A vehicle control method, device and electronic equipment

Abstract

A vehicle control method, device and electronic equipment, the method comprising: determining a relative collision time corresponding to a front vehicle of an intelligent vehicle and vehicle driving information; when the relative collision time is lower than a preset relative collision time, determining a first vehicle behavior corresponding to the relative collision time based on a mapping relationship between the preset relative collision time and the first preset vehicle behavior, and adjusting the intelligent vehicle behavior of the intelligent vehicle based on the first vehicle behavior; or when the relative collision time is not lower than the preset relative collision time, determining a second vehicle behavior corresponding to the vehicle driving information based on a mapping relationship between the preset vehicle driving information and the second preset vehicle behavior, and adjusting the intelligent vehicle behavior of the intelligent vehicle based on the second vehicle behavior. Through the above method, the intelligent vehicle can predict the vehicle behavior of the front vehicle, and adjust the intelligent vehicle behavior based on the prediction result, ensuring the safety of the intelligent vehicle during driving.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent vehicles, in particular to a vehicle control method and device and electronic equipment. BACKGROUND

[0002] In the process of driving an intelligent vehicle, in order to improve the safety of the intelligent vehicle in the driving process, the intelligent vehicle needs to make a vehicle behavior prediction on the front vehicle in a preset range and / or the front vehicle in the adjacent lane in the driving process, and the intelligent vehicle makes vehicle driving planning based on the vehicle behavior prediction result, so as to reduce the risk in the driving process of the intelligent vehicle.

[0003] At present, the specific process of vehicle driving planning of the intelligent vehicle is as follows: in a three-lane scene, the distribution diagram of the intelligent vehicle and other vehicles in the three lanes is as shown in Figure 1 In Figure 1 , the intelligent vehicle obtains the respective vehicle speeds and vehicle behaviors of the first vehicle, the second vehicle and the third vehicle, determines the risk levels of the vehicles based on the vehicle speeds and vehicle behaviors of the vehicles, determines the lane with the lowest risk level from the risk levels, and takes the lane with the lowest risk level as the target lane, and controls the intelligent vehicle to drive in the target lane.

[0004] Based on the above description, in the process of determining the target lane, if there is a vehicle collision in front of the lane to which the intelligent vehicle belongs, since the intelligent vehicle can only detect after the actual driving track of the front vehicle deviates from the preset driving track, the intelligent vehicle cannot respond in time based on the vehicle collision, so that the intelligent vehicle has the risk of colliding with the front vehicle, thereby failing to ensure the safety of the intelligent vehicle in the driving process. SUMMARY

[0005] The present application provides a processing control method and device and electronic equipment for enabling the intelligent vehicle to predict the vehicle behavior of the front vehicle, thereby improving the safety of the intelligent vehicle in the driving process.

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

[0007] determining the relative collision time corresponding to the front vehicle of the intelligent vehicle and the vehicle driving information, wherein the relative collision time records the interval time of the collision between the front vehicle and other vehicles;

[0008] when the relative collision time is lower than the preset relative collision time, determining a first vehicle behavior corresponding to the relative collision time based on a mapping relationship between the preset relative collision time and the first preset vehicle behavior, and adjusting the intelligent vehicle behavior of the intelligent vehicle based on the first vehicle behavior; or

[0009] when the relative collision time is not lower than the preset relative collision time, determining a second vehicle behavior corresponding to the vehicle driving information based on a mapping relationship between preset vehicle driving information and the second preset vehicle behavior, and adjusting the intelligent vehicle behavior of the intelligent vehicle based on the second vehicle behavior.

[0010] By the above method, the intelligent vehicle predicts the vehicle behavior of the front vehicle in different ways through the size relationship between the relative collision time and the preset relative collision time, so that the intelligent vehicle can adjust the intelligent vehicle behavior based on the predicted first vehicle behavior or second vehicle behavior in advance.

[0011] In a possible design, the relative collision time corresponding to the front vehicle of the intelligent vehicle is determined, including:

[0012] determining a relative speed and a relative distance between the front vehicle and the other vehicle;

[0013] calculating a ratio between the relative distance and the relative speed, and taking the ratio as the relative collision time.

[0014] By the above method, the relative collision time is calculated through the relative distance and the relative speed, so that the intelligent vehicle can predict the vehicle behavior of the front vehicle based on the relative collision time.

[0015] In a possible design, the first vehicle behavior corresponding to the relative collision time is determined based on a mapping relationship between the preset relative collision time and the first preset vehicle behavior, including:

[0016] matching the relative collision time with the preset relative collision time to determine a first relative collision time consistent with the relative collision time;

[0017] determining a first risk probability value set corresponding to the first relative collision time based on a mapping relationship between the preset relative collision time and the first preset risk probability value set, wherein the first preset risk probability value set records probabilities of occurrence of each vehicle behavior;

[0018] filtering a maximum first risk probability value from the first risk probability value set, and determining a first vehicle behavior corresponding to the maximum first risk probability value based on a mapping relationship between the first preset risk probability value set and the first preset vehicle behavior.

[0019] By the above method, the first vehicle behavior corresponding to the relative collision time is determined through the mapping relationship between the preset relative collision time and the first preset vehicle behavior, so that the purpose of predicting the vehicle behavior of the front vehicle in advance is achieved.

[0020] In a possible design, the second vehicle behavior corresponding to the vehicle driving information is determined based on a mapping relationship between preset vehicle driving information and the second preset vehicle behavior, including:

[0021] The vehicle driving information is matched with the preset vehicle driving information to determine second vehicle driving information consistent with the vehicle driving information;

[0022] A second risk probability value set corresponding to the second vehicle driving information is determined based on a mapping relationship between the preset vehicle driving information and the second preset risk probability value set;

[0023] A maximum second risk probability value is filtered out from the second risk probability value set, and a vehicle behavior corresponding to the maximum second risk probability value is taken as the second vehicle behavior of the front vehicle.

[0024] By the above method, the second vehicle behavior corresponding to the vehicle driving information is determined based on the mapping relationship between the preset vehicle driving information and the second preset vehicle behavior, so that the purpose of predicting the vehicle behavior of the front vehicle in advance is achieved.

[0025] In a possible design, matching the vehicle driving information with the preset vehicle driving information includes:

[0026] Lane information, vehicle speed information and vehicle type information in the vehicle driving information are extracted;

[0027] The lane information is matched with preset lane information in the preset vehicle driving information; and,

[0028] The vehicle speed information is matched with preset vehicle speed information in the preset vehicle driving information; and,

[0029] The vehicle type information is matched with preset vehicle type information in the preset vehicle driving information.

[0030] By the above method, the matching of the vehicle driving information and the preset vehicle driving information is realized in multiple ways, which improves the accuracy of the intelligent vehicle in predicting the vehicle behavior of the front vehicle.

[0031] In a second aspect, the present application provides a vehicle control device, which includes:

[0032] determining a relative collision time corresponding to a front vehicle of the intelligent vehicle and vehicle driving information, wherein the relative collision time records an interval time of a collision between the front vehicle and other vehicles;

[0033] a first module configured to, when the relative collision time is lower than a preset relative collision time, determine a first vehicle behavior corresponding to the relative collision time based on a mapping relationship between the preset relative collision time and the first preset vehicle behavior, and adjust the intelligent vehicle behavior of the intelligent vehicle based on the first vehicle behavior; or,

[0034] a second module configured to, when the relative collision time is not lower than the preset relative collision time, determine a second vehicle behavior corresponding to the vehicle driving information based on a mapping relationship between preset vehicle driving information and the second preset vehicle behavior, and adjust the intelligent vehicle behavior of the intelligent vehicle based on the second vehicle behavior.

[0035] In a possible design, the determining module is specifically configured to determine a relative speed and a relative distance between the front vehicle and the other vehicles, calculate a ratio between the relative distance and the relative speed, and take the ratio as the relative collision time.

[0036] In a possible design, the first module is specifically configured to match the relative collision time with the preset relative collision time, determine a first relative collision time consistent with the relative collision time, determine a first risk probability value set corresponding to the first relative collision time based on a mapping relationship between the preset relative collision time and the first preset risk probability value set, select a maximum first risk probability value from the first risk probability value set, and determine a first vehicle behavior corresponding to the maximum first risk probability value based on a mapping relationship between the first preset risk probability value set and the first preset vehicle behavior.

[0037] In a possible design, the second module is specifically configured to match the vehicle driving information with the preset vehicle driving information, determine second vehicle driving information consistent with the vehicle driving information, determine a second risk probability value set corresponding to the second vehicle driving information based on a mapping relationship between the preset vehicle driving information and the second preset risk probability value set, select a maximum second risk probability value from the second risk probability value set, and take a vehicle behavior corresponding to the maximum second risk probability value as the second vehicle behavior of the front vehicle.

[0038] In a possible design, the second module is further configured to extract lane information, vehicle speed information and vehicle type information from the vehicle driving information, match the lane information with preset lane information in the preset vehicle driving information, match the vehicle speed information with preset vehicle speed information in the preset vehicle driving information, and match the vehicle type information with preset vehicle type information in the preset vehicle driving information.

[0039] In a third aspect, the present application provides an electronic device, comprising:

[0040] a memory configured to store a computer program;

[0041] a processor configured to execute the computer program stored in the memory, thereby implementing the vehicle control method.

[0042] In a fourth aspect, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program. The computer program is executed by a processor, thereby implementing the vehicle control method.

[0043] The technical effects of the first aspect to the fourth aspect and each possible solution of the first aspect can be referred to the above description, and will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 A distribution diagram of an intelligent vehicle and other vehicles in three lanes is provided for the present application;

[0045] Figure 2 A flowchart of a vehicle control method is provided for the present application;

[0046] Figure 3 A diagram of other vehicles in front of a front vehicle in three lanes is provided for the present application;

[0047] Figure 4 A structural diagram of a vehicle control device is provided for the present application;

[0048] Figure 5 A structural diagram of an electronic device is provided for the present application. DETAILED DESCRIPTION

[0049] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings. The specific operation methods in the method embodiments can also be applied to the device embodiments or system embodiments. It should be noted that in the description of the present application, "multiple" is understood as "at least two". The association relationship of the associated objects is described, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone. A is connected with B, which means that A is directly connected with B and A is connected with B through C. In addition, in the description of the present application, "first", "second" and the like are only used for the purpose of distinguishing description and cannot be understood as indicating or implying relative importance or indicating or implying sequence.

[0050] In the prior art, in order to realize the safety in the driving process, the intelligent vehicle needs to determine the vehicle behavior of the front vehicle and / or the front vehicle in the adjacent lane, such as Figure 1 As shown, the intelligent vehicle will detect the respective vehicle speeds and vehicle behaviors of the first vehicle, the second vehicle and the third vehicle, the intelligent vehicle will determine the respective risk levels based on the respective vehicle speeds and the respective vehicle behaviors corresponding to the respective vehicle speeds, and then filter out the target lane corresponding to the lowest risk level, and control the intelligent vehicle to drive in the target lane. However, when the front vehicle collides with other vehicles, since the intelligent vehicle can only detect after the actual driving track of the front vehicle deviates from the preset driving track, the intelligent vehicle cannot respond in time based on the vehicle behavior of the front vehicle, so that the intelligent vehicle has the risk of colliding with the front vehicle, thereby failing to ensure the safety of the intelligent vehicle in the driving process.

[0051] In order to solve the above-mentioned problems, the present application provides a vehicle control method for the intelligent vehicle to predict the vehicle behavior of the front vehicle, so as to timely adjust the behavior of the intelligent vehicle before the front vehicle collides, and thus ensure the safety of the intelligent vehicle in the driving process. The method and the device of the present application are based on the same technical concept, and since the principles of the problems solved by the method and the device are similar, the embodiments of the device and the method can be mutually referred to, and the repeated parts will not be described again.

[0052] The embodiments of the present application will be described in detail below with reference to the drawings.

[0053] With reference to Figure 2 The present application provides a vehicle control method, which can predict the vehicle behavior of the front vehicle of the intelligent vehicle, thereby ensuring the safety of the intelligent vehicle in the driving process, and the implementation process of the method is as follows:

[0054] Step S21: Determine the relative collision time corresponding to the front vehicle of the intelligent vehicle and the vehicle driving information.

[0055] In order to ensure the safety of the intelligent vehicle during driving, the intelligent vehicle needs to predict the vehicle behavior of the front vehicle, prevent the intelligent vehicle from responding to the vehicle behavior of the front vehicle in time, and thus reduce the problem of driving safety of the intelligent vehicle. First, the intelligent vehicle needs to obtain the relative collision time of the front vehicle and other vehicles and the vehicle driving information, which records the interval time of the collision between the front vehicle and other vehicles, and determines whether the front vehicle of the intelligent vehicle collides with other vehicles based on the relative interval time. The specific process of obtaining the relative collision time is as follows:

[0056] In order to enable the intelligent vehicle to respond to the vehicle behavior of the front vehicle in advance, the intelligent vehicle will determine the relative speed and relative distance between the front vehicle and other vehicles, and calculate the ratio between the relative speed and the relative distance, which will be used as the relative collision time. For example, the relative speed of the front vehicle and other vehicles is 4 m / s, and the relative distance is 20 m. Then the collision interval time is 20 / 4=5s, which represents that the front vehicle will collide with other vehicles in 5s.

[0057] In addition, the intelligent vehicle also needs to obtain the vehicle driving information, which at least includes the vehicle speed information of the front vehicle, the lane information of the lane to which the front vehicle belongs, and the vehicle type information of the front vehicle. In the embodiment of the application, the vehicle speed information and the vehicle type information can be obtained according to the high-precision map and the visual detection device. Since the vehicle speed information and the vehicle type information are obtained based on the high-precision map and the visual detection device, which are well known to those skilled in the art, they will not be described in detail here.

[0058] Since the lane to which the intelligent vehicle belongs can be a single lane, a double lane, a three-lane, etc. in the actual traffic scene, and the number of front vehicles of the intelligent vehicle is at least one, here, a three-lane is taken as an example for description. The schematic diagram of other vehicles in front of the front vehicle in a three-lane is shown in Figure 3 In Figure 3 , there are a first front vehicle, a second front vehicle and a third front vehicle in front of the intelligent vehicle, and there are other vehicles in the lane to which the second front vehicle belongs. The number of other vehicles can be adjusted according to the actual scene, which is only used as an example for description.

[0059] Through the above method, the intelligent vehicle detects whether the front vehicle collides with other vehicles through the relative collision time, so as to predict the traffic conditions of the front lane in advance, and thus ensure the safety of the intelligent vehicle during driving.

[0060] Step S22: When the relative collision time is lower than the preset relative collision time, a first vehicle behavior corresponding to the relative collision time is determined based on a mapping relationship between the preset relative collision time and the first preset vehicle behavior, and the intelligent vehicle behavior of the intelligent vehicle is adjusted based on the first vehicle behavior.

[0061] After the relative collision interval time is determined, it is detected whether the relative collision interval time is lower than a preset interval time. When the collision interval time is lower than the preset interval time, it is represented that the front vehicle and the other vehicle will collide; for example, the preset interval time is 12s, and when the collision interval time is 4s, 4s<12s, it is represented that the front vehicle will collide due to too short response time.

[0062] After it is determined that the front vehicle and the other vehicle will collide, the intelligent vehicle matches the relative collision time of the collision between the front vehicle and the other vehicle with the preset relative collision time, takes the matched preset relative collision time as a first relative collision time, and determines a first risk probability value set corresponding to the first relative collision time based on a mapping relationship between the preset relative collision time and the first preset risk probability value set, the first preset risk probability value set recording probabilities of occurrence of each vehicle behavior. The mapping relationship between the preset relative collision time and the first preset vehicle behavior is shown in Table 1 as follows:

[0063] Pre-set relative collision time T1 T2 T3 First pre-set risk probability value set P1 P2 P3

[0064] Table 1

[0065] In the above Table 1, the preset relative collision times are T1, T2 and T3, when the preset relative collision time is T1, the risk probability value set corresponding to T1 is P1, when the preset relative collision time is T2, the risk probability value set corresponding to T1 is P2, when the preset relative collision time is T3, the risk probability value set corresponding to T3 is P3, P1+P2+P3=1, and the preset collision time in Table 1 can be a certain time or a certain time period, which is not described in detail here.

[0066] After the first risk probability value set is determined based on the above Table 1, in order to determine the vehicle behavior of the front vehicle, the maximum first risk probability value is selected from the first risk probability value set, and the first vehicle behavior corresponding to the maximum first risk probability value is determined based on a mapping relationship between the first preset risk probability value set and the first preset vehicle behavior, the mapping relationship between the first preset risk probability value set and the first preset vehicle behavior is shown in Table 2 as follows:

[0067]

[0068] Table 2

[0069] The first preset risk probability value set is recorded in Table 2, and each first preset risk probability value set records the risk probability value corresponding to each first preset vehicle behavior A, B, and C, respectively. A, B, and C in Table 2 can be acceleration, braking, and lane changing in turn. Here, only an example is given, and the first preset vehicle behavior can be set according to the vehicle behavior in the actual scene.

[0070] For example:

[0071]

[0072] Table 3

[0073] The first preset risk probability value set is recorded in Table 3, and each first preset risk probability value set records the risk probability value corresponding to each first preset vehicle behavior A, B, and C, respectively. A, B, and C in Table 2 can be acceleration, braking, and lane changing in turn. Here, only an example is given, and the first preset vehicle behavior can be set according to the vehicle behavior in the actual scene.

[0074] It should be noted that when the maximum first risk probability value is at least 2, the intelligent vehicle can determine any one of the multiple vehicle behaviors as the first vehicle behavior.

[0075] After the intelligent vehicle determines the first vehicle behavior, the intelligent vehicle can take the first vehicle behavior as the actual vehicle behavior of the front vehicle, and adjust the planning path of the intelligent vehicle based on the actual vehicle behavior, thereby realizing the adjustment of the intelligent vehicle behavior.

[0076] Through the above method, the first vehicle behavior of the front vehicle can be determined through the mapping relationship between the preset relative collision time and the first preset vehicle behavior, thereby realizing the prediction of the first vehicle behavior of the front vehicle by the intelligent vehicle.

[0077] Step 23: When the relative collision time is not less than the preset relative collision time, the second vehicle behavior corresponding to the vehicle driving information is determined based on the mapping relationship between the preset vehicle driving information and the second preset vehicle behavior, and the intelligent vehicle behavior of the intelligent vehicle is adjusted based on the second vehicle behavior.

[0078] After determining the relative collision time, when the relative collision interval time is not less than the preset interval time, it represents that the front vehicle does not collide with other vehicles. If the preset interval time is 12s, when the collision interval time is 15s, 15s>12s, which represents that the front vehicle has sufficient reaction time to avoid collision with other vehicles.

[0079] When the intelligent vehicle detects that the front vehicle does not collide with other vehicles, in order to determine the vehicle behavior of the front vehicle, the intelligent vehicle needs to judge the vehicle behavior of the front vehicle based on the mapping relationship between the preset vehicle driving information and the second preset vehicle behavior. First, the vehicle driving information needs to be matched with the preset vehicle driving information. The specific matching process is as follows:

[0080] Since the vehicle driving information includes lane information, vehicle speed information and vehicle type information, the preset vehicle driving information includes preset lane information, preset vehicle speed information and preset vehicle type information. In order to determine the vehicle behavior of the front vehicle, it is necessary to determine whether the lane information matches the preset lane information, whether the vehicle speed information matches the preset vehicle speed information, and whether the vehicle type information matches the preset vehicle type information. Based on the matching results of the vehicle speed information, the lane information and the vehicle type information and the preset vehicle driving information, the second vehicle driving information is determined from the preset vehicle driving information.

[0081] After determining the second vehicle driving information, in order to determine the vehicle behavior of the front vehicle, it is necessary to determine the second risk probability value set corresponding to the second vehicle driving information based on the mapping relationship between the preset vehicle driving information and the second preset risk probability value set. The mapping relationship between the preset vehicle driving information and the second preset risk probability value set is shown in Table 4:

[0082]

[0083]

[0084] Table 4

[0085] The above table 4 records four kinds of preset lane information. The type of lane information can be adjusted according to the actual traffic conditions. The lane information can be the speed section in the driving lane. Based on the lane information, the vehicle speed information and the vehicle type information in the second vehicle driving information, the row and the column can be determined from table 4. Based on the row and the column, the corresponding second risk probability value set can be determined. The second risk probability value set in the above table 4 is only as an example. The actual parameters can be limited based on the actual situation. Here is not too much description.

[0086] In addition, it should be noted that Q11+Q12+Q13+Q14=1;Q21+Q22+Q23+Q24=1;Q31+Q32+Q33+Q34=1;Q41+Q42+Q43+Q44=1.

[0087] After determining the second risk probability value set based on the above table 4, the maximum second risk probability value needs to be selected from the second risk probability value set, and the vehicle behavior corresponding to the maximum second risk probability value is taken as the second vehicle behavior of the front vehicle.

[0088] For example, when the speed information of the front vehicle is 80-100 kph, the vehicle type information of the front vehicle is a small passenger car, the lane information of the front vehicle is an expressway, the speed information of the front vehicle is 110 kph, and the vehicle type information of the front vehicle is a small passenger car, the speed information does not conform to 80-100 kph, and it can be determined that Q11 is the second risk probability value set.

[0089] If Q11=(0.1, 0.3, 0.2), the Q11 records the probabilities of the front vehicle accelerating, decelerating, and changing lanes, respectively. Since the probability value of 0.3 is the largest, deceleration is determined as the second vehicle behavior of the small passenger car.

[0090] When there are multiple maximum second risk probability value sets, the intelligent vehicle can determine any one of the vehicle behaviors corresponding to the multiple maximum second risk probability value sets as the second vehicle behavior.

[0091] Through the above method, after the intelligent vehicle determines that the front vehicle does not collide with other vehicles, the intelligent vehicle will predict the front vehicle based on the mapping relationship between the preset vehicle driving information and the second preset risk probability value set, solving the problem that the front vehicle can only be detected after the driving trajectory deviates, thereby improving the safety of the intelligent vehicle during driving.

[0092] Based on the same inventive concept, the embodiments of the present application also provide a vehicle control device for realizing the functions of the vehicle control method. Referring to Figure 4 , the device comprises:

[0093] A determination module 401 is configured to determine a relative collision time corresponding to a front vehicle of an intelligent vehicle and vehicle driving information, wherein the relative collision time records an interval time of collision between the front vehicle and other vehicles.

[0094] A first module 402 is configured to, when the relative collision time is lower than a preset relative collision time, determine a first vehicle behavior corresponding to the relative collision time based on a mapping relationship between the preset relative collision time and the first preset vehicle behavior, and adjust an intelligent vehicle behavior of the intelligent vehicle based on the first vehicle behavior; or,

[0095] A second module 403 is configured to, when the relative collision time is not lower than the preset relative collision time, determine a second vehicle behavior corresponding to the vehicle driving information based on a mapping relationship between preset vehicle driving information and the second preset vehicle behavior, and adjust the intelligent vehicle behavior of the intelligent vehicle based on the second vehicle behavior.

[0096] In a possible design, the determining module 401, in particular, is configured to determine the relative speed and the relative distance between the front vehicle and the other vehicle, calculate a ratio between the relative distance and the relative speed, and take the ratio as the relative collision time.

[0097] In a possible design, the first module 402, in particular, is configured to match the relative collision time with the preset relative collision time, determine a first relative collision time consistent with the relative collision time, determine a first risk probability value set corresponding to the first relative collision time based on a mapping relationship between the preset relative collision time and the first preset risk probability value set, filter a maximum first risk probability value from the first risk probability value set, and determine a first vehicle behavior corresponding to the maximum first risk probability value based on a mapping relationship between the first preset risk probability value set and the first preset vehicle behavior.

[0098] In a possible design, the second module 403, in particular, is configured to match the vehicle driving information with the preset vehicle driving information, determine second vehicle driving information consistent with the vehicle driving information, determine a second risk probability value set corresponding to the second vehicle driving information based on a mapping relationship between the preset vehicle driving information and the second preset risk probability value set, filter a maximum second risk probability value from the second risk probability value set, and take a vehicle behavior corresponding to the maximum second risk probability value as the second vehicle behavior of the front vehicle.

[0099] In a possible design, the second module 403 is further configured to extract lane information, vehicle speed information and vehicle type information from the vehicle driving information, match the lane information with preset lane information in the preset vehicle driving information, match the vehicle speed information with preset vehicle speed information in the preset vehicle driving information, and match the vehicle type information with preset vehicle type information in the preset vehicle driving information.

[0100] Based on the same inventive concept, the embodiment of the present application further provides an electronic device, which can implement the function of the vehicle control device described above, and refer to Figure 5 , the electronic device comprises:

[0101] at least one processor 501 and a memory 502 connected with the at least one processor 501, and the specific connection medium between the processor 501 and the memory 502 is not limited in the embodiment of the present application, Figure 5 In the embodiment of the present application, the connection between the processor 501 and the memory 502 is taken as an example of connection through a bus 500. The bus 500 is used for connection between the processor 501 and the memory 502 in Figure 5The connection between the other components is indicated by a thick line, which is only illustrative and not limited. The bus 500 can be divided into an address bus, a data bus, a control bus, etc. For the sake of simplicity, Figure 5 The bus is indicated by a thick line, but it does not mean that there is only one bus or only one type of bus. Alternatively, the processor 501 can also be referred to as a controller, and the name is not limited.

[0102] In the embodiment of the application, the memory 502 stores instructions executable by the at least one processor 501, and the at least one processor 501 can execute the vehicle control method discussed above by executing the instructions stored in the memory 502. The processor 501 can realize Figure 4 The functions of each module in the device shown.

[0103] The processor 501 is the control center of the device, and can connect each part of the control device through various interfaces and lines. By running or executing the instructions stored in the memory 502 and calling the data stored in the memory 502, the device can process data and perform various functions, thereby monitoring the device as a whole.

[0104] In a possible design, the processor 501 can include one or more processing units, and the processor 501 can integrate an application processor and a modem processor. The application processor mainly processes the operating system, user interface, and application program, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 501. In some embodiments, the processor 501 and the memory 502 can be implemented on the same chip, and in some embodiments, they can also be implemented on separate chips respectively.

[0105] The processor 501 can be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, which can realize or execute the methods, steps and logic block diagrams disclosed in the embodiments of the application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the vehicle control method disclosed in the embodiments of the application can be directly embodied by a hardware processor for execution, or by a combination of hardware and software modules in the processor for execution.

[0106] The memory 502, as a non-volatile computer readable storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 502 can include at least one type of storage medium, for example, can include flash memory, hard disk, multimedia card, card type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic storage, magnetic disk, optical disk, etc. The memory 502 is any other medium capable of carrying or storing desired program code in the form of instructions or data structures and capable of being accessed by a computer, but is not limited thereto. The memory 502 in the embodiments of the present application can also be a circuit or any other device capable of realizing a storage function, used to store program instructions and / or data.

[0107] By designing and programming the processor 501, the code corresponding to the vehicle control method introduced in the foregoing embodiments can be fixed in the chip, so that the chip can execute the vehicle control step of the embodiment shown in the running time. Figure 2 How to design and program the processor 501 is a technology known to those skilled in the art, which will not be described here.

[0108] Based on the same inventive concept, the embodiments of the present application also provide a storage medium storing computer instructions, when the computer instructions run on a computer, the computer instructions make the computer execute the vehicle control method discussed above.

[0109] In some possible implementations, the present application provides that various aspects of the vehicle control method can also be implemented in the form of a program product, which includes program code, when the program product runs on the device, the program code is used to make the control device execute the steps in the vehicle control method according to various exemplary embodiments of the present application described above in the specification.

[0110] Those skilled in the art will appreciate that embodiments of the present application can be devised for a variety of other systems which are currently developed or later developed. Therefore, the present application is intended to cover all such modifications and variations of this application that are within the scope of the appended claims and their equivalents. It is intended that each element of claim 1 and 2 is independent of one another. No element of claim 1 and 2, or any other claim, is implied to depend on any other element or limitation of claim 1 and 2 or any other claim except where expressly recited in that claim.

[0111] The present application is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to this application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 means for performing each of the functions specified in the flowchart and / or block diagram block or blocks.

[0112] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 means for performing each of the functions specified in the flowchart and / or block diagram block or blocks.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 means for performing each of the functions specified in the flowchart and / or block diagram block or blocks.

[0114] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A vehicle control method, characterized in that, include: The relative collision time and vehicle driving information of the vehicle in front of the intelligent vehicle are determined. The relative collision time records the interval between the collision between the vehicle in front and other vehicles. The vehicle driving information includes at least the speed information of the vehicle in front, the lane information of the lane to which the vehicle in front belongs, and the vehicle type information of the vehicle in front. When the relative collision time is lower than a preset relative collision time, based on the mapping relationship between the preset relative collision time and a first preset vehicle behavior, the first vehicle behavior corresponding to the relative collision time is determined, and the intelligent vehicle behavior of the intelligent vehicle is adjusted based on the first vehicle behavior; or When the relative collision time is not less than the preset relative collision time, the second vehicle behavior corresponding to the vehicle driving information is determined based on the mapping relationship between the preset vehicle driving information and the second preset vehicle behavior, and the intelligent vehicle behavior of the intelligent vehicle is adjusted based on the second vehicle behavior. The behavior of the first vehicle is determined through the following steps: The relative collision time is matched with the preset relative collision time to determine a first relative collision time that is consistent with the relative collision time; Based on the mapping relationship between the preset relative collision time and the first preset risk probability value set, the first risk probability value set corresponding to the first relative collision time is determined, wherein the first preset risk probability value set records the probability of each vehicle behavior occurring. The maximum first risk probability value is selected from the first set of risk probability values, and the first vehicle behavior corresponding to the maximum first risk probability value is determined based on the mapping relationship between the first preset set of risk probability values ​​and the first preset vehicle behavior.

2. The method as described in claim 1, characterized in that, Determine the relative collision time between the intelligent vehicle and the vehicle in front, including: Determine the relative speed and relative distance between the vehicle ahead and the other vehicles; The ratio between the relative distance and the relative velocity is calculated, and the ratio is used as the relative collision time.

3. The method as described in claim 1, characterized in that, Based on the mapping relationship between preset vehicle driving information and second preset vehicle behavior, the second vehicle behavior corresponding to the vehicle driving information is determined, including: The vehicle driving information is matched with the preset vehicle driving information to determine the second vehicle driving information that is consistent with the vehicle driving information; Based on the mapping relationship between the preset vehicle driving information and the second preset risk probability value set, the second risk probability value set corresponding to the second vehicle driving information is determined; The maximum second risk probability value is selected from the set of second risk probability values, and the vehicle behavior corresponding to the maximum second risk probability value is taken as the second vehicle behavior of the vehicle in front.

4. The method as described in claim 3, characterized in that, Matching the vehicle driving information with the preset vehicle driving information includes: Extract lane information, speed information, and vehicle type information from the vehicle driving information; Match the lane information with the preset lane information in the preset vehicle driving information; and, Match the vehicle speed information with the preset vehicle speed information in the preset vehicle driving information; and, The vehicle type information is matched with the preset vehicle type information in the preset vehicle driving information.

5. A vehicle control device, characterized in that, include: The determination module is used to determine the relative collision time and vehicle driving information of the vehicle in front of the intelligent vehicle. The relative collision time records the interval between the collision between the vehicle in front and other vehicles. The vehicle driving information includes at least: the speed information of the vehicle in front, the lane information of the lane to which the vehicle in front belongs, and the vehicle type information of the vehicle in front. The first module is configured to, when the relative collision time is lower than a preset relative collision time, determine the first vehicle behavior corresponding to the preset relative collision time based on the mapping relationship between the preset relative collision time and the first preset vehicle behavior, and adjust the intelligent vehicle behavior of the intelligent vehicle based on the first vehicle behavior; or The second module is used to determine the second vehicle behavior corresponding to the vehicle driving information based on the mapping relationship between the preset vehicle driving information and the second preset vehicle behavior when the relative collision time is not less than the preset relative collision time, and to adjust the intelligent vehicle behavior of the intelligent vehicle based on the second vehicle behavior. The first module determines the behavior of the first vehicle through the following steps: The relative collision time is matched with the preset relative collision time to determine a first relative collision time that is consistent with the relative collision time; Based on the mapping relationship between the preset relative collision time and the first preset risk probability value set, the first risk probability value set corresponding to the first relative collision time is determined, wherein the first preset risk probability value set records the probability of each vehicle behavior occurring. The maximum first risk probability value is selected from the first set of risk probability values, and the first vehicle behavior corresponding to the maximum first risk probability value is determined based on the mapping relationship between the first preset set of risk probability values ​​and the first preset vehicle behavior.

6. The apparatus as claimed in claim 5, characterized in that, The determining module is specifically used to determine the relative speed and relative distance between the vehicle in front and the other vehicles, calculate the ratio between the relative distance and the relative speed, and use the ratio as the relative collision time.

7. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, when executing a computer program stored in the memory, implements the steps of the method according to any one of claims 1-4.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1-4.

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