Intelligent driving control method

Abstract

The invention relates to an intelligent driving control method, and relates to the technical field of vehicle intelligent driving. The method comprises the steps that the rear vehicle speed, the self vehicle speed and the relative speed and the relative distance of the rear vehicle are obtained; determining a collision risk distance according to the relative speed, the relative distance and a preset system reaction duration; when the collision risk distance is smaller than or equal to a preset risk distance threshold value, a safe vehicle distance is determined according to the vehicle speed of the rear vehicle, the vehicle speed of the vehicle, a preset adjustment coefficient and the relative distance; based on the road condition in front of the vehicle, the acceleration of the vehicle is adjusted, so that the relative distance of the rear vehicle reaches a safe vehicle distance or the vehicle decelerates and the rear vehicle overtakes. By adopting the method, the rear-end collision risk of the rear vehicle can be reduced.

Description

Technical Field

[0001] The present application relates to the field of vehicle intelligent driving technology, and in particular to a control method for intelligent driving. Background Art

[0002] At present, with the rapid development of intelligent driving technology, the accuracy and stability of the perception system have an important impact on driving safety. However, most existing systems focus on forward or lateral perception and strategy optimization, while relatively few studies have been conducted on the detection and response strategies of rear-end vehicles. The detection of rear-end vehicles is crucial on highways or complex road environments, especially in densely populated and high-speed situations. The lack of accurate detection of rear-end vehicles can easily lead to rear-end collisions. Summary of the invention

[0003] Based on this, it is necessary to provide a control method for intelligent driving in view of the above technical problems, the method comprising:

[0004] Obtain the speed of the following vehicle, the speed of the vehicle itself, the relative speed and relative distance of the following vehicle;

[0005] Determining a collision risk distance according to the relative speed, the relative distance, and a preset system reaction time;

[0006] 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 vehicle, a preset adjustment coefficient and the relative distance;

[0007] Based on the road conditions in front of the vehicle, the acceleration of the vehicle is adjusted so that the relative distance to the rear vehicle reaches the safe distance or the vehicle is decelerated to allow the rear vehicle to overtake.

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

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

[0010] Based on a preset data fusion algorithm, the rear vehicle driving data collected by the driving information sensors are fused to determine the rear vehicle speed and rear vehicle position in real time;

[0011] The relative speed and relative distance of the rear vehicle are determined according to the speed of the rear vehicle, the position of the rear vehicle, the speed of the own vehicle and the position of the own vehicle.

[0012] As an optional implementation manner, the formula for determining the collision risk distance according to the relative speed, the relative distance and the preset system reaction time is:

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

[0014] Among them, D risk is the collision risk distance, D current is the relative distance, V relative is the relative speed, t reaction It is the preset system response time.

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

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

[0017] Among them, D new is the safe distance between vehicles, D current is the relative distance, K adjust is the preset adjustment coefficient, V rear is the speed of the following vehicle, V ego is the vehicle speed.

[0018] As an optional implementation manner, adjusting the acceleration of the vehicle based on the road condition in front of the vehicle so that the relative distance of the rear vehicle reaches the safe distance or the vehicle decelerates and allows the rear vehicle to overtake includes:

[0019] In the case where the road condition ahead of the own vehicle is safe, if the speed of the rear vehicle is greater than the speed of the own vehicle, determining a first acceleration adjustment amount according to a preset acceleration coefficient, the speed of the rear vehicle and the speed of the own vehicle;

[0020] According to the first acceleration adjustment amount, adjusting the acceleration of the vehicle so that the relative distance reaches the safe vehicle distance;

[0021] In the case where the road condition ahead of the own vehicle is unsafe, if the speed of the rear vehicle is greater than the speed of the own vehicle, determining a second acceleration adjustment amount according to a preset deceleration coefficient, the speed of the rear vehicle and the speed of the own vehicle;

[0022] According to the second acceleration adjustment amount, the acceleration is adjusted to decelerate the vehicle and allow the following vehicle to overtake.

[0023] As an optional implementation manner, when the road condition ahead of the self-vehicle is safe, if the speed of the rear vehicle is greater than the speed of the self-vehicle, a formula for determining the first acceleration adjustment amount is as follows according to a preset acceleration coefficient, the speed of the rear vehicle and the speed of the self-vehicle:

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

[0025] Among them, A adjust1 is the first acceleration adjustment, K accel is the preset acceleration factor, V rear is the speed of the following vehicle, V ego is the vehicle speed.

[0026] As an optional implementation manner, when the road condition ahead of the self-vehicle is unsafe, if the speed of the rear vehicle is greater than the speed of the self-vehicle, a formula for determining the second acceleration adjustment amount is as follows according to a preset deceleration coefficient, the speed of the rear vehicle and the speed of the self-vehicle:

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

[0028] Among them, A adjust2 is the second acceleration adjustment, K decel is the preset deceleration factor, V rear is the speed of the following vehicle, V ego is the vehicle speed.

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

[0030] The rear vehicle driving data collected by each driving information sensor is corrected and processed respectively by using an extended Kalman filter.

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

[0032] Get the road slope;

[0033] When it is detected that the road slope is greater than or equal to a preset slope threshold, determining the braking distance of the rear vehicle under the condition of the road slope according to the speed of the rear vehicle, the preset automatic emergency braking acceleration, the road slope and the acceleration of gravity;

[0034] If the braking distance of the rear vehicle is greater than the safe vehicle distance, the acceleration of the own vehicle is adjusted based on the road conditions in front of the own vehicle so that the braking distance of the rear vehicle reaches the safe vehicle distance or the own vehicle is decelerated and the rear vehicle is allowed to overtake.

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

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

[0037] Among them, D brake is the braking distance of the rear vehicle, V rear is the speed of the following vehicle, a brake 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, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and when the processor executes the computer program, the method steps described in any one of the first aspects are implemented.

[0039] According to a third aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method steps described in any one of the first aspects are implemented.

[0040] The present application provides a control method for intelligent driving. The technical solution provided by the embodiments of the present application brings at least the following beneficial effects. The method includes: obtaining the speed of a rear vehicle, the speed of the own vehicle, the relative speed and 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; when the collision risk distance is less than or equal to a preset risk distance threshold, determining a safe distance according to the speed of the rear vehicle, the speed of the own vehicle, a preset adjustment coefficient and the relative distance; and adjusting the acceleration of the own vehicle based on the road conditions in front of the own vehicle so that the relative distance of the rear vehicle reaches the safe distance or the own vehicle slows down and allows the rear vehicle to overtake.

[0041] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A flowchart of an intelligent driving control method provided in an embodiment of the present application;

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

[0045] Figure 3 A flow chart of a method for adjusting the acceleration of a vehicle provided in an embodiment of the present application;

[0046] Figure 4 A flowchart of another intelligent driving control method provided in an embodiment of the present application;

[0047] Figure 5 A schematic diagram of the structure of an intelligent driving control system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] The following will describe in detail a method for controlling intelligent driving provided by an embodiment of the present application in combination with a specific implementation manner. Figure 1 A flowchart of a control method for intelligent driving provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the specific steps are as follows:

[0050] Step 101, obtaining the speed of the rear vehicle, the speed of the vehicle itself, and the relative speed and relative distance of the rear vehicle.

[0051] During implementation, the vehicle can obtain the speed of the following vehicle, the speed of the vehicle itself, the relative speed and relative distance of the following vehicle.

[0052] As an optional implementation, Figure 2 A flowchart of a method for obtaining vehicle driving information provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the specific steps of obtaining the speed of the rear vehicle, the speed of the vehicle, the relative speed and the relative distance of the rear vehicle in step 101 are as follows:

[0053] Step 201, obtaining in real time the vehicle speed, the vehicle position and the driving data of the following vehicle collected by various driving information sensors.

[0054] In practice, the vehicle can obtain its own speed in real time through the vehicle computer, its own position in real time through the navigation system, and the driving data of the rear vehicle collected by driving information sensors such as cameras, lidar and millimeter-wave radar. For example: the camera can provide image information of the rear vehicle, and the system can identify the rear vehicle and calculate the rear vehicle speed and position through a preset image processing algorithm. Lidar can detect the rear vehicle position and speed through point cloud data. Millimeter-wave radar can be used for the detection of medium and long-distance targets, and can measure the rear vehicle position and speed without being affected by weather.

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

[0056] The driving data of the following vehicle collected by each driving information sensor is corrected and processed respectively through the extended Kalman filter.

[0057] In practice, the driving data of the rear vehicle collected by each driving information sensor is independently estimated through the extended Kalman filter. The extended Kalman filter first predicts the state based on the sensor's observation value, including the rear vehicle position, rear vehicle speed and acceleration. The state prediction equation is as follows:

[0058]

[0059] The covariance prediction equation is:

[0060]

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

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

[0063] The status update steps are as follows:

[0064] Observation residual calculation:

[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 can dynamically adjust the uncertainty of each driving information sensor according to the sampling frequency and error characteristics, ensuring that the information of various sensors is fully utilized, thereby obtaining a more accurate state estimation result. For example: Since the camera is easily affected by lighting conditions, the rear vehicle driving data collected by the camera under poor lighting conditions has large noise. Therefore, when the extended Kalman filter is processed, the system can give a larger uncertainty to the rear vehicle driving data collected by the camera. Another example: Since the laser radar has a higher detection accuracy under high-speed driving conditions, the rear vehicle driving data collected by the laser radar can provide reliable spatial position and speed estimation under complex road conditions. Therefore, during the extended Kalman filter processing, the noise covariance of the laser radar data is low, and the system gives a higher degree of trust. Another example: The detection accuracy of the millimeter-wave radar is not affected by the weather. Therefore, under severe weather conditions, the rear vehicle driving data collected by the millimeter-wave radar is more stable. The system can give a higher weight to the rear vehicle driving data collected by the millimeter-wave radar in such an environment.

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

[0073] In implementation, the fusion of the rear vehicle driving data collected by each driving information sensor with a preset data fusion algorithm can improve the prediction accuracy and predict the rear vehicle speed and position. The fused rear vehicle speed and position will serve as the basis for adjusting the intelligent driving control strategy.

[0074] Step 203, determining the relative speed and relative distance of the rear vehicle according to the speed and position of the rear vehicle, the speed and position of the own vehicle.

[0075] During implementation, the system can determine the relative speed and relative distance of the following vehicle based on the fused speed and position of the following vehicle, as well as the speed and position of the own vehicle.

[0076] Step 102, determining the collision risk distance according to the relative speed, the relative distance and the preset system reaction time.

[0077] During implementation, the system can judge whether the rear vehicle is moving faster and the relative distance is gradually decreasing based on the relative speed and relative distance, determine whether there is a risk of rear-end collision, and quantify the collision risk.

[0078] As an optional implementation, in step 102, the formula for determining the collision risk distance according to the relative speed, relative distance and preset system reaction time is:

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

[0080] Among them, D risk is the collision risk distance, D current is the relative distance, V relative is the relative speed, t reaction It is the preset system response time.

[0081] Step 103, when the collision risk distance is less than or equal to a preset risk distance threshold, a safe vehicle distance is determined according to the speed of the rear vehicle, the speed of the vehicle, a preset adjustment coefficient and the relative distance.

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

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

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

[0085] Among them, D new is the safe distance between vehicles, D current is the relative distance, K adjust is the preset adjustment coefficient, V rear is the speed of the following vehicle, V ego is the vehicle speed.

[0086] Step 104, based on the road conditions ahead of the ego vehicle, adjust the acceleration of the ego vehicle so that the relative distance to the rear vehicle reaches a safe distance or the ego vehicle slows down and allows the rear vehicle to overtake.

[0087] In practice, after determining the safe distance, the vehicle can accelerate to make the relative distance reach the safe distance. However, before accelerating, the vehicle must consider the road conditions in front of the vehicle, such as whether the relative distance between the vehicle and the front vehicle supports the vehicle to accelerate to the safe distance with the rear vehicle. If it supports, the vehicle can reduce the following distance with the front vehicle to provide more braking space for the rear vehicle, reducing the probability of rear-end collision. If it does not support, the vehicle can slow down and let the rear vehicle overtake to avoid rear-end collision, while also ensuring the safety of the vehicle and the front vehicle. This process can be achieved by adjusting the acceleration.

[0088] As an optional implementation, Figure 3 A flowchart of a method for adjusting the acceleration of a vehicle provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, in step 104, based on the road conditions ahead of the vehicle, the specific steps of adjusting the acceleration of the vehicle so that the relative distance of the rear vehicle reaches a safe distance or the vehicle decelerates and allows the rear vehicle to overtake are as follows:

[0089] Step 301, when the road condition ahead of the ego vehicle is safe, if the speed of the rear vehicle is greater than the speed of the ego vehicle, a first acceleration adjustment amount is determined according to a preset acceleration coefficient, the speed of the rear vehicle and the speed of the ego vehicle.

[0090] In implementation, when the road condition ahead of the ego vehicle is safe, such as there is no vehicle in the lane ahead or the relative distance between the front vehicle and the ego vehicle is greater than a preset following distance threshold, if the speed of the rear vehicle is greater than the speed of the ego vehicle, it indicates that the rear vehicle is approaching, and the system can determine the first acceleration adjustment amount according to the preset acceleration coefficient, the speed of the rear vehicle and the speed of the ego vehicle.

[0091] As an optional implementation, in step 301, when the road condition ahead of the ego vehicle is safe, if the speed of the rear vehicle is greater than the speed of the ego vehicle, the formula for determining the first acceleration adjustment amount is as follows according to the preset acceleration coefficient, the speed of the rear vehicle and the speed of the ego vehicle:

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

[0093] Among them, A adjust1 is the first acceleration adjustment, K accel is the preset acceleration factor, V rear is the speed of the following vehicle, V ego is the vehicle speed.

[0094] Step 302: adjusting the acceleration of the vehicle according to the first acceleration adjustment amount so that the relative distance reaches the safe vehicle distance.

[0095] During implementation, the system can gradually increase the acceleration of the vehicle according to the first acceleration adjustment amount so that the relative distance reaches a safe distance and avoids rear-end collision with the rear vehicle.

[0096] Step 303, when the road condition ahead of the own vehicle is unsafe, if the speed of the rear vehicle is greater than the speed of the own vehicle, determine the second acceleration adjustment amount according to the preset deceleration coefficient, the speed of the rear vehicle and the speed of the own vehicle.

[0097] In implementation, when the road condition ahead of the ego vehicle is unsafe, such as when there is an obstacle in the lane ahead or the relative distance between the front vehicle and the ego vehicle is close to or less than the preset following distance threshold. If the speed of the rear vehicle is greater than the speed of the ego vehicle, it means that the rear vehicle is approaching, and the ego vehicle cannot accelerate to further shorten the distance with the front vehicle. The system can determine the first acceleration adjustment amount according to the preset acceleration coefficient, the speed of the rear vehicle and the speed of the ego vehicle, so as to slow down the ego vehicle in advance and allow the rear vehicle to overtake.

[0098] As an optional implementation, in step 303, when the road condition ahead of the ego vehicle is unsafe, if the speed of the rear vehicle is greater than the speed of the ego vehicle, the formula for determining the second acceleration adjustment amount is as follows according to the preset deceleration coefficient, the speed of the rear vehicle and the speed of the ego vehicle:

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

[0100] Among them, A adjust2 is the second acceleration adjustment, K decel is the preset deceleration coefficient, V rear is the speed of the following vehicle, V ego is the vehicle speed.

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

[0102] During implementation, the system can gradually reduce the acceleration of the ego vehicle according to the second acceleration adjustment amount. While giving priority to ensuring a safe distance between the ego vehicle and the vehicle in front, the ego vehicle can be decelerated in time to avoid a rear-end collision caused by the ego vehicle decelerating due to untimely deceleration and the following vehicle being closer.

[0103] As an optional implementation, Figure 4 A flowchart of another intelligent driving control method provided in an embodiment of the present application, such as Figure 4 As shown, the specific steps are as follows:

[0104] Step 401, obtaining the road slope.

[0105] In implementation, on sloped roads, the braking distance of the following vehicle is affected by the slope, so the system can obtain the road slope to improve the accuracy of dynamic adjustment of intelligent driving.

[0106] Step 402, when it is detected that the road slope is greater than or equal to a preset slope threshold, the braking distance of the rear vehicle under the condition of the road slope is determined according to the speed of the rear vehicle, the preset automatic emergency braking acceleration, the road slope and the gravity acceleration.

[0107] In implementation, when it is detected that the road slope is greater than or equal to a preset slope threshold, the system can determine the braking distance of the rear vehicle under the conditions of the road slope based on the rear vehicle speed, the preset automatic emergency braking acceleration, the road slope and the acceleration of gravity.

[0108] As an optional implementation, in step 402, when it is detected that the road slope is greater than or equal to a preset slope threshold, the formula for determining the braking distance of the rear vehicle under the condition of the road slope is:

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

[0110] Among them, D brake is the braking distance of the rear vehicle, V rear is the speed of the following vehicle, a brake 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 rear vehicle is greater than the safe vehicle distance, based on the road conditions in front of the ego vehicle, adjust the acceleration of the ego vehicle so that the braking distance of the rear vehicle reaches the safe vehicle distance or the ego vehicle slows down and allows the rear vehicle to overtake.

[0112] In practice, if the braking distance of the rear vehicle is greater than the safe distance, the system obtains the road conditions in front of the vehicle. If the road conditions in front of the vehicle are safe, the system can accelerate to make the braking distance of the rear vehicle reach the safe distance. If the road conditions in front of the vehicle are unsafe, the vehicle cannot accelerate to further shorten the distance with the vehicle in front, and the system can issue a safety reminder in advance to remind the driver to take appropriate measures, such as slowing down or changing lanes.

[0113] The embodiment of the present application provides a control method for intelligent driving, the method comprising: obtaining the speed of the rear vehicle, the speed of the vehicle, the relative speed and relative distance of the rear vehicle; determining the collision risk distance according to the relative speed, relative distance and preset system reaction time; when the collision risk distance is less than or equal to the preset risk distance threshold, determining the safe distance according to the speed of the rear vehicle, the speed of the vehicle, the preset adjustment coefficient and the relative distance; adjusting the acceleration of the vehicle based on the road conditions in front of the vehicle so that the relative distance of the rear vehicle reaches the safe distance or the vehicle slows down and allows the rear vehicle to overtake. The embodiment of the present application adopts a post-fusion Kalman filter solution, comprehensively utilizes the advantages of various driving information sensors such as cameras, laser radars and millimeter-wave radars, eliminates the deviations between various driving information sensors, and improves the accuracy and robustness of detection. And according to the real-time detected state of the rear vehicle, automatically adjust the relative or issue a collision risk warning, which significantly improves driving safety. In addition, the embodiment of the present application can accurately estimate the braking distance of the rear vehicle by considering the slope, speed and acceleration, helping the driver to take defensive driving measures in time on ramps or other complex environments.

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

[0115] It can be understood that the same / similar parts between the various embodiments of the above method in this specification can refer to each other, and each embodiment focuses on the differences from other embodiments. For related points, please refer to the description of other method embodiments.

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

[0117] A plurality of driving information collection devices 510 are used to independently obtain the driving data of the following vehicle.

[0118] The processing device 520 is used to independently process the rear vehicle driving data of each driving information collection device 510 through a Kalman filter, adjust the prediction result according to the difference between the historical data and the current observation value, and generate an independent rear vehicle state estimation.

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

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

[0121] The present application embodiment provides a control system for intelligent driving, such as Figure 5 As shown, the system includes: a plurality of driving information collection devices 510, which are used to independently obtain the driving data of the rear vehicle. A processing device 520, which is used to independently process the driving data of the rear vehicle of each driving information collection device 510 through a Kalman filter, adjust the prediction result according to the difference between the historical data and the current observation value, and generate an independent estimation of the state of the rear vehicle. A fusion device 530, which is used to assign different weights to the driving data of the rear vehicle of different driving information collection devices 510 according to their reliability through a weighted fusion algorithm, and finally generate the comprehensive state information of the rear vehicle. A strategy adjustment device 540, which is used to adjust the driving strategy of the vehicle in real time or issue a warning according to the comprehensive state information of the rear vehicle. The embodiment of the present application adopts a post-fusion Kalman filter solution, comprehensively utilizes the advantages of various driving information collection devices such as cameras, lidars and millimeter-wave radars, eliminates the deviations between the driving information collection devices, and improves the accuracy and robustness of detection. And according to the real-time detected status of the rear vehicle, the relative position is automatically adjusted or a collision risk warning is issued, which significantly improves driving safety. In addition, the embodiment of the present application can accurately estimate the braking distance of the rear vehicle by considering the slope, speed and acceleration, helping the driver to take defensive driving measures in time on slopes or other complex environments.

[0122] For the specific definition of the control system of intelligent driving, please refer to the definition of the control method of intelligent driving above, which will not be repeated here. Each module in the above-mentioned intelligent driving control system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0123] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database 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. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0124] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[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 for analysis, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0126] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0127] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0128] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A control method for intelligent driving, characterized in that: The method comprises: Obtain 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 according to 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 vehicle distance according to the speed of the rear vehicle, the speed of the vehicle, a preset adjustment coefficient and the relative distance; Based on the road conditions in front of the vehicle, the acceleration of the vehicle is adjusted so that the relative distance to the rear vehicle reaches the safe distance or the vehicle is decelerated to allow the rear vehicle to overtake.

2. The method according to claim 1, characterized in that The obtaining of the speed of the rear vehicle, the speed of the vehicle itself, the relative speed and the relative distance of the rear vehicle includes: Real-time acquisition of the vehicle speed, vehicle position and driving data of the following vehicle collected by various driving information sensors; Based on a preset data fusion algorithm, the rear vehicle driving data collected by the driving information sensors are fused to determine the rear vehicle speed and rear vehicle position in real time; The relative speed and relative distance of the rear vehicle are determined according to the speed of the rear vehicle, the position of the rear vehicle, the speed of the own vehicle and the position of the own vehicle.

3. The method according to claim 1, characterized in that The formula for determining the collision risk distance according to the relative speed, the relative distance and the preset system reaction time is: D risk =D current -V relative ×t reaction ; Among them, D risk is the collision risk distance, D current is the relative distance, V relative is the relative speed, t reaction It is the preset system response time.

4. The method according to claim 1, characterized in that: When the collision risk distance is less than or equal to the preset risk distance threshold, the formula for determining the safe vehicle distance is as follows according to the speed of the rear vehicle, the speed of the vehicle, the preset adjustment coefficient and the relative distance: D new =D current +K adjust ×(V rear -V ego ); Among them, D new is the safe distance between vehicles, D current is the relative distance, K adjust is the preset adjustment coefficient, V rear is the speed of the following vehicle, V ego is the vehicle speed.

5. The method according to claim 1, characterized in that The adjusting the acceleration of the vehicle based on the road condition in front of the vehicle so that the relative distance of the rear vehicle reaches the safe distance or the vehicle decelerates and allows the rear vehicle to overtake includes: In the case where the road condition ahead of the own vehicle is safe, if the speed of the rear vehicle is greater than the speed of the own vehicle, determining a first acceleration adjustment amount according to a preset acceleration coefficient, the speed of the rear vehicle and the speed of the own vehicle; According to the first acceleration adjustment amount, adjusting the acceleration of the vehicle so that the relative distance reaches the safe vehicle distance; In the case where the road condition ahead of the own vehicle is unsafe, if the speed of the rear vehicle is greater than the speed of the own vehicle, determining a second acceleration adjustment amount according to a preset deceleration coefficient, the speed of the rear vehicle and the speed of the own vehicle; According to the second acceleration adjustment amount, the acceleration is adjusted to decelerate the vehicle and allow the following vehicle to overtake.

6. The method according to claim 5, characterized in that In the case where the road condition ahead of the vehicle is safe, if the speed of the rear vehicle is greater than the speed of the vehicle, the formula for determining the first acceleration adjustment amount is as follows according to the preset acceleration coefficient, the speed of the rear vehicle and the speed of the vehicle: A adjust1 =K accel ×(V rear -V ego ); Among them, A adjust1 is the first acceleration adjustment, K accel is the preset acceleration factor, V rear is the speed of the following vehicle, V ego is the vehicle speed.

7. The method according to claim 5, characterized in that In the case where the road condition ahead of the vehicle is unsafe, if the speed of the rear vehicle is greater than the speed of the vehicle, the formula for determining the second acceleration adjustment amount is as follows according to the preset deceleration coefficient, the speed of the rear vehicle and the speed of the vehicle: A adjust2 =-K decel ×(V rear -V ego ); Among them, A adjust2 is the second acceleration adjustment, K decel is the preset deceleration coefficient, V rear is the speed of the following vehicle, V ego is the vehicle speed.

8. The method according to claim 2, characterized in that: After acquiring the vehicle speed, the vehicle position and the driving data of the following vehicle collected by the driving information sensors in real time, the method further includes: The rear vehicle driving data collected by each driving information sensor is corrected and processed respectively by using an extended Kalman filter.

9. The method according to claim 1, characterized in that: The method further comprises: Get the road slope; When it is detected that the road slope is greater than or equal to a preset slope threshold, determining the braking distance of the rear vehicle under the condition of the road slope according to the speed of the rear vehicle, the preset automatic emergency braking acceleration, the road slope and the acceleration of gravity; If the braking distance of the rear vehicle is greater than the safe vehicle distance, the acceleration of the own vehicle is adjusted based on the road conditions in front of the own vehicle so that the braking distance of the rear vehicle reaches the safe vehicle distance or the own vehicle is decelerated and the rear vehicle is allowed to overtake.

10. The method according to claim 9, characterized in that When it is detected that the road slope is greater than or equal to a preset slope threshold, the formula for determining the braking distance of the rear vehicle under the condition of the road slope is as follows according to the rear vehicle speed, the preset automatic emergency braking acceleration, the road slope and the gravity acceleration: D brake =V rear 2 / [2(a brake +gsinθ)]; Among them, D brake is the braking distance of the rear vehicle, V rear is the speed of the following vehicle, a brake is the preset automatic emergency braking acceleration, g is the acceleration due to gravity, and θ is the road slope.

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