Automatic emergency braking control method and device, computer equipment and vehicle

Abstract

The invention relates to an automatic emergency braking control method and device, computer equipment and a vehicle. The method comprises the following steps: acquiring own vehicle state information, and determining whether an own vehicle meets a preset effective condition or not according to the own vehicle state information; under the condition that the own vehicle state information meets a preset effective condition, target vehicle state information is obtained, and the longitudinal collision time between the own vehicle and the target vehicle is calculated according to the target vehicle state information and the own vehicle state information; under the condition that the longitudinal collision time meets a preset collision condition, determining that the target vehicle is a short-distance early warning target, and giving an alarm; and detecting the definition of the current lane line, and controlling the vehicle to brake and decelerate according to the current road curvature under the condition that the current lane line is not clear and the vehicle is in a stable straight moving state. The method can adapt to vehicle requirements, reduces the false triggering probability, avoids vehicle instability, and improves the vehicle driving safety and reliability.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent vehicles, and in particular, to an automatic emergency braking control method, device, computer device, and vehicle. Background Art

[0002] Compared with passenger vehicles, commercial vehicles have the characteristics of long body, high center of mass, and heavy load, and are more likely to have serious traffic accidents during driving. Therefore, AEB (Automatic Emergency Braking) as a key safety technology has gradually received wide attention. However, the traditional AEB system cannot adapt to the characteristics of commercial vehicles, the system is prone to false triggering, the braking effect is poor, there is a warning risk, which seriously affects driving safety. Summary of the Invention

[0003] Based on this, in view of the above technical problems, it is necessary to provide an automatic emergency braking control method, device, computer device, and vehicle that can adapt to the characteristics of commercial vehicles.

[0004] In a first aspect, the present application provides an automatic emergency braking control method, which includes:

[0005] Obtain the self-vehicle state information, and determine whether the self-vehicle meets the preset valid condition according to the self-vehicle state information;

[0006] When the self-vehicle state information meets the preset valid condition, obtain the target vehicle state information, and calculate the longitudinal collision time between the self-vehicle and the target vehicle according to the target vehicle state information and the self-vehicle state information;

[0007] When the longitudinal collision time meets the preset collision condition, determine the target vehicle as a close-range warning target and issue an alarm;

[0008] Detect the clarity of the current lane line. When the current lane line is not clear and the self-vehicle is in a stable straight-ahead state, control the self-vehicle to brake and decelerate according to the current road curvature.

[0009] In one embodiment, obtaining the self-vehicle state information and determining whether the self-vehicle meets the preset valid condition according to the self-vehicle state information includes:

[0010] Obtain the self-vehicle state information, where the self-vehicle state information includes the confidence level of the current lane line quality;

[0011] When the confidence level of the current lane line quality is greater than the preset threshold, or when the confidence level of the current lane line quality is less than the preset threshold and the self-vehicle is in a stable straight-ahead state, determine that the self-vehicle meets the preset valid condition.

[0012] In one embodiment, when the confidence level of the current lane line quality is less than a preset threshold and the host vehicle is in a stable straight - driving state, determining that the host vehicle meets the preset valid conditions includes:

[0013] When it meets the condition that the heading angle of the host vehicle is less than a first angle and the steering wheel angle of the host vehicle is less than a second angle within a preset time, it is determined that the host vehicle is in a stable straight - driving state.

[0014] In one embodiment, obtaining the host vehicle state information and determining whether the host vehicle meets the preset valid conditions according to the host vehicle state information further includes:

[0015] Obtain the host vehicle state information, where the host vehicle state information includes the confidence level of the current lane line quality;

[0016] When the confidence level of the current lane line quality is less than a preset threshold and the host vehicle is not in a stable straight - driving state, it is determined that the host vehicle does not meet the preset valid conditions, and no automatic emergency braking is required.

[0017] In one embodiment, the host vehicle state information includes the host vehicle speed, and the target vehicle state information includes the longitudinal and lateral distances and the longitudinal and lateral speeds of the target vehicle; according to the target vehicle state information and the host vehicle state information, calculating the longitudinal collision time between the host vehicle and the target vehicle includes:

[0018] Filter the longitudinal and lateral speed signals of the target vehicle;

[0019] Calculate the longitudinal collision time according to the host vehicle speed, the longitudinal and lateral distances, the longitudinal and lateral speeds of the target vehicle, and a preset safety distance.

[0020] In one embodiment, when the longitudinal collision time meets the preset collision condition, determining that the target vehicle is a close - range warning target and issuing an alarm includes:

[0021] When the longitudinal collision time is greater than the collision time threshold and the relative speed between the host vehicle and the target vehicle is not greater than a first speed, determine whether the longitudinal and lateral distance meets the close - range alarm condition according to the longitudinal and lateral distance;

[0022] When the longitudinal and lateral distance meets the close - range alarm condition, determine that the target vehicle is a close - range warning target and issue an alarm.

[0023] In one embodiment, controlling the host vehicle to brake and decelerate according to the current road curvature further includes:

[0024] Calculate the straight - road braking acceleration according to the braking coefficient and the theoretical braking acceleration;

[0025] Calculate the centripetal acceleration according to the host vehicle speed and the road curvature radius;

[0026] Calculate the maximum braking acceleration on a curve based on the straight - line braking acceleration and centripetal acceleration;

[0027] Control the braking and deceleration of the host vehicle according to the maximum braking acceleration on the curve.

[0028] In a second aspect, the present application also provides an automatic emergency braking control device. The device includes:

[0029] An information acquisition module, configured to acquire the host vehicle state information and determine whether the host vehicle meets a preset valid condition according to the host vehicle state information;

[0030] A collision recognition module, configured to acquire the target vehicle state information when the host vehicle state information meets the preset valid condition, and calculate the longitudinal collision time between the host vehicle and the target vehicle according to the target vehicle state information and the host vehicle state information;

[0031] A close - range warning module, configured to determine that the target vehicle is a close - range warning target and issue an alarm when the longitudinal collision time meets a preset collision condition;

[0032] A braking module, configured to detect the clarity of the current lane line, and control the braking and deceleration of the host vehicle according to the current road curvature when the current lane line is not clear and the host vehicle is in a stable straight - line state.

[0033] In a third aspect, the present application also provides a computer device. The computer device includes a memory and a processor. When the processor executes the computer program, the steps in any one of the embodiments of the first aspect are implemented.

[0034] In a fourth aspect, the present application also provides a vehicle. The vehicle includes the automatic emergency braking control device in the embodiment of the second aspect.

[0035] The above - mentioned automatic emergency braking control method improves the safety of commercial vehicles in close - range dangerous scenarios through a close - range alarm strategy; when the lane line is not clear, by judging the stable straight - line condition of the host vehicle, the false - triggering probability is reduced; at the same time, by comprehensively considering the influence of road curvature and brake coefficient on the braking intensity, the braking deceleration on different road curvatures is limited to avoid vehicle instability, thereby improving the driving safety and reliability of commercial vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is an application environment diagram of the automatic emergency braking control method in an embodiment;

[0037] Figure 2 It is a flowchart of the automatic emergency braking control method in an embodiment;

[0038] Figure 3A flowchart of the steps for obtaining the self-vehicle status information in an embodiment and determining whether the self-vehicle meets the preset valid conditions based on the self-vehicle status information;

[0039] Figure 4 A flowchart of the steps for obtaining the self-vehicle status information in another embodiment and determining whether the self-vehicle meets the preset valid conditions based on the self-vehicle status information;

[0040] Figure 5 A flowchart of the steps for calculating the longitudinal collision time between the self-vehicle and the target vehicle according to the target vehicle status information and the self-vehicle status information in an embodiment;

[0041] Figure 6 A flowchart of the steps for determining the target vehicle as a close-range warning target and issuing an alarm when the longitudinal collision time meets the preset collision conditions in an embodiment;

[0042] Figure 7 A flowchart of the steps for controlling the self-vehicle to brake and decelerate according to the current road curvature in an embodiment;

[0043] Figure 8 A structural block diagram of an automatic emergency braking control device in an embodiment;

[0044] Figure 9 An internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0045] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to 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.

[0047] As a key safety technology, the automatic emergency braking system (AEB) has gradually received wide attention in recent years. Research shows that vehicles equipped with the AEB system can effectively reduce the occurrence of traffic accidents, especially showing significant effects in avoiding rear-end collisions and reducing the severity of accidents. However, due to the special nature of commercial vehicles (such as long braking distances, large inertia, high center of gravity, etc.), the actual application performance of the AEB system on commercial vehicles has attracted people's attention. The AEB system for commercial vehicles not only requires higher reliability, but also needs to be specifically optimized according to its characteristics to ensure that it can effectively play its role under various complex working conditions.

[0048] Currently, the AEB system generally consists of three stages: the first-level warning of FCW (Forward Collision Warning), the second-level warning of FCW, and the AEB emergency braking stage. The system obtains data such as the longitudinal and lateral distances, longitudinal and lateral speeds, target life cycle, current lane line information, and the speed of the host vehicle of the target vehicle through millimeter-wave radar and cameras, calculates the Time to Collision (TTC), and implements a hierarchical braking control strategy according to the driver's acceptance and the degree of target danger. However, the existing AEB systems still have the following problems in commercial vehicle applications:

[0049] Insufficient warning at close range: Commercial vehicles have a longer braking distance, and the existing systems may have insufficient warning and braking responses in close-range dangerous scenarios, increasing the collision risk.

[0050] False triggering when lane lines are unclear: In the case of unclear or missing lane lines, the system may misjudge the danger of the target vehicle, resulting in unnecessary braking triggers, affecting the driving experience and driving safety.

[0051] Influence of road curvature and brake coefficient: The braking intensity and vehicle stability of commercial vehicles are significantly affected by road curvature and brake coefficient. The existing systems do not fully consider these factors, which may lead to vehicle instability or poor braking effect.

[0052] In view of the above problems, the present invention proposes an automatic emergency braking control method, aiming to solve the practical application problems of the AEB system for commercial vehicles and provide strong support for the further development of commercial vehicle safety technology.

[0053] The automatic emergency braking control method provided by the embodiments of the present application can be applied to an application environment as shown in Figure 1 . Among them, the data acquisition device 102 and the terminal 104 perform data transmission through a communication interface. The data acquisition device 102 collects automatic braking parameters such as the state information of the host vehicle, the state information of the target vehicle, and the current lane information, and sends them to the terminal 104; the terminal 104 receives and processes these automatic braking parameters, calculates the collision time, and issues a control instruction. Among them, the terminal 104 can be but is not limited to various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices.

[0054] In an exemplary embodiment, as shown in Figure 2 , an automatic emergency braking control method is provided. Taking the method applied to the terminal 104 in Figure 1 as an example, the method includes the following steps:

[0055] Step 202, obtain the state information of the host vehicle, and determine whether the host vehicle meets the preset effective conditions according to the state information of the host vehicle.

[0056] Optionally, obtain the state information of the host vehicle in real time through in-vehicle sensors (such as inertial measurement units, wheel speed sensors, etc.), including the host vehicle speed, acceleration, yaw rate, steering wheel angle, etc. According to the preset valid conditions (such as whether the host vehicle speed is within a reasonable range, whether the sensor data is valid, etc.), determine whether the host vehicle is in a state where AEB control can be executed. If the state information of the host vehicle meets the preset valid conditions, proceed to the next step; otherwise, pause the AEB function and filter out the warning for the target vehicle.

[0057] Step 204, when the state information of the host vehicle meets the preset valid conditions, obtain the state information of the target vehicle, and calculate the longitudinal collision time between the host vehicle and the target vehicle based on the state information of the target vehicle and the host vehicle.

[0058] Optionally, obtain the state information of the target vehicle through millimeter-wave radar and cameras, including the longitudinal and lateral distances, longitudinal and lateral speeds, target life cycle, etc. of the target vehicle. Combining the state information such as the host vehicle speed and acceleration, use the Time To Collision (TTC) algorithm to calculate the longitudinal collision time between the host vehicle and the target vehicle to evaluate the potential threat level of the target vehicle to the host vehicle.

[0059] Step 206, when the longitudinal collision time meets the preset collision conditions, determine the target vehicle as a close-range warning target and issue an alarm.

[0060] Optionally, according to the preset collision conditions, determine whether the target vehicle is a close-range warning target. If the conditions are met, issue an alarm to the driver through the in-vehicle human-machine interface (such as the instrument panel, sound alarm, etc.) to provide sufficient reaction time and effectively avoid collision accidents.

[0061] Step 208, detect the clarity of the current lane line. When the current lane line is not clear and the host vehicle is in a stable straight-ahead state, control the host vehicle to brake and decelerate according to the current road curvature.

[0062] Optionally, detect the clarity of the current lane line through a camera. If the lane line is not clear (such as due to road surface wear, bad weather conditions, etc.), the system further determines whether the host vehicle is in a stable straight-ahead state (such as whether the steering wheel angle is close to zero, whether the yaw rate is within a reasonable range). If the host vehicle is in a stable straight-ahead state, adjust the braking deceleration according to the current road curvature (obtained through in-vehicle sensors or high-precision maps). For example, when driving on a curve, the system will appropriately reduce the braking deceleration to avoid vehicle instability or skidding caused by excessive braking.

[0063] The above-mentioned automatic emergency braking control method realizes accurate collision warning and braking control by obtaining the state information of the host vehicle and the target vehicle and combining the road environment data, improving the safety of commercial vehicles in close-range dangerous scenarios; when the lane lines are unclear, the false trigger probability is reduced by judging the stable straight-line condition of the host vehicle; at the same time, considering the influence of road curvature and brake coefficient on braking intensity, the braking deceleration on different road curvatures is limited to avoid vehicle instability, thereby improving the driving safety and reliability of commercial vehicles.

[0064] The following several embodiments specifically elaborate on the effective condition determination logic of the AEB system of the present application.

[0065] In an exemplary embodiment, as Figure 3 shown, obtaining the state information of the host vehicle and determining whether the host vehicle meets the preset effective conditions according to the state information of the host vehicle includes:

[0066] Step 302, obtain the state information of the host vehicle, where the state information of the host vehicle includes the confidence level of the current lane line quality.

[0067] Step 304, when the confidence level of the current lane line quality is greater than the preset threshold, or when the confidence level of the current lane line quality is less than the preset threshold and the host vehicle is in a stable straight-line state, determine that the host vehicle meets the preset effective conditions.

[0068] Exemplarily, the state information of the host vehicle further includes data such as the speed of the host vehicle, the width of the host vehicle, and the rotational speed of the steering angle of the host vehicle. These information are jointly used to evaluate the driving state and stability of the host vehicle. The confidence level threshold of the lane line quality is dynamically adjusted according to the speed of the host vehicle. The corresponding relationship between the threshold and the speed of the host vehicle is shown in Table 1. For example, when the speed of the host vehicle is relatively high, the threshold is correspondingly increased to ensure the reliability of the lane line recognition result. When the confidence level of the lane line quality is greater than the threshold or the confidence level of the current lane line quality is less than the preset threshold and the host vehicle is in a stable straight-line state, the target is effective and the AEB function can operate and enter the next stage for judgment.

[0069] Table 1 Confidence Level Threshold Table of Lane Line Quality

[0070]

[0071] In this embodiment, by obtaining the vehicle speed, width, steering angle rotation speed of the vehicle itself, and the current lane line information, and combining the lane line quality confidence and the motion state of the vehicle itself, an accurate judgment of the target effectiveness is achieved. The dynamic threshold adjustment is realized, and the lane line quality confidence threshold is dynamically adjusted according to the vehicle speed to ensure the judgment accuracy at different vehicle speeds; the false triggering is effectively suppressed, and the straight-ahead state judgment is stabilized. By judging the vehicle's heading angle and steering wheel angle, the reliability of the target effectiveness determination is ensured, and the false triggering of the AEB system caused by misjudgment is avoided, improving the safety and user experience of the system. This method is applicable to vehicle safety control in complex road environments and provides a strong guarantee for the reliable operation of the AEB system.

[0072] In an exemplary embodiment, when the current lane line quality confidence is less than the preset threshold and the vehicle itself is in a stable straight-ahead state, determining that the vehicle itself meets the preset effective condition includes the steps of:

[0073] When it is satisfied that the vehicle's heading angle is less than the first angle and the vehicle's steering wheel angle is less than the second angle within the preset time, it is determined that the vehicle itself is in a stable straight-ahead state.

[0074] Exemplarily, referring to Table 1, if the lane line quality confidence is less than the threshold, it is further determined whether the vehicle itself is in a stable straight-ahead state. The vehicle's heading angle is obtained through an inertial measurement unit or a yaw rate sensor to ensure that the vehicle does not have an obvious lateral offset; the steering wheel angle is obtained through a steering wheel angle sensor to ensure that the driver does not perform an obvious steering operation. The determination condition for the stable straight-ahead state is that within the preset time of 0.5 s, it is satisfied that the vehicle's heading angle is less than 0.038° and the steering wheel angle is less than 45°. The preset time, the first angle, and the second angle can all be adjusted according to experimental conditions and actual requirements.

[0075] In an exemplary embodiment, as Figure 4 shown, obtaining the vehicle state information of the vehicle itself and determining whether the vehicle itself meets the preset effective condition according to the vehicle state information further includes:

[0076] Step 402, obtaining the vehicle state information of the vehicle itself, where the vehicle state information includes the current lane line quality confidence.

[0077] Step 404, when the current lane line quality confidence is less than the preset threshold and the vehicle itself is not in a stable straight-ahead state, it is determined that the vehicle itself does not meet the preset effective condition, and automatic emergency braking is not required.

[0078] Exemplarily, road images are collected by a camera or a vision sensor, lane lines are identified using image processing algorithms, and the lane line quality confidence is calculated. When the lane line quality confidence is greater than the lane line quality confidence threshold and the host vehicle is not in a stable straight - driving state, the host vehicle does not meet the preset effective conditions, and there is no need for automatic emergency braking. The target is filtered to avoid unnecessary braking triggers caused by misjudgment.

[0079] In the above - mentioned several embodiments, by combining the lane line quality confidence and the host vehicle motion state, accurate judgment of target effectiveness is achieved. Considering the lane line quality confidence and the host vehicle driving state comprehensively, the determination logic of the effective conditions of the AEB system is optimized, ensuring that the AEB function can still operate safely and reliably in complex road environments. This design effectively reduces the probability of mis - triggering, improves the adaptability and robustness of the system, and provides a strong guarantee for the safe driving of the vehicle under various working conditions.

[0080] The following several embodiments specifically illustrate the close - range warning and braking mechanism of the present application.

[0081] In an exemplary embodiment, as Figure 5 shown, the host vehicle state information includes the host vehicle speed, and the target vehicle state information includes the longitudinal and lateral distances and the longitudinal and lateral speeds of the target vehicle; calculating the longitudinal collision time between the host vehicle and the target vehicle according to the target vehicle state information and the host vehicle state information includes:

[0082] Step 502, filter the longitudinal and lateral speed signals of the target vehicle.

[0083] Step 504, calculate the longitudinal collision time according to the host vehicle speed, longitudinal and lateral distances, longitudinal and lateral speeds of the target vehicle, and a preset safety distance.

[0084] Exemplarily, the host vehicle state information includes the host vehicle speed, and the target vehicle state information includes the longitudinal and lateral distances, the longitudinal and lateral speeds of the target vehicle, and the target life cycle. The target life cycle is used to track the existence time of the target vehicle, judge whether the target is stable and reliable, and evaluate the persistence of the target vehicle in the system's field of view. The core role of the target life cycle is to improve the reliability of target detection, optimize system resource allocation, enhance the accuracy of trajectory prediction, and suppress mis - triggering. It is an important parameter in the AEB system to ensure the authenticity and stability of target vehicle information, and is directly related to the safety and robustness of the system.

[0085] The longitudinal and lateral speed signals of the target vehicle are usually collected by millimeter - wave radar or cameras. However, due to factors such as sensor noise and environmental interference, the original signals may be fluctuating or unstable. To improve the calculation accuracy, the system performs filtering processing on the longitudinal and lateral speed signals of the target vehicle. Kalman filtering or low - pass filtering algorithms are used to filter out high - frequency noise and retain the low - frequency components of the signals, making the speed signals smoother and more stable.

[0086] The formula for calculating the longitudinal collision time is as follows:

[0087]

[0088] Wherein, represents the longitudinal collision time; represents the horizontal and vertical distance between the host vehicle and the target vehicle; represents the safety distance, which can be set to 2m; represents the speed of the host vehicle; represents the horizontal and vertical speed of the target vehicle.

[0089] In this embodiment, the formula for calculating the collision time based on the relative speed and distance can accurately evaluate the collision risk, providing a scientific basis for the warning and braking of the AEB system. The preset safety distance and collision time threshold can be dynamically adjusted according to the vehicle type and driving environment, improving the adaptability and reliability of the system.

[0090] In an exemplary embodiment, as Figure 6 shown, when the longitudinal collision time meets the preset collision condition, the target vehicle is determined as a close-range warning target, and the alarm includes:

[0091] Step 602, when the longitudinal collision time is greater than the collision time threshold and the relative speed between the host vehicle and the target vehicle is not greater than the first speed, determine whether the horizontal and vertical distance meets the close-range alarm condition according to the horizontal and vertical distance.

[0092] Step 604, when the horizontal and vertical distance meets the close-range alarm condition, determine the target vehicle as a close-range warning target and issue an alarm.

[0093] Exemplarily, is the collision time threshold. When , it is determined that the target can be the target of the FCW stage. When , that is, when the target is not the target of the FCW stage, when the relative speed between the host vehicle and the target vehicle ≤ 3 km / h, determine whether the relative distance is less than the close-range alarm threshold. The close-range alarm threshold is shown in Table 2, and the close-range alarm threshold is calibrated according to the host vehicle speed. If the relative distance is less than the close-range alarm threshold, it is determined as a close-range warning target. In the case of relatively low collision risk but still requiring attention, the driver is reminded to stay vigilant to avoid potential dangers caused by negligence or slow reaction.

[0094] Table 2 Close-range alarm threshold table

[0095]

[0096] In this embodiment, by comprehensively judging the longitudinal collision time, relative speed, and longitudinal and lateral distances, the precise triggering of the short-distance alarm function is achieved, providing an important auxiliary function for the vehicle automatic emergency braking system and effectively improving driving safety and the driver experience.

[0097] In an exemplary embodiment, as Figure 7 shown, controlling the vehicle's own braking and deceleration according to the current road curvature includes:

[0098] Step 702, calculate the straight-road braking acceleration according to the braking coefficient and the theoretical braking acceleration.

[0099] Step 704, calculate the centripetal acceleration according to the vehicle's own speed and the road curvature radius.

[0100] Step 706, calculate the maximum braking acceleration in the curve according to the straight-road braking acceleration and the centripetal acceleration.

[0101] Step 708, control the vehicle's own braking and deceleration according to the maximum braking acceleration in the curve.

[0102] Exemplarily, during the actual braking execution process, the tires will deform, and there will also be reasons such as brake pad wear, braking clearance, and return springs, resulting in not being completely in the state of peak longitudinal force, and the braking intensity is less than the peak adhesion coefficient, and the actual straight-road braking acceleration is difficult to reach the theoretical braking acceleration.

[0103] Therefore, the straight-road braking acceleration is:

[0104]

[0105] Wherein, is the braking coefficient, representing the friction coefficient between the tire and the road surface, usually determined according to road conditions (such as dry, wet, icy, etc.) and tire performance, and can be exemplarily taken as 0.91; represents the straight-road braking acceleration, represents the theoretical braking acceleration.

[0106] On the curve, to avoid the vehicle from losing lateral stability and rolling over, part of the braking force is used to resist the centripetal force, and the centripetal acceleration is:

[0107]

[0108] Wherein, represents the centripetal acceleration, represents the square of the vehicle's own speed; represents the road curvature radius.

[0109] Therefore, on the curve, the maximum braking deceleration of the vehicle is:

[0110]

[0111] During the braking execution process, the above formula is used to limit the deceleration to avoid rollover caused by excessive deceleration on a curve.

[0112] In this embodiment, by comprehensively considering the road curvature, the vehicle's own speed, and the braking performance, the braking control strategy for the vehicle when driving on a curve is optimized. Based on the calculation of the maximum braking acceleration on a curve considering the straight-line braking acceleration and the centripetal acceleration, the scientificity and rationality of the braking control are ensured; the braking acceleration is dynamically adjusted according to the real-time monitoring data to improve the adaptability and reliability of the system; by reserving a safety margin and providing human-machine interaction prompts, the risk of vehicle instability is reduced, and the driving safety and driving experience are improved, providing important support for the application of the vehicle automatic emergency braking system in a complex road environment, and is particularly suitable for the safety control of large vehicles such as commercial vehicles.

[0113] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0114] Based on the same inventive concept, the embodiments of the present application also provide an automatic emergency braking control device for implementing the automatic emergency braking control method described above. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the automatic emergency braking control device provided below can refer to the limitations on the automatic emergency braking control method in the above text, and will not be repeated here.

[0115] In an exemplary embodiment, as Figure 8 shown, an automatic emergency braking control device is provided, including: an information acquisition module 810, a collision recognition module 820, a close-range warning module 830, and a braking module 840, where:

[0116] The information acquisition module 810 is configured to acquire the vehicle's own state information and determine whether the vehicle meets a preset valid condition according to the vehicle's own state information;

[0117] A collision recognition module 820, configured to obtain target vehicle state information when the self-vehicle state information meets a preset valid condition, and calculate the longitudinal collision time between the self-vehicle and the target vehicle according to the target vehicle state information and the self-vehicle state information;

[0118] A close-range warning module 830, configured to determine the target vehicle as a close-range warning target and issue an alarm when the longitudinal collision time meets a preset collision condition;

[0119] A braking module 840, configured to detect the clarity of the current lane line, and control the self-vehicle to brake and decelerate according to the current road curvature when the current lane line is not clear and the self-vehicle is in a stable straight-ahead state.

[0120] In an exemplary embodiment, the information acquisition module 810 includes a lane line evaluation module, and the lane line evaluation module is configured to obtain self-vehicle state information, where the self-vehicle state information includes the confidence level of the current lane line quality;

[0121] When the confidence level of the current lane line quality is greater than a preset threshold, or when the confidence level of the current lane line quality is less than the preset threshold and the self-vehicle is in a stable straight-ahead state, it is determined that the self-vehicle meets the preset valid condition.

[0122] In an exemplary embodiment, the lane line evaluation module is further configured to determine that the self-vehicle is in a stable straight-ahead state when the self-vehicle heading angle is less than a first angle and the self-vehicle steering wheel angle is less than a second angle within a preset time.

[0123] In an exemplary embodiment, the lane line evaluation module is further configured to obtain self-vehicle state information, where the self-vehicle state information includes the confidence level of the current lane line quality;

[0124] When the confidence level of the current lane line quality is less than the preset threshold and the self-vehicle is not in a stable straight-ahead state, it is determined that the self-vehicle does not meet the preset valid condition and no automatic emergency braking is required.

[0125] In an exemplary embodiment, the collision recognition module 820 is further configured to filter the lateral and longitudinal speed signals of the target vehicle;

[0126] Calculate the longitudinal collision time according to the self-vehicle speed, lateral and longitudinal distances, the lateral and longitudinal speeds of the target vehicle, and a preset safety distance.

[0127] In an exemplary embodiment, the close-range warning module 830 is further configured to determine whether the lateral and longitudinal distance meets the close-range alarm condition according to the lateral and longitudinal distance when the longitudinal collision time is greater than the collision time threshold and the relative speed between the self-vehicle and the target vehicle is not greater than a first speed;

[0128] When the horizontal and vertical distances meet the conditions for short-distance alarm, determine the target vehicle as a short-distance warning target and issue an alarm.

[0129] In an exemplary embodiment, the braking module 840 is further configured to calculate the straight-line braking acceleration according to the braking coefficient and the theoretical braking acceleration;

[0130] Calculate the centripetal acceleration according to the vehicle speed and the road curvature radius;

[0131] Calculate the maximum braking acceleration in a curve according to the straight-line braking acceleration and the centripetal acceleration;

[0132] Control the vehicle to brake and decelerate according to the maximum braking acceleration in a curve.

[0133] Each module in the above automatic emergency braking control device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0134] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 9 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used for the processor to exchange information with external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements an automatic emergency braking control method.

[0135] Those skilled in the art can understand that Figure 9 the structure shown in

[0136] In an exemplary embodiment, a computer device is provided, which includes a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in any one of the above method embodiments are implemented.

[0137] In an exemplary embodiment, a vehicle is provided, which includes the automatic emergency braking control device in any one of the above device embodiments.

[0138] 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 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, and the collection, use, and processing of relevant data need to comply with the relevant laws, regulations, and standards of relevant countries and regions.

[0139] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. 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. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0140] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope described in this specification.

[0141] The above-described embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. An automatic emergency braking control method, characterized in that: The method comprises: Obtaining vehicle status information, and determining whether the vehicle satisfies a preset validity condition based on the vehicle status information; When the self-vehicle state information satisfies the preset validity condition, acquiring the target vehicle state information, and calculating the longitudinal collision time between the self-vehicle and the target vehicle according to the target vehicle state information and the self-vehicle state information; When the longitudinal collision time satisfies a preset collision condition, determining that the target vehicle is a close-range warning target and issuing an alarm; The clarity of the current lane line is detected, and when the current lane line is unclear and the vehicle is in a stable straight-moving state, the vehicle is controlled to brake and decelerate according to the current road curvature.

2. The automatic emergency braking control method according to claim 1, characterized in that: The obtaining of the vehicle status information and determining whether the vehicle satisfies a preset validity condition according to the vehicle status information includes: Acquiring the vehicle state information, wherein the vehicle state information includes a current lane line quality confidence; When the current lane line quality confidence is greater than a preset threshold, or when the current lane line quality confidence is less than a preset threshold and the ego vehicle is in a stable straight driving state, it is determined that the ego vehicle meets the preset validity condition.

3. The automatic emergency braking control method according to claim 2, characterized in that: When the current lane line quality confidence is less than a preset threshold and the vehicle is in a stable straight-moving state, determining that the vehicle satisfies the preset validity condition includes: When the heading angle of the vehicle is less than the first angle and the steering wheel angle of the vehicle is less than the second angle within a preset time, it is determined that the vehicle is in a stable straight-moving state.

4. The automatic emergency braking control method according to claim 2, characterized in that: The obtaining of the vehicle status information and determining whether the vehicle satisfies the preset validity condition according to the vehicle status information further includes: Acquiring the vehicle state information, wherein the vehicle state information includes a current lane line quality confidence; When the current lane line quality confidence is less than a preset threshold and the vehicle is not in a stable straight-moving state, it is determined that the vehicle does not meet the preset validity condition and automatic emergency braking is not required.

5. The automatic emergency braking control method according to claim 1, characterized in that: The vehicle state information includes the vehicle speed, and the target vehicle state information includes the lateral and longitudinal distances and the lateral and longitudinal speeds of the target vehicle; and calculating the longitudinal collision time between the vehicle and the target vehicle based on the target vehicle state information and the vehicle state information includes: Filtering the lateral and longitudinal speed signals of the target vehicle; The longitudinal collision time is calculated based on the vehicle speed, the lateral and longitudinal distances, the target vehicle lateral and longitudinal speeds and a preset safety distance.

6. The automatic emergency braking control method according to claim 1, characterized in that: When the longitudinal collision time satisfies a preset collision condition, determining that the target vehicle is a close-range warning target and issuing an alarm includes: When the longitudinal collision time is greater than the collision time threshold and the relative speed between the vehicle and the target vehicle is not greater than the first speed, determining whether the lateral and longitudinal distances meet the close-range alarm condition according to the lateral and longitudinal distances; When the lateral and longitudinal distances meet the close-range alarm condition, the target vehicle is determined to be a close-range warning target and an alarm is issued.

7. The automatic emergency braking control method according to claim 1, characterized in that: The controlling the braking deceleration of the vehicle according to the current road curvature comprises: Calculate the straight road braking acceleration based on the braking coefficient and theoretical braking acceleration; Calculate the centripetal acceleration based on the vehicle speed and the road curvature radius; Calculating a maximum braking acceleration on a curve according to the straight road braking acceleration and the centripetal acceleration; The braking deceleration of the vehicle is controlled according to the maximum braking acceleration of the curve.

8. An automatic emergency brake control device, characterized in that: The device comprises: An information acquisition module, used to acquire the vehicle status information and determine whether the vehicle satisfies a preset validity condition according to the vehicle status information; A collision recognition module, configured to obtain target vehicle state information when the self-vehicle state information satisfies the preset validity condition, and calculate the longitudinal collision time between the self-vehicle and the target vehicle based on the target vehicle state information and the self-vehicle state information; A close-range warning module, configured to determine that the target vehicle is a close-range warning target and issue an alarm when the longitudinal collision time satisfies a preset collision condition; The braking module is used to detect the clarity of the current lane line. When the current lane line is unclear and the vehicle is in a stable straight-moving state, the braking and deceleration of the vehicle are controlled according to the current road curvature.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A vehicle, characterized in that: The vehicle includes the automatic emergency brake control device as claimed in claim 8.

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