A control method for intelligent driving

Abstract

The application relates to a control method for intelligent driving and relates to the technical field of intelligent driving of vehicles. The method comprises the following steps: acquiring the speed of a rear vehicle, the speed of a self vehicle, the relative speed and the relative distance of the rear vehicle; determining a collision risk distance according to the relative speed, the relative distance and a preset system reaction time length; when the collision risk distance is less than or equal to a preset risk distance threshold, determining a safe vehicle distance according to the speed of the rear vehicle, the speed of the self vehicle, a preset adjustment coefficient and the relative distance; and adjusting the acceleration of the self vehicle based on the road conditions in front of the self vehicle so that the relative distance of the rear vehicle reaches the safe vehicle distance or the self vehicle slows down and lets the rear vehicle overtake. The application can reduce the rear-end collision risk of the rear vehicle.

Description

Technical Field

[0001] This application relates to the field of intelligent driving technology for vehicles, and in particular to a control method for intelligent driving. Background Technology

[0002] Currently, with the rapid development of intelligent driving technology, the accuracy and stability of perception systems have a significant impact on driving safety. However, most existing systems focus on forward or lateral perception and strategy optimization, while research on detection and response strategies for rear-end vehicles is relatively limited. Rear-end vehicle detection is crucial on highways or in complex road environments, especially under conditions of dense traffic and high speeds; a lack of accurate rear-end vehicle detection can easily lead to rear-end collisions. Summary of the Invention

[0003] Therefore, it is necessary to provide a control method for intelligent driving to address the aforementioned technical problems, the method comprising:

[0004] Get the speed of the vehicle behind, the speed of your own vehicle, the relative speed of the vehicle behind, and the relative distance;

[0005] The collision risk distance is determined based on the relative speed, the relative distance, and the preset system reaction time;

[0006] When the collision risk distance is less than or equal to a preset risk distance threshold, a safe following distance is determined based on the following vehicle speed, the following vehicle speed, a preset adjustment coefficient, and the relative distance.

[0007] Based on the road conditions ahead, adjust the vehicle's acceleration to ensure that the relative distance to the following vehicle reaches the safe following distance, or decelerate the vehicle to allow the following vehicle to overtake.

[0008] As an optional implementation, obtaining the speed of the following vehicle, the speed of the vehicle itself, the relative speed of the following vehicle, and the relative distance includes:

[0009] Real-time acquisition of vehicle speed, vehicle position, and following vehicle driving data collected by various driving information sensors;

[0010] Based on a preset data fusion algorithm, the following vehicle driving data collected by each driving information sensor is fused to determine the following vehicle speed and position in real time.

[0011] The relative speed and relative distance of the following vehicle are determined based on the following vehicle's speed, the following vehicle's position, the following vehicle's speed, and the following vehicle's position.

[0012] As an optional implementation, the formula for determining the collision risk distance based on the relative speed, the relative distance, and the preset system reaction time is as follows:

[0013] Drisk =D current -V relative ×t reaction ;

[0014] Among them, D risk D is the collision risk distance. current V is the relative distance. relative Let t be the relative velocity. reaction This is the preset system response time.

[0015] As an optional implementation, when the collision risk distance is less than or equal to a preset risk distance threshold, the formula for determining the safe following distance based on the following vehicle speed, the following vehicle speed, a preset adjustment coefficient, and the relative distance is as follows:

[0016] D new =D current +K adjust ×(V rear -V ego );

[0017] Among them, D new For a safe following distance, D current K represents the relative distance. adjust V is the preset adjustment coefficient. rear V represents the speed of the following vehicle. ego This refers to the vehicle's speed.

[0018] As an optional implementation, adjusting the vehicle's acceleration based on the road conditions ahead to ensure the relative distance to the following vehicle reaches the safe following distance, or decelerating the vehicle to allow the following vehicle to overtake, includes:

[0019] If the road conditions ahead of the vehicle are safe, and the speed of the vehicle behind is greater than the speed of the vehicle itself, then a first acceleration adjustment amount is determined based on a preset acceleration coefficient, the speed of the vehicle behind, and the speed of the vehicle itself.

[0020] Adjust the vehicle's acceleration according to the first acceleration adjustment amount so that the relative distance reaches the safe vehicle distance;

[0021] If the road conditions ahead of the vehicle are unsafe, and the speed of the vehicle behind is greater than the speed of the vehicle itself, then a second acceleration adjustment amount is determined based on a preset deceleration coefficient, the speed of the vehicle behind, and the speed of the vehicle itself.

[0022] Adjust the acceleration according to the second acceleration adjustment amount to decelerate the vehicle and allow the vehicle behind to overtake.

[0023] As an optional implementation, if the speed of the following vehicle is greater than the speed of the vehicle itself, and the road conditions ahead are safe, the formula for determining the first acceleration adjustment amount based on the preset acceleration coefficient, the speed of the following vehicle, and the speed of the vehicle itself is as follows:

[0024] A adjust1 =K accel ×(V rear -V ego );

[0025] Among them, A adjust1 K is the first acceleration adjustment amount. accel V is the preset acceleration coefficient. rear V represents the speed of the following vehicle. ego This refers to the vehicle's speed.

[0026] As an optional implementation, if the road conditions ahead of the vehicle are unsafe and the speed of the following vehicle is greater than the speed of the vehicle itself, the formula for determining the second acceleration adjustment amount based on the preset deceleration coefficient, the speed of the following vehicle, and the speed of the vehicle itself is as follows:

[0027] A adjust2 =-K decel ×(V rear -V ego );

[0028] Among them, A adjust2 K is the second acceleration adjustment amount. decel V is the preset deceleration coefficient. rear V represents the speed of the following vehicle. ego This refers to the vehicle's speed.

[0029] As an optional implementation, after acquiring the vehicle speed, vehicle position, and following vehicle driving data collected by various driving information sensors in real time, the method further includes:

[0030] The following vehicle driving data collected by each of the driving information sensors are corrected by using an extended Kalman filter.

[0031] As an optional implementation, the method further includes:

[0032] Obtain the road slope;

[0033] When the road gradient is detected to be greater than or equal to a preset gradient threshold, the braking distance of the following vehicle under the road gradient condition is determined based on the following vehicle speed, the preset automatic emergency braking acceleration, the road gradient, and the gravitational acceleration.

[0034] If the braking distance of the following vehicle is greater than the safe following distance, the vehicle's acceleration is adjusted based on the road conditions ahead, so that the braking distance of the following vehicle reaches the safe following distance or the vehicle decelerates and allows the following vehicle to overtake.

[0035] As an optional implementation, when the road slope is detected to be greater than or equal to a preset slope threshold, the formula for determining the braking distance of the following vehicle under the condition of the road slope, based on the following vehicle speed, the preset automatic emergency braking acceleration, the road slope, and gravitational acceleration, is as follows:

[0036] D brake =V rear 2 / [2(a brake +gsinθ)];

[0037] Among them, D brake V is the braking distance of the following vehicle. rear Let a be the speed of the following vehicle. brake θ is the preset automatic emergency braking acceleration, g is the acceleration due to gravity, and θ is the road slope.

[0038] In a second aspect, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor executes the computer program to implement the steps of the method as described in any of the first aspects.

[0039] Thirdly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method as described in any of the first aspects.

[0040] This application provides a control method for intelligent driving. The technical solution provided by the embodiments of this application brings at least the following beneficial effects. The method includes: acquiring the speed of the following vehicle, the speed of the vehicle itself, the relative speed and relative distance of the following vehicle; determining a collision risk distance based on the relative speed, the relative distance and a preset system reaction time; when the collision risk distance is less than or equal to a preset risk distance threshold, determining a safe following distance based on the speed of the following vehicle, the speed of the vehicle itself, a preset adjustment coefficient and the relative distance; and adjusting the acceleration of the vehicle itself based on the road conditions ahead, so that the relative distance of the following vehicle reaches the safe following distance or the vehicle itself decelerates and allows the following vehicle to overtake.

[0041] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 A flowchart of a control method for intelligent driving provided in an embodiment of this application;

[0044] Figure 2 A flowchart illustrating a method for obtaining vehicle driving information provided in an embodiment of this application;

[0045] Figure 3 A flowchart illustrating a method for adjusting the acceleration of a vehicle, as provided in this application embodiment;

[0046] Figure 4 A flowchart illustrating another intelligent driving control method provided in this application embodiment;

[0047] Figure 5 This is a schematic diagram of the structure of a smart driving control system provided in an embodiment of this application. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0049] The following will describe in detail, with reference to specific embodiments, a control method for intelligent driving provided in this application. Figure 1 A flowchart of a control method for intelligent driving provided in an embodiment of this application is shown below. Figure 1 As shown, the specific steps are as follows:

[0050] Step 101: Obtain the speed of the following vehicle, the speed of your own vehicle, the relative speed of the following vehicle, and the relative distance.

[0051] In practice, the vehicle can obtain the speed of the vehicle behind, the speed of the vehicle itself, the relative speed of the vehicle behind, and the relative distance.

[0052] As an optional implementation method, Figure 2 A flowchart illustrating a method for obtaining vehicle driving information provided in this application embodiment is shown below. Figure 2 As shown, the specific steps for obtaining the following vehicle speed, the vehicle's speed, the relative speed and relative distance in step 101 are as follows:

[0053] Step 201: Real-time acquisition of vehicle speed, vehicle position, and following vehicle driving data collected by various driving information sensors.

[0054] In practice, vehicles can obtain their own speed in real time through their vehicle computer, their own position in real time through the navigation system, and driving information data from following vehicles collected by sensors such as cameras, lidar, and millimeter-wave radar. For example, cameras can provide image information of following vehicles, and the system can identify the following vehicles and calculate their speed and position using preset image processing algorithms. LiDAR can detect the position and speed of following vehicles using point cloud data. Millimeter-wave radar can be used to detect targets at medium to long distances and can measure the position and speed of following vehicles unaffected by weather conditions.

[0055] As an optional implementation, after step 201, the method further includes:

[0056] The following vehicle driving data collected by each driving information sensor is corrected by using an extended Kalman filter.

[0057] In implementation, the following vehicle's driving data collected by each driving information sensor is used for independent state estimation via an extended Kalman filter. The extended Kalman filter first predicts the state based on the sensor observations, including the following vehicle's position, speed, and acceleration. The state prediction equation is as follows:

[0058]

[0059] The covariance prediction equation is:

[0060]

[0061] Among them, F k Q is the Jacobian matrix of the state transition function. k Let be the process noise covariance matrix.

[0062] The extended Kalman filter can correct the prediction results based on the difference between historical state estimates and current observations, thereby reducing noise in sensor data and improving the accuracy of the estimation.

[0063] The status update steps are as follows:

[0064] Calculation of observation residuals:

[0065] y = z - Hx k+1|k

[0066] Kalman gain calculation:

[0067] K = P k+1|k H T HP k+1|k H T +R)-1

[0068] State and covariance updates:

[0069] x k+1|k+1 =x k+1|k +Ky

[0070] P k+1|k+1 =(I-KH)P k+1|k

[0071] The Extended Kalman Filter (EKF) can dynamically adjust its uncertainty based on the sampling frequency and error characteristics of each driving information sensor, ensuring that information from various sensors is fully utilized, thereby obtaining more accurate state estimation results. For example, because cameras are susceptible to lighting conditions, the following vehicle driving data collected by cameras under poor lighting conditions has high noise. Therefore, during EKF processing, the system can assign greater uncertainty to the following vehicle driving data collected by cameras. As another example, because lidar has high detection accuracy under high-speed driving conditions, the following vehicle driving data collected by lidar can provide reliable spatial position and speed estimates in complex road conditions. Therefore, during EKF processing, the noise covariance of lidar data is low, and the system assigns a higher confidence level. Furthermore, the detection accuracy of millimeter-wave radar is not affected by weather. Therefore, under adverse weather conditions, the following vehicle driving data collected by millimeter-wave radar has high stability, and the system can assign higher weight to the following vehicle driving data collected by millimeter-wave radar in such environments.

[0072] Step 202: Based on the preset data fusion algorithm, the following vehicle driving data collected by each driving information sensor is fused to determine the following vehicle speed and position in real time.

[0073] In practice, combining the following vehicle driving data collected by various driving information sensors with a preset data fusion algorithm can improve prediction accuracy and predict the following vehicle speed and position. The fused following vehicle speed and position will serve as the basis for adjusting the intelligent driving control strategy.

[0074] Step 203: Determine the relative speed and relative distance of the following vehicle based on the following vehicle's speed, position, and the vehicle's own speed and position.

[0075] In practice, the system can determine the relative speed and relative distance of the following vehicle based on the merged following vehicle speed and position, as well as the vehicle's own speed and position.

[0076] Step 102: Determine the collision risk distance based on relative speed, relative distance, and preset system reaction time.

[0077] In practice, the system can determine whether a vehicle behind is approaching at a high speed and the relative distance is gradually decreasing based on relative speed and relative distance, thereby determining whether there is a risk of a rear-end collision and quantifying the collision risk.

[0078] As an optional implementation, the formula for determining the collision risk distance in step 102 based on relative speed, relative distance, and a preset system reaction time is as follows:

[0079] D risk =D current -V relative ×t reaction ;

[0080] Among them, D risk D is the collision risk distance. current V is the relative distance. relative Let t be the relative velocity. reaction This is the preset system response time.

[0081] Step 103: When the collision risk distance is less than or equal to the preset risk distance threshold, determine the safe distance based on the following vehicle speed, the vehicle speed, the preset adjustment coefficient, and the relative distance.

[0082] In practice, if the collision risk distance is less than or equal to the preset risk distance threshold, the system determines that the following vehicle has a high collision risk. Therefore, based on the following vehicle speed, the vehicle's own speed, the preset adjustment coefficient, and the relative distance, a safe following distance is determined so that the vehicle can adjust the relative distance in time to avoid a rear-end collision.

[0083] As an optional implementation, in step 103, when the collision risk distance is less than or equal to a preset risk distance threshold, the formula for determining the safe following distance based on the following vehicle speed, the following vehicle speed, a preset adjustment coefficient, and the relative distance is as follows:

[0084] D new =D current +K adjust ×(V rear -V ego );

[0085] Among them, D new For a safe following distance, D current K represents the relative distance. adjust V is the preset adjustment coefficient. rear V represents the speed of the following vehicle. ego This refers to the vehicle's speed.

[0086] Step 104: Based on the road conditions ahead, adjust the vehicle's acceleration to ensure a safe distance from the following vehicle or to decelerate the vehicle and allow the following vehicle to overtake.

[0087] In practice, once a safe following distance is determined, a vehicle can accelerate to achieve this distance. However, before accelerating, the road conditions ahead must be considered. For example, does the relative distance between the vehicle and the vehicle in front support acceleration to a safe following distance? If so, reducing the following distance provides more braking space for the vehicle behind, reducing the likelihood of a rear-end collision. If not, the vehicle can decelerate to allow the vehicle behind to overtake, avoiding a rear-end collision while ensuring the safety of both vehicles. This process can be achieved by adjusting acceleration.

[0088] As an optional implementation method, Figure 3 A flowchart of a vehicle acceleration adjustment method provided in this application embodiment is shown below. Figure 3 As shown, the specific steps in step 104, which involve adjusting the vehicle's acceleration based on the road conditions ahead to ensure a safe distance from the following vehicle or to decelerate the vehicle and allow it to overtake, are as follows:

[0089] Step 301: If the road conditions ahead of the vehicle are safe, and the speed of the vehicle behind is greater than the speed of the vehicle itself, then determine the first acceleration adjustment amount based on the preset acceleration coefficient, the speed of the vehicle behind, and the speed of the vehicle itself.

[0090] In practice, assuming the road conditions ahead are safe, such as no vehicles in the lane ahead or the relative distance between the vehicle in front and the vehicle in front is greater than the preset following distance threshold, if the speed of the vehicle behind is greater than the speed of the vehicle in front, it indicates that the vehicle behind is approaching. The system can then determine the first acceleration adjustment amount based on the preset acceleration coefficient, the speed of the vehicle behind, and the speed of the vehicle in front.

[0091] As an optional implementation, in step 301, if the road conditions ahead of the vehicle are safe and the speed of the following vehicle is greater than the speed of the vehicle itself, then the formula for determining the first acceleration adjustment amount based on the preset acceleration coefficient, the speed of the following vehicle, and the speed of the vehicle itself is as follows:

[0092] A adjust1 =K accel ×(V rear -V ego );

[0093] Among them, A adjust1 K is the first acceleration adjustment amount. accel V is the preset acceleration coefficient. rear V represents the speed of the following vehicle. ego This refers to the vehicle's speed.

[0094] Step 302: Adjust the vehicle's acceleration according to the first acceleration adjustment amount to achieve a safe distance between vehicles.

[0095] During implementation, the system can adjust the acceleration of the vehicle according to the first acceleration adjustment amount, gradually increasing the vehicle's acceleration to achieve a safe distance and avoid rear-end collisions.

[0096] Step 303: If the road conditions ahead of the vehicle are unsafe and the speed of the vehicle behind is greater than the speed of the vehicle itself, then determine the second acceleration adjustment amount based on the preset deceleration coefficient, the speed of the vehicle behind, and the speed of the vehicle itself.

[0097] In practice, when the road conditions ahead are unsafe, such as obstacles in the lane ahead or the relative distance between the vehicle in front and the vehicle being close to or less than the preset following distance threshold, if the speed of the vehicle behind is greater than the speed of the vehicle behind, it indicates that the vehicle behind is approaching. At this time, the vehicle behind cannot accelerate to further shorten the distance to the vehicle in front. The system can determine the first acceleration adjustment amount based on the preset acceleration coefficient, the speed of the vehicle behind, and the speed of the vehicle behind, so as to make the vehicle behind decelerate in advance and allow the vehicle behind to overtake.

[0098] As an optional implementation, in step 303, if the road conditions ahead of the vehicle are unsafe and the speed of the following vehicle is greater than the vehicle's speed, the formula for determining the second acceleration adjustment amount based on the preset deceleration coefficient, the speed of the following vehicle, and the vehicle's speed is as follows:

[0099] A adjust2 =-K decel ×(V rear -V ego );

[0100] Among them, A adjust2 K is the second acceleration adjustment amount. decel V is the preset deceleration coefficient. rear V represents the speed of the following vehicle. ego This refers to the vehicle's speed.

[0101] Step 304: Adjust the acceleration according to the second acceleration adjustment amount to decelerate the vehicle and allow the following vehicle to overtake.

[0102] In practice, the system can adjust the second acceleration amount to gradually reduce the vehicle's acceleration. While prioritizing the safe distance between the vehicle and the vehicle in front, the vehicle can decelerate in time, avoiding a rear-end collision caused by the vehicle decelerating too late and the vehicle behind getting closer.

[0103] As an optional implementation method, Figure 4 A flowchart of another intelligent driving control method provided in the embodiments of this application is shown below. Figure 4 As shown, the specific steps are as follows:

[0104] Step 401: Obtain the road slope.

[0105] In practice, on sloping roads, the braking distance of the following vehicle is affected by the slope. Therefore, the system can obtain the road slope to improve the accuracy of dynamic adjustments in intelligent driving.

[0106] Step 402: When the road slope is detected to be greater than or equal to the preset slope threshold, the braking distance of the following vehicle under the road slope condition is determined based on the following vehicle speed, the preset automatic emergency braking acceleration, the road slope and gravity acceleration.

[0107] In practice, when the road slope is detected to be greater than or equal to the preset slope threshold, the system can determine the braking distance of the following vehicle under the road slope condition based on the following vehicle speed, the preset automatic emergency braking acceleration, the road slope, and the gravitational acceleration.

[0108] As an optional implementation, in step 402, when the road slope is detected to be greater than or equal to a preset slope threshold, the formula for determining the braking distance of the following vehicle under the road slope condition is as follows: based on the following vehicle speed, the preset automatic emergency braking acceleration, the road slope, and gravitational acceleration.

[0109] D brake =V rear 2 / [2(a brake +gsinθ)];

[0110] Among them, D brake V is the braking distance of the following vehicle. rear Let a be the speed of the following vehicle. brake θ is the preset automatic emergency braking acceleration, g is the acceleration due to gravity, and θ is the road slope.

[0111] Step 403: If the braking distance of the following vehicle is greater than the safe following distance, adjust the acceleration of the following vehicle based on the road conditions ahead, so that the braking distance of the following vehicle reaches the safe following distance or the following vehicle decelerates and allows the following vehicle to overtake.

[0112] In practice, if the braking distance of the following vehicle exceeds the safe following distance, the system will assess the road conditions ahead of the vehicle. If the road conditions ahead are safe, the system can accelerate to bring the following vehicle's braking distance to the safe following distance. If the road conditions ahead are unsafe, and the vehicle cannot accelerate to further shorten the distance to the vehicle ahead, the system can issue a safety warning in advance, reminding the driver to take appropriate measures, such as slowing down or changing lanes.

[0113] This application provides a control method for intelligent driving, comprising: acquiring the speed of a following vehicle, the speed of the driver, the relative speed of the following vehicle, and the relative distance; determining a collision risk distance based on the relative speed, relative distance, and a preset system reaction time; determining a safe following distance based on the following vehicle speed, the driver's speed, a preset adjustment coefficient, and the relative distance when the collision risk distance is less than or equal to a preset risk distance threshold; and adjusting the driver's acceleration based on the road conditions ahead to ensure the relative distance to the following vehicle reaches a safe following distance or to decelerate the driver and allow the following vehicle to overtake. This application employs a post-fusion Kalman filter scheme, comprehensively utilizing the advantages of various driving information sensors such as cameras, lidar, and millimeter-wave radar, eliminating deviations between different driving information sensors, and improving detection accuracy and robustness. Furthermore, based on the real-time detected following vehicle status, it automatically adjusts the relative distance or issues a collision risk warning, significantly improving driving safety. In addition, by considering slope, speed, and acceleration, this application can accurately predict the braking distance of the following vehicle, helping the driver to take timely defensive driving measures on slopes or other complex environments.

[0114] It should be understood that, although Figures 1 to 4 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figures 1 to 4 At least some of the steps in the process may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least some of the steps or stages in other steps.

[0115] It is understood that the same / similar parts between the various embodiments of the methods described above in this specification can be referred to each other. Each embodiment focuses on the differences from other embodiments, and relevant parts can be referred to the description of other method embodiments.

[0116] This application also provides a control system for intelligent driving, such as... Figure 5 As shown, the system includes:

[0117] Several driving information collection devices 510 are used to independently acquire driving data of following vehicles.

[0118] The processing unit 520 is used to independently process the following vehicle driving data of each driving information acquisition device 510 through a Kalman filter, adjust the prediction results according to the difference between historical data and current observations, and generate an independent following vehicle state estimate.

[0119] The fusion device 530 is used to assign different weights to the following vehicle driving data from different driving information collection devices 510 according to their reliability through a weighted fusion algorithm, and finally generate comprehensive status information of the following vehicle.

[0120] The strategy adjustment device 540 is used to adjust the driving strategy of the vehicle in real time or issue a warning based on the comprehensive status information of the following vehicle.

[0121] This application provides a control system for intelligent driving, such as... Figure 5 As shown, the system includes: several driving information acquisition devices 510, used to independently acquire driving data of following vehicles; a processing device 520, used to independently process the driving data of following vehicles from each driving information acquisition device 510 using a Kalman filter, adjusting the prediction results based on the differences between historical data and current observations, and generating independent estimates of the state of following vehicles; a fusion device 530, used to assign different weights to the driving data of following vehicles from different driving information acquisition devices 510 according to their reliability using a weighted fusion algorithm, and finally generate comprehensive state information of following vehicles; and a strategy adjustment device 540, used to adjust the driving strategy of the vehicle itself or issue warnings in real time based on the comprehensive state information of following vehicles. This embodiment of the application, by adopting a post-fusion Kalman filter scheme, comprehensively utilizes the advantages of various driving information acquisition devices such as cameras, lidar, and millimeter-wave radar, eliminating deviations between driving information acquisition devices, and improving detection accuracy and robustness. Furthermore, based on the real-time detected status of the following vehicle, it automatically adjusts the relative position or issues a collision risk warning, significantly improving driving safety. In addition, by considering the slope, speed, and acceleration, the embodiments of this application can accurately predict the braking distance of the following vehicle, helping the driver to take timely defensive driving measures on slopes or other complex environments.

[0122] Specific limitations regarding the control system for intelligent driving can be found in the limitations on the control methods for intelligent driving described above, and will not be repeated here. Each module in the aforementioned intelligent driving control system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0123] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0124] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0125] It should also be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0126] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0127] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0128] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A control method of intelligent driving, characterized by, The method comprises: acquiring the speed of the rear vehicle, the speed of the ego vehicle, the relative speed and the relative distance of the rear vehicle; determining the collision risk distance according to the relative speed, the relative distance and a preset system reaction time length; when the collision risk distance is less than or equal to a preset risk distance threshold, determining the safe distance according to the speed of the rear vehicle, the speed of the ego vehicle, a preset adjustment coefficient and the relative distance; adjusting the acceleration of the ego vehicle based on the road condition in front of the ego vehicle so that the relative distance of the rear vehicle reaches the safe distance or the ego vehicle slows down and lets the rear vehicle overtake; the formula for determining the collision risk distance according to the relative speed, the relative distance and a preset system reaction time length is: D risk =D current -V relative ×t reaction ; wherein D risk is the collision risk distance, D current is the relative distance, V relative is the relative speed, t reaction is the preset system reaction time length; the formula for determining the safe distance according to the speed of the rear vehicle, the speed of the ego vehicle, a preset adjustment coefficient and the relative distance when the collision risk distance is less than or equal to a preset risk distance threshold is: D new =D current +K adjust ×(V rear -V ego ); Wherein, D new is a safe distance, D current is a relative distance, K adjust is a preset adjustment coefficient, V rear is a speed of the rear vehicle, V ego is a speed of the ego vehicle.

2. The method of claim 1, wherein, the acquiring the speed of the rear vehicle, the speed of the ego vehicle, the relative speed and the relative distance of the rear vehicle comprises: acquiring the speed of the ego vehicle, the position of the ego vehicle and the rear vehicle driving data collected by each driving information sensor in real time; fusing the rear vehicle driving data collected by each driving information sensor based on a preset data fusion algorithm to determine the speed of the rear vehicle and the position of the rear vehicle in real time; determining the relative speed and the relative distance of the rear vehicle according to the speed of the rear vehicle, the position of the rear vehicle, the speed of the ego vehicle and the position of the ego vehicle.

3. The method of claim 1, wherein, the adjusting the acceleration of the ego vehicle based on the road condition in front of the ego vehicle so that the relative distance of the rear vehicle reaches the safe distance or the ego vehicle slows down and lets the rear vehicle overtake comprises: if the speed of the rear vehicle is greater than the speed of the ego vehicle when the road condition in front of the ego vehicle is safe, determining a first acceleration adjustment amount according to a preset acceleration coefficient, the speed of the rear vehicle and the speed of the ego vehicle; adjusting the acceleration of the ego vehicle according to the first acceleration adjustment amount so that the relative distance reaches the safe distance; if the speed of the rear vehicle is greater than the speed of the ego vehicle when the road condition in front of the ego vehicle is not safe, determining a second acceleration adjustment amount according to a preset deceleration coefficient, the speed of the rear vehicle and the speed of the ego vehicle; adjusting the acceleration according to the second acceleration adjustment amount so that the ego vehicle slows down and lets the rear vehicle overtake.

4. The method of claim 3, wherein, the formula for determining the first acceleration adjustment amount according to a preset acceleration coefficient, the speed of the rear vehicle and the speed of the ego vehicle if the speed of the rear vehicle is greater than the speed of the ego vehicle when the road condition in front of the ego vehicle is safe is: A adjust1 =K accel ×(V rear -V ego ); Wherein, A adjust1 is the first acceleration adjustment amount, K accel is a preset acceleration coefficient, V rear is the speed of the rear vehicle, V ego is the speed of the ego vehicle.

5. The method of claim 3, wherein, the formula for determining the second acceleration adjustment amount according to a preset deceleration coefficient, the speed of the rear vehicle and the speed of the ego vehicle if the speed of the rear vehicle is greater than the speed of the ego vehicle when the road condition in front of the ego vehicle is not safe is: A adjust2 =-K decel ×(V rear -V ego ); Wherein, A adjust2 is the second acceleration adjustment amount, K decel is a preset deceleration coefficient, V rear is the speed of the rear vehicle, V ego is the speed of the ego vehicle.

6. The method of claim 2, wherein, after the acquiring the speed of the ego vehicle, the position of the ego vehicle and the rear vehicle driving data collected by each driving information sensor in real time, the method further comprises: correcting the rear vehicle driving data collected by each driving information sensor respectively through an extended Kalman filter.

7. The method of claim 1, wherein, the method further comprises: acquiring the road slope; When it is detected that the road slope is greater than or equal to a preset slope threshold, a rear vehicle braking distance under the condition of the road slope is determined according to the rear vehicle speed, a preset automatic emergency braking acceleration, the road slope and a gravitational acceleration; If the rear vehicle braking distance is greater than the safe vehicle distance, a host vehicle acceleration is adjusted based on a road condition in front of the host vehicle, so that the rear vehicle braking distance reaches the safe vehicle distance or the host vehicle slows down and lets the rear vehicle overtake.

8. The method of claim 7, wherein, The formula for determining the rear vehicle braking distance under the condition of the road slope when it is detected that the road slope is greater than or equal to a preset slope threshold is: D brake =V rear 2 / [2(a brake +gsinθ)]; where D brake is the distance of the rear vehicle, V rear is the speed of the rear vehicle, a brake is the preset automatic emergency braking acceleration, g is the acceleration of gravity, and θ is the road slope.

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