A method for controlling the formation of an intelligent networked vehicle fleet, a computing device, a storage medium and a vehicle

Abstract

The present invention provides a method for controlling the formation of an intelligent networked vehicle fleet, comprising the following steps: S1, obtaining information about the preceding vehicle, including the type of the preceding vehicle and the relative distance S between the preceding vehicle and the vehicle. S2, if the relative distance S to the preceding vehicle is greater than or equal to S 1 , the vehicle switches to pilot mode. If the relative distance S to the vehicle ahead is less than S 1 , the vehicle switches to the degradation mode. S3. If the relative distance S to the vehicle ahead is greater than S 2 , the vehicle switches to pilot mode. If the relative distance to the vehicle ahead is less than or equal to S 2 and greater than or equal to S 3 , the vehicle switches to the chasing mode. If the relative distance to the vehicle ahead is less than or equal to S 4 , the vehicle switches to the following mode. If the relative distance to the vehicle in front is less than S 3 and greater than S 4 , judge the size relationship between the front vehicle queue size m and the maximum fleet size N, and then determine the following mode. Under the premise of ensuring that the decision-making control of the fleet members remains consistent, the present invention controls the vehicle driving mode based on the fleet formation process rules to ensure the real-time and accuracy of the control scheme.

Description

Technical Field

[0001] The present invention relates to the field of intelligent networked vehicle fleet formation control technology, and specifically to an intelligent networked vehicle fleet formation control method, a computing device, a storage medium and a vehicle. Background Art

[0002] With the development of wireless communication technology and the automobile industry, vehicle-road cooperative autonomous driving technology represented by intelligent connected vehicles has received more and more attention. Intelligent connected vehicles can not only perceive the surrounding environment information through advanced sensors, but also use precise control systems to reduce traffic accidents and traffic jams caused by human driving errors. At the same time, CACC (Cooperative Adaptive Cruise Control) technology enables multiple intelligent connected vehicles to form a convoy in a more compact manner, with members in the convoy moving at the same speed, effectively improving road traffic capacity and reducing exhaust emissions and energy loss.

[0003] However, there are still many obstacles to the large-scale popularization of intelligent connected vehicles. Mixed traffic flows consisting of intelligent connected vehicles and manually driven vehicles will exist for a long time in the future. When intelligent connected vehicles follow manually driven vehicles, the control of the CACC system will degenerate to the control of the ACC (Adaptive Cruise Control) system, resulting in a decrease in traffic efficiency, stability and safety. In addition, when facing emergencies caused by manual drivers, an overly long convoy will also cause safety risks due to the vehicles driving too closely together.

[0004] In the current control method for the formation of intelligent networked fleets, the decision-making process of each vehicle is relatively independent, which may lead to decision conflicts among fleet members and excessive fleet length. At the same time, the decision-making control algorithm usually requires too long a calculation time, which affects the real-time performance of the control scheme. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method for controlling the formation of an intelligent networked vehicle fleet in view of the above-mentioned deficiencies in the prior art, comprising the following steps:

[0006] S1. Obtain the preceding vehicle information, including the preceding vehicle type and the relative distance S between the preceding vehicle and the vehicle. The preceding vehicle types include manually driven vehicles and intelligent networked vehicles. If the preceding vehicle is a manually driven vehicle, go to step S2. If the preceding vehicle is an intelligent networked vehicle, go to step S3.

[0007] S2: If the relative distance S to the vehicle in front is greater than or equal to S1, the vehicle switches to the pilot mode. If there is a following vehicle in the vehicle's convoy, the following vehicle switches to the following mode. If the relative distance S to the vehicle in front is less than S1, the vehicle switches to the degraded mode. If there is a following vehicle in the vehicle's convoy, the following vehicle switches to the following mode. S1 is the preset distance at which the intelligent connected vehicle's function degrades when the vehicle in front is a manually driven vehicle.

[0008] S3. If the relative distance S to the vehicle in front is greater than S2, the vehicle switches to the pilot mode. If there is a following vehicle in the vehicle's convoy, the following vehicle switches to the following mode. If the relative distance to the vehicle in front is less than or equal to S2 and greater than or equal to S3, the vehicle switches to the chasing mode. If there is a following vehicle in the vehicle's convoy, the following vehicle switches to the following chasing mode. If the relative distance to the vehicle in front is less than or equal to S4, the vehicle switches to the following mode. If the relative distance to the vehicle in front is less than S3 and greater than S4, the relationship between the size of the vehicle queue in front m and the maximum size of the convoy N is determined. If the size of the vehicle queue in front is equal to N, the vehicle switches to the pilot mode. If there is a following vehicle in the vehicle's convoy, the following vehicle switches to the following mode. If the size of the front vehicle queue is less than N, the vehicle switches to the pursuit mode. If there are following vehicles in the vehicle's convoy, the (Nm-1)th following vehicle behind the vehicle's convoy switches to the following pursuit mode. If there are still following vehicles behind the (Nm)th following vehicle in the vehicle's convoy, the (N-m+1)th following vehicle in the vehicle's convoy switches to the pilot mode. If there are still following vehicles behind the (N-m+1)th following vehicle in the vehicle's convoy, the following vehicle switches to the following mode. S2, S3, and S4 are the pursuit threshold, pilot threshold, and following threshold for the intelligent connected vehicle to switch modes when the preceding vehicle is also an intelligent connected vehicle.

[0009] The degradation mode indicates that when the intelligent connected vehicle is the head vehicle in a convoy and the vehicle in front is a manually driven vehicle, a larger following distance mode is implemented to ensure safety due to the randomness and uncertainty of the driving behavior of manually driven vehicles. The pilot mode indicates that when the intelligent connected vehicle is the head vehicle in a convoy and there is no vehicle in front or the vehicle in front is the last vehicle in a convoy that has reached the maximum size, the mode is adopted to ensure the traffic efficiency and the distance between different convoys. The following mode indicates that when the driving mode of the head vehicle in the convoy is the pilot mode or the degradation mode, in order to maintain a smaller distance within the convoy, the non-head vehicle in the same convoy implements a smaller following distance mode. The chasing mode indicates that when the intelligent connected vehicle is the head vehicle in a convoy and the vehicle in front is an intelligent connected vehicle and the convoy in which the vehicle in front is located has not reached the maximum size of the convoy, the high speed mode is implemented to reduce the distance with the vehicle in front and form a new convoy with the vehicle in front as soon as possible. The following and catching-up mode means that when the driving mode of the head vehicle in the queue is the catching-up mode, in order to maintain a smaller distance within the queue and the stability of the convoy, the non-head vehicles in the same convoy will adopt a high-speed and smaller following distance mode.

[0010] Through the above-mentioned switching of the following mode of the intelligent connected vehicle fleet, while ensuring that the decision-making control of the fleet members remains consistent, the vehicle driving mode is controlled based on the fleet formation process rules to ensure the real-time and accuracy of the control plan.

[0011] Preferably, the intelligent connected vehicle collects the vehicle type of the preceding vehicle and the relative distance S between the preceding vehicle and the vehicle itself through on-vehicle sensors and vehicle-to-vehicle communication equipment, and transmits the current real-time information to the fleet formation controller, which controls the vehicle mode.

[0012] Preferably, S1 is set to 250-350m.

[0013] Preferably, S2 is set to 100-140m.

[0014] Preferably, S3 = V max h Leading ,S4=V max h Following , where V max is the maximum speed of the vehicle, h Leading is the expected headway distance in pilot mode, h Following Expected headway for following mode.

[0015] Preferably, h Leading Set to 0.9-1.1s.

[0016] Preferably, h Following Set to 0.5-0.7s.

[0017] The present invention also provides a computing device, including a processor and a memory, wherein the memory stores executable codes, and when the executable codes are executed by the processor, the processor executes the above method.

[0018] The present invention also provides a non-temporary machine-readable storage medium on which executable codes are stored. When the executable codes are executed by a processor of an electronic device, the processor is enabled to execute the above method.

[0019] The present invention also provides a vehicle, and the vehicle uses the above method.

[0020] The present invention performs control based on the running status information and vehicle information of the vehicle itself and its surrounding vehicles, taking into account the vehicle itself and the following vehicles in its convoy, thus ensuring the accuracy of the control logic.

[0021] The present invention decomposes the formation of an intelligent connected vehicle fleet into different stages based on the rules of the vehicle fleet formation process, refines the driving modes of vehicles at different positions and stages, promotes the formation of the vehicle fleet, and avoids the phenomenon of excessive vehicle fleet size.

[0022] The control rule of the present invention takes into account various fleet operation processes such as fleet formation, fleet splitting and fleet reorganization, and is complete. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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.

[0024] Figure 1 It is a schematic diagram of the process of the present invention;

[0025] Figure 2 It is a schematic diagram of the pilot mode in which the front vehicle is a manually driven vehicle in the present invention;

[0026] Figure 3 is a schematic diagram of a degradation mode in which the front vehicle is a manually driven vehicle in the present invention;

[0027] Figure 4 is a schematic diagram of a pilot mode in which the front vehicle is an intelligent network-connected vehicle in the present invention;

[0028] Figure 5 is a schematic diagram of a chasing mode in which the front vehicle is an intelligent network-connected vehicle in the present invention;

[0029] Figure 6 It is a schematic diagram of the pilot mode in which the front vehicle is an intelligent network-connected fleet in the present invention;

[0030] Figure 7 It is a schematic diagram of the catching-up mode in which the front vehicle is an intelligent network-connected vehicle fleet in the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0032] See also Figure 1 The intelligent networked vehicle fleet formation control method of this embodiment includes the following steps:

[0033] S1. Obtain the preceding vehicle information, including the preceding vehicle type and the relative distance S between the preceding vehicle and the vehicle. The preceding vehicle types include manually driven vehicles and intelligent networked vehicles. If the preceding vehicle is a manually driven vehicle, go to step S2. If the preceding vehicle is an intelligent networked vehicle, go to step S3.

[0034] S2, see Figure 2 , if the relative distance S to the leading vehicle is greater than or equal to S1, the ego vehicle switches to the pilot mode. If there is a following vehicle in the convoy where the ego vehicle is located, the following vehicle switches to the following mode. Figure 3 If the relative distance S to the front vehicle is less than S1, the vehicle switches to the degradation mode. If there is a following vehicle in the convoy where the vehicle is located, the following vehicle switches to the following mode. S1 is the preset distance at which the function of the intelligent networked vehicle degrades when the front vehicle is a manually driven vehicle. In this embodiment, S1 is preferably set to 250-350m.

[0035] S3, see Figure 4 , if the relative distance S to the leading vehicle is greater than S2, the ego vehicle switches to the pilot mode. If there is a following vehicle in the convoy where the ego vehicle is located, the following vehicle switches to the following mode. Figure 5 If the relative distance to the front vehicle is less than or equal to S2 and greater than or equal to S3, the vehicle switches to the pursuit mode. If there is a following vehicle in the convoy, the following vehicle switches to the following pursuit mode. If the relative distance to the front vehicle is less than or equal to S4, the vehicle switches to the following mode. If the relative distance to the front vehicle is less than S3 and greater than S4, the relationship between the size of the front vehicle queue m and the maximum size of the convoy N is determined. See Figure 6 , if the size of the leading vehicle queue is equal to N, the ego vehicle switches to the pilot mode. If there is a following vehicle in the ego vehicle's convoy, the following vehicle switches to the following mode. Figure 7 , if the size of the front vehicle queue is less than N, the vehicle switches to the catching-up mode. If there are following vehicles in the convoy where the vehicle is located, the (Nm-1)th following vehicle behind the convoy where the vehicle is located switches to the following catching-up mode. If there are still following vehicles behind the (Nm)th following vehicle in the convoy where the vehicle is located, the (N-m+1)th following vehicle in the convoy where the vehicle is located switches to the pilot mode. If there are still following vehicles behind the (N-m+1)th following vehicle in the convoy where the vehicle is located, the following vehicle switches to the following mode. In this embodiment, N=5 is taken as an example, the front vehicle queue m=3, the rear vehicle queue n=4, the first vehicle in the rear vehicle queue, i.e., the vehicle, switches to the catching-up mode, the vehicle behind the vehicle follows and switches to the following catching-up mode, the third vehicle in the rear vehicle queue switches to the pilot mode, and the remaining following vehicles switch to the following mode. In this way, when the front vehicle queue is less than N, the rear vehicle queue can make up for it, thereby improving the economy and preventing the convoy queue from being too long.

[0036] S2, S3, and S4 are the catch-up threshold, pilot threshold, and following threshold for the intelligent networked vehicle to switch modes when the preceding vehicle is also an intelligent networked vehicle. In this embodiment, S2 is preferably set to 100-140m. S3 and S4 in this embodiment are determined by the formula S3=V max h Leading 、S4=V max h Following Determine, where V max is the maximum speed of the vehicle, h Leading is the expected headway distance in pilot mode, h Following is the expected headway time in the following mode. Leading Set to 0.9-1.1s, h Following Set to 0.5-0.7s.

[0037] Preferably, in this embodiment, the intelligent connected vehicle collects the type of the preceding vehicle and the relative distance S between the preceding vehicle and the vehicle through sensors and vehicle-to-vehicle communication equipment on the vehicle, and transmits the current real-time information to the fleet formation controller, which controls the vehicle mode.

[0038] Through the above-mentioned switching of the following mode of the intelligent connected vehicle fleet, while ensuring that the decision-making control of the fleet members remains consistent, the vehicle driving mode is controlled based on the fleet formation process rules to ensure the real-time and accuracy of the control plan. Example

[0039] This embodiment is a computing device, including a processor and a memory, wherein the memory stores codes for executing the method in the above embodiment.

[0040] The processor may be a multi-core processor or may include multiple processors. In some embodiments, the processor may include a general-purpose main processor and one or more special coprocessors, such as a graphics processing unit (GPU), a digital signal processor (DSP), etc. In some embodiments, the processor may be implemented using a customized circuit, such as an application-specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

[0041] The memory may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage. Among them, ROM can store static data or instructions required by the processor or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at run time. In addition, the memory may include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (e.g., DVD-ROM, double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. The computer-readable storage medium does not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0042] The memory stores executable codes thereon, and when the executable codes are executed by the processor, the processor is enabled to execute the above method. Example

[0043] This embodiment provides a non-transitory machine-readable memory having executable codes stored thereon. When the executable codes are executed by a processor of an electronic device, the processor is enabled to execute the above-mentioned system.

[0044] A non-temporary machine-readable memory (or computer-readable memory, or machine-readable memory) having executable code (or computer program, or computer instruction code) stored thereon, which, when executed by a processor of an electronic device (or computing device, server, etc.), enables the processor to perform the various steps of the above-mentioned method according to the present invention.

[0045] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or a combination of both. Example

[0046] This embodiment is a vehicle, and the vehicle uses the method in the above embodiment.

[0047] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for controlling the formation of an intelligent networked vehicle fleet, characterized in that: The following steps are involved: S1. Obtaining the preceding vehicle information, including the preceding vehicle type and the relative distance S between the preceding vehicle and the vehicle. The preceding vehicle types include manually driven vehicles and intelligent networked vehicles. If the preceding vehicle is a manually driven vehicle, proceed to step S2; if the preceding vehicle is an intelligent networked vehicle, proceed to step S3. S2: If the relative distance S to the vehicle in front is greater than or equal to S1, the vehicle switches to the pilot mode. If there is a following vehicle in the vehicle's convoy, the following vehicle switches to the following mode. If the relative distance S to the vehicle in front is less than S1, the vehicle switches to the degraded mode. If there is a following vehicle in the vehicle's convoy, the following vehicle switches to the following mode. S1 is the preset distance at which the intelligent connected vehicle's function degrades when the vehicle in front is a manually driven vehicle. S3. If the relative distance S to the vehicle in front is greater than S2, the vehicle switches to the pilot mode. If there is a following vehicle in the vehicle's convoy, the following vehicle switches to the following mode. If the relative distance to the vehicle in front is less than or equal to S2 and greater than or equal to S3, the vehicle switches to the chasing mode. If there is a following vehicle in the vehicle's convoy, the following vehicle switches to the following chasing mode. If the relative distance to the vehicle in front is less than or equal to S4, the vehicle switches to the following mode. If the relative distance to the vehicle in front is less than S3 and greater than S4, the relationship between the size of the vehicle queue in front and the maximum size of the convoy N is determined. If the size of the vehicle queue in front is equal to N, the vehicle switches to the pilot mode. If there is a following vehicle in the vehicle's convoy, the following vehicle switches to the following mode. Switch to following mode; if the size of the leading vehicle queue is less than N, the vehicle switches to the chasing mode; if there are following vehicles in the vehicle's convoy, the (Nm-1)th following vehicle behind the vehicle's convoy switches to the following chasing mode; if there are still following vehicles behind the (Nm)th following vehicle in the vehicle's convoy, the (N-m+1)th following vehicle in the vehicle's convoy switches to the pilot mode; if there are still following vehicles behind the (N-m+1)th following vehicle in the vehicle's convoy, the following vehicle switches to the following mode; S2, S3, and S4 are the chasing threshold, pilot threshold, and following threshold for mode switching of the intelligent connected vehicle when the leading vehicle is also an intelligent connected vehicle.

2. The intelligent networked vehicle fleet formation control method according to claim 1, characterized in that: Intelligent connected vehicles collect the type of the vehicle in front and the relative distance S between the vehicle in front and the vehicle itself through on-board sensors and vehicle-to-vehicle communication equipment, and transmit the real-time information at the current moment to the fleet formation controller, which controls the vehicle mode.

3. The intelligent networked vehicle fleet formation control method according to claim 1, characterized in that: S1 is set to 250-350m.

4. The intelligent networked vehicle fleet formation control method according to claim 1, characterized in that: S2 is set to 100-140m.

5. The intelligent networked vehicle fleet formation control method according to claim 1, characterized in that: S3=V max h Leading ,S4=V max h Following , where V max is the maximum speed of the vehicle, h Leading is the expected headway distance in pilot mode, h Following Expected headway for following mode.

6. The intelligent networked vehicle fleet formation control method according to claim 5, characterized in that: h Leading Set to 0.9-1.1s.

7. The intelligent networked vehicle fleet formation control method according to claim 5, characterized in that: h Following Set to 0.5-0.7s.

8. A computing device, characterized in that: The method comprises a processor and a memory, wherein the memory stores executable codes, and when the executable codes are executed by the processor, the processor executes the method according to any one of claims 1 to 7.

9. A non-transitory machine-readable storage medium, characterized in that: An executable code is stored thereon, and when the executable code is executed by a processor of an electronic device, the processor is caused to execute any method as claimed in claims 1-7.

10. A vehicle, characterized in that: The vehicle uses the method according to any one of claims 1 to 7.

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