Vehicle lamp control method and vehicle-mounted electronic device

Abstract

The invention relates to a vehicle lamp control method and a vehicle-mounted electronic terminal. The method comprises the following steps: acquiring a real-time image obtained by acquiring a road environment through a camera; performing target identification on the live image, and extracting environment visibility information based on a target identification result; wherein the environment visibility information comprises the following distance and / or the turn-on ratio of surrounding vehicles; under the condition that the safety level corresponding to the environment visibility information is lower than a preset level, a first vehicle lamp signal is triggered to turn on a vehicle lamp; and the intellectualization of vehicle lamp control is improved.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle safe driving, and in particular to a vehicle light control method and a vehicle-mounted electronic device. Background Art

[0002] With the development of automotive electronic technology, the safety protection measures of automobiles are becoming more and more perfect. During the driving process, the vehicle's automatic sensing headlights can automatically light up before the ambient light becomes dark, and automatically turn off when the ambient light becomes brighter. This helps to reduce the driver's vehicle control intensity and reduce driving safety hazards.

[0003] At present, headlight control mainly evaluates the visibility of the road environment by sensing the ambient light intensity, thereby deciding whether to turn on the headlights. The ambient light intensity is mainly sensed by the photosensitive element, which sends an electronic signal to the controller to decide whether to turn on or off the vehicle headlights. Since the photosensitive element only obtains light intensity information and has a low recognition rate of the light source, it is easy to cause the headlight control to be wrong, which may cause the headlights to be turned on too late or too much in some cases.

[0004] Currently, no effective solution has been proposed for the insufficiently intelligent method of headlight control in related technologies. Summary of the invention

[0005] Based on this, it is necessary to provide a more intelligent vehicle light control method and a vehicle-mounted electronic device to address the above technical issues.

[0006] In a first aspect, the present application provides a vehicle light control method, comprising:

[0007] Acquire the real-time image of the road environment captured by the camera;

[0008] Performing target recognition on the live image, and extracting environmental visibility information based on the target recognition result; wherein the environmental visibility information includes the following distance and / or the proportion of lights on of surrounding vehicles;

[0009] When the safety level corresponding to the environmental visibility information is lower than a preset level, a first vehicle light signal is triggered to turn on the vehicle lights.

[0010] In some embodiments, performing target recognition on the live image and extracting environmental visibility information based on the target recognition result includes:

[0011] In the case that there are other vehicles within the field of view of the camera, vehicle recognition is performed on the live image to determine a first vehicle that is traveling in the same direction as the vehicle and is following the vehicle;

[0012] Calculating the following distance between the first vehicle and other vehicles, and determining whether the following distance is increasing within a preset time;

[0013] When it is determined that the following distance shows an increasing trend within a preset time, it is determined that the following distance of the first vehicle increases, and first environmental visibility information is generated according to the determination result.

[0014] In some embodiments, when there are other vehicles within the field of view of the camera, vehicle recognition is performed on the live image to determine the first vehicle that is traveling in the same direction as the vehicle and has a following behavior, including:

[0015] When it is identified that there are a plurality of vehicles in a single lane that are traveling in the same direction as the own vehicle and are following the own vehicle, the vehicle closest to the own vehicle among the plurality of vehicles is determined as the first vehicle.

[0016] In some embodiments, when it is identified that the first vehicle exists in multiple lanes, when it is determined that the following distance of the first vehicle is increasing within a preset time, it is determined that the following distance of the first vehicle is increasing, and first environmental visibility information is generated according to the determination result, including:

[0017] Determining a first confidence level of each lane according to the distance between each lane and the lane of the vehicle;

[0018] Determining a second confidence level of each relative position relationship according to the relative position relationship between each of the first vehicles and the host vehicle;

[0019] The following vehicle confidence of the target vehicle is calculated according to the first confidence and the second confidence, and the first environmental visibility information is generated according to the following vehicle confidence of the target vehicle; wherein the target vehicle is the first vehicle for which the following distance is determined to increase.

[0020] In some embodiments, performing target recognition on the live image and extracting environmental visibility information based on the target recognition result includes:

[0021] In the case that there are other vehicles within the field of view of the camera, performing vehicle recognition and headlight recognition on the live image to determine a second vehicle with its headlights turned on;

[0022] Determining the light-on ratio according to the ratio of the second vehicle in the identified vehicles;

[0023] Second environmental visibility information is generated according to the light-on ratio.

[0024] In some embodiments, after generating the second environment visibility information according to the light-on ratio, the method further includes:

[0025] When the safety level corresponding to the second environmental visibility information is lower than a preset level, the type of lights of the host vehicle waiting to be turned on is determined according to the type of lights turned on by the second vehicle.

[0026] In some embodiments, after performing vehicle recognition and vehicle light recognition on the live image and determining the second vehicle with its lights turned on, the method further includes:

[0027] Determining a relative position relationship between the second vehicle and the host vehicle;

[0028] According to the relative position relationship, the corresponding headlight type of the second vehicle is identified; wherein,

[0029] When the second vehicle is located behind the vehicle, identifying the second vehicle with its headlights turned on;

[0030] When the second vehicle is located in front of the vehicle, identifying the second vehicle with its fog lights turned on;

[0031] When the second vehicle is located in front of and behind the vehicle successively, the second vehicle is identified by turning on the double flash warning lights.

[0032] In some embodiments, identifying the type of the corresponding headlight of the second vehicle according to the relative position relationship includes:

[0033] Extracting the outline boundary pixel points of the second vehicle in the live image to obtain a vehicle outline point information layer;

[0034] Binarizing the live image of the second vehicle to obtain a brightness information layer;

[0035] Positionally matching the vehicle contour point information layer with the corresponding brightness information layer to obtain the position relationship of the headlight bright spot of the second vehicle relative to the vehicle contour;

[0036] The type of headlights turned on by the second vehicle is determined according to the positional relationship of the headlight bright spot relative to the vehicle outline.

[0037] In some embodiments, determining the type of headlights turned on by the second vehicle according to the positional relationship of the headlight bright spot relative to the vehicle outline includes:

[0038] When the second vehicle is located behind the host vehicle, if the bright spot of the headlight is located within the outline of the vehicle and is symmetrical about the central axis of the outline of the vehicle, it is determined that the second vehicle has turned on its headlights; or,

[0039] When the second vehicle is located in front of the vehicle, if the bright spot of the headlight is located within the outline of the vehicle, there is a grayscale difference in the red layer, and the bright spot of the headlight is symmetrical about the central axis of the vehicle outline, it is determined that the second vehicle has turned on the fog lights.

[0040] In some embodiments, after position matching the vehicle contour point information layer with the corresponding brightness information layer to obtain the position relationship of the headlight bright spot of the second vehicle relative to the vehicle contour, the method further includes:

[0041] When the second vehicle is located in front of and behind the vehicle successively, and the bright spot of the vehicle light is located within the outline of the vehicle, a plurality of frames of continuous live images are acquired, and brightness information layers of the same second vehicle are extracted from the plurality of frames of continuous live images based on a preset time interval for comparison;

[0042] If the comparison result shows that the bright spot of the vehicle light appears intermittently bright in the yellow layer, it is determined that the second vehicle has turned on the hazard warning lights.

[0043] In some embodiments, when the safety level corresponding to the environmental visibility information is lower than a preset level, after triggering the first vehicle light signal to turn on the vehicle light, the method further includes:

[0044] Controlling the vehicle lighting system to remain in an on state within a preset time period;

[0045] After the preset time period has passed, the vehicle light is turned off in response to a second vehicle light signal.

[0046] In some embodiments, after the preset time period has elapsed, responding to the second vehicle light signal to turn off the vehicle light comprises:

[0047] When it is detected that the safety level corresponding to the environmental visibility information is higher than a preset level, the second vehicle light signal is triggered; wherein the environmental visibility information includes the following vehicle distance or the proportion of lights on.

[0048] In a second aspect, the present application provides a vehicle-mounted electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method described in the first aspect are implemented.

[0049] The above-mentioned headlight control method and vehicle-mounted electronic device collect real-time images of the road environment, analyze the following distance and / or the proportion of lights on of surrounding vehicles through target recognition in the real-time images, generate environmental visibility information based on this, and determine whether to turn on the headlights according to the safety level corresponding to the environmental visibility information. This enables the headlight control method to evaluate the road environment more intelligently and comprehensively, improves the rationality of decision-making, and avoids the problems of misjudgment and missed judgment that may occur in traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A hardware structure block diagram of a vehicle-mounted electronic device in one embodiment;

[0051] Figure 2 A schematic diagram of the structure of a vehicle system in an embodiment;

[0052] Figure 3 is a flow chart of a vehicle light control method in one embodiment;

[0053] Figure 4 is a flow chart of a target recognition method for a first vehicle in one embodiment;

[0054] Figure 5 is an exploded schematic diagram of an overtaking process between a vehicle and a neighboring vehicle in one embodiment;

[0055] Figure 6 is a schematic diagram of a lane in an embodiment;

[0056] Figure 7 is a flow chart of a target recognition method for a second vehicle in one embodiment;

[0057] Figure 8 FIG. 4 is a flow chart of a vehicle light control method in another embodiment. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0059] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the general meaning understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the", "these" and the like in this application do not represent quantitative restrictions, and they can be singular or plural. The terms "include", "comprise", "have" and any variants thereof involved in this application are intended to cover non-exclusive inclusions; for example, a process, method and system, product or device comprising a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether directly or indirectly. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. Usually, the character " / " indicates that the objects associated with each other are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0060] In one embodiment, a vehicle-mounted electronic device is provided. The vehicle-mounted electronic device may be a vehicle central control unit (SOC), a vehicle-mounted electronic terminal or a similar computing device. The method embodiments provided in the present application may be executed in the vehicle-mounted electronic device. Figure 1 1 is a hardware structure block diagram of a vehicle-mounted electronic device according to an embodiment of the present application. Figure 1 As shown, the vehicle-mounted electronic device may include one or more ( Figure 1 Only one is shown in the figure) processor 101 and memory 102 for storing data, wherein processor 101 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. The above-mentioned vehicle-mounted electronic device may also include a transmission device 103 and an input and output device 104 for communication functions. It can be understood by ordinary technicians in this field that Figure 1 The structure shown is for illustration only and does not limit the structure of the above-mentioned vehicle-mounted electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0061] The memory 102 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the vehicle light control method in this embodiment. The processor 101 executes various functional applications and data processing by running the computer program stored in the memory 102, that is, to implement the above method. The memory 102 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 102 may further include a memory remotely arranged relative to the processor 101, and these remote memories may be connected to the vehicle-mounted electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0062] The transmission device 103 is used to receive or send data via a network. The above network includes a wireless network provided by a communication provider of the vehicle-mounted electronic device. In one embodiment, the transmission device 103 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one embodiment, the transmission device 103 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0063] In one embodiment, a vehicle system is provided. Figure 2 It is a structural diagram of the vehicle system. Figure 2 As shown, the vehicle system includes: an on-board electronic device 100, a sensor 200, an execution controller 300 and a vehicle light system 400; the sensor 200 and the execution controller 300 are respectively connected to the on-board electronic device 100, and the execution controller 300 is also connected to the vehicle light system 400. Among them, the sensor 200 includes but is not limited to a light sensor, a camera (monocular and / or binocular), and a radar (Lidar and / or Radar); the execution controller 300 includes but is not limited to a motor and an optical mechanism; the vehicle light system 400 includes but is not limited to a fog lamp, a high beam lamp, a low beam lamp, and a flash lamp. The on-board electronic device 100 executes the vehicle light control method by receiving the sensor data (such as a live image, an actual shooting distance) collected by the sensor 200, generates and sends a control signal to the execution controller 300, and the execution controller 300 controls the operating state of the vehicle light system 400.

[0064] Exemplarily, the vehicle-mounted electronic device 100 extracts environmental visibility information by executing the vehicle light control method, and determines the corresponding safety level based on the environmental visibility information. When the safety level is lower than the preset level, the first vehicle light signal is triggered, and after the execution controller 300 receives the first vehicle light signal, the vehicle light system 400 is turned on, such as turning on the high and low beam lights or intelligently adjusting the lighting range and angle, etc., to improve driving visibility. For another example, when the safety level is higher than the preset level, the second vehicle light signal is triggered, and after the execution controller 300 receives the second vehicle light signal, the vehicle light system 400 is turned off.

[0065] In actual applications, when the vehicle-mounted electronic device 100 controls the vehicle lighting system 400 to turn on, it can automatically switch between high and low beams or intelligently adjust the lighting range and angle. Among them, the automatic switching between high and low beams can be when the safety level is lower than a preset level, the high and low beams are automatically switched to avoid interfering with other road users. Intelligent adjustment of the lighting range and angle can be when the safety level is lower than a preset level, the lighting angle and lighting range of the headlights are automatically adjusted to provide the best lighting effect. In this embodiment, by effectively extracting environmental visibility information, determining the corresponding safety level based on the environmental visibility information, and automatically controlling the operating state of the vehicle system 400 based on the safety level, the driver's field of vision can be improved and driving safety can be improved.

[0066] Figure 3 Flowchart of the vehicle light control method of this embodiment is shown in FIG. 1 , which is described by taking the method applied to the above-mentioned vehicle electronic device as an example, and includes the following steps:

[0067] Step S101, obtaining a real-time image of the road environment captured by a camera.

[0068] When the vehicle is driving, the camera on board will collect the road environment and obtain the real-time image. The on-board electronic device receives the real-time image sent by the camera and performs target recognition on it.

[0069] In some embodiments, the driving area within a certain distance range centered on the vehicle can be determined as a driving geofence. The driving geofence corresponds to a certain range on the map. This part of the area can obtain local real-time or weather forecast conditions for a period of time from the map or the cloud. When the weather forecast within the driving geofence shows that the current weather will affect the driving visual distance, the system will output an environmental request signal. The on-board electronic terminal (or vehicle system) responds to the environmental request signal and triggers the camera to collect real-time images of the road environment to facilitate further judgment through visual analysis.

[0070] Considering that cloud weather information is limited by update frequency and network signal strength, in order to enhance the effectiveness of the system's judgment on special weather conditions, the vehicle also provides its own sensors to obtain environmental information for system-assisted judgment. Examples are as follows:

[0071] By configuring sunlight and rainfall sensors, real-time sunlight and rainfall parameters can be obtained during driving, and accurate judgments can be made when the vehicle is driving in darkness or heavy rain. By configuring temperature and humidity sensors, the temperature and humidity in the driving environment of the vehicle can be evaluated to assist in foggy judgments. That is, when the vehicle finds that the clarity of the target of the vehicle in front drops below a certain level, there is no input signal from the rain sensor, the humidity is higher than a certain threshold, and the temperature is within a certain range, it is judged as heavy fog. By configuring PM2.5 environmental sensors, the content of particulate matter in the environment is sensed. When the PM2.5 outside the vehicle is seriously exceeded, the humidity is very low, and the wind speed in the driving area is high, it is judged as a sandstorm.

[0072] The vehicle can combine cloud-based meteorological information and local sensor data collection to make a comprehensive judgment on the current environment and selectively output two results. One is hardware identification parameters, such as: sunlight and rain sensors. In heavy rain, the vehicle will automatically output a rainfall signal to the vehicle fog light controller after driving for a period of time; when the vehicle fog light controller receives this parameter, it turns on the fog lights. The other is to feedback weather result information to the vehicle system. At this time, if the cloud data is available, if the special weather (such as fog, sandstorm, rainstorm) obtained is inconsistent with the parameters detected locally by the vehicle, the system will not feedback the weather results. If they are consistent, the weather results will be fed back. For example, the cloud judges that the weather is a sandstorm, but at this time the illumination parameters fed back by the sunlight and rain sensors should be less than a certain value, and the PM2.5 monitoring results should be higher than a certain value.

[0073] Step S102, performing target recognition on the live image, and extracting environmental visibility information based on the target recognition result; wherein the environmental visibility information includes the following distance of surrounding vehicles and / or the proportion of lights on.

[0074] When there are other vehicles around the vehicle, the vehicle can perform target recognition based on the vehicle, for example, identifying a first vehicle that is following the vehicle in the same direction as the vehicle and monitors the following distance of the first vehicle. Or, the second vehicle with its lights turned on can be identified, and the proportion of lights on can be determined based on the proportion of the second vehicle in the identified vehicles. These two target recognition methods can be performed simultaneously or one can be selected.

[0075] The key to target recognition in live images is to extract the feature points of surrounding vehicles. For example, when the host vehicle approaches from the right rear and enters a certain area behind the target vehicle, the target vehicle in front enters the front camera screen of the host vehicle. At this time, the front camera of the host vehicle will capture the target vehicle in front of it in the screen, and the forward binocular system can measure the distance of the target vehicle in front. Through target recognition, the center point (feature point) of the upper edge of the target vehicle is located, and the target vehicle is simulated as a feature point for continuous tracking. Using this method, for the case of multiple vehicles behind, each vehicle model is fitted as a feature point (the center point of the highest point position in the Z direction of the target close to the side of the host vehicle can be selected), and each vehicle feature point is continuously tracked for a period of time to obtain the motion trajectory of each vehicle. For the multi-vehicle mode, a parallel processing strategy can be used for target recognition.

[0076] Step S103 : when the safety level corresponding to the environmental visibility information is lower than a preset level, triggering a first vehicle light signal to turn on the vehicle lights.

[0077] Environmental visibility information can be quantified into a safety level, and then the safety level is compared with the preset level. If the safety level is lower than the preset level, it means that the current road environment is not conducive to driving, and the lights need to be turned on to improve the driving vision. On the contrary, if the safety level is not lower than the preset level, it means that the current road environment is acceptable and there is no need to turn on the lights.

[0078] Environmental visibility information can be one of the following distance and the proportion of lights on, or a combination of multiple information. The following distance and the proportion of lights on can be normalized into safety levels, respectively. The decision to turn on the lights can be made based on a single indicator, which is convenient to calculate and has a fast decision speed. For example, the smaller the following distance, the higher the corresponding safety level; the smaller the proportion of lights on, the higher the corresponding safety level. Alternatively, the following distance and the proportion of lights on can be normalized and transformed into the [0, 1] interval, and weights can be assigned according to the importance of each indicator in the comprehensive evaluation. The standardized indicator values ​​are multiplied by their corresponding weights and summed to obtain the final comprehensive value, which is the safety level. This multi-indicator decision is more accurate than a single indicator decision. Alternatively, as long as one of the following distance and the proportion of lights on meets the preset conditions (the following distance is greater than the first threshold, and the proportion of lights on is greater than the second threshold), the system determines that the safety level is lower than the preset level and the lights need to be turned on.

[0079] In the above steps S101 to S103, real-time images of the road environment are collected, and the following distances and / or the proportion of lights on of surrounding vehicles are analyzed by object recognition in the real-time images. Based on this, environmental visibility information is generated, and whether to turn on the lights is determined according to the safety level corresponding to the environmental visibility information. This enables the headlight control method to evaluate the road environment more intelligently and comprehensively, improves the rationality of decision-making, and avoids the problems of misjudgment and missed judgment that may occur in traditional methods.

[0080] The target recognition of the first vehicle and the second vehicle will be further introduced below.

[0081] In one embodiment, Figure 4 A flow chart of a target recognition method for a first vehicle is given, such as Figure 4 As shown, the process includes the following steps:

[0082] Step S201: When there are other vehicles within the field of view of the camera, vehicle recognition is performed on the live image to determine the first vehicle that has the same driving direction as the vehicle and is following the vehicle.

[0083] During driving, the vehicle camera monitors other vehicles in the lanes around the vehicle in real time, and first determines whether the other vehicles are traveling in the same direction as the vehicle; if they are traveling in the same direction, it determines whether the other vehicles are following the vehicle, that is, whether there is a car in front of the other vehicles. If there is a car, it is determined to be following the vehicle.

[0084] When multiple vehicles are identified in a single lane that are traveling in the same direction as the vehicle and following the vehicle, the vehicle closest to the vehicle is determined as the first vehicle. This is because in a multi-vehicle scenario, for multiple target vehicles at different locations and different lane distances around the vehicle, the error in the distance measurement by the sensor will increase with the increase of distance, so only the vehicle closest to the vehicle is tracked in each adjacent lane as the tracking target.

[0085] Step S202, calculating the following distance between the first vehicle and the remaining vehicles, and determining whether the following distance is increasing within a preset time.

[0086] Determining whether the following distance shows an increasing trend within a preset time may be determining whether the following distance between the first vehicle and the preceding vehicle continues to increase within a certain period of time, or may be determining whether a vehicle distance increase rate exceeds a certain threshold.

[0087] Step S203 : when it is determined that the following distance shows an increasing trend within a preset time, it is determined that the following distance of the first vehicle increases, and first environmental visibility information is generated according to the determination result.

[0088] For the first vehicle that has following behavior, this vehicle monitors the following distance between the first vehicle and the vehicle in front of it in real time. When it is found that the following distance between the first vehicle and the vehicle in front continues to increase within a certain period of time, or the rate of increase of the vehicle distance exceeds a certain threshold, it is determined that the following distance of the first vehicle has increased, and an increase signal of the vehicle distance of the first vehicle is output.

[0089] Exemplarily, inter-camera target tracking calibration will be further introduced below.

[0090] Figure 5 is a decomposition diagram of the overtaking process between the vehicle and the neighboring vehicle, as shown in Figure 5 As shown, the left side shows the scene of the first vehicle overtaking on the right, and the right side shows the scene of the first vehicle overtaking on the left. Overtaking can be divided into two driving processes:

[0091] The first process: from the time when the first vehicle appears in the front camera image of the vehicle until the first vehicle exits the front camera image during the overtaking process;

[0092] The second process: from the time when the first vehicle appears in the side camera image of the vehicle until the first vehicle drives to a certain distance behind the rear of the vehicle during the overtaking process.

[0093] Taking the first process mentioned above as an example, vehicle reference point recognition is performed. When the vehicle approaches from the right rear and enters a certain area behind the first vehicle, the first vehicle enters the front camera screen of the vehicle. The front camera of the vehicle will capture the target in the screen. At this time, the forward binocular system can achieve the distance measurement of the front vehicle target. Through the target recognition method, the center point (feature point) of the upper edge of the first vehicle is located, and the first vehicle is simulated as a feature point for continuous tracking. Using this method, in the case of multiple vehicles behind, each vehicle model is fitted as a feature point, and each vehicle feature point is continuously tracked for a period of time to obtain the motion trajectory of each vehicle. The same treatment is applied to the multi-vehicle mode, and a parallel target recognition strategy can be adopted.

[0094] In one embodiment, when it is identified that there are multiple lanes with the first vehicle, when it is determined that the following distance of the first vehicle is increasing within a preset time, it is determined that the following distance of the first vehicle is increasing, and first environmental visibility information is generated according to the determination result, including:

[0095] A first confidence level of each lane is determined based on the distance between each lane and the lane of the host vehicle; a second confidence level of each relative position relationship is determined based on the relative position relationship between each first vehicle and the host vehicle; a following confidence level of a target vehicle is calculated based on the first confidence level and the second confidence level, and first environmental visibility information is generated based on the following confidence level of the target vehicle; wherein the target vehicle is the first vehicle for which an increase in following distance is determined.

[0096] In this embodiment, a first confidence level is set for the following distance increase results outputted from different lanes, and a second confidence level is set for the relative positional relationship between each first vehicle and the host vehicle. The first confidence level changes in a gradient as the distance between each lane and the host lane changes, that is, the closer a lane is to the host lane, the higher the first confidence level of the lane; otherwise, the opposite is true. Figure 6 is a schematic diagram of the lanes of this embodiment, as shown in Figure 6As shown, assuming that the current road environment has 4 lanes, from left to right they are lane 1, lane 2, lane 3, and lane 4, and the vehicle is driving in lane 2. If the confidence of lane 2 is a, then the confidences of lane 1, lane 3, and lane 4 are a / 2, a / 2, and a / 3 respectively. The first vehicle can be located in front of, behind, on the left, or on the right of the vehicle, and each relative position relationship corresponds to a second confidence, which can be 1 / 2.

[0097] Continue to refer Figure 5 , assuming that the vehicle is in lane 2, and there is a first vehicle in lane 2, and the first vehicle is behind the vehicle, then the following confidence of the first vehicle is 1 / 2(a / (a+a / 2+a / 2+a / 3))×100%. For another example, on this basis, there is a first vehicle in lane 4, and the first vehicle is located on the right side of the vehicle, then the following confidence of the first vehicle in lane 4 is 1 / 2(a / 3 / (a+a / 2+a / 2+a / 3))×100%. The sum of the following confidences of lanes 2 and 3 is accumulated. For multiple target vehicles, the sum of the following confidences of all target vehicles around the vehicle is accumulated. When the sum of the following confidences is greater than a certain threshold, a vehicle distance increase signal is output.

[0098] In one embodiment, Figure 7 A flow chart of a target recognition method for a second vehicle is given, such as Figure 7 As shown, the process includes the following steps:

[0099] Step S301, performing headlight recognition on a vehicle to determine a second vehicle with headlights turned on;

[0100] Step S302, determining the proportion of lights on according to the proportion of the second vehicle in the identified vehicles;

[0101] Step S303: Generate second environment visibility information according to the proportion of lights on.

[0102] In the following vehicle scenario, the vehicle will use the camera to detect the status of the lights of other vehicles around it, and use the status of the lights of the surrounding vehicles as a reference to decide whether to turn on the lights of the vehicle. When the proportion of lights on is higher than a certain percentage, it means that the current road environment is poor and the lights need to be turned on. Therefore, the first light signal will be triggered to turn on the lights of the vehicle. This setting can maximize the accuracy of light control.

[0103] In some embodiments, after step S303, when the safety level corresponding to the second environmental visibility information is lower than the preset level, the type of lights to be turned on by the vehicle can be determined according to the type of lights turned on by the second vehicle. Using the lights of surrounding vehicles as a reference to determine the type of lights to be turned on by the vehicle can make the control of the lights of the vehicle more reasonable.

[0104] In one embodiment, the relative position relationship between the second vehicle and the vehicle may be determined first, and the type of headlights corresponding to the second vehicle may be identified according to the relative position relationship. The following will introduce headlight type identification solutions for different relative position relationships.

[0105] (1) When the second vehicle is located behind the vehicle (including vehicles located behind the vehicle in other lanes), the headlights of the second vehicle are identified as on. Specifically, the contour boundary pixel points of the second vehicle are extracted from the live image to obtain a vehicle contour point information layer; the live image of the second vehicle is binarized to obtain a brightness information layer; the vehicle contour point information layer is positionally matched with the corresponding brightness information layer to obtain the positional relationship of the bright spot of the second vehicle's headlights relative to the vehicle contour; if the bright spot of the headlights is located within the vehicle contour and is symmetrical about the central axis of the vehicle contour, it is determined that the second vehicle has turned on its headlights.

[0106] (2) When the second vehicle is located in front of the vehicle (including vehicles in other lanes in front of the vehicle), the fog lights of the second vehicle are identified as on. Specifically, the contour boundary pixel points of the second vehicle are extracted from the live image to obtain a vehicle contour point information layer; the live image of the second vehicle is binarized, the red (R) layer is extracted, and the brightness information layer is obtained; the vehicle contour point information layer is matched with the corresponding brightness information layer to obtain the position relationship of the bright spot of the second vehicle's headlight relative to the vehicle contour; considering the difference in red brightness between the fog lights and the rear lights, if the bright spot of the headlight is located within the vehicle contour, there is a grayscale difference in the red layer, and the bright spot of the headlight is symmetrical about the central axis of the vehicle contour, it is determined that the second vehicle has turned on the fog lights.

[0107] (3) When the second vehicle is located in front of and behind the vehicle (including vehicles located in front of and behind the vehicle in other lanes), the second vehicle is identified as having its hazard lights on. Specifically, the contour boundary pixel points of the second vehicle are extracted from the live image to obtain a vehicle contour point information layer; the live image of the second vehicle is binarized to extract the yellow (R+Y) layer to obtain a brightness information layer; the vehicle contour point information layer is positionally matched with the corresponding brightness information layer to obtain the positional relationship of the bright spot of the second vehicle's headlight relative to the vehicle contour; when the bright spot of the headlight is located within the vehicle contour, multiple frames of continuous live images are obtained, and the brightness information layers of the same second vehicle are extracted from the multiple frames of continuous live images based on a preset time interval for comparison; if the comparison result shows that the bright spot of the headlight appears intermittently bright in the yellow layer, that is, a cyclic yellow light is identified at the position of the bright spot of the headlight, then it is determined that the second vehicle has turned on its hazard lights.

[0108] In the above embodiment, different headlight type recognition methods are adopted for different relative position relationships, which can improve the accuracy and efficiency of headlight type recognition. The multi-vehicle headlight type recognition scenario can be processed in a similar manner to the vehicle following scenario, and the confidence of the periodic vehicle headlight activation in the road environment is calculated based on the confidence gradient.

[0109] In one embodiment, Figure 8 A flow chart of another vehicle light control method is provided, such as Figure 8 As shown, the process includes the following steps:

[0110] Step S401, in response to the environment request signal, obtain the real-time image obtained by the camera collecting the road environment. When the vehicle is driving in a geographical fence area with extreme weather, when the vehicle receives the environment request signal, the vehicle will collect the real-time image and make further judgment on the conditions for turning on the headlights. Before this, a corresponding user operation switch can be set on the vehicle's on-board electronic terminal. When the user turns on this function switch, the function of judging the conditions for turning on the headlights is effective, and the vehicle begins to monitor the driving environment in real time.

[0111] Step S402, performing target recognition on the live image, and extracting environmental visibility information based on the target recognition result; wherein the environmental visibility information includes the following distance of surrounding vehicles and / or the proportion of lights on.

[0112] Step S403, when the safety level corresponding to the environmental visibility information is lower than the preset level, trigger the first headlight signal to turn on the headlights. When the vehicle meets the headlight turn-on judgment conditions, the vehicle is ready to automatically turn on the headlights. Based on visual judgment, the light-on status of surrounding vehicles is obtained, and a certain type of headlight (headlight, fog light or double flash warning light) is turned on accordingly. Optionally, as long as one of the following vehicle distance and the ratio of lights on meets the preset conditions (the following vehicle distance is greater than the first threshold, and the ratio of lights on is greater than the second threshold), it is considered that the safety level is lower than the preset level, and the headlights can be turned on at this time.

[0113] Step S404, controlling the vehicle light system to remain in an on state within a preset time period.

[0114] Step S405: after a preset time period, when it is detected that the safety level corresponding to the environmental visibility information is higher than a preset level, trigger a second vehicle light signal to turn off the vehicle lights.

[0115] In this embodiment, when the safety level corresponding to the environmental visibility information is lower than the preset level, after the first headlight signal is triggered to turn on the headlight, the headlight system can be controlled to maintain the on state within the preset time; after the preset time, the headlight is turned off in response to the second headlight signal. The second headlight signal can be triggered when it is detected that the safety level corresponding to the environmental visibility information is higher than the preset level, or it can be triggered by the user's input operation (key, screen gesture, voice command). Such a setting can avoid the system misjudgment causing the headlight to frequently send out the off / on signal, resulting in a poor user experience. Therefore, a delay switch can be set at the system level, and within a period of time after the headlight turn-on signal is sent, the vehicle does not accept the headlight turn-off signal sent by the system. When the delay is over, when the vehicle outputs the headlight turn-off signal again, the headlight responds and turns off. In the above process, when the user intervenes in the adjustment of the headlights, the vehicle responds to the system with the human intervention requirement as the highest priority, and enters the next detection cycle after the response is completed.

[0116] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0117] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0118] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0119] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A vehicle light control method, characterized in that: include: Acquire the real-time image of the road environment captured by the camera; Performing target recognition on the live image, and extracting environmental visibility information based on the target recognition result; wherein the environmental visibility information includes the following distance and / or the proportion of lights on of surrounding vehicles; When the safety level corresponding to the environmental visibility information is lower than a preset level, a first vehicle light signal is triggered to turn on the vehicle lights.

2. The vehicle light control method according to claim 1, characterized in that: Performing target recognition on the live image and extracting environmental visibility information based on the target recognition result, including: In the case that there are other vehicles within the field of view of the camera, vehicle recognition is performed on the live image to determine a first vehicle that is traveling in the same direction as the vehicle and is following the vehicle; Calculating the following distance between the first vehicle and other vehicles, and determining whether the following distance is increasing within a preset time; When it is determined that the following distance shows an increasing trend within a preset time, it is determined that the following distance of the first vehicle increases, and first environmental visibility information is generated according to the determination result.

3. The vehicle light control method according to claim 2, characterized in that: In the case that there are other vehicles within the field of view of the camera, vehicle recognition is performed on the live image to determine a first vehicle that is traveling in the same direction as the vehicle and has a following behavior, including: When it is identified that there are a plurality of vehicles in a single lane that are traveling in the same direction as the own vehicle and are following the own vehicle, the vehicle closest to the own vehicle among the plurality of vehicles is determined as the first vehicle.

4. The vehicle light control method according to claim 2, characterized in that: When it is identified that the first vehicle exists in a plurality of lanes, and when it is determined that the following distance shows an increasing trend within a preset time, it is determined that the following distance of the first vehicle increases, and first environmental visibility information is generated according to the determination result, including: Determining a first confidence level of each lane according to the distance between each lane and the lane of the vehicle; Determining a second confidence level of each relative position relationship according to the relative position relationship between each of the first vehicles and the host vehicle; The following vehicle confidence of the target vehicle is calculated according to the first confidence and the second confidence, and the first environmental visibility information is generated according to the following vehicle confidence of the target vehicle; wherein the target vehicle is the first vehicle for which the following distance is determined to increase.

5. The vehicle light control method according to claim 1, characterized in that: Performing target recognition on the live image and extracting environmental visibility information based on the target recognition result, including: In the case that there are other vehicles within the field of view of the camera, performing vehicle recognition and headlight recognition on the live image to determine a second vehicle with its headlights turned on; Determining the light-on ratio according to the ratio of the second vehicle in the identified vehicles; Second environmental visibility information is generated according to the light-on ratio.

6. The vehicle light control method according to claim 5, characterized in that: After generating the second environment visibility information according to the light-on ratio, the method further includes: When the safety level corresponding to the second environmental visibility information is lower than a preset level, the type of lights of the host vehicle waiting to be turned on is determined according to the type of lights turned on by the second vehicle.

7. The vehicle light control method according to claim 5, characterized in that: After performing vehicle recognition and vehicle light recognition on the live image and determining the second vehicle with its lights turned on, the method further includes: Determining a relative position relationship between the second vehicle and the host vehicle; According to the relative position relationship, the corresponding headlight type of the second vehicle is identified; wherein, When the second vehicle is located behind the vehicle, identifying the second vehicle with its headlights turned on; When the second vehicle is located in front of the vehicle, identifying the second vehicle with its fog lights turned on; When the second vehicle is located in front of and behind the vehicle successively, the second vehicle is identified by turning on the double flash warning lights.

8. The vehicle light control method according to claim 7, characterized in that: Identifying the type of the corresponding headlight of the second vehicle according to the relative position relationship includes: Extracting the outline boundary pixel points of the second vehicle in the live image to obtain a vehicle outline point information layer; Binarizing the live image of the second vehicle to obtain a brightness information layer; Positionally matching the vehicle contour point information layer with the corresponding brightness information layer to obtain the position relationship of the headlight bright spot of the second vehicle relative to the vehicle contour; The type of headlights turned on by the second vehicle is determined according to the positional relationship of the headlight bright spot relative to the vehicle outline.

9. The vehicle light control method according to claim 8, characterized in that: Determining the type of the headlight turned on by the second vehicle according to the positional relationship of the headlight bright spot relative to the vehicle outline includes: When the second vehicle is located behind the vehicle, if the bright spot of the headlight is located within the outline of the vehicle and is symmetrical about the central axis of the outline of the vehicle, it is determined that the second vehicle has turned on its headlights; or, When the second vehicle is located in front of the vehicle, if the bright spot of the headlight is located within the outline of the vehicle, there is a grayscale difference in the red layer, and the bright spot of the headlight is symmetrical about the central axis of the vehicle outline, it is determined that the second vehicle has turned on the fog lights.

10. The vehicle light control method according to claim 8, characterized in that: After position matching the vehicle contour point information layer with the corresponding brightness information layer to obtain the position relationship of the headlight bright spot of the second vehicle relative to the vehicle contour, the method further includes: When the second vehicle is located in front of and behind the vehicle successively, and the bright spot of the vehicle light is located within the outline of the vehicle, a plurality of frames of continuous live images are acquired, and brightness information layers of the same second vehicle are extracted from the plurality of frames of continuous live images based on a preset time interval for comparison; If the comparison result shows that the bright spot of the vehicle light appears intermittently bright in the yellow layer, it is determined that the second vehicle has turned on the hazard warning lights.

11. The vehicle light control method according to claim 1, characterized in that: When the safety level corresponding to the environmental visibility information is lower than a preset level, after triggering the first vehicle light signal to turn on the vehicle light, the method further includes: Controlling the vehicle lighting system to remain in an on state within a preset time period; After the preset time period has passed, the vehicle light is turned off in response to a second vehicle light signal.

12. The vehicle light control method according to claim 11, characterized in that: After the preset time has passed, responding to a second vehicle light signal to turn off the vehicle light comprises: When it is detected that the safety level corresponding to the environmental visibility information is higher than a preset level, the second vehicle light signal is triggered; wherein the environmental visibility information includes the following vehicle distance or the proportion of lights on.

13. An in-vehicle electronic device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 12 are implemented.

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