electromagnetic energy according to the first transmission beam pattern, the second transmission beam pattern, and the third transmission beam pattern.

[0182] In some embodiments, method 700 also involves applying a range compression filter and a zero Doppler filter to data representing a 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 a transmit phase shifter based on the transmit calibration parameters. The computing system can then estimate the mutual coupling matrix further based on the transmit phase shifter.

[0183] In some embodiments, the computing system can estimate a transport delay corresponding to the second transmission beam pattern and use the transport delay to perform waveform alignment to determine a difference between the second transmission beam pattern and a corresponding free space beam pattern. The computing system can then further estimate the mutual coupling matrix based on the difference.

[0184] In some embodiments, the vehicle radar can be used with control electronics that can include one or more field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), and / or tensor processing units (TPUs). For example, the 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 signals, enhance signal quality, and improve target detection and tracking. Additionally, the radar system often involves the conversion of analog signals to digital form for further processing. The control electronics can include analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) to facilitate these conversions. The control electronics can receive analog signals from the radar sensor, digitize them, and process the digital data for analysis and interpretation.

[0185] In addition, the control electronics can also provide the ability for real-time control and adjustment of various radar system components. For example, the 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 important for the transmission and reception of accurate and synchronized signals. The control electronics can efficiently handle the large amount of data generated by the radar system. The 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. The control electronics can also integrate various interfaces and protocols required for radar signal transmission, such as processors, memory modules, communication modules, and display units. The control electronics can provide the interface logic necessary to facilitate seamless data exchange between these components, enabling efficient data flow and system integration. The 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 applications, improving performance and efficiency.

[0186] This disclosure is not limited to the specific embodiments described in this application, and the specific embodiments are intended as examples 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 apparatuses recited herein, functionally equivalent methods and apparatuses 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 be within the scope of the appended claims.

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

[0188] Regarding any or all of the message flow diagrams, scenarios, and flowcharts considered in the figures and herein, each step, block, operation, and / or communication can represent the processing of information and / or the transmission of information according to an exemplary embodiment. Alternative embodiments are within the scope of these exemplary embodiments. In these alternative embodiments, for example, operations described as steps, blocks, transmissions, communications, requests, responses, and / or messages can be executed in an order different from that shown or discussed, such as substantially simultaneously or in the reverse order, depending on the associated functions. Further, more or fewer blocks and / or operations can be used in any of the message flow diagrams, scenarios, and flowcharts considered herein, and these message flow diagrams, scenarios, and flowcharts can be combined with each other, in part or in whole.

[0189] Steps, blocks, or operations corresponding to the processing of information may correspond to circuitry configured to perform specific logical functions of the methods or techniques described herein. Alternatively or additionally, steps or blocks corresponding to the processing of information may correspond to a portion of a module, segment, or program code (including associated data). The program code may include one or more instructions executable by a processor to perform specific logical operations or actions in a 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, disk drive, solid state drive, or another storage medium.

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

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

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

Claims

1. 1. A method comprising: triggering, by a computing system, each transmit antenna element of the radar to individually transmit electromagnetic energy according to a first transmit beam pattern; generating data representative of a collection pattern based on reflections of the electromagnetic energy transmitted by the computing system and according to the first transmit beam pattern; synthesizing a second transmit beam pattern different from the first transmit beam pattern using the data representative of the acquisition pattern; estimating a mutual coupling matrix for processing reflections of electromagnetic energy transmitted according to the second transmit beam pattern; generating, by the computing system and based on the mutual coupling matrix, a model for operating the radar mounted on the vehicle, the model enabling a vehicle radar system having one or more radars consistent with the radar to transmit and receive electromagnetic energy according to the first transmit beam pattern and the second transmit beam pattern.

2. estimating the mutual coupling matrix The method of claim 1 , comprising estimating the mutual coupling matrix based on a reference array response matrix, the reference array response matrix being dependent on an environment of the radar.

3. 2. The method of claim 1, further comprising receiving, from a receive antenna element of the radar, reflections of the electromagnetic energy transmitted according to the first transmit beam pattern, the reflections of the electromagnetic energy transmitted according to the first transmit beam pattern being reflected off a calibration target located within the environment of the radar before reaching the receive antenna element of the radar at a first plurality of angles.

4. estimating the mutual coupling matrix for processing reflections of electromagnetic energy transmitted according to the second transmit beam pattern; 4. The method of claim 3, comprising estimating the mutual coupling matrix based on reflections of the electromagnetic energy transmitted according to the second transmit beam pattern that reach the receive antenna elements of the radar at a second plurality of angles.

5. synthesizing a third transmit beam pattern different from the first transmit beam pattern and the second transmit beam pattern based on the data representative of the acquisition pattern; The method of claim 4 , further comprising: estimating a second mutual coupling matrix for processing reflections of electromagnetic energy transmitted according to the third transmit beam pattern.

6. estimating the second mutual coupling matrix for processing reflections of electromagnetic energy transmitted according to the third transmit beam pattern; 6. The method of claim 5, comprising estimating the second mutual coupling matrix based on reflections of the electromagnetic energy transmitted according to the third transmit beam pattern that reach the receive antenna elements of the radar at a third plurality of angles, the third plurality of angles being different from the first plurality of angles and the second plurality of angles.

7. generating the model for operating the radar on the vehicle, 7. The method of claim 6, further comprising generating the model based on both the mutual coupling matrix and the second mutual coupling matrix, the model enabling 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.

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

9. 10. The method of claim 8, further comprising providing the data representing the collection pattern along with the model to the vehicle as part of the over-the-air update, the combination of the model and the data representing the collection pattern enabling each vehicle radar system to synthesize multiple transmit beam patterns and estimate mutual coupling matrices corresponding to the multiple transmit beam patterns for use during navigation by the vehicle.

10. performing a manifold matching process to model a free space array response for a main lobe of the second transmit beam pattern; determining a difference between the free space array response and a composite array response for the main lobe of the second transmit beam pattern; and calculating a correction based on the difference; estimating the mutual coupling matrix The method of claim 1 , comprising estimating the mutual coupling matrix based on the correction.

11. triggering each transmit antenna element of the radar to individually transmit electromagnetic energy; triggering a first transmitting antenna element to transmit a first electromagnetic energy at a first time; triggering a second transmitting antenna element to transmit second electromagnetic energy at a second time after the first time; and triggering a third transmitting antenna element to transmit a third electromagnetic energy at a third time after the second time.

12. triggering each transmit antenna element of the radar to individually transmit electromagnetic energy; 12. The method of claim 11, comprising triggering each transmit antenna element of the radar to transmit electromagnetic energy individually over a plurality of azimuth angles, the plurality of azimuth angles depending on the first transmit beam pattern.

13. generating data representative of the collection pattern, The method of claim 1 , comprising generating data representative of the acquisition pattern such that the acquisition pattern is independent of locations of lobes and nulls.

14. applying a range compression filter and a zero Doppler filter to the data representing the acquisition pattern; estimating transmit calibration parameters based on the data representative of the acquisition pattern after applying the range compression filter and the zero Doppler filter; The method of claim 1 , further comprising: calibrating a transmit phase shifter based on the transmit calibration parameters.

15. estimating the mutual coupling matrix The method of claim 14 comprising estimating the mutual coupling matrix further based on the transmit phase shifter.

16. 1. A system comprising: Radar, a computing system, the computing system comprising: Triggering each transmit antenna element of the radar to individually transmit electromagnetic energy according to a first transmit beam pattern; generating data representative of a collection pattern based on reflections of the electromagnetic energy transmitted according to the first transmit beam pattern; synthesizing a second transmit beam pattern different from the first transmit beam pattern using the data representative of the acquisition pattern; estimating a mutual coupling matrix for processing reflections of electromagnetic energy transmitted according to the second transmit beam pattern; A system configured to generate a model for operating the radar mounted on a vehicle based on the mutual coupling matrix, the model enabling a vehicle radar system having one or more radars consistent with the radar to transmit and receive electromagnetic energy according to the first transmit beam pattern and the second transmit beam pattern.

17. The computing system comprises: Estimating a transport delay corresponding to the second transmit beam pattern; 17. The system of claim 16, further configured to perform waveform matching using the transport delay to determine a difference between the second transmit beam pattern and a corresponding free-space beam pattern.

18. The computing system comprises: The system of claim 17 , further configured to estimate the mutual coupling matrix further based on the difference.

19. The computing system comprises: synthesizing a plurality of transmit beam patterns different from the first transmit beam pattern and the second transmit beam pattern using the data representative of the acquisition pattern; 20. The system of claim 16, further configured to estimate a plurality of mutual coupling matrices for processing reflections of electromagnetic energy transmitted according to the plurality of transmit beam patterns.

20. A non-transitory computer-readable medium configured to store instructions that, when executed by a computing system having one or more processors, cause the computing system to: triggering each transmit antenna element of the radar to individually transmit electromagnetic energy according to a first transmit beam pattern; generating data representative of a collection pattern based on reflections of the electromagnetic energy transmitted according to the first transmit beam pattern; synthesizing a second transmit beam pattern different from the first transmit beam pattern using the data representative of the acquisition pattern; estimating a mutual coupling matrix for processing reflections of electromagnetic energy transmitted according to the second transmit beam pattern; and generating a model for operating the radar mounted on a vehicle based on the mutual coupling matrix, the model enabling a vehicle radar system having one or more radars consistent with the radar to transmit and receive electromagnetic energy according to the first transmit beam pattern and the second transmit beam pattern.

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