A networking intelligent headlamp system based on occlusion risk prediction and a control method thereof

Abstract

This invention discloses a networked intelligent headlight system and control method based on occlusion risk prediction, relating to the fields of automotive lighting and vehicle networking technology. The system includes a forward-facing environment sensor, a vehicle networking communicator, a vehicle status collector, a headlight controller, and matrix LED headlights. The headlight controller identifies occlusion risk targets that are obscured by objects but have not entered the vehicle's direct perception range based on remote target information and road environment information ahead of the vehicle. It determines the expected exposure position based on the movement state of the occlusion risk target and the boundary of the obstruction object, and forms a road pre-illumination area accordingly. The controller then controls the corresponding LED beads in the matrix LED headlights based on the road pre-illumination area to provide pre-illumination before the occlusion risk target enters the vehicle's direct perception range. This invention can utilize networked sensing information to illuminate the road area where the risk target is expected to appear in advance, improving road visibility and vehicle safety during nighttime driving in occlusion scenarios.

Description

Technical Field

[0001] This invention belongs to the field of automotive lighting and vehicle networking technology, specifically relating to a networked intelligent headlight system and control method based on occlusion risk prediction. Background Technology

[0002] Automotive headlights are a crucial component for ensuring road visibility during nighttime driving and in low-light conditions. With the development of automotive electronics technology, traditional fixed low beam and high beam headlights have gradually evolved into adaptive headlight systems and matrix LED headlights.

[0003] Existing matrix LED headlights typically consist of multiple individually controllable LED beads. Sensors such as a front-facing camera and millimeter-wave radar detect vehicles, pedestrians, or the road environment ahead, and then adjust the on / off status or brightness of different LED beads based on the detection results to change the headlight's illumination area. For example, when an oncoming vehicle is detected, the brightness of the corresponding area can be reduced to avoid glare; when a vehicle enters a curve, the light distribution can be adjusted according to the vehicle's turning direction.

[0004] However, the control of existing intelligent headlights typically relies on the vehicle's own environmental sensors. In environments such as urban intersections, mountain curves, in front of or behind large vehicles, and at the crest of a road, the presence of buildings, green belts, mountains, large vehicles, or other obstructions may prevent the vehicle's forward-facing cameras and other environmental sensors from detecting pedestrians, bicycles, non-motorized vehicles, or other vehicles behind these obstructions in advance.

[0005] Once a risky target enters the vehicle's direct perception range from behind an obstruction, the existing system can only identify it and adjust the headlights. By this time, the distance between the vehicle and the risky target has shortened, leaving the driver with limited time to observe and react.

[0006] With the development of vehicle-to-everything (V2X) technology, vehicles can obtain environmental target information collected by other connected vehicles or roadside sensing devices through V2X communication. Therefore, even if the vehicle itself has not yet directly detected a certain target, other connected vehicles or roadside sensing devices may have already detected that target in advance.

[0007] However, if the target location detected remotely is used directly to control the vehicle's headlights, the following problems still exist: Firstly, there is a certain time lag between the generation of remote target information and its reception by the vehicle, during which the position of the moving target may have changed. Secondly, the target being obscured may be located in a position where the headlights cannot directly illuminate it, such as behind a building or a large vehicle. Directly pointing the headlights at the target's current location will not produce effective illumination. Third, distant targets may not necessarily enter the vehicle's driving area. If all distant targets are illuminated in advance, it is easy to produce ineffective illumination. Fourth, while enhancing road lighting in advance, it is also necessary to consider vehicles ahead and oncoming vehicles to avoid glare to other drivers from the enhanced lighting area; Fifth, when the obscured target changes from a remote sensing state to a direct sensing state of the vehicle, it is necessary to maintain the continuity of the light illumination area.

[0008] Therefore, it is necessary to provide a networked intelligent headlight system that can predict the location where the target will leave the obstruction by using information from a distant target before the vehicle itself directly senses the obstruction, and illuminate the corresponding road area in advance. Summary of the Invention

[0009] To address the aforementioned problems, this invention provides a networked intelligent headlight system and control method based on occlusion risk prediction. Its core lies in: firstly, utilizing vehicle-to-everything (V2X) networks to obtain information about distant targets that are temporarily invisible to the vehicle; then, combining this with road environment information to determine whether the target is occluded and whether it might be associated with the vehicle's subsequent movement; thereby predicting the target's location where it will emerge from the occlusion, and converting this location and the road area it might subsequently traverse into specific LED control areas for matrix LED headlights.

[0010] To achieve the above objectives, this invention provides a networked intelligent headlight system based on occlusion risk prediction, comprising a forward environment sensor, a vehicle-to-everything (V2X) communicator, a vehicle status acquisition unit, a headlight controller, and matrix LED headlights. The forward environment sensor acquires road environment information ahead of the vehicle; the V2X communicator receives remote target information from other networked vehicles or roadside sensing devices; the vehicle status acquisition unit acquires the vehicle's driving status; the headlight controller fuses the above information and outputs lighting control commands; and the matrix LED headlights generate localized illumination corresponding to the area where the target is expected to appear, based on the lighting control commands.

[0011] Matrix LED headlights include an LED light source board, multiple LED beads arranged in a matrix on the LED light source board, and LED driver circuits connected to each LED bead. Each LED bead can be individually turned on, off, or have its brightness adjusted by the LED driver circuit, enabling the headlights to form zoned lighting according to the spatial location of the road, and providing the basis for subsequent conversion of the pre-illuminated road area into specific LED bead control actions.

[0012] The remote target information includes at least the target's position, speed, direction of movement, and the time the information was generated. The remote target can be a pedestrian, bicycle, non-motorized vehicle, motorized vehicle, or other road target that may affect the vehicle's movement. Because there is a time lag between the generation and transmission of the remote target information to the vehicle's controller, the headlight controller uses this time difference between the information generation time and the current control time, combined with the target's speed and direction of movement, to perform motion compensation on the remote target's position, obtaining a predicted target position corresponding to the current control moment. This avoids directly using outdated positions, which could lead to occlusion detection and pre-illumination position offsets.

[0013] Furthermore, the headlight controller determines the predicted target area based on the predicted target position, the target size of the distant target, and the target position error. The predicted target area is the spatial range formed around the predicted target position, used to characterize the target entity size and the uncertainty range introduced by communication positioning, detection positioning, and motion prediction, rather than using a single coordinate point as the object of occlusion judgment. When the predicted target area is outside the direct sensing range of the forward environment sensor, and the observation direction from the vehicle towards the predicted target area is blocked by an obstruction, the corresponding distant target is identified as an occlusion risk target. Obstructions can be fixed obstructions such as buildings, walls, green belts, mountains, and roadside slopes, or mobile obstructions such as trucks, buses, and coaches.

[0014] Furthermore, the vehicle status acquisition unit obtains the vehicle speed, steering wheel angle, and vehicle heading angle, and the headlight controller uses this information to determine the vehicle's expected travel area within a set prediction time range. Simultaneously, it determines the predicted motion area of ​​a target based on the predicted position and motion state of the occlusion risk target. When the predicted motion area of ​​a target overlaps with the vehicle's expected travel area within the set prediction time range, it indicates that the occlusion risk target has the potential to enter the vehicle's subsequent travel area, and it is identified as an occlusion risk target posing a driving risk to the vehicle. For distant targets whose predicted motion areas do not overlap with the vehicle's expected travel area, pre-illumination for that target may not be triggered.

[0015] Furthermore, for obstruction risk targets that pose a driving risk to the vehicle, the headlight controller determines the expected exposure position based on the predicted movement direction of the obstruction risk target and the obstruction boundary of the obstruction object. The expected exposure position refers to the position where, after the obstruction risk target moves along the predicted movement direction, it is no longer obstructed by the obstruction object and can enter the vehicle's observation direction. For fixed obstructions, the obstruction boundary can be determined based on its contour boundary on the side closest to the road; for moving obstructions, the obstruction boundary can be updated based on the position and contour of the obstruction object continuously obtained by the forward environmental sensor, so that the expected exposure position is dynamically updated with the relative movement of the obstruction object and the risk target.

[0016] Furthermore, the headlight controller determines the road pre-illumination area based on the expected exposure location. The road pre-illumination area begins in the road area where the expected exposure location is located and extends along the predicted movement direction of the occluded target towards the vehicle's expected travel area, covering at least a portion of the road traversed by the occluded target from the moment it is unobstructed until it may enter the driving conflict area. The width and extension of the road pre-illumination area can be determined based on the target size, target position error, target movement speed, and the target's predicted movement area, ensuring that the pre-illumination is not directed at the current location of the target still obscured by the object, but rather acts on the road area where the target is expected to appear, which the light can reach in advance.

[0017] Furthermore, the headlight controller determines the currently illuminated area of ​​the matrix LED headlights and determines the restricted lighting area based on the positions of vehicles ahead and oncoming vehicles. The illuminated area excludes portions that are actually not covered by light due to headlight mounting angle, vehicle body posture, road geometry, or physical obstructions; the restricted lighting area excludes directions that may cause glare to drivers of vehicles traveling in the same or oncoming traffic. The portion of the road pre-illuminated area that lies within the illuminated area but outside the restricted lighting area is defined as the actual pre-illuminated area.

[0018] Furthermore, the headlight controller pre-stores the light mapping relationship between each LED and different road projection areas in front of the vehicle. This mapping relationship can be obtained during headlight installation calibration or vehicle calibration, and is used to describe the main projection area formed on the road in front of the vehicle after each LED is lit. During vehicle operation, the headlight controller corrects the position of the road projection area based on the vehicle's pitch and steering wheel angle, selects LEDs whose corrected road projection area overlaps with the actual pre-illuminated area, and adjusts the drive current or pulse width modulation duty cycle of the selected LEDs through the LED driver circuit to ensure that the actual enhanced lighting area corresponds to the actual pre-illuminated area.

[0019] During pre-lighting, the vehicle-to-everything (V2X) communicator continuously receives updated information on distant targets, while the forward environment sensor continuously monitors the road environment ahead of the vehicle. When a potential target that could obstruct the view moves beyond its predicted exposure position and enters the direct sensing range of the forward environment sensor, the headlight controller continues to control the corresponding LEDs based on the target's location obtained from the sensor. This gradually switches the control basis from predicted information about distant targets to information directly perceived by the vehicle, maintaining continuity in the lighting area during the switching process.

[0020] This invention also provides a networked intelligent headlight control method based on occlusion risk prediction, comprising the following steps: S1, receiving remote target information sent by other networked vehicles or roadside sensing devices through a vehicle network communicator, obtaining road environment information in front of the vehicle through a forward environment sensor, and obtaining the vehicle's driving status; S2, determining occlusion risk targets that are occluded by objects but have not entered the direct sensing range of the forward environment sensor based on the remote target information and road environment information; S3, determining the expected exposure position of the occlusion risk target when it is expected to detach from the occlusion based on the movement state of the occlusion risk target and the occlusion boundary of the object; S4, determining the road pre-illumination area based on the expected exposure position; S5, determining the LED beads corresponding to the road pre-illumination area based on the light mapping relationship between multiple LED beads arranged in a matrix in the matrix LED headlight and the road projection area in front of the vehicle; S6, controlling the corresponding LED beads to light up or increase their brightness before the occlusion risk target enters the direct sensing range of the forward environment sensor to pre-illuminate the road pre-illumination area.

[0021] Further, in step S2, motion compensation is performed based on the time difference between the information generation time of the remote target and the current control time, the movement speed and direction of the remote target, to obtain the predicted target position. Then, a predicted target area is formed based on the predicted target position, target size, and target position error, and it is determined whether the predicted target area is outside the direct perception range of the vehicle and is obstructed by the position of the obstruction. Subsequently, the expected driving area of ​​the vehicle is determined based on the driving state of the vehicle, and the predicted target movement area is determined based on the predicted target position and movement state. Only when the two overlap within the set prediction time range, the calculation of the expected exposure position and pre-illumination area for the target continues.

[0022] Further, in step S4, the road area where the expected exposure location is located is taken as the starting area, and the road pre-illumination area is formed by extending along the predicted movement direction of the occlusion risk target toward the expected driving area of ​​the vehicle; in step S5, the part of the road pre-illumination area that is within the light reach area and outside the restricted lighting area is determined as the actual pre-illumination area, and the corresponding LED bead is determined according to the overlapping relationship between the corrected LED bead road projection area and the actual pre-illumination area; after the pre-illumination is performed, when the vehicle's forward environment sensor detects the occlusion risk target, the corresponding LED bead is controlled according to the target position directly detected by the vehicle.

[0023] Compared with the prior art, the present invention has at least the following beneficial effects: By obtaining information about risk targets that are temporarily invisible to the vehicle in advance through vehicle-to-everything (V2X) communication, and combining this with time compensation, occlusion judgment, and driving risk assessment, the vehicle can determine the expected exposure location of the target before it enters the vehicle's direct perception range. By further converting the expected exposure location into a road pre-illumination area, and filtering and executing the illumination based on the light-accessible area, restricted lighting area, and LED light bead road mapping relationship, the vehicle can illuminate the road area where the risk target is about to appear in advance while taking into account anti-glare measures. After the target enters the vehicle's direct perception range, the vehicle continues to control the lights based on the vehicle's detection results, which allows for a smooth transition between networked predictive pre-illumination and conventional direct perception lighting. Attached Figure Description

[0024] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a block diagram of the overall structure of the networked intelligent headlight system based on occlusion risk prediction of the present invention. Figure 2 This is a schematic diagram of the matrix LED headlight of the present invention; Figure 3 This is a schematic diagram illustrating the principle of remote target position compensation and predicted target area formation in this invention; Figure 4 This is a schematic diagram illustrating the expected exposure location and the principle of road pre-illumination area formation according to the present invention; Figure 5 This is a schematic diagram showing the mapping between the road pre-lighting area and the LED lamp bead road projection area of ​​the present invention; Figure 6 This is a schematic diagram illustrating the working principle of the present invention in a building-shaded scenario at an urban intersection. Figure 7 This is a schematic diagram illustrating the working principle of the present invention in a scenario where large vehicles obstruct the view. Figure 8 This is a flowchart of the networked intelligent headlight control method based on occlusion risk prediction according to the present invention.

[0025] Explanation of reference numerals in the attached figures: 1. Forward-facing environmental sensor; 11. Forward-facing camera; 12. Millimeter-wave radar; 2. Vehicle-to-everything (V2X) communicator; 3. Vehicle status acquisition unit; 4. Headlight controller; 5. Matrix LED headlights; 51. LED light source board; 52. LED beads; 53. LED driver circuit; 6. Obstruction; 61. Obstruction boundary; 7. Obstruction risk target; 71. Target predicted position; 72. Predicted target area; 73. Target predicted movement area; 8. Expected exposure position; 9. Road pre-illuminated area; 91. Actual pre-illuminated area; 10. Expected driving area of ​​this vehicle; 101. Restricted lighting area; 102. Light-accessible area. Detailed Implementation

[0026] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are used to illustrate the data relationships, judgment process and lighting control process between the various components, so that those skilled in the art can implement the present invention accordingly; without changing the basic technical concept of the present invention, the sensor types, coordinate expression methods, prediction models and lighting calibration methods can be adapted according to specific vehicle models and headlight products.

[0027] Example 1: System Overall Structure and Basic Control Process Reference Figures 1 to 5 This embodiment provides a networked intelligent headlight system based on occlusion risk prediction. The system includes a forward-facing environment sensor 1, a vehicle-to-everything (V2X) communicator 2, a vehicle status collector 3, a headlight controller 4, and matrix LED headlights 5. The forward-facing environment sensor 1, V2X communicator 2, and vehicle status collector 3 provide the headlight controller 4 with information on the vehicle's road environment, distant targets, and the vehicle's driving status, respectively. The headlight controller 4 performs target position correction, occlusion risk assessment, predicted exposure location calculation, road pre-illumination area generation, and LED selection, ultimately implementing local pre-illumination through the matrix LED headlights 5.

[0028] A forward-facing environment sensor 1 is located at the front of the vehicle. In this embodiment, the forward-facing environment sensor 1 includes a forward-facing camera 11 and a millimeter-wave radar 12. The forward-facing camera 11 acquires images of the road ahead of the vehicle to obtain contour information of lane boundaries, road direction, vehicles ahead, oncoming vehicles, and building edges, green belt edges, and large vehicle outlines that can cause visual obstruction. The millimeter-wave radar 12 is used to assist in obtaining the distance and relative speed of vehicles ahead or other moving targets. For vehicles equipped only with a forward-facing camera 11, the road environment and the outlines of obstructions can also be obtained through image recognition and the vehicle's own positioning information.

[0029] The vehicle-to-everything (V2X) communicator 2 is connected to the vehicle communication network and is used to receive remote target information sent by other connected vehicles or roadside sensing devices. The remote target information includes at least the target location, speed, direction of movement, and information generation time, and may also include the target type, target size, target position error, or target recognition confidence level. The remote target location can be expressed in road coordinates, geographic coordinates, or local coordinates of the sending end. In this embodiment, the headlight controller 4 converts the received remote target location into road coordinates or vehicle coordinates with the vehicle as a reference, based on the vehicle's position and heading, enabling geometric comparison of the remote target, the outline of the obstruction, the vehicle's expected driving area, and the light projection area under the same coordinate reference.

[0030] The vehicle status acquisition unit 3 can acquire the vehicle speed, steering wheel angle, and vehicle heading angle via the vehicle CAN bus or in-vehicle Ethernet. It can also acquire the yaw rate, vehicle pitch status, and vehicle position. The headlight controller 4 can be implemented by the processing unit in the vehicle lighting domain controller, intelligent driving domain controller, or an independent controller, as long as it can receive the above information and output corresponding control signals to the LED driver circuit 53.

[0031] Reference Figure 2 The matrix LED headlight 5 includes an LED light source board 51, multiple LED beads 52, and an LED driving circuit 53. The multiple LED beads 52 are arranged in a matrix on the LED light source board 51, and each LED bead 52 is connected to a corresponding driving channel in the LED driving circuit 53. The headlight controller 4 can output on / off or brightness adjustment commands to different LED beads 52. Brightness adjustment can be achieved by changing the driving current or changing the pulse width modulation duty cycle.

[0032] During vehicle factory calibration or headlight system calibration, a light mapping relationship between each LED bead 52 and the projected area of ​​the road in front of the vehicle is established in advance. Specifically, a single LED bead 52 or a group of adjacent LED beads 52 can be lit separately, and the corresponding main road lighting area under standard vehicle posture can be recorded. The boundary position of each lighting area and the corresponding LED bead 52 are then stored in the headlight controller 4 to establish a correspondence. Thus, after the controller obtains the road area that needs enhanced lighting, it can directly determine the LED bead 52 that needs to be controlled by looking up a table or by geometric overlap judgment.

[0033] Reference Figure 3When the vehicle-to-everything (V2X) communicator 2 receives information about a remote target, the headlight controller 4 first reads the information generation time. Let the target's position at the time of information generation be P0, its velocity vector be V, and the time difference between the current control time and the information generation time be Δt. Then, under the uniform speed prediction method, the predicted target position 71 can be expressed as P1 = P0 + VΔt. If the remote target information also includes acceleration, the target position can be further corrected using acceleration. The purpose of the above position compensation is to convert the position detected by the remote device at a previous moment into the predicted position at the current control moment, rather than requiring the use of a specific mathematical model.

[0034] After obtaining the predicted target position 71, the headlight controller 4 forms a predicted target area 72 based on the target size and position error of the distant target. In practice, a basic area can be formed around the predicted target position 71 according to the target's external dimensions, and then an extension corresponding to the position error can be superimposed along the target's movement direction and laterally. For targets such as pedestrians or bicycles whose movement direction is prone to change, the lateral range can be appropriately increased; for motor vehicles with stable movement direction, the predicted target area 72 can be appropriately extended along their movement direction. By using an area instead of a single point, the frequent changes in occlusion judgment at critical boundaries caused by positioning errors can be avoided.

[0035] The headlight controller 4 then combines the road environment information obtained from the forward environment sensor 1 to determine occlusion. Specifically, an observation direction can be established from the vehicle's observation reference point toward the predicted target area 72, and it can be determined whether this observation direction intersects with the outline area of ​​the occupant 6. When the predicted target area 72 has not yet entered the direct perception range where the forward environment sensor 1 can stably detect the target, and the vehicle's line of sight toward the predicted target area 72 is blocked by the occupant 6, the corresponding distant target is identified as the occlusion risk target 7. If the predicted target area 72 can be stably detected by the vehicle's forward environment sensor 1, the vehicle's detection result is directly used for regular headlight control, without having to treat it as an occupancy risk target that only relies on distant information.

[0036] To eliminate obstructions unrelated to the vehicle's movement, the headlight controller 4 also performs a driving risk assessment. The vehicle status acquisition unit 3 provides the vehicle's speed, steering wheel angle, and heading angle. Based on this, the headlight controller 4 calculates the vehicle's trajectory within a set prediction time and, combined with the vehicle's width and necessary safety margin, forms the vehicle's expected driving area 10. For straight-ahead driving, the expected driving area 10 extends forward along the current vehicle heading; for turning driving, a curved expected driving area is formed based on the steering wheel angle or the vehicle's yaw rate.

[0037] Meanwhile, the headlight controller 4 starts from the predicted target position 71 and calculates the position change of the occluded risk target 7 within the same predicted time range based on its speed and direction of movement. It then expands the trajectory using the target size and positional error to form the predicted target movement area 73. When the predicted target movement area 73 overlaps with the vehicle's expected driving area 10, it indicates that the risk target may enter the vehicle's subsequent driving area, so the calculation of the expected exposure position and pre-illumination area continues. When there is no overlap and the distance between them continues to increase, the pre-illumination calculation corresponding to that target can be terminated to reduce ineffective brightening.

[0038] Reference Figure 4 After determining that there is a driving risk, the headlight controller 4 determines the obstruction boundary 61 of the obstruction 6. For fixed obstructions such as buildings and walls, the obstruction boundary 61 can be taken as the outer contour edge of the side of the risk target expected to enter the road; for moving obstructions such as large trucks, the obstruction boundary 61 is continuously updated based on the real-time position and contour obtained by the forward-facing camera 11 and the millimeter-wave radar 12.

[0039] The expected exposure location 8 can be determined as follows: Following the predicted movement direction of the occlusion risk target 7, search forward from the current predicted target location 71, determining whether the line of sight between each predicted location and the vehicle's observation reference point still intersects with the obstruction 6; when the target moves to a certain location and the line of sight is no longer interrupted by the obstruction 6 for the first time, that location or its corresponding road area is determined as the expected exposure location 8. Equivalently, when the predicted target movement path is clear, the critical position facing the visible side after the predicted target movement path intersects with the obstruction boundary 61 can also be taken as the expected exposure location 8. This method directly corresponds the expected exposure location 8 to the physical meaning of "the target first escaping the obstruction."

[0040] After obtaining the expected exposure location 8, the headlight controller 4 forms a road pre-illumination area 9. The road pre-illumination area 9 does not need to cover the target location behind the obstruction 6 that is currently unilluminated. Instead, it starts from the illuminated road area near the expected exposure location 8 and extends along the predicted movement direction of the obstructing risk target 7 towards the vehicle's expected driving area 10. In actual formation, the road area between the expected exposure location 8 and the overlapping area between the target's predicted movement area 73 and the vehicle's expected driving area 10 can be used as the basic area, and then expanded to both sides according to the width of the predicted target area 72, thereby covering a possible movement path of the risk target after it has just emerged from obstruction.

[0041] The headlight controller 4 further determines the reachable area 102. The reachable area 102 can be determined based on the installation position of the matrix LED headlights 5, the calibrated illumination range, the current vehicle pitch state, and any obstructions from objects in front of the vehicle. If a portion of the road pre-illuminated area 9 is located behind a wall, mountain, or large vehicle, and the headlights cannot actually reach that portion, it is excluded from the executable illumination range.

[0042] Simultaneously, the headlight controller 4 determines the restricted lighting area 101 based on the positions of vehicles ahead and oncoming vehicles. The restricted lighting area 101 corresponds to the area in the rearview mirror of the vehicle traveling in the same direction ahead, the observation direction of the driver of the oncoming vehicle, or other directions where high brightness needs to be avoided. The headlight controller 4 performs a region calculation on the road pre-illuminated area 9, the light-accessible area 102, and the restricted lighting area 101, and determines the portion of the road pre-illuminated area 9 that is within the light-accessible area 102 and outside the restricted lighting area 101 as the actual pre-illuminated area 91. In this way, even if a part of the road pre-illuminated area 9 coincides with the direction of oncoming vehicles, other illuminated portions that do not produce obvious glare can be preserved.

[0043] Reference Figure 5 The headlight controller 4 reads the pre-stored light mapping relationship between the LED beads 52 and the road projection area. Considering that vehicle steering and body pitch will change the position of the lights relative to the road, the headlight controller 4 corrects the position of the road projection area of ​​each LED bead 52 according to the steering wheel angle and body pitch state. Then, it determines whether the corrected road projection area overlaps with the actual pre-illuminated area 91, and selects the LED beads 52 with overlap as the beads to be controlled. For beads with only partial overlap, a lower brightness enhancement can be selected according to the degree of overlap; for beads whose main projection area is located within the actual pre-illuminated area 91, a higher brightness enhancement can be used. This hierarchical method is only one optional implementation. The core is to enable the selected beads 52 to provide local enhanced illumination to the actual pre-illuminated area 91 by using the drive current or pulse width modulation duty cycle.

[0044] During pre-illumination, the headlight controller 4 continuously updates information on distant targets, the position of obstructions 6, the vehicle's driving status, the expected exposure position 8, and the actual pre-illumination area 91 according to the control cycle. When the obstruction risk target 7 has not yet entered the vehicle's direct perception range, the headlights are mainly controlled based on the prediction results of distant targets. When the obstruction risk target 7 crosses the expected exposure position 8 and is stably detected by the forward-facing camera 11 or other vehicle sensors, the headlight controller 4 uses the target position detected by the vehicle as the new main control basis, and gradually moves or shrinks the original actual pre-illumination area 91 toward or towards the road area where the actual target is located, thereby avoiding abrupt changes in the light area.

[0045] Example 2: Buildings obstructing pedestrians at urban intersections Reference Figure 6 This embodiment illustrates a scenario with a fixed obstruction. Assume the vehicle is approaching an intersection along the first road. A building exists on the right front side of the intersection, constituting an obstruction 6. The near corner of the building is located between the vehicle and the pedestrian area on the right, preventing the forward-facing camera 11 from directly seeing the pedestrian behind the building. Another networked vehicle on the intersecting road or a roadside sensing device at the intersection has detected the pedestrian and sent the pedestrian's location, walking speed, walking direction, and information generation time to the vehicle.

[0046] After receiving the remote target information, the vehicle-to-everything (V2X) communicator 2 first converts the pedestrian's position to the road coordinates used by the vehicle, and performs motion compensation based on the time difference between the information generation time and the current control time to obtain the predicted target position 71. Then, a predicted target area 72 is formed according to the pedestrian's body size and positional error. Since this predicted target area 72 is located outside the currently stable detection area of ​​the forward-facing camera 11, and the line of sight from the vehicle towards this area intersects with the outline of a building, the pedestrian is identified as an occlusion risk target 7.

[0047] Vehicle status acquisition unit 3 provides vehicle speed, steering wheel angle, and vehicle heading angle. Headlight controller 4 forms the vehicle's expected driving area 10 based on the vehicle's direction of travel as it approaches the intersection. Headlight controller 4 then forms a target prediction motion area 73 based on the pedestrian's predicted position 71 and walking direction. When the target prediction motion area 73 extends to the pedestrian crossing or roadway and overlaps with the vehicle's expected driving area 10 at the intersection, it is considered that the pedestrian may enter the vehicle's driving area.

[0048] The headlight controller 4 defines the outer edge of the building near the intersection as the obstruction boundary 61, and calculates when a pedestrian will be able to cross this edge along the predicted walking direction. The location where the pedestrian's predicted movement path enters the outer edge of the building and is no longer obstructed by the building from the vehicle's perspective is defined as the expected exposure location 8. Subsequently, starting from the pedestrian crossing or curb area near the aforementioned expected exposure location 8, the road pre-illumination area 9 is formed by extending along the pedestrian's predicted movement direction towards the vehicle's expected driving area 10.

[0049] If there are no other vehicles at the intersection causing glare, the portion of the road pre-illuminated area 9 within the light-accessible area 102 can be considered the actual pre-illuminated area 91. If there are oncoming vehicles, a restricted lighting area 101 is formed based on the position of the oncoming vehicles, and the portion overlapping with the restricted lighting area 101 is excluded from the road pre-illuminated area 9. The headlight controller 4 selects the corresponding LED beads 52 based on the actual pre-illuminated area 91 and increases their brightness, so that the outer edges of buildings and the positions where pedestrians are expected to enter the road are already illuminated before the pedestrians actually enter the vehicle's field of vision.

[0050] As the pedestrian continues to move, the headlight controller 4 continuously corrects the predicted target position 71 and the expected exposure position 8 based on the newly received remote target information. When the pedestrian crosses the edge of a building and enters the direct sensing range of the forward-facing camera 11, the headlight controller 4 updates the lamp control area using the pedestrian position detected by the vehicle. This forms a continuous control chain of "remote perception prediction - predicted exposure area pre-illumination - vehicle direct perception following illumination".

[0051] Example 3: Dynamic obstruction of bicycles by large vehicles Reference Figure 7 This embodiment illustrates a scenario with a moving obstruction. The vehicle is traveling along a road, and a large truck is in front of it, obstructing the view of the vehicle's forward-facing camera 11 of the road area to its right front. Another connected vehicle or roadside sensing device detects a bicycle to the right front of the large truck, and that the bicycle is moving towards the vehicle's lane. The device then sends the bicycle's position, speed, direction of movement, and the time of information generation to the vehicle.

[0052] The headlight controller 4 performs time compensation on the far end position of the bicycle to obtain the target prediction position 71, and forms a predicted target area 72 based on the size of the bicycle and positional error. Simultaneously, the forward-facing camera 11 and millimeter-wave radar 12 continuously acquire the position, relative speed, and outer contour of the large truck. When the aforementioned predicted target area 72 is outside the direct perception range of the vehicle, and the large truck body is positioned in front of the predicted target area 72 from the vehicle's viewing direction, the large truck is designated as a moving obstruction 6, and the bicycle as an obstruction risk target 7.

[0053] The headlight controller 4 forms the vehicle's expected driving area 10 based on the vehicle's speed, steering wheel angle, and heading angle, and forms a target prediction motion area 73 based on the bicycle target prediction position 71, speed, and direction of movement. When the bicycle target prediction motion area 73 is expected to enter the vehicle's expected driving area 10, pre-illumination is confirmed to be required. Since the large truck itself is also in motion, the headlight controller 4 does not use a fixed static edge, but updates its right-side occlusion boundary 61 based on the large truck's position and profile obtained in each control cycle.

[0054] In each update cycle, the headlight controller 4 calculates forward from the bicycle's current target predicted position 71 along its predicted direction of movement to determine the position where the bicycle first enters the vehicle's observable area relative to the updated right-side obstruction boundary 61 of the truck, and identifies this position as the expected exposure position 8. Subsequently, a road pre-illumination area 9 is formed on the right side of the truck, near the expected location of the bicycle, and moves synchronously with the relative positions of the truck, bicycle, and the vehicle.

[0055] To avoid illuminating the rearview mirrors of large trucks or other vehicle drivers with the enhanced beam, the headlight controller 4 creates a restricted lighting zone 101 based on the positions of large trucks and other vehicles on the road. Only the portion of the road pre-illuminated area 9 that the light can reach but is not located within the restricted lighting zone 101 is considered the actual pre-illuminated area 91. The corresponding LED beads 52 can be controlled by gradually increasing their brightness, making the road lighting change smoother.

[0056] When a bicycle enters the direct sensing range of the vehicle's forward environment sensor 1 from the right side of a large truck, the headlight controller 4 continues to adjust the LED beads 52 based on the actual position of the bicycle directly detected by the vehicle. If the large truck changes lanes, accelerates or decelerates, or the bicycle changes direction, the headlight controller 4 recalculates the target prediction movement area 73, the expected exposure position 8, and the actual pre-illuminated area 91 based on the latest information, and does not continue to use the old prediction results that have become invalid.

[0057] Specific implementation process of control methods Reference Figure 8 The control method of the present invention can be executed by the headlight controller 4 according to a continuous control cycle, and includes steps S1 to S6. Each step can be executed sequentially within a single controller, or different control modules of the vehicle can complete data acquisition and calculation separately and then send the results to the headlight controller 4, as long as the corresponding pre-lighting control result can be formed in the end.

[0058] S1. Acquire environmental and remote target information. Receive remote target information from other networked vehicles or roadside sensing devices via the vehicle-to-everything (V2X) communicator 2. Acquire road environment information such as the outlines of the road, vehicles, and obstructions ahead of the vehicle via the forward-facing environment sensor 1. Acquire vehicle speed, steering wheel angle, and heading angle via the vehicle state acquisition device 3. For remote target information expressed in different coordinate systems, convert it to a coordinate system consistent with the vehicle's road environment information before proceeding to subsequent judgments.

[0059] S2. Determine the occlusion risk target. First, based on the time difference between the information generation time and the current control time, and combined with the target's movement speed and direction, motion compensation is performed on the remote target position to obtain the predicted target position 71. Then, based on the predicted target position 71, the target size, and the target position error, a predicted target area 72 is formed. If the current environmental sensor 1 cannot reliably detect the target, and the vehicle's observation direction towards the predicted target area 72 is blocked by an obstruction 6, the corresponding remote target is selected as an occlusion candidate target.

[0060] In step S2, driving risk screening is further performed. The vehicle's predicted driving area 10 is formed based on the vehicle's speed, steering wheel angle, and heading angle. A predicted target movement area 73 is formed based on the predicted target position 71, speed, and direction of movement of the candidate occlusion target. Only when the predicted target movement area 73 overlaps with the vehicle's predicted driving area 10 within a set prediction time range is the target identified as an occlusion risk target 7 requiring pre-illumination.

[0061] S3. Determine the expected exposure location. Based on the outer contour of the obstruction 6, determine the obstruction boundary 61 corresponding to the predicted movement direction of the target. Determine whether the observation direction from the vehicle to the target is still interrupted by the obstruction 6 along the predicted movement path of the obstruction risk target 7. Determine the road position when the target first leaves the obstruction as the expected exposure location 8. For moving obstructions 6, redetermine the obstruction boundary 61 in each control cycle based on the new position and new contour of the moving obstruction.

[0062] S4. Forming a road pre-illumination area. Starting from the road area where the expected exposure location 8 is located, a road pre-illumination area 9 is formed by extending along the predicted movement direction of the obstruction risk target 7 towards the expected driving area 10 of the vehicle. The road pre-illumination area 9 can be laterally expanded according to the width of the predicted target area 72. Subsequently, the light reachable area 102 of the matrix LED headlights 5 and the restricted illumination area 101 formed by the vehicles in front of the vehicle and oncoming vehicles are determined. The portion of the road pre-illumination area 9 that is located within the light reachable area 102 and outside the restricted illumination area 101 is determined as the actual pre-illumination area 91.

[0063] S5. Identify LED beads. Read the light mapping relationship between each LED bead 52 and the road projection area. Correct the position of the road projection area according to the vehicle's pitch state and steering wheel angle, and select the LED beads 52 that overlap with the actual pre-illumination area 91 after correction. This completes the conversion from the pre-illumination area in the road space to the specific LED bead driving channel.

[0064] S6. Perform pre-illumination. Before the occlusion risk target 7 enters the direct sensing range of the forward environment sensor 1, the LED driving circuit 53 illuminates or increases the brightness of the LED beads 52 determined in step S5, thus providing advance illumination to the actual pre-illumination area 91. Steps S1 to S5 are continuously repeated during the control process to update the target and road conditions; after the forward environment sensor 1 detects the occlusion risk target 7, the corresponding LED beads 52 are controlled according to the target position detected by the vehicle to smoothly transition the pre-illumination area to the actual road area where the target is located.

[0065] Other optional implementation methods In other implementations, remote target information can be provided by roadside cameras, roadside radar devices, or other traffic sensing equipment located near road intersections, mountain bends, tunnel entrances / exits, or road crests. For bend scenarios, the line-of-sight boundary formed by mountains, road slopes, or the bend's shape can be used as an occlusion boundary 61. The expected exposure location 8 is determined based on the position of the risk target moving along the road into the vehicle's observable area, and a road pre-illumination area 9 is formed along the bend's shape.

[0066] For road crest scenarios, the occlusion boundary 61 formed by the crest can be determined based on the longitudinal slope of the road and the visible road boundary of the forward environment sensor 1. When oncoming vehicles, pedestrians, or other targets are still on the other side of the crest and the remote sensing device has already provided their motion information, the position of them entering the observable area of ​​the vehicle after crossing the crest can be predicted, and pre-illumination can be provided only for the road area near the crest that can be actually illuminated by the vehicle's headlights.

[0067] When multiple targets 7 pose a risk of obstruction, the headlight controller 4 can calculate the actual pre-illumination area 91 corresponding to each target separately, and then merge the LED beads 52 that need to be lit without entering the restricted illumination area 101. If the information of a certain distant target has not been updated for a long time, the target position error has increased significantly, or the target's predicted movement area 73 no longer overlaps with the vehicle's expected driving area 10, the pre-illumination corresponding to that target can be canceled to avoid continuously increasing the illumination based on the failure information.

[0068] The number, arrangement, and road projection area corresponding to each LED in the matrix LED headlight 5 are not limited to those shown in the attached diagram; the LED driving circuit 53 can control the brightness of the LEDs 52 using constant current regulation, pulse width modulation, or a combination of both. The forward environment sensor 1 can also be a camera, millimeter-wave radar, or other sensor combination capable of obtaining information about the road environment and obstructions, depending on the vehicle configuration.

[0069] The technical features in the above embodiments can be combined with each other without contradicting each other. For those skilled in the art, conventional adjustments to the data representation, prediction model, sensor combination, LED calibration method, or control cycle based on the technical concept disclosed in this invention do not change the basic principle of this invention: using remote target information to predict the expected exposure location of occlusion risk targets and to pre-illuminate the corresponding road areas.

Claims

1. A networked intelligent headlight system based on occlusion risk prediction, characterized in that, This includes forward-facing environmental sensors, vehicle-to-everything (V2X) communication devices, vehicle status data acquisition units, headlight controllers, and matrix LED headlights. The matrix LED headlight includes an LED light source board, a plurality of LED beads arranged in a matrix on the LED light source board, and an LED driving circuit connected to each of the LED beads. The vehicle-to-everything (V2X) communicator is used to receive remote target information sent by other connected vehicles or roadside sensing devices; the forward-facing environment sensor is used to acquire road environment information in front of the vehicle; and the vehicle status collector is used to acquire the vehicle's driving status. The headlight controller determines, based on the remote target information and the road environment information, a potential occlusion target that is obscured by an obstruction object but has not entered the direct sensing range of the forward environment sensor. Based on the movement state of the potential occlusion target and the obstruction boundary of the obstruction object, it determines the expected exposure position of the potential occlusion target when it is expected to be removed from the obstruction. Based on the expected exposure position, it determines the road pre-illumination area. The headlight controller controls the corresponding LED beads according to the road pre-illumination area to pre-illuminate the road pre-illumination area before the obstruction risk target enters the direct sensing range of the forward environment sensor.

2. The networked intelligent headlight system based on occlusion risk prediction according to claim 1, characterized in that, The forward-facing environment sensor includes a forward-facing camera, and the remote target information includes the position, speed, direction of motion, and time of information generation of the remote target. The headlight controller performs motion compensation on the position of the distant target based on the time difference between the information generation time and the current control time, the movement speed, and the movement direction, thereby obtaining the predicted position of the target.

3. The networked intelligent headlight system based on occlusion risk prediction according to claim 2, characterized in that, The headlight controller determines the predicted target area based on the predicted target position, the target size of the distant target, and the target position error. When the predicted target area is outside the direct sensing range of the forward environment sensor, and the observation direction from the vehicle toward the predicted target area is blocked by the obstruction, the corresponding far target is identified as the obstruction risk target.

4. The networked intelligent headlight system based on occlusion risk prediction according to claim 3, characterized in that, The vehicle status acquisition device is used to obtain the vehicle speed, steering wheel angle and vehicle heading angle. The headlight controller determines the vehicle's expected driving area based on the vehicle speed, steering wheel angle, and vehicle heading angle, and determines the target's predicted motion area based on the target's predicted position and motion state of the obstruction risk target. When the predicted motion area of ​​the target overlaps with the expected driving area of ​​the vehicle within a set prediction time range, it is determined that the occlusion risk target and the vehicle pose a driving risk.

5. The networked intelligent headlight system based on occlusion risk prediction according to claim 4, characterized in that, The predicted exposure location is the location where the occlusion risk target moves along the predicted direction of movement to a point where it is no longer obscured by the occlusion object; The road pre-illumination area starts from the road area where the expected exposure location is located and extends towards the expected driving area of ​​the vehicle along the predicted movement direction of the occlusion risk target.

6. The networked intelligent headlight system based on occlusion risk prediction according to claim 5, characterized in that, The headlight controller is also used to determine the area that the matrix LED headlights can illuminate, and to determine the restricted lighting area based on the position of vehicles in front of the vehicle and oncoming vehicles. The headlight controller defines the portion of the road pre-illuminated area that is located within the reachable area of ​​the light and outside the restricted lighting area as the actual pre-illuminated area.

7. The networked intelligent headlight system based on occlusion risk prediction according to claim 6, characterized in that, The headlight controller pre-stores the light mapping relationship between each LED and different road projection areas in front of the vehicle; The headlight controller corrects the position of the road projection area based on the vehicle's pitch and steering wheel angle, selects LED beads that overlap with the actual pre-illuminated area in the corrected road projection area, and adjusts the driving current or pulse width modulation duty cycle of the selected LED beads through the LED driving circuit.

8. A networked intelligent headlight control method based on occlusion risk prediction, characterized in that, Includes the following steps: S1. Receive remote target information sent by other connected vehicles or roadside sensing devices through the vehicle-to-everything (V2X) communicator, obtain road environment information in front of the vehicle through the forward-facing environmental sensor, and obtain the vehicle's driving status. S2. Based on the remote target information and the road environment information, identify the occlusion risk targets that are obscured by the obstruction object and have not entered the direct sensing range of the forward environment sensor. S3. Based on the movement state of the occlusion risk target and the occlusion boundary of the occlusion object, determine the expected exposure position of the occlusion risk target when it is expected to leave the occlusion. S4. Determine the road pre-lighting area based on the expected exposure location; S5. Based on the light mapping relationship between the multiple LED beads arranged in a matrix in the matrix LED headlight and the road projection area in front of the vehicle, determine the LED beads corresponding to the road pre-illumination area. S6. Before the occlusion risk target enters the direct sensing range of the forward environment sensor, control the corresponding LED beads to light up or increase the brightness to pre-illuminate the road pre-illumination area.

9. The networked intelligent headlight control method based on occlusion risk prediction according to claim 8, characterized in that, In step S2, based on the time difference between the information generation time of the remote target information and the current control time, the movement speed and direction of the remote target, motion compensation is performed on the position of the remote target to obtain the predicted position of the target; The predicted target area is determined based on the predicted target location, target size, and target location error. When the predicted target area is outside the direct sensing range of the forward-facing environmental sensor and is blocked by the obstruction, the distant target is identified as the obstruction risk target. The expected driving area of ​​the vehicle is determined based on the vehicle's driving status, and when the predicted movement area of ​​the occlusion risk target overlaps with the expected driving area of ​​the vehicle within a set prediction time range, it is determined that there is a driving risk between the occlusion risk target and the vehicle.

10. The networked intelligent headlight control method based on occlusion risk prediction according to claim 8, characterized in that, In step S4, the road pre-illumination area is formed by extending along the predicted movement direction of the occlusion risk target toward the expected driving area of ​​the vehicle, starting from the road area where the expected exposure location is located; In step S5, the portion of the road pre-lighting area that is within the reachable area of ​​the matrix LED headlights and outside the restricted lighting areas corresponding to the vehicles in front of the vehicle and oncoming vehicles is defined as the actual pre-lighting area, and the corresponding LED beads are determined based on the actual pre-lighting area. After pre-illumination, when the forward ambient sensor detects the occlusion risk target, the corresponding LED beads are controlled according to the target position obtained by the forward ambient sensor.

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