A seatbelt-based braking warning method, system, terminal and storage medium

Abstract

The present application relates to a braking warning method, system, terminal and storage medium based on a seat belt, and relates to the technical field of intelligent driving. The method includes: obtaining the current speed and current distance of a vehicle in the front area of the current vehicle; taking the minimum value in the current distances to obtain a target current distance and the target current speed corresponding to the minimum current distance, and marking the vehicle corresponding to the target current distance as the target vehicle; obtaining a forward view image of the current vehicle; identifying the target vehicle from the forward view image according to the minimum current distance; determining the vehicle light information of the target vehicle through the forward view image; obtaining the accident probability of the current vehicle according to the target current distance, the target current speed and the vehicle light information; when the accident probability is greater than a preset probability, controlling the seat belt of the current vehicle to shake, and adjusting the binding force corresponding to the seat belt in use on the current vehicle so that the binding force is greater than a preset threshold. The present application has the effect of improving braking warning.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology, and particularly to a braking warning method, system, terminal, and storage medium based on a seat belt. Background Art

[0002] The braking system is a key component to ensure driving safety, and its main function is to decelerate or stop. With the development of intelligent driving technology, an early warning function of the braking system has also been added to vehicles.

[0003] Related technologies detect whether there are obstacles in front of the vehicle through a radar system. When the distance between the vehicle and the obstacle is less than the safe distance, a loudspeaker installed on the driver's seat will emit an alarm sound to remind the driver to brake in time.

[0004] Regarding the above related technologies, the inventors believe that the real road conditions are very complex, and a simple alarm sound lacks sufficient reminder effect. For example, in a noisy scenario, it is difficult for the driver to notice the alarm sound, resulting in the driver being unable to brake in time and thus causing traffic accidents. Summary of the Invention

[0005] In order to improve the braking warning effect, this application provides a braking warning method, system, terminal, and storage medium based on a seat belt.

[0006] In a first aspect, this application provides a braking warning method based on a seat belt, adopting the following technical solution:

[0007] A braking warning method based on a seat belt includes:

[0008] Obtaining the current speed and current distance of the vehicles in the front area of the current vehicle through a radar system;

[0009] Taking the minimum value of the current distances to obtain the target current distance and the target current speed corresponding to the target current distance, and marking the vehicle corresponding to the target current distance as the target vehicle;

[0010] Obtaining the front view image of the current vehicle through a front view monitoring system;

[0011] Identifying the target vehicle from the front view image according to the target current distance;

[0012] Determining the vehicle light information of the target vehicle through the front view image;

[0013] Obtaining the accident probability of the current vehicle according to the target current distance, the target current speed, and the vehicle light information;

[0014] When the accident probability is greater than a preset probability, control the seat belt of the current vehicle to shake, and adjust the restraint force corresponding to the seat belt in use on the current vehicle so that the restraint force is greater than a preset threshold.

[0015] By adopting the above technical solution, the accident probability of the current vehicle is obtained through the target current distance, the target current speed, and the vehicle light information. When the accident probability is greater than the preset probability, control the seat belt of the current vehicle to shake, and adjust the restraint force corresponding to the seat belt in use on the current vehicle. Not only can the driver be reminded to brake in time by the shaking of the seat belt, reducing the probability of the vehicle encountering a traffic accident, but also the restraint force of the seat belt can be adjusted to improve the restraint effect of the seat belt.

[0016] Optionally, obtain the driving speed and driving direction of the current vehicle;

[0017] Perform motion analysis according to the driving speed and the driving direction to generate the first prediction trajectory of the current vehicle;

[0018] Perform motion analysis according to the target current speed and the vehicle light information to generate the second prediction trajectory of the target vehicle;

[0019] Combine the first prediction trajectory, the second prediction trajectory, and the target current distance to obtain the accident probability.

[0020] By adopting the above technical solution, motion analysis can be performed in combination with the target current speed and vehicle light information of the target vehicle, making the second prediction trajectory more accurate. Further improve the accuracy of the accident probability, so that the seat belt can remind the driver in time by shaking.

[0021] Optionally, identify the vehicle type of the target vehicle;

[0022] When the vehicle type belongs to a preset type, judge whether the target vehicle is in a loaded state through the front view image;

[0023] If so, identify the type of goods of the target vehicle through the front view image; identify the stacking height of the goods on the target vehicle through the front view image; determine the first predicted weight of the target vehicle in the first preset mapping relationship according to the type of goods, the stacking height, and the vehicle type; update the accident probability according to the stacking height and the first predicted weight;

[0024] If not, determine the second predicted weight of the target vehicle in the second preset mapping relationship according to the vehicle type; update the accident probability according to the second predicted weight.

[0025] By adopting the above technical solution, when the target vehicle is of a preset type, the front view image is used to identify the type of goods and the stacking height, so that the accident probability is updated according to the stacking height, the type of goods and the vehicle type, further improving the accuracy of the accident probability, enabling the seat belt to timely remind the driver by jittering.

[0026] Optionally, in response to receiving a braking signal sent by another vehicle, the number of the braking signals is counted, the distance between the other vehicle and the current vehicle is less than the vehicle influence distance, and the braking signal is broadcast after the other vehicle receives a braking operation;

[0027] Judge whether the number of the signals is greater than a number threshold;

[0028] If so, control the seat belt of the current vehicle to jitter, and adjust the binding force corresponding to the seat belt in use on the current vehicle so that the binding force is greater than a preset threshold;

[0029] If not, maintain the driving state of the current vehicle.

[0030] By adopting the above technical solution, when the number of braking signals is greater than the number threshold, the seat belt will be controlled to jitter to remind the driver that all surrounding vehicles are braking and attention should be paid to an accident nearby. Therefore, this solution can warn the state near the vehicle, and also remind the driver to pay attention to braking after a certain number of vehicles brake, effectively reducing the probability of traffic accidents.

[0031] Optionally, the area where the current vehicle is located is obtained through a navigation system;

[0032] When the area is a vehicle passing area, perform the step of judging whether the number of the signals is greater than the number threshold;

[0033] When the area is not the vehicle passing area, receive a status signal sent by the other vehicle, where the status signal is used to indicate whether the other vehicle has stopped; count the number of stop signals and the number of movement signals in the status signal; cancel the jitter of the seat belt when the ratio of the number of movement signals to the number of stop signals is less than a ratio threshold.

[0034] By adopting the above technical solution, different execution schemes are selected according to whether the area where the current vehicle is located is a vehicle passing area. Among them, when the current vehicle is not in the vehicle passing area, it will be judged whether it is necessary to make the seat belt jitter according to the number of vehicles started, reducing unnecessary jitter of the seat belt and improving the use experience.

[0035] Optionally, when the area is the vehicle passing area and the number of signals is greater than the number threshold, obtain the total number of vehicles within the peripheral range of the current vehicle;

[0036] When the total number of vehicles is greater than the vehicle number threshold, count the frequency of brake signal generation of the vehicles within the peripheral range within a preset duration;

[0037] When the frequency of brake signal generation is greater than the frequency threshold, count the braking frequency of the current vehicle within the preset duration;

[0038] When the braking frequency is greater than the frequency threshold, generate a pause jitter signal, and the pause jitter signal is used to indicate that the seat belt of the current vehicle stops jittering within a preset specified duration.

[0039] By adopting the above technical solution, when the frequency of brake signal generation of most vehicles near the current vehicle is greater than the frequency threshold within a preset duration, the current vehicle is probably in a congested section, so the jitter of the seat belt is reduced to avoid the frequent jitter of the seat belt from affecting the driver.

[0040] Optionally, taking the driving direction of the current vehicle as the reference direction and the current vehicle as the origin, set a detection angle, and the angular bisector of the detection angle points to the driving direction;

[0041] Broadcast a status request into the detection angle;

[0042] Receive the return signal and count the number of returned signals;

[0043] Read the candidate lane signals of the candidate vehicles from the return signal;

[0044] Obtain the current lane signal of the current vehicle;

[0045] From the lane mapping relationship, determine the matching lane signal corresponding to the current lane signal;

[0046] Count the number of signals in the candidate lane signals that are the same as the matching lane signal to obtain the total number of vehicles within the peripheral range of the current vehicle.

[0047] By adopting the above technical solution, select a detection angle according to the driving direction of the current vehicle and broadcast a status request into the detection angle, so as to determine the total number of vehicles by using the return signal. This makes the judgment of the environment where the current vehicle is located more accurate.

[0048] In a second aspect, the present application provides a brake warning system based on a seat belt, adopting the following technical solution:

[0049] A brake warning system based on a seat belt, comprising:

[0050] An acquisition module, configured to acquire the current speed, the current distance, the forward view image, the preset probability, the driving speed, the driving direction, the preset type, the first preset mapping relationship, the second preset mapping relationship, the brake signal, the quantity threshold, the return signal, and the lane mapping relationship;

[0051] A memory, configured to store the program of the seatbelt-based braking warning method described in any one of the above;

[0052] A processor, the program in the memory can be loaded and executed by the processor and implement the seatbelt-based braking warning method described in any one of the above.

[0053] By adopting the above technical solution, the accident probability of the current vehicle is obtained through the target current distance, the target current speed, and the vehicle light information, and when the accident probability is greater than the preset probability, the seatbelt of the current vehicle is controlled to shake, and the binding force corresponding to the seatbelt in use on the current vehicle is adjusted. It not only reminds the driver to brake in time by the shaking of the seatbelt, reduces the probability of the vehicle encountering a traffic accident, but also can adjust the binding force of the seatbelt to improve the restraint effect of the seatbelt.

[0054] In a third aspect, the present application provides an intelligent terminal, adopting the following technical solution:

[0055] An intelligent terminal, including a memory and a processor, and a computer program capable of being loaded and executed by the processor is stored on the memory and implements the method described in any one of the above.

[0056] In a fourth aspect, the present application provides a computer storage medium, which can store the corresponding program and has the characteristic of being convenient to implement the improvement of the braking warning effect. The following technical solution is adopted:

[0057] A computer-readable storage medium stores a computer program capable of being loaded and executed by the processor and implementing any one of the seatbelt-based braking warning methods described above.

[0058] In summary, the present application includes at least one of the following beneficial technical effects:

[0059] 1. The accident probability of the current vehicle is obtained through the target current distance, the target current speed, and the vehicle light information, and when the accident probability is greater than the preset probability, the seatbelt of the current vehicle is controlled to shake, and the binding force corresponding to the seatbelt in use on the current vehicle is adjusted. It not only reminds the driver to brake in time by the shaking of the seatbelt, reduces the probability of the vehicle encountering a traffic accident, but also can adjust the binding force of the seatbelt to improve the restraint effect of the seatbelt;

[0060] 2. Motion analysis can be combined with the target current speed and vehicle lighting information of the target vehicle to make the second predicted trajectory more accurate. Further improve the accuracy of the accident probability, so that the seat belt can timely remind the driver through jitter;

[0061] 3. Different execution schemes are selected according to whether the area where the current vehicle is located is a vehicle passing area. Among them, when the current vehicle is not in the vehicle passing area, it will be judged whether it is necessary to make the seat belt jitter according to the number of vehicles turned on, reducing unnecessary jitter of the seat belt and improving the user experience. Brief Description of the Drawings

[0062] Figure 1 is a schematic flowchart of a braking warning method based on a seat belt provided by an embodiment of the present application.

[0063] Figure 2 is a schematic flowchart of a calculation method for accident probability provided by an embodiment of the present application.

[0064] Figure 3 is a schematic flowchart of an update method for accident probability provided by an embodiment of the present application.

[0065] Figure 4 is a schematic flowchart of a braking warning method based on a seat belt and vehicle networking technology provided by an embodiment of the present application.

[0066] Figure 5 is a schematic flowchart of a method for judging the area where the current vehicle is located provided by an embodiment of the present application.

[0067] Figure 6 is a schematic flowchart of a braking warning method in a preset scenario provided by an embodiment of the present application.

[0068] Figure 7 is a schematic flowchart of a method for counting the total number of vehicles provided by an embodiment of the present application.

[0069] Figure 8 is a schematic structural diagram of a braking warning system based on a seat belt provided by an embodiment of the present application. Detailed Embodiment

[0070] In order to make the purpose, technical solutions and advantages of the present application clearer, the following further describes the present application in detail with reference to the attached Figure 1 to the attached Figure 8 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.

[0071] An embodiment of the present application discloses a braking warning method based on a seat belt. Refer to Figure 1, the method includes:

[0072] Step S101: Obtain the current speed and current distance of the vehicles in the front area of the current vehicle through a radar system.

[0073] The front area is in front of the front of the current vehicle. For example, obtain the driving direction of the current vehicle. Taking the current vehicle as the origin, a dividing line passing through the origin and perpendicular to the driving direction is set on the horizontal plane. According to the dividing line and the driving direction, the front area and the rear area are obtained, and the front area and the rear area are on both sides of the dividing line.

[0074] Exemplarily, electromagnetic waves are emitted towards the front area through the radar system and the emission timestamp of the electromagnetic waves is recorded. The reflected wave of the electromagnetic wave is obtained, and the reception timestamp of the reflected wave is recorded. According to the difference between the emission timestamp and the reception timestamp, the current distance of the vehicle is calculated. Further, according to the ratio of the difference between adjacent current distances and the emission interval of the electromagnetic waves, the current speed of the vehicle is calculated.

[0075] Step S102: Take the minimum value in the current distances to obtain the target current distance and the target current speed corresponding to the target current distance, and mark the vehicle corresponding to the target current distance as the target vehicle.

[0076] Optionally, at least two groups of data records located in the front area are obtained through the radar system, and the data records include the current speed and the current distance. The minimum value in the current distances is taken out from at least two groups of data records to obtain the target current distance.

[0077] Step S103: Obtain the front view image of the current vehicle through a front view monitoring system.

[0078] The front view monitoring system includes a front view camera on the vehicle, and the front view camera is installed at any one or more of the positions behind the windshield of the current vehicle, the front grille, behind the vehicle logo, on the roof or the front bumper.

[0079] Step S104: Identify the target vehicle from the front view image according to the target current distance.

[0080] Exemplarily, identify the vehicle position in the front view image. Through a depth prediction algorithm, taking the vehicle position as a standard, obtain the estimated distance from the vehicle position to the current vehicle. The vehicle positions that match the target current distance are screened from the estimated distances to obtain the target vehicle.

[0081] In some other embodiments, the minimum value is screened out from the estimated distances to obtain the minimum estimated distance. The vehicle corresponding to the minimum estimated distance is determined as the target vehicle.

[0082] Step S105: Determine the vehicle light information of the target vehicle through the front view image.

[0083] The vehicle lighting information includes the types of vehicle lights. The types of lights include brake lights, hazard warning lights, turn signals, and fog lights.

[0084] Exemplarily, frame the regional image of the target vehicle in the front view image. Identify the headlight image of the target vehicle from the regional image. Obtain the vehicle lighting information based on the headlight position and headlight color in the headlight image. Further, acquire the front view video corresponding to the front view image. Read the flashing condition of the target vehicle's headlights from the front view video. In an actual scenario, the use of some headlights is often accompanied by flashing. For example, after the hazard warning light is turned on, it will flash. If it is only judged whether the target vehicle turns on the hazard warning light based on the headlight position and headlight color, misjudgment may occur.

[0085] Step S106: Obtain the accident probability of the current vehicle based on the target current distance, target current speed, and vehicle lighting information.

[0086] The accident probability refers to the probability that the current vehicle collides with the target vehicle on the premise of maintaining its own motion state unchanged.

[0087] Optionally, the accident probability of the current vehicle can be obtained through a preset accident probability model. For example, call the accident probability model, and use the target current distance, target current speed, and vehicle lighting information as input parameters to obtain the accident probability of the current vehicle.

[0088] Step S107: When the accident probability is greater than the preset probability, control the seat belt of the current vehicle to shake, and adjust the binding force corresponding to the seat belt in use on the current vehicle so that the binding force is greater than the preset threshold.

[0089] It should be noted that when the seat belt shakes, the binding force on the seat belt is always greater than the preset threshold. For example, use the preset threshold as the minimum binding force. Calculate the sum value of the preset threshold and the preset force value to obtain the maximum binding force. Control the binding force on the seat belt to reciprocate between the minimum binding force and the maximum binding force.

[0090] In some other embodiments, when the accident probability is less than the preset probability, it is not necessary to control the seat belt of the current vehicle to shake.

[0091] By adopting the above technical solution, the accident probability of the current vehicle is obtained through the target current distance, target current speed, and vehicle lighting information, and when the accident probability is greater than the preset probability, the seat belt of the current vehicle is controlled to shake, and the binding force corresponding to the seat belt in use on the current vehicle is adjusted. Not only can the driver be reminded to brake in time through the shaking of the seat belt, reducing the probability of the vehicle encountering traffic accidents, but also the binding force of the seat belt can be adjusted to improve the restraint effect of the seat belt.

[0092] An embodiment of this application discloses a method for calculating accident probability. Refer to Figure 2 , the method includes:

[0093] Step S201: Obtain the driving speed and driving direction of the current vehicle.

[0094] Optionally, a speed sensor is installed on the current vehicle, and the speed sensor can measure the driving speed of the current vehicle. Optionally, through a positioning system, obtain the position change of the current vehicle and the time difference corresponding to the position change. Calculate the driving speed of the current vehicle through the position change and the time difference.

[0095] Optionally, a magnetometer is installed on the current vehicle, and the magnetometer can measure the driving direction of the current vehicle through the earth's magnetic field. Optionally, through a positioning system, obtain the position change of the current vehicle. Calculate the driving direction of the current vehicle through the position change.

[0096] Step S202: Perform motion analysis based on the driving speed and driving direction to generate a first prediction trajectory of the current vehicle.

[0097] The first prediction trajectory is used to represent the moving trajectory of the current vehicle in the future time period. The first prediction trajectory includes the relationship between the position of the current vehicle and time change in the future time period.

[0098] Exemplarily, take the current position of the current vehicle as the starting point of the first prediction trajectory. Perform motion analysis on the current vehicle according to the driving direction and driving speed to obtain the first prediction trajectory.

[0099] Step S203: Perform motion analysis based on the target current speed and vehicle light information to generate a second prediction trajectory of the target vehicle.

[0100] The second prediction trajectory is used to represent the moving trajectory of the target vehicle in the future time period. The second prediction trajectory includes the relationship between the position of the target vehicle and time change in the future time period.

[0101] Exemplarily, according to the vehicle light information, determine the predicted motion state of the target vehicle in a preset light - motion state relationship table. The predicted motion states include maintaining motion, braking, accelerating, decelerating, and steering. Perform motion analysis on the target vehicle according to the target current speed, predicted motion state, and target driving direction to obtain the second prediction trajectory. Among them, the target driving direction of the target vehicle can be the same as the driving direction of the current vehicle.

[0102] Step S204: Combine the first prediction trajectory, the second prediction trajectory, and the target current distance to obtain the accident probability.

[0103] Exemplarily, motion analysis is performed by combining the first predicted trajectory, the second predicted trajectory, and the target current distance to determine whether the shortest distance between the current vehicle and the target vehicle is less than a preset safe driving distance. If the shortest distance between the current vehicle and the target vehicle is less than the safe driving distance, it is considered that a collision may occur between the current vehicle and the target vehicle, and an accident probability is generated according to the minimum distance between the current vehicle and the target vehicle. The value of the accident probability is negatively correlated with the above-mentioned minimum distance. If the shortest distance between the current vehicle and the target vehicle is greater than the safe driving distance, the accident probability is set to 0.

[0104] By adopting the above technical solution, motion analysis can be performed by combining the target current speed of the target vehicle and vehicle light information, making the second predicted trajectory more accurate. Further improving the accuracy of the accident probability enables the seat belt to timely remind the driver by jittering.

[0105] In a real scenario, the vehicles running on the road are diverse, and different safe driving distances need to be set for different types of vehicles to ensure vehicle safety. Therefore, an embodiment of the present application discloses a method for updating the accident probability. Refer to Figure 3 , the method includes:

[0106] Step S301: Identify the vehicle type of the target vehicle.

[0107] The vehicle types include sedans, trucks, buses, lorries, and special vehicles.

[0108] In one implementation, the vehicle shape corresponding to the target vehicle is obtained from the front view image. The vehicle type of the target vehicle is identified through the vehicle shape.

[0109] In another implementation, the license plate information of the target vehicle is obtained from the front view image. The license plate information includes the license plate number and the license plate color. The vehicle type of the target vehicle is determined through the license plate number and the license plate color.

[0110] Step S302: When the vehicle type belongs to a preset type, determine whether the target vehicle is in a loaded state through the front view image.

[0111] The preset type refers to the vehicle types used for cargo transportation. For example, the preset type includes trucks and lorries.

[0112] Optionally, the cargo structure of the target vehicle is determined according to the vehicle type of the target vehicle. The cargo structure refers to the structure of the vehicle for loading goods. In the front view image, the cargo image corresponding to the cargo structure of the target vehicle is searched for. Through object recognition technology, it is determined whether there is cargo in the cargo image. If there is, the target vehicle is in a loaded state. If not, the target vehicle is not in a loaded state. Exemplarily, if the target vehicle is a truck, the cargo structure is the cargo box.

[0113] Step S303: If so, identify the type of goods on the target vehicle through the front view image.

[0114] Optionally, in the front view image, search for the cargo image corresponding to the cargo structure of the target vehicle. Identify the type of goods located within the cargo image through object type recognition technology.

[0115] Furthermore, the type of goods is used to describe the type and shape of the goods. For example, the type of goods refers to the goods on the target vehicle being metal round tubes.

[0116] Step S304: Identify the stacking height of the goods on the target vehicle through the front view image.

[0117] Exemplarily, calculate and analyze the height of the cargo structure in the cargo image. Calculate and analyze the exposed height of the goods protruding from the cargo structure in the cargo image. Calculate the sum of the cargo structure height and the exposed height to obtain the stacking height.

[0118] Step S305: Determine the first predicted weight of the target vehicle in the first preset mapping relationship according to the type of goods, the stacking height, and the vehicle type.

[0119] The first preset mapping relationship is used to record the relationship between the type of goods, the stacking height, and the vehicle type and the predicted weight of the target vehicle.

[0120] In some other embodiments, perform weight analysis based on the type of goods and the stacking height to obtain the weight of the goods. Retrieve the vehicle weight from the vehicle type - weight relationship table according to the vehicle type. Calculate the sum of the goods weight and the vehicle weight to obtain the first predicted weight.

[0121] Step S306: Update the accident probability according to the stacking height and the first predicted weight.

[0122] On the one hand, as the weight of the vehicle increases, the inertia of the vehicle itself becomes greater and its braking becomes more difficult. Therefore, the impact of the vehicle's weight on the safe driving distance needs to be considered. On the other hand, as the stacking height of the goods on the vehicle increases, the probability of the goods falling during braking also increases. Therefore, the impact of the stacking height of the goods on the safe driving distance also needs to be considered.

[0123] Exemplarily, update the safe driving distance according to the stacking height and the first predicted weight. And use the updated safe driving distance to regenerate the accident probability. The way to update the safe driving distance can be obtained by looking up a table.

[0124] Step S307: If not, determine the second predicted weight of the target vehicle in the second preset mapping relationship according to the vehicle type.

[0125] It should be noted that in this application, even if it is determined from the front view image that the target vehicle is not in a loaded state, it is still not considered that the target vehicle has no load, but is in a pending state. This is because the loading situation of some vehicles cannot be directly obtained from the front view image. For example, when the target vehicle is equipped with an enclosed cargo box, the loading situation of the target vehicle cannot be obtained, nor can the loading information of the target vehicle be obtained.

[0126] The second preset relationship is used to record the relationship between the vehicle type and the predicted weight of the target vehicle. Further, based on the vehicle type analysis, the maximum load weight corresponding to the vehicle type and the maximum vehicle weight corresponding to the vehicle type are obtained. Calculate the product of the maximum load weight and the preset coefficient to obtain the overweight load weight. Calculate the sum of the maximum vehicle weight and the overweight load weight to obtain the second predicted weight.

[0127] Step S308: Update the accident probability according to the second predicted weight.

[0128] Exemplarily, update the safe driving distance according to the second predicted weight. And use the updated safe driving distance to regenerate the accident probability.

[0129] By adopting the above technical solution, when the target vehicle is of a preset type, the front view image is used to identify the type of goods and the stacking height, so that the accident probability is updated according to the stacking height, the type of goods and the vehicle type, further improving the accuracy of the accident probability, and enabling the seat belt to timely remind the driver through jitter.

[0130] In a real scenario, if the vehicles within a certain range of the current vehicle are all braking, it indicates that an unexpected situation may have occurred near the current vehicle. At this time, the driver needs to be reminded to slow down or brake to a stop in order to observe the surrounding situation. Therefore, an embodiment of the present application discloses a seat belt braking warning method based on vehicle networking technology. Refer to Figure 4 , the method includes:

[0131] Step S401: In response to receiving a braking signal sent by another vehicle, count the number of braking signals. The distance between the other vehicle and the current vehicle is less than the vehicle influence distance, and the braking signal is broadcast after the other vehicle receives a braking operation.

[0132] The braking signal includes at least one of the number, braking time, signal sending time, and position information of the other vehicle.

[0133] The vehicle influence distance is a preset empirical value, and relevant personnel can adjust the specific value of the vehicle influence distance according to actual needs.

[0134] Exemplarily, the signal reception time of the brake signal is recorded. The signal transmission time is extracted from the brake signal. Based on the difference between the signal reception time and the signal transmission time, the distance between other vehicles and the current vehicle is calculated. Determine whether the aforementioned distance is less than the vehicle impact distance. If so, the signal quantity of the brake signal is updated. If not, the signal quantity of the brake signal is not updated. In some other embodiments, the position information of other vehicles can also be extracted from the brake signal. Based on the position information of other vehicles and the position information of the current vehicle, the distance between other vehicles and the current vehicle is calculated and analyzed.

[0135] Optionally, when counting brake signals, only brake signals within a preset time interval are counted, for example, only brake signals received between 12:00:00 and 12:00:10 are counted.

[0136] Step S402: Determine whether the signal quantity is greater than a quantity threshold.

[0137] The quantity threshold is a preset empirical value, and relevant personnel can adjust the value of the quantity threshold according to actual needs.

[0138] If the number of signals is greater than the number threshold, step S403 is executed;

[0139] If the signal quantity is less than the quantity threshold, step S404 is executed.

[0140] Step S403: If yes, the seat belt of the current vehicle is controlled to vibrate, and the restraint force corresponding to the seat belt in use on the current vehicle is adjusted so that the restraint force is greater than a preset threshold.

[0141] When the number of signals is greater than the number threshold, it means that many vehicles are braking near the current vehicle, and a traffic accident, traffic jam or other emergency has occurred near the current vehicle, and the current vehicle needs to slow down or brake. Therefore, in this case, the seat belt of the current vehicle is controlled to vibrate, and the restraint force of the seat belt in use on the current vehicle is adjusted.

[0142] Step S404: If not, maintain the current driving state of the vehicle.

[0143] When the number of signals is less than the number threshold, it indicates that the traffic near the current vehicle is running normally and the driving state of the current vehicle is maintained.

[0144] By adopting the above technical solution, when the number of brake signal signals is greater than the number threshold, the seat belt will be controlled to vibrate to remind the driver that surrounding vehicles are braking and that they need to pay attention to accidents nearby. Therefore, this solution can warn the status of the vehicle nearby and remind the driver to pay attention to braking after a certain number of vehicles brake, effectively reducing the probability of traffic accidents.

[0145] In the following embodiments, the area where the current vehicle is located affects the vehicle's warning. For example, when the current vehicle is inside a parking lot, other nearby vehicles will frequently brake, affecting the judgment of the current vehicle. Therefore, the embodiments of the present application disclose a method for determining the area where the current vehicle is located. Refer to Figure 5 , the method includes:

[0146] Step S501: Obtain the area where the current vehicle is located through the navigation system.

[0147] Exemplarily, through the navigation system, identify the position of the current vehicle on the map. According to the foregoing position, obtain the area where the current vehicle is located.

[0148] Step S502: When the area is a vehicle passage area, execute the step of determining whether the number of signals is greater than the number threshold.

[0149] If the area is a vehicle communication area, it means that the current vehicle is driving normally on the road, and directly execute the step of determining whether the number of signals is greater than the number threshold to determine whether a warning needs to be issued.

[0150] Step S503: When the area is not a vehicle passage area, receive the status signals sent by other vehicles, and the status signals are used to indicate whether other vehicles have stopped.

[0151] If the area is not a vehicle communication area, it is necessary to further determine whether the seat belt should be jittered according to the area.

[0152] Step S504: Count the number of stop signals and the number of motion signals in the status signals.

[0153] The status signals include motion status signals and stationary status signals. The motion status signal means that other vehicles are in a motion state, that is, other vehicles are driving. The stationary status signal means that other vehicles are in a stationary state.

[0154] Step S505: Cancel the jitter of the seat belt when the ratio of the number of motion signals to the number of stop signals is less than the ratio threshold.

[0155] The ratio threshold is a preset empirical value, and relevant personnel can adjust the specific value of the ratio threshold according to the actual situation.

[0156] In some other embodiments, when the ratio of the number of motion signals to the number of stop signals is greater than the ratio threshold, maintain the jitter of the seat belt.

[0157] By adopting the above technical solution, different execution schemes are selected according to whether the area where the current vehicle is located is a vehicle passing area. Among them, when the current vehicle is not in the vehicle passing area, it is determined whether it is necessary to make the seat belt shake according to the number of vehicles turned on, reducing unnecessary shaking of the seat belt and improving the user experience.

[0158] In the following embodiments, when the current vehicle is located in the vehicle passing area, if the current vehicle encounters a traffic jam, the shaking of the seat belt can be stopped for a period of time to avoid the frequent shaking of the seat belt affecting the driver. Therefore, the embodiments of the present application disclose a braking warning method under a preset scenario. Refer to Figure 6 , the method includes:

[0159] Step S601: When the area where the vehicle is located is a vehicle passing area and the signal quantity is greater than the quantity threshold, obtain the total number of vehicles within the peripheral range of the current vehicle.

[0160] Optionally, an electromagnetic wave is emitted to the peripheral range through a radar system, and the reflected wave of the electromagnetic wave is received. The total number of vehicles is determined according to the number of reflected waves.

[0161] In some other embodiments, through object recognition technology, vehicles and the distances from the vehicles to the current vehicle are recognized from the front view image. According to the aforementioned distances, the total number of vehicle connections within the peripheral range is calculated.

[0162] Step S602: When the total number of vehicles is greater than the vehicle quantity threshold, count the frequency of brake signal generation of the vehicles within the peripheral range within a preset duration.

[0163] Both the vehicle quantity threshold and the preset duration are preset empirical values, and relevant personnel can adjust the specific values according to actual needs.

[0164] The frequency of brake signal generation represents the total number of brake signals generated by any vehicle within the peripheral range within the preset duration.

[0165] In some other embodiments, when the total number of vehicles is less than the vehicle quantity threshold, the steps in the Figure 5 shown embodiments are executed.

[0166] Step S603: When the frequency of brake signal generation is greater than the frequency threshold, count the braking frequency of the current vehicle within the preset duration.

[0167] The frequency threshold is a preset empirical value, and relevant personnel can adjust the specific value according to actual needs.

[0168] Step S604: When the braking frequency is greater than the frequency threshold, generate a pause shaking signal, and the pause shaking signal is used to instruct the seat belt of the current vehicle to stop shaking within a preset specified duration.

[0169] If the braking frequency is greater than the frequency threshold, it indicates that the current vehicle is most likely in a traffic jam. At this time, the frequent jitter of the seat belt will instead cause the driver to become desensitized to the jitter of the seat belt, affecting the warning function of the seat belt. Therefore, it is necessary to generate a pause jitter signal to stop the jitter of the seat belt of the current vehicle within a preset specified duration.

[0170] In some other embodiments, if the driving speed of the current vehicle is greater than the preset speed threshold, the pause jitter signal is cancelled, and the seat belt of the current vehicle is allowed to jitter.

[0171] In some other embodiments, if the braking frequency is less than the frequency threshold, there is no need to generate a pause jitter signal.

[0172] By adopting the above technical solution, when the generation frequency of the braking signal of most vehicles near the current vehicle within the preset duration is greater than the frequency threshold, the current vehicle is most likely in a congested section. Therefore, the jitter of the seat belt is reduced to avoid the frequent jitter of the seat belt from affecting the driver.

[0173] The embodiment of the present application discloses a method for counting the total number of vehicles. Refer to Figure 7 , this method includes:

[0174] Step S701: Taking the driving direction of the current vehicle as the reference direction and the current vehicle as the origin, set a detection angle, and the angular bisector of the detection angle points to the driving direction.

[0175] The angle of the included angle formed by the detection angle is a preset empirical value.

[0176] Step S702: Broadcast a status request into the detection angle.

[0177] The status request is used to request the status information of the vehicle, and the status information includes at least one of the lane information, position information, and speed information of the vehicle.

[0178] Exemplarily, set the transmission angle of the communication system according to the detection angle. Broadcast the status request according to the transmission angle.

[0179] Step S703: Receive the return signal and count the number of returns of the return signal.

[0180] The return signal carries the status information of the candidate vehicle. The candidate vehicle refers to the vehicle located within the detection angle and responding to the status request.

[0181] Step S704: Read the candidate lane signal of the candidate vehicle from the return signal.

[0182] The lane signal is used to indicate the lane where the vehicle is located. Among them, the lane signal includes a straight lane, a right-turn lane, and a left-turn lane.

[0183] Optionally, after the candidate vehicle obtains its own position information, the lane information of the candidate vehicle is extracted from the position information of the candidate vehicle. The lane information is recoded into a candidate lane signal, and the candidate lane signal is added to the return signal.

[0184] Step S705: Obtain the current lane signal of the current vehicle.

[0185] Optionally, obtain the position information of the current vehicle. The current lane signal is extracted from the position information of the current vehicle.

[0186] Step S706: Determine the matching lane signal corresponding to the current lane signal from the lane mapping relationship.

[0187] In an actual scenario, it is not the case that as long as the road near the current vehicle is congested, it will affect the driving of the current vehicle. For example, if the congested section is located in the oncoming lane of the current vehicle, then the congested section will not affect the driving of the current vehicle or will have a relatively small impact. Therefore, in this step, it is necessary to determine from the perspective of the lane which lanes will affect the driving of the current vehicle and determine the matching lane signal.

[0188] Step S707: Count the number of signals in the candidate lane signals that are the same as the matching lane signal to obtain the total number of vehicles within the peripheral range of the current vehicle.

[0189] In some embodiments, if the candidate lane signal indicates that the vehicle is located in two lanes at the same time, and one of the lanes corresponds to the matching lane signal while the other lane does not correspond to the matching lane signal, then such a candidate lane signal is also counted into the total number of vehicles.

[0190] By adopting the above technical solution, the detection angle is selected according to the driving direction of the current vehicle, and a status request is broadcast in the detection angle, so as to determine the total number of vehicles by using the return signal. This makes the judgment of the environment where the current vehicle is located more accurate.

[0191] Based on the same inventive concept, an embodiment of the present application provides a seat belt-based brake warning system. Please refer to Figure 8 , the system includes:

[0192] An acquisition module 801, configured to acquire the current speed, current distance, forward view image, preset probability, driving speed, driving direction, preset type, first preset mapping relationship, second preset mapping relationship, brake signal, quantity threshold, return signal, and lane mapping relationship;

[0193] A memory 802, configured to store the program of the seat belt-based brake warning method described in any one of the above.

[0194] A processor 803, and a program in the memory can be loaded and executed by the processor to implement the seatbelt-based braking warning method described in any of the above.

[0195] By adopting the above technical solution, the accident probability of the current vehicle is obtained based on the current distance to the target, the current speed of the target, and the vehicle lighting information. When the accident probability is greater than a preset probability, the seatbelt of the current vehicle is controlled to shake, and the binding force corresponding to the seatbelt in use on the current vehicle is adjusted. This not only reminds the driver to brake in time by the shaking of the seatbelt, reducing the probability of the vehicle encountering a traffic accident, but also can adjust the binding force of the seatbelt to enhance the restraint effect of the seatbelt.

[0196] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above division of each functional module is used as an example. In practical applications, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0197] The embodiments of the present application provide a computer-readable storage medium storing a computer program that can be loaded and executed by a processor to implement the seatbelt-based braking warning method.

[0198] Computer storage media include, for example: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, etc., all kinds of media that can store program codes.

[0199] Based on the same inventive concept, the embodiments of the present application provide an intelligent terminal including a memory and a processor, and a computer program capable of being loaded and executed by the processor to implement the seatbelt-based braking warning method is stored on the memory.

[0200] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above division of each functional module is used as an example. In practical applications, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0201] The above are all preferred embodiments of the present application, and do not limit the protection scope of the present application accordingly. Any feature disclosed in this specification (including the abstract and drawings), unless specifically described, can be replaced by other equivalent or similar-purpose alternative features. That is, unless specifically described, each feature is only an example of a series of equivalent or similar features.

Claims

1. A brake warning method based on a seat belt, characterized in that: The method comprises: Obtain the current speed and current distance of vehicles in the area in front of the current vehicle through the radar system; Taking the minimum value of the current distances, obtaining the target current distance and the target current speed corresponding to the target current distance, and marking the vehicle corresponding to the target current distance as the target vehicle; Acquiring a front view image of the current vehicle through a front view monitoring system; identifying the target vehicle from the front view image according to the current distance of the target; Determining vehicle lighting information of the target vehicle through the front view image; Obtaining the accident probability of the current vehicle according to the current distance of the target, the current speed of the target and the vehicle light information; When the accident probability is greater than a preset probability, controlling the seat belt of the current vehicle to vibrate, and adjusting the restraint force corresponding to the seat belt in use on the current vehicle so that the restraint force is greater than a preset threshold; Among them, before obtaining the current speed and current distance of the vehicle in the area in front of the current vehicle through the radar system, it also includes: in response to receiving a brake signal sent by other vehicles, counting the number of brake signals, the distance between the other vehicles and the current vehicle is less than the vehicle impact distance, and the brake signal is broadcast after the other vehicle receives the brake operation; judging whether the number of signals is greater than a number threshold; if so, controlling the seat belt of the current vehicle to shake, and adjusting the restraint force corresponding to the seat belt in use on the current vehicle so that the restraint force is greater than a preset threshold; if not, maintaining the driving state of the current vehicle.

2. The seat belt-based brake warning method according to claim 1, characterized in that: The obtaining the accident probability of the current vehicle according to the current distance of the target, the current speed of the target and the vehicle light information includes: Obtaining the current driving speed and driving direction of the vehicle; Performing motion analysis according to the driving speed and the driving direction to generate a first predicted trajectory of the current vehicle; Performing motion analysis based on the current speed of the target and the vehicle light information to generate a second predicted trajectory of the target vehicle; The accident probability is obtained by combining the first predicted trajectory, the second predicted trajectory, and the current distance of the target.

3. The seat belt-based brake warning method according to claim 2, characterized in that: The method further comprises: identifying a vehicle type of the target vehicle; When the vehicle type belongs to a preset type, judging whether the target vehicle is in a cargo-carrying state through the front view image; If so, identifying the type of cargo on the target vehicle through the front view image; identifying the stacking height of the cargo type on the target vehicle through the front view image; determining a first predicted weight of the target vehicle in a first preset mapping relationship according to the cargo type, the stacking height and the vehicle type; and updating the accident probability according to the stacking height and the first predicted weight; If not, then according to the vehicle type, a second predicted weight of the target vehicle is determined in a second preset mapping relationship; and the accident probability is updated according to the second predicted weight.

4. The seat belt-based brake warning method according to claim 1, characterized in that: The method further comprises: Obtaining the area where the current vehicle is located through a navigation system; When the area is a vehicle traffic area, executing the step of determining whether the number of signals is greater than a number threshold; When the area where the vehicle is located is not the vehicle passage area, receiving the status signal sent by the other vehicle, the status signal is used to indicate whether the other vehicle has stopped; counting the number of stop signals and the number of motion signals in the status signal; and when the ratio of the number of motion signals to the number of stop signals is less than a ratio threshold, canceling the shaking of the seat belt.

5. The seat belt-based brake warning method according to claim 4, characterized in that: After executing the step of determining whether the number of signals is greater than the number threshold, the method further includes: When the area where the current vehicle is located is the vehicle traffic area and the number of signals is greater than the number threshold, obtaining the total number of vehicles in the surrounding area of ​​the current vehicle; When the total number of vehicles is greater than the vehicle number threshold, counting the frequency of brake signal generation by vehicles within the surrounding area within a preset time period; When the braking signal generation frequency is greater than a frequency threshold, counting the braking frequency of the current vehicle within the preset time period; When the braking frequency is greater than the frequency threshold, a pause shaking signal is generated, and the pause shaking signal is used to instruct the seat belt of the current vehicle to stop shaking within a preset specified time period.

6. The seat belt-based brake warning method according to claim 5, characterized in that: The obtaining of the total number of vehicles in the surrounding area of ​​the current vehicle includes: Taking the driving direction of the current vehicle as the reference direction and the current vehicle as the origin, a detection angle is set, and the angle bisector of the detection angle points to the driving direction; Broadcasting a status request within the detection angle; receiving a return signal, and counting the number of return signals; reading a candidate lane signal of the candidate vehicle from the return signal; Acquiring a current lane signal of the current vehicle; Determine a matching lane signal corresponding to the current lane signal from the lane mapping relationship; The number of signals in the candidate lane signals that are identical to the matching lane signal is counted to obtain the total number of vehicles within the surrounding range of the current vehicle.

7. A brake warning system based on a seat belt, characterized in that: The system comprises: An acquisition module, used to acquire current speed, current distance, front view image, preset probability, driving speed, driving direction, preset type, first preset mapping relationship, second preset mapping relationship, brake signal, quantity threshold, return signal and lane mapping relationship; A memory, used to store a program of the seat belt-based braking warning method according to any one of claims 1 to 6; The program in the processor memory can be loaded and executed by the processor to implement the seat belt-based braking warning method as described in any one of claims 1 to 6.

8. An intelligent terminal, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 6.

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