Method and system for controlling an automatic remote lighting functionality for headlights and vehicle

Abstract

A method (300) for controlling an automatic high beam functionality for headlights (104) of a vehicle (100), the method (300) comprising: Obtaining camera data relating to an object in front of the vehicle (100); identifying, via a processor (222), a radial gradient (402) of pixels in a region of interest from the camera data; automatically controlling, via the processor (222), the high beam functionality for the headlights (104) based on the radial gradient (402); and Calculating, via the processor (222), a density of the radial gradient (402) from the camera data; wherein the automatic control comprises automatically controlling, via the processor (222), the automatic high beam functionality for the headlights (104) based on the density of the radial gradient (402); wherein: calculating the density of the radial gradient (402) comprises calculating, via the processor (222), a difference between a maximum hue and a minimum hue in the radial gradient (402) from the camera data; and the automatic control comprises automatically reducing, via the processor (222), an intensity of the headlights (104) when the difference between the maximum hue and the minimum hue in the radial gradient (402) exceeds a predetermined threshold.

Description

[0001] The technical field generally refers to the area of ​​vehicles and, in particular, to the control of high beam functionality in vehicles.

[0002] Many vehicles today are equipped with headlights with automatic high beam functionality, which, for example, automatically controls the high beam of the vehicle's headlights under various circumstances. In such vehicles, the high beam can be switched off when an approaching vehicle is detected. However, in certain situations, the existing automatic high beam control systems may not always be optimally controlled, for example, when driving on a road with an incline or other decline.

[0003] DE 10 2013 112 163 A1 describes real-time object detection, where a preprocessor performs run-length encoding and generates an integral image of an image. The run-length encoding and the integral image are used to identify potential objects and iteratively refine their boundaries. A gradient histogram and a support vector machine then classify the object. The method can be part of a driver assistance system.

[0004] It may be considered an object to provide improved systems and methods for controlling automatic high beam functionality for vehicle headlights and a vehicle having the system.

[0005] A method according to the invention is provided for controlling automatic high beam functionality for headlights of a vehicle, the method comprising: obtaining camera data corresponding to an object in front of the vehicle; identifying, via a processor, a radial gradient of pixels in a region of interest from the camera data; and automatically controlling, via the processor, the automatic high beam functionality for the headlights based on the radial gradient. The method according to the invention further comprises calculating, via the processor, a density of the radial gradient from the camera data; wherein the automatic controlling comprises automatically controlling, via the processor, the automatic high beam functionality for the headlights based on the density of the radial gradient.The inventive method further comprises calculating the density of the radial gradient, calculating a difference between a maximum shadow and a minimum shadow in the radial gradient from the camera data via the processor; and automatically controlling comprises automatically reducing an intensity of the headlights via the processor when the difference between the maximum shadow and the minimum shadow in the radial gradient exceeds a predetermined threshold.

[0006] In one embodiment, the method further comprises: calculating, via the processor, a magnitude of the radial gradient from the camera data; wherein the automatically controlling comprises automatically controlling, via the processor, the automatic high beam functionality for the headlights based on the magnitude of the radial gradient.

[0007] Also in one embodiment, calculating the magnitude of the radial gradient comprises calculating, via the processor, a number of pixels in the radial gradient from the camera data; and automatically controlling comprises automatically reducing, via the processor, an intensity of the headlights when the number of pixels in the radial gradient exceeds a predetermined threshold.

[0008] In one embodiment, the method further comprises: calculating, via the processor, a magnitude of the radial gradient from the camera data; and calculating, via the processor, a density of the radial gradient from the camera data; wherein automatically controlling comprises automatically controlling, via the processor, the automatic high beam functionality for the headlights based on both the magnitude and the density of the radial gradient.

[0009] Also in one embodiment, calculating the magnitude of the radial gradient comprises calculating, via the processor, a number of pixels in the radial gradient from the camera data; calculating the density of the radial gradient comprises calculating, via the processor, a difference between a maximum hue and a minimum hue in the radial gradient from the camera data; and automatically controlling comprises automatically reducing, via the processor, an intensity of the headlights based on both the number of pixels and the difference between the maximum hue and the minimum hue in the radial gradient from the camera data.

[0010] A system according to the invention for controlling automatic high beam functionality for headlights of a vehicle is provided, the system comprising: a camera configured to provide camera data associated with an object in front of the vehicle; and a processor coupled to the camera and configured to enable at least the following: identifying a radial gradient of pixels in a region of interest from the camera data; and automatically controlling the automatic high beam functionality for the headlights based on the radial gradient.The processor is further configured to enable at least the following: calculating the magnitude by calculating a number of pixels in the radial gradient from the camera data; calculating the density by calculating a difference between a maximum hue and a minimum hue in the radial gradient from the camera data; and automatically reducing an intensity of the headlights via the processor based on both the number of pixels and the difference between the maximum hue and the minimum hue in the radial gradient from the camera data.

[0011] In one embodiment, the processor is further configured to enable at least the following: calculating a magnitude of the radial gradient from the camera data; and automatically controlling the automatic high beam functionality for the headlights based on the magnitude of the radial gradient.

[0012] In one embodiment, the processor is further configured to enable at least the following: calculating the magnitude by calculating a number of pixels in the radial gradient from the camera data; and automatically reducing an intensity of the headlights when the number of pixels in the radial gradient exceeds a predetermined threshold.

[0013] In one embodiment, the processor is further configured to enable at least the following: calculating a radial gradient density from the camera data; and automatically controlling, via the processor, automatic high beam functionality for the headlights based on the radial gradient density.

[0014] In one embodiment, the processor is further configured to enable at least the following: calculating the density by calculating a difference between a maximum hue and a minimum hue in the radial gradient from the camera data; and automatically reducing an intensity of the headlights when the difference between the maximum hue and the minimum hue in the radial gradient exceeds a predetermined threshold.

[0015] In one embodiment, the processor is further configured to enable at least the following: calculating a magnitude of the radial gradient from the camera data; calculating a density of the radial gradient from the camera data; and automatically controlling the automatic high beam function for the headlights based on both the magnitude and the density of the radial gradient.

[0016] A vehicle according to the invention is provided, comprising: one or more headlights having automatic high beam functionality; and a control system for controlling the automatic high beam functionality for the headlights, the control system comprising: a camera configured to provide camera data relating to an object in front of the vehicle; and a processor coupled to the camera and configured to enable at least the following: identifying a radial gradient of pixels in a region of interest from the camera data; and automatically controlling the automatic high beam functionality for the headlights based on the radial gradient.The processor is further configured to enable at least the following: calculating a magnitude of the radial gradient from the camera data by calculating a number of pixels in the radial gradient from the camera data; calculating a density of the radial gradient from the camera data by calculating a difference between a maximum hue and a minimum hue in the radial gradient from the camera data; and automatically reducing an intensity of the headlights via the processor based on both the number of pixels and the difference between the maximum hue and the minimum hue in the radial gradient from the camera data.

[0017] In one embodiment, the processor is further configured to enable at least the following: calculating a magnitude of the radial gradient from the camera data; and automatically controlling the automatic high beam functionality for the headlights based on the magnitude of the radial gradient.

[0018] In one embodiment, the processor is further configured to enable at least the following: calculating the magnitude by calculating a number of pixels in the radial gradient from the camera data; and automatically reducing an intensity of the headlights when the number of pixels in the radial gradient exceeds a predetermined threshold.

[0019] In one embodiment, the processor is further configured to enable at least the following: calculating a radial gradient density from the camera data; and automatically controlling, via the processor, automatic high beam functionality for the headlights based on the radial gradient density.

[0020] In one embodiment, the processor is further configured to enable at least the following: calculating the density by calculating a difference between a maximum hue and a minimum hue in the radial gradient from the camera data; and automatically reducing an intensity of the headlights when the difference between the maximum hue and the minimum hue in the radial gradient exceeds a predetermined threshold.

[0021] The present specification will now be described in conjunction with the following figures, wherein like reference numerals designate like elements, and wherein: Fig. 1 is a functional block diagram of a vehicle including vehicle headlights and a control system that controls the headlights, including automatic high beam functionality for the vehicle headlights; Fig. 2 is a functional block diagram of a computer system of a control system for controlling headlights of a vehicle, including for controlling the automatic high beam functionality, and which in conjunction with the control system of Fig. 1 can be implemented; Fig. 3 is a flowchart of a method for controlling the automatic high beam functionality for headlights of a vehicle, which method is used in conjunction with the vehicle of Fig. 1, the tax system of Fig. 1 and the computer system of Fig. 2 can be implemented; and Fig. 4 and Fig. 5 are schematic diagrams of an illustrative example of an implementation of the method of Fig. 3 in connection with the vehicle of Fig. 1, as shown on a roadway together with one or more other vehicles.

[0022] Fig. 1 shows a vehicle 100 according to an exemplary embodiment. As described in more detail below, the vehicle 100 includes a control system 102 for controlling automatic high beam functionality for the headlights 104 of the vehicle 100. As described in more detail below, the control system 102 controls the high beam functionality of the headlights 104 based on a radial gradient in the camera data relative to a region of interest for an object ahead of the vehicle 100, according to exemplary embodiments.

[0023] In certain embodiments, the vehicle 100 comprises an automobile. In various embodiments, the vehicle 100 may be any of a number of different types of automobiles, such as a sedan, a station wagon, a truck, or a sport utility vehicle (SUV), and may be two-wheel drive (2WD) (i.e., rear-wheel drive or front-wheel drive), four-wheel drive (4WD), or all-wheel drive (AWD), and / or various other types of vehicles in certain embodiments. In certain embodiments, the vehicle 100 may also include a motorcycle and / or one or more other types of vehicles. Furthermore, in various embodiments, the vehicle 100 may also include any number of other types of mobile platforms.

[0024] In the illustrated embodiment, the vehicle 100 includes a body 106 that substantially encloses other components of the vehicle 100. Also in the illustrated embodiment, the vehicle 100 includes a plurality of axles and wheels (in Fig. 1 not shown) that enable the movement of the vehicle 100 as part of or together with a drive system 108 of the vehicle 100.

[0025] In various embodiments, the drive system 108 includes a propulsion system. In certain exemplary embodiments, the drive system 108 includes an internal combustion engine and / or an electric motor / generator. In certain embodiments, the drive system 108 may vary, and / or two or more drive systems 108 may be used. By way of example, the vehicle 100 may also include any one or a combination of different types of drive systems, such as a gasoline or diesel-powered internal combustion engine, a flex-fuel vehicle engine (i.e., a mixture of gasoline and alcohol), an engine powered by a gaseous compound (e.g., hydrogen and / or natural gas), an internal combustion / electric motor hybrid engine, and an electric motor. As in Fig. 1, in various embodiments, the control system 102 includes one or more of the following elements: a vision system (FOM) 112, an instrument cluster (IPC) 116, a body control module (BCM) 118, and an exterior lighting module (ELM). In various embodiments, the image processing system 112 receives camera data for the vehicle 100, identifies and performs calculations related to a radial gradient with respect to a region of interest in the camera data corresponding to an object in front of the vehicle 100 (including calculations related to a size and a density of the radial gradient), and provides instructions for controlling automatic high beam functionality for the headlights 104 of the vehicle 100.

[0026] In various embodiments, the image processing system 112 provides these features via machine vision and image processing 114 with respect to the camera data and the radial gradient identified therein. Furthermore, in various embodiments, the image processing system 112 controls the automatic high beam functionality for the headlights 104 via instructions provided from the image processing system 112 through the body control module 118 and on to the exterior lighting module 120 coupled to the headlights 104. In various embodiments, these steps are described further below in connection with the method 300 of Fig. 3 and the implementations of Fig. 4 and Fig. 5 described in more detail.

[0027] In various embodiments, the body control module 118 also uses other data, calculations, and requests to control the automatic high beam functionality for the headlights 104 via instructions provided to the exterior lighting module 120, e.g., using other data such as vehicle speed as well as user inputs (e.g., user instructions and / or overrides) from the instrument cluster 116.

[0028] In relation to Fig. 2, a functional block diagram is provided for a control system 200 that controls automatic high beam functionality for headlights for a vehicle according to exemplary embodiments. In various embodiments, the control system 200 corresponds to the control system 102 of the vehicle 100 of Fig. 1, and / or components thereof. In certain embodiments, the control system 200 and / or components thereof are part of the vision system 112 of Fig. 1. In certain embodiments, the control system 200 and / or its components may be part of and / or coupled to the vision system 112, the instrument cluster 116, the body control module 118, and / or the exterior lighting module 120. While Fig. 2 shows a control system 200 having a sensor array 202 (with a camera 212 and other sensors) and a computer system 204 (with a processor 222, a memory 224 and other components), and while the control system 200 in one embodiment is at least partially similar to the vision system 112 of Fig. 1, it will be understood that in various embodiments, each of the image processing systems 112, the instrument cluster 116, the body control module 118, and the exterior lighting module 120 may include the same or similar components as in Fig. 2 and / or as described below, for example including corresponding sensors and / or corresponding processors and memory and so on.

[0029] As in Fig. 2, in various embodiments, the control system 200 includes a sensor assembly 202 and a controller 204. In various embodiments, the sensor assembly 202 includes one or more cameras 212. In various embodiments, one or more of the cameras 212 are directed in front of the vehicle 100, for example, to detect objects on or near a roadway or path in front of the vehicle 100. In certain embodiments, the sensor assembly 202 may also include one or more other types of sensing sensors 214 (e.g., in some embodiments, RADAR, LIDAR, SONAR, or the like), one or more vehicle speed sensors 216 (e.g., wheel speed sensors, accelerometers, and / or other sensors for measuring data to determine a speed of the vehicle 100), and / or one or more other sensors 218 (e.g.,in certain embodiments, user input sensors, GPS sensors, and so on).

[0030] As also in Fig. 2, the controller is coupled to the sensor assembly 202. In various embodiments, the controller 204 controls the automatic high beam functionality for the vehicle's headlights based on an identified radial assignment of camera data from the camera 212 relating to one or more detected objects in front of the vehicle (e.g., along a path or roadway in front of the vehicle), as described below in connection with the method 300 of Fig. 3 and the implementations of Fig. 4 and Fig. 5 is explained in more detail. As in Fig. 2, in various embodiments, the controller 204 includes a computer system having a processor 222, a memory 224, an interface, a storage device 228, a bus 230, and a disk 236.

[0031] As in Fig. 2, the controller 204 includes a computer system. In certain embodiments, the controller 204 may also include the sensor assembly 202 and / or one or more other vehicle components. Furthermore, the controller 204 may differ in other ways from the Fig. 2. For example, the controller 204 may be coupled to or otherwise utilize one or more remote computer systems and / or other control systems, for example, as part of one or more of the vehicle devices and systems mentioned above.

[0032] In the illustrated embodiment, the computer system of the controller 204 includes a processor 222, a memory 224, an interface 226, a storage device 228, and a bus 230. The processor 222 performs the computation and control functions of the controller 204 and may include any type of processor or multiple processors, individual integrated circuits such as a microprocessor, or any suitable number of integrated circuits and / or printed circuit boards that cooperate to perform the functions of a processing unit. During operation, the processor 222 executes one or more programs 232 contained in the memory 224 and, as such, controls the general operation of the controller 204 and the computer system of the controller 204, generally in carrying out the processes described herein, such as the method 300 described further below in connection with Fig. 2 is discussed.

[0033] The memory 224 may be any suitable type of memory. For example, the memory 224 may include various types of dynamic random access memory (DRAM) such as SDRAM, various types of static RAM (SRAM), and various types of non-volatile memory (PROM, EPROM, and Flash). In certain examples, the memory 224 is located on and / or disposed on the same computer chip as the processor 222. In the illustrated embodiment, the memory 224 stores the aforementioned program 232 along with one or more stored values ​​234 (e.g., in various embodiments, including predetermined thresholds for controlling the automatic high beam function).

[0034] Bus 230 is used to transfer programs, data, status, and other information or signals between the various components of the computer system of controller 204. Interface 226 enables communication with the computer system of controller 204, e.g., from a system driver and / or another computer system, and may be implemented using any suitable method and apparatus. In one embodiment, interface 226 receives the various data from sensor assembly 202, drive system 108, suspension system 106, and / or one or more other components and / or systems of vehicle 100. Interface 226 may include one or more network interfaces to communicate with other systems or components.The interface 226 may also include one or more network interfaces to communicate with technicians and / or one or more storage interfaces to connect to storage devices, such as storage device 228.

[0035] The storage device 228 may be any suitable type of storage device, including various types of random access memories and / or other storage devices. In an exemplary embodiment, the storage device 228 comprises a program product from which the memory 224 can receive a program 232 that performs one or more embodiments of one or more processes of the present disclosure, such as the steps of the method 300 described further below in connection with Fig. 2. In another exemplary embodiment, the program product may be stored and / or otherwise accessed directly in the memory 224 and / or one or more other disks 236 and / or other storage devices.

[0036] Bus 230 may be any suitable physical or logical means for interconnecting computer systems and components. These include, but are not limited to, direct hard-wired connections, fiber optic, infrared, and wireless bus technologies. During operation, program 232 is stored in memory 224 and executed by processor 222.

[0037] While this exemplary embodiment is described in the context of a fully functional computer system, those skilled in the art will recognize that the mechanisms of the present disclosure may be distributed as a program product having one or more types of non-transitory, computer-readable, signal-bearing media used to store the program and its instructions and to effect its distribution, such as a non-transitory, computer-readable medium carrying the program and including computer instructions stored therein for causing a computer processor (such as processor 222) to perform and execute the program. Such a program product may take a variety of forms, and the present disclosure applies equally regardless of the particular type of computer-readable, signal-bearing medium used to effect distribution.Examples of signal-bearing media include writable media such as floppy disks, hard disks, memory cards, and optical disks, as well as transmission media such as digital and analog communication links. In certain embodiments, cloud-based storage and / or other technologies may also be used. It is also recognized that the computer system of the controller 204 may otherwise differ from the system illustrated in FIG. Fig. 2, for example, in that the computer system of the controller 204 may be coupled to or otherwise utilize one or more remote computer systems and / or other control systems.

[0038] Fig. 3 is a flowchart of a method 300 for controlling high beam functionality for headlights of a vehicle, according to exemplary embodiments. In various embodiments, the method 300 may be used in connection with the vehicle 100 of Fig. 1, including its control system 102, and including the control system 200 of Fig. 2 (and / or components thereof). The method 300 is also described below in connection with the Fig. 4 and Fig. 5, which illustrates examples of an implementation of the method 300 of Fig. 3 in connection with the vehicle 100 of Fig. 1 as shown on a roadway with other vehicles in front of the vehicle 100, in accordance with various example embodiments.

[0039] As in Fig. 3, in various embodiments, method 300 begins at 302. In various embodiments, method 300 begins when one or more events occur that indicate that a vehicle trip is occurring or will occur, such as when a driver, operator, or passenger enters vehicle 100, an engine of vehicle 100 is turned on, a transmission of vehicle 100 is placed in a "drive" mode, or the like.

[0040] Sensor data is collected at 303. In various embodiments, camera data from the one or more cameras 212 is collected by Fig. 2, including camera data with images of a path or roadway and any detected objects therein or near it, in front of the vehicle 100 (i.e., in certain embodiments, additional sensor data may also be obtained from one or more other sensors of the sensor array 202 of Fig. 2, e.g., including other types of sensor data from other detection sensors 214 to identify objects in front of the vehicle 100 (e.g., using RADAR, LIDAR, SONAR, or the like) and / or vehicle speed (e.g., via one or more speed sensors 216) and / or other vehicle data.

[0041] In various embodiments, an image frame is obtained from the camera data at 304. In various embodiments, each image frame corresponds to the camera data for areas in front of the vehicle 100 at a particular time.

[0042] In various embodiments, the horizontal field of view (HFOV) and the vertical field of view (VFOV) are calibrated at 306 using the sensor data. In various embodiments, the HFOV and VFOV are calculated by the processor 222 of Fig. 2 using the sensor data 303. Also in various embodiments, the region of interest (ROI) in 308 can only be accurately identified after the exact calibration of the HFOV and VFOV.

[0043] In various embodiments, a region of interest is identified at 308. In various embodiments, the region of interest (ROI) is determined by the processor 222 from Fig. 2 as a region of the image from the camera data surrounding a detected object in front of the vehicle 100 (e.g., on or near a path or roadway in front of the vehicle 100) based on the horizontal and vertical field of view. In various embodiments, further processing is then limited to this specific region of the image.

[0044] A radial gradient is identified for the image frame at 310. In various embodiments, the processor 222 identifies Fig. 2, a radial gradient within the region of interest of 308 as a region of transition through multiple levels of brightness to darkness (or vice versa) within the region of interest of the image. In various embodiments, the pixels of the region of interest are sampled via processor 222 to identify a gradient.

[0045] For example, with reference to Fig. 4, a first implementation is provided, which shows a first image 400 with a detected object located along a roadway in front of the vehicle 100 (in Fig. 4 not shown). As in Fig. 4, the first image 400 includes a radial gradient 402 surrounding the headlights of the detected object (i.e., a detected oncoming vehicle). As shown in Fig. 4, the radial gradient 402 in this example extends from a center 404 to an outer edge 406. As also shown in Fig. 4, the radial gradient 402 exemplifies a transition between a brightest area in the center 404, a darkest area at the outer edge 406, and various different shades (e.g., different shades of gray), each of which is incrementally darker from the center 404 to the outer edge 406.

[0046] Back to Fig. 3: In various embodiments, the magnitude of the radial gradient is calculated and monitored at 312. In various embodiments, the magnitude of the radial gradient comprises a count of the number of pixels in the gradient and / or in a component region therein. For example, in one embodiment, the magnitude of the radial gradient comprises a count of pixels from the center 404 to a single outer corner of the outer edge 406 (e.g., corresponding to a radius of the radial gradient 402). As an additional example, in certain other embodiments, the magnitude of the radial gradient comprises a number of pixels across the entire surface of the outer edge 406 (e.g., corresponding to an area of ​​the radial gradient 402).

[0047] In various embodiments, a density of the radial gradient is also calculated and monitored at 314. In various embodiments, the density of the radial gradient includes a difference between the minimum and maximum hues in the radial gradient.

[0048] In various embodiments, it is determined at 316 whether the magnitude of the radial gradient is greater than a predetermined threshold. In various embodiments, the processor 222 takes Fig. 2, a determination is made as to whether the number of pixels in the radial gradient, as counted at 312, exceeds a predetermined threshold. In various embodiments, the threshold is also a calibratable lookup table consisting of both radius counts and area counts. In various embodiments, if the magnitude of the radial gradient is determined to be greater than the predetermined threshold, the process proceeds to step 320, described further below. Also in various embodiments, the process otherwise proceeds to step 310, described above.

[0049] In various embodiments, it is determined at 318 whether the density of the radial gradient is greater than a predetermined threshold. In various embodiments, the processor 222 takes Fig. 2, a determination is made as to whether the difference between the minimum and maximum hues of the number of pixels in the radial gradient, as determined at 314, exceeds a predetermined threshold. In various embodiments, the threshold for the density is also a calibratable lookup table that includes an exponential / linear / logarithmic increase in the density values. In various embodiments, if the density of the radial gradient is determined to be greater than the predetermined threshold, the process proceeds to step 320, described further below. Also in various embodiments, the process otherwise proceeds to step 310 described above.

[0050] With respect to steps 316 and 318, in certain embodiments, the process proceeds to step 320 if both the magnitude and density of the radial gradient exceed their respective thresholds (and otherwise returns to step 310). Conversely, in certain other embodiments, the process proceeds to step 320 if either the magnitude or the density, or both, are greater than their respective predetermined thresholds (and otherwise returns to step 310).

[0051] In step 320, a gradient index is assigned. In various embodiments, the processor 222 of Fig. 2 an index value representing a geographical location of the radial gradient. Furthermore, in certain embodiments, an intensity of the automatic high beam for the headlights is reduced in step 322, particularly by instructions received from the processor 222 of Fig. 2 (e.g., as provided via the vision system 112 by the body control module 118 to the exterior lighting module 120 of Fig. 1). In addition, in certain embodiments, the process also proceeds to step 323, which is described below.

[0052] During step 322, a scan of possible headlights within the radial gradient is performed, and it is determined whether headlights of another vehicle have been identified within the radial gradient. In certain embodiments, step 322 includes a determination by the processor 222 of Fig. 2, whether a closer examination of the camera data (i.e., in a future image when the detected object approaches the vehicle 100) reveals that headlights of another vehicle are indeed represented by the radial gradient.

[0053] In Fig. 5, for example, a second image 500 is shown, which is temporally related to the first image 400 of Fig. 4 follows. As in Fig. 5, the following (second) image 500 shows when the detected object is approaching the vehicle 100 from Fig. 1 that two headlights 502 of another vehicle 100 are present in the second image. In various embodiments, this serves as confirmation of the initial determination (based on the radial gradient) that another vehicle is approaching the vehicle 100 from Fig. 1 approaches.

[0054] Back to Fig. 3: In various embodiments, if it is determined that headlights of another vehicle are not found within the radial gradient, the automatic high beam function for the headlights 104 of the vehicle 100 is turned on (or re-turned on) at 324. In various embodiments, the process then returns to 304.

[0055] Conversely, in various embodiments, if it is determined that the headlights of another vehicle are within the radial gradient, tracking of the other vehicle is initiated at 326 (e.g., via commands sent from processor 222 to sensor array 202 of Fig. 2), and the automatic high beam is switched off at 328 by commands received from the processor 222 of Fig. 2 (e.g., as transmitted via the vision system 112 by the body control module 118 to the exterior lighting module 120 of Fig. 1).

[0056] In various embodiments, the headlights of the other vehicle are assigned a headlight index (e.g., related to geographic location) at 330, and two-dimensional coordinates calculated from the image area are provided for the headlights of the other vehicle at 332 based on the geographic location of the physical vehicle. Additionally, in various embodiments, the two-dimensional coordinates are transformed into latitude and longitude values ​​using eigenvalues ​​at 334.

[0057] In certain embodiments, the automatic high beams are partially turned off at 336. For example, in certain embodiments, certain high beams directed toward the additional vehicle may be Fig. 5 are switched off at 336, while other high beams that are not directed towards the additional vehicle are switched off by Fig. 5 may remain in high beam mode at 336. In various embodiments, such instructions are transmitted via the processor 222 of Fig. 2 (e.g., as provided via the vision system 112 by the body control module 118 to the exterior lighting module 120 of Fig. 1). Also in certain embodiments, tracking of the additional vehicle continues in various iterations of step 326 until the additional vehicle is no longer present in the camera data image frames, whereupon the process returns to step 304 for detection of a new object.

[0058] Accordingly, methods, systems, and vehicles are provided for controlling automatic high beam functionality for vehicle headlights. In various embodiments, camera data is used to determine a radial gradient in the camera images of headlights of a detected vehicle located in front of the vehicle 100 of Fig. 1 to control the automatic high beam functionality. In various embodiments, the automatic high beam is reduced or turned off when the radial gradient indicates that another vehicle is in front of the vehicle 100, thereby reducing glare to the other vehicle. By utilizing the radial gradient, the disclosed methods, systems, and vehicles can potentially enable earlier detection of an approaching vehicle, particularly in situations where there is a hill and / or a sloped road, further minimizing glare to the driver of the approaching vehicle.

[0059] It will be appreciated that systems, vehicles, applications, and implementations may vary from those depicted in the figures and described herein. For example, in various embodiments, the vehicle 100, the control system 102, its components, and / or other components may differ from those depicted in Fig. 1 and / or described above in connection therewith. It is also recognized that the components of the control system 200 may differ from Fig. 2 in various embodiments. It is further recognized that the steps of method 300 may be different and / or that various steps thereof may be performed simultaneously and / or in a different order than those described in Fig. 3 and / or described above. It is also appreciated that implementations of the method 300 may differ from those described in Fig. 4 and / or Fig.5 and / or as described above.

Claims

[1] A method (300) for controlling an automatic high beam functionality for headlights (104) of a vehicle (100), the method (300) comprising: Obtaining camera data relating to an object in front of the vehicle (100); identifying, via a processor (222), a radial gradient (402) of pixels in a region of interest from the camera data; automatically controlling, via the processor (222), the high beam functionality for the headlights (104) based on the radial gradient (402); and Calculating, via the processor (222), a density of the radial gradient (402) from the camera data; wherein the automatic control comprises automatically controlling, via the processor (222), the automatic high beam functionality for the headlights (104) based on the density of the radial gradient (402); wherein: calculating the density of the radial gradient (402) comprises calculating, via the processor (222), a difference between a maximum hue and a minimum hue in the radial gradient (402) from the camera data; and the automatic control comprises automatically reducing, via the processor (222), an intensity of the headlights (104) when the difference between the maximum hue and the minimum hue in the radial gradient (402) exceeds a predetermined threshold. [2] The method (300) of claim 1, further comprising: Calculating, via the processor (222), a magnitude of the radial gradient (402) from the camera data; wherein said automatically controlling comprises automatically controlling, via said processor (222), said automatic high beam functionality for said headlights (104) based on the magnitude of said radial gradient (402). [3] The method (300) of claim 2, wherein: calculating the magnitude of the radial gradient (402) comprises calculating, via the processor (222), a number of pixels in the radial gradient (402) from the camera data; and the automatic control comprises automatically reducing, via the processor (222), an intensity of the headlights (104) when the number of pixels in the radial gradient (402) exceeds a predetermined threshold. [4] The method (300) of claim 1, further comprising: Calculating, via the processor (222), a magnitude of the radial gradient (402) from the camera data; and Calculating, via the processor (222), a density of the radial gradient (402) from the camera data; wherein said automatically controlling comprises automatically controlling, via said processor (222), said automatic high beam functionality for said headlights (104) based on both the magnitude and density of said radial gradient (402). [5] The method (300) of claim 4, wherein: calculating the magnitude of the radial gradient (402) comprises calculating, via the processor (222), a number of pixels in the radial gradient (402) from the camera data; calculating the density of the radial gradient (402) comprises calculating, via the processor (222), a difference between a maximum hue and a minimum hue in the radial gradient (402) from the camera data; and the automatic control comprises automatically reducing, via the processor (222), an intensity of the headlights (104) based on both the number of pixels and the difference between the maximum hue and the minimum hue in the radial gradient (402) from the camera data. [6] A system (200) for controlling an automatic high beam functionality for headlights (104) of a vehicle (100), the system comprising: a camera configured to provide camera data relating to an object in front of the vehicle (100); and a processor (222) coupled to the camera and configured to enable at least the following: identifying a radial gradient (402) of pixels in a region of interest from the camera data; and automatically controlling the high beam functionality for the headlights (104) based on the radial gradient (402); wherein the processor (222) is further configured to enable at least the following: Calculating a magnitude of the radial gradient (402) from the camera data by calculating a number of pixels in the radial gradient (402) from the camera data; Calculating a density of the radial gradient (402) from the camera data by calculating a difference between a maximum hue and a minimum hue in the radial gradient (402) from the camera data; and automatically reducing, via the processor (222), an intensity of the headlights (104) based on both the number of pixels and the difference between the maximum and minimum hue in the radial gradient (402) from the camera data. [7] Vehicle (100), comprising: one or more headlights (104) with automatic high beam functionality; and a control system for controlling the automatic high beam functionality for the headlights (104), where the tax system includes: a camera configured to provide camera data relating to an object in front of the vehicle (100); and a processor (222) coupled to the camera and configured to enable at least the following: identifying a radial gradient (402) of pixels in a region of interest from the camera data; and automatically controlling the high beam functionality for the headlights (104) based on the radial gradient (402); wherein the processor (222) is further configured to enable at least the following: Calculating a magnitude of the radial gradient (402) from the camera data by calculating a number of pixels in the radial gradient (402) from the camera data; Calculating a density of the radial gradient (402) from the camera data by calculating a difference between a maximum hue and a minimum hue in the radial gradient (402) from the camera data; and automatically reducing, via the processor (222), an intensity of the headlights (104) based on both the number of pixels and the difference between the maximum and minimum hue in the radial gradient (402) from the camera data.

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