Vehicle driving optimization control method, device, storage medium and electronic device

Abstract

The present invention discloses a vehicle driving optimization control method, device, storage medium and electronic device. When a vehicle is detected to enter an intelligent networked vehicle control area, the type of the vehicle is judged; when the vehicle is a networked vehicle with manual assisted driving, whether there is a vehicle queue in front of the lane where the networked vehicle is located is judged; when there is a vehicle queue in front of the lane where the networked vehicle is located, whether the networked vehicle can be included in the vehicle queue is judged according to the queue length of the vehicle queue and the drivable speed range of the networked vehicle; when the networked vehicle can be included in the vehicle queue, the networked vehicle in the new vehicle queue is optimized and controlled by using a preset trajectory optimization algorithm, thereby realizing vehicle driving optimization control under mixed traffic flow, and reducing the energy consumption of vehicles passing through intersections through optimization control.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to a vehicle driving optimization control method, device, storage medium and electronic equipment. Background Art

[0002] On urban roads, intersections are one of the main causes of traffic congestion. During traffic congestion, vehicles tend to accelerate / decelerate frequently, increasing energy consumption. It is particularly important to improve the traffic capacity of urban intersections and energy-saving driving capabilities.

[0003] There are two main types of intersection traffic algorithms for energy-saving driving of intelligent connected vehicles. The first type is aimed at reducing the energy consumption of a single vehicle. By obtaining the phase information of multiple consecutive signal lights on the vehicle path, the optimal speed of the vehicle is determined with the goal of passing through the downstream signal-controlled intersection without stopping. In addition, there are also studies that consider energy consumption and driving comfort to select the optimal speed and acceleration. The second type is aimed at the fleet of intelligent connected vehicles to achieve the lowest overall energy consumption of the fleet. Some studies have achieved this through a hierarchical centralized controller. The upper layer optimizes the structure and stability of the queue, and the lower layer optimizes the coordinated control of the hybrid vehicle engine and motor to achieve fuel economy. Most of the existing research is oriented to all intelligent connected vehicles, and there is little research on energy-saving optimization control under mixed traffic. Therefore, it is urgent to propose a new vehicle driving optimization control method to achieve energy-saving optimization control under mixed traffic. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a vehicle driving optimization control method, device, storage medium and electronic device to achieve energy-saving optimization control under mixed traffic.

[0005] The technical solution proposed by the present invention is as follows:

[0006] A first aspect of an embodiment of the present invention provides a vehicle driving optimization control method, which includes: when a vehicle is detected to enter an intelligent connected vehicle control area, determining the type of the vehicle; when the vehicle is a connected vehicle with manual assisted driving, determining whether there is a vehicle queue in front of the lane where the connected vehicle is located; when there is a vehicle queue in front of the lane where the connected vehicle is located, determining whether the connected vehicle vehicle can be included in the vehicle queue based on the queue length of the vehicle queue and the drivable speed range of the connected vehicle vehicle; when the connected vehicle vehicle can be included in the vehicle queue, optimizing the control of the connected vehicle vehicle in the new vehicle queue using a preset trajectory optimization algorithm.

[0007] Optionally, the method further includes: when the vehicle is an intelligent connected vehicle, taking the intelligent connected vehicle as the first vehicle of a new vehicle queue and optimizing and controlling the intelligent connected vehicle using the preset trajectory optimization algorithm.

[0008] Optionally, the method further includes: when the connected vehicle vehicle cannot be included in the vehicle queue or when the vehicle queue does not exist in front of the lane where the connected vehicle vehicle is located, optimizing the driving speed of the connected vehicle vehicle according to the drivable speed range of the connected vehicle vehicle.

[0009] Optionally, when there is a vehicle queue in front of the lane where the connected vehicle vehicle is located, whether the connected vehicle vehicle can be included in the vehicle queue is judged according to the queue length of the vehicle queue and the drivable speed range of the connected vehicle vehicle, including: judging whether the connected vehicle vehicle can pass the stop line within the same green light phase as other vehicles in the vehicle queue according to the drivable speed range of the connected vehicle vehicle; calculating the maximum queue length of the vehicle queue according to the drivable speed range of the connected vehicle vehicle and comparing the queue length of the vehicle queue with the maximum queue length of the vehicle queue; judging whether the connected vehicle vehicle can be included in the vehicle queue according to the queue length comparison result and the judgment result of whether the connected vehicle vehicle can pass the stop line within the same green light phase.

[0010] Optionally, when the connected vehicle vehicle can be incorporated into the vehicle queue, the connected vehicle vehicle in the new vehicle queue is optimized and controlled using a preset trajectory optimization algorithm, including: obtaining a preset longitudinal dynamics model corresponding to the connected vehicle vehicle; determining an actual acceleration of the connected vehicle vehicle according to the preset longitudinal dynamics model; constructing a corresponding target control model according to the actual acceleration of the connected vehicle vehicle; and optimizing and controlling the connected vehicle vehicle according to the target control model.

[0011] Optionally, the preset longitudinal dynamics model is as follows:

[0012]

[0013] Where: x i (t) represents the longitudinal position of the connected vehicle i in the lane at time t; v i (t) represents the speed of the connected vehicle i at time t; α = 0.5ρ a C D A f , ρ a Indicates air density; C D Indicates the air resistance coefficient; A f represents the frontal area of ​​the connected vehicle; f represents the rolling resistance coefficient of the connected vehicle; Mi represents the mass of the connected vehicle i; g represents the gravitational acceleration of the connected vehicle; θ represents the road slope of the lane; a i (t) represents the actual acceleration of the connected vehicle i after considering the driver's operating error at time t; a i,exp (t) represents the expected acceleration of the connected vehicle i during the optimization control process; a i,err (t) represents the driver's control error in controlling the connected vehicle i.

[0014] Optionally, the method further includes: when there is a vehicle that is not included in the vehicle queue in front of the lane where the vehicle queue is located or when there is a non-networked vehicle in front of the lane where the vehicle queue is located, using a preset vehicle following model to calculate the maximum following speed of the networked vehicle in the vehicle queue within the same green light phase interval.

[0015] Optionally, when the connected vehicle vehicle cannot be included in the vehicle queue or when the vehicle queue does not exist in front of the lane where the connected vehicle vehicle is located, the driving speed of the connected vehicle vehicle is optimized and controlled according to the drivable speed range of the connected vehicle vehicle, including: obtaining the current driving speed of the connected vehicle vehicle; comparing the current driving speed with the drivable speed range of the connected vehicle vehicle; when the current driving speed is less than the minimum drivable speed corresponding to the drivable speed range, sending an acceleration control instruction to the connected vehicle vehicle and controlling the connected vehicle to accelerate; when the current driving speed is greater than the maximum drivable speed corresponding to the drivable speed range, sending a deceleration control instruction to the connected vehicle and controlling the connected vehicle to decelerate.

[0016] A second aspect of an embodiment of the present invention provides a vehicle driving optimization control device, which includes: a first judgment module, which is used to judge the type of the vehicle when it is detected that the vehicle enters the control area of ​​an intelligent networked vehicle; a second judgment module, which is used to judge whether there is a vehicle queue in front of the lane where the networked vehicle is located when the vehicle is a networked vehicle with manual assisted driving; a third judgment module, which is used to judge whether the networked vehicle can be included in the vehicle queue according to the queue length of the vehicle queue and the drivable speed range of the networked vehicle when there is a vehicle queue in front of the lane where the networked vehicle is located; and a control module, which is used to optimize the control of the networked vehicle in the new vehicle queue using a preset trajectory optimization algorithm when the networked vehicle can be included in the vehicle queue.

[0017] A third aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the vehicle driving optimization control method as described in the first aspect of the embodiment of the present invention and any one of the first aspects.

[0018] A fourth aspect of an embodiment of the present invention provides an electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to execute the vehicle driving optimization control method as described in the first aspect of the embodiment of the present invention and any one of the first aspects.

[0019] The technical solution provided by the present invention has the following effects:

[0020] The vehicle driving optimization control method provided by the embodiment of the present invention determines the type of the vehicle when a vehicle is detected to enter a control area of ​​an intelligent networked vehicle; when the vehicle is a networked vehicle with manual assisted driving, determines whether there is a vehicle queue in front of the lane where the networked vehicle is located; when there is a vehicle queue in front of the lane where the networked vehicle is located, determines whether the networked vehicle can be included in the vehicle queue according to the queue length of the vehicle queue and the drivable speed range of the networked vehicle; when the networked vehicle can be included in the vehicle queue, optimizes and controls the networked vehicle in the new vehicle queue using a preset trajectory optimization algorithm, thereby realizing vehicle driving optimization control under mixed traffic flow, and reducing the energy consumption of vehicles passing through intersections through optimization control. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 is a flow chart of a vehicle driving optimization control method according to an embodiment of the present invention;

[0023] Figure 2 is a structural block diagram of a vehicle driving optimization control device according to an embodiment of the present invention;

[0024] Figure 3 is a schematic diagram of the structure of a computer-readable storage medium provided according to an embodiment of the present invention;

[0025] Figure 4 is a schematic structural diagram of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0027] The embodiment of the present invention provides a vehicle driving optimization control method, such as Figure 1 As shown, the method comprises the following steps:

[0028] Step S101: When a vehicle is detected to enter the intelligent networked vehicle control area, the type of the vehicle is determined. Specifically, an intelligent networked vehicle cloud control system is set at the road intersection; the intelligent networked vehicle control area is the intersection area covered by the intelligent networked vehicle cloud control system. When a vehicle enters the intelligent networked vehicle control area, the type of the vehicle is determined. Among them, the vehicle type can include intelligent networked vehicles, networked vehicles that require manual assisted driving, and non-networked vehicles.

[0029] Step S102: When the vehicle is a connected vehicle with manual assisted driving, determine whether there is a vehicle queue in front of the lane where the connected vehicle is located. Specifically, a connected vehicle with manual assisted driving refers to a connected vehicle that can receive control information sent from the cloud like an intelligent connected vehicle but requires manual assisted driving. When a connected vehicle with manual assisted driving enters the control area of ​​the intelligent connected vehicle, determine whether there is an existing vehicle queue in front of the lane where the connected vehicle is located.

[0030] Step S103: When there is a vehicle queue in front of the lane where the connected vehicle vehicle is located, determine whether the connected vehicle vehicle can be included in the vehicle queue according to the queue length of the vehicle queue and the drivable speed range of the connected vehicle vehicle. Specifically, when there is a corresponding vehicle queue in front of the lane where the connected vehicle vehicle is located, determine whether to include the connected vehicle vehicle in the vehicle queue for optimization control;

[0031] First, it is possible to determine whether the current vehicle queue is saturated based on the number of vehicles in the vehicle queue. If it is not saturated, it means that a new connected vehicle vehicle can be added to the end of the vehicle queue; if it is saturated, it means that no new vehicle can be added to the vehicle queue.

[0032] Then, when a new connected vehicle vehicle can be added at the tail of the vehicle queue, in order to reduce the addition error, it can be further determined whether the current connected vehicle vehicle can pass the current intersection within the same traffic light phase as the vehicle queue. Specifically, when the current connected vehicle vehicle is traveling at the current speed, it can pass the current intersection within the same traffic light phase as the vehicle queue, then the new connected vehicle vehicle can be added at the tail of the vehicle queue; otherwise, the new connected vehicle vehicle cannot be added at the tail of the vehicle queue.

[0033] Step S104: When the Internet of Vehicles vehicle can be incorporated into the vehicle queue, the Internet of Vehicles vehicle in the new vehicle queue is optimized and controlled using a preset trajectory optimization algorithm. Specifically, after the Internet of Vehicles vehicle is incorporated into the corresponding vehicle queue, the preset trajectory optimization algorithm is used for optimization control. The preset trajectory optimization algorithm may be a quadratic programming algorithm, a neural network genetic algorithm, a secure multi-party computing method (SMPC), etc. The present invention does not make specific limitations on this, as long as the requirements are met.

[0034] The vehicle driving optimization control method provided by the embodiment of the present invention realizes the vehicle driving optimization control under mixed traffic flow, and reduces the energy consumption of vehicles passing through intersections through optimization control.

[0035] As an optional implementation of the embodiment of the present invention, the method also includes: when the vehicle is an intelligent networked vehicle, the intelligent networked vehicle is used as the first vehicle in a new vehicle queue and the intelligent networked vehicle is optimized and controlled using a preset trajectory optimization algorithm. Specifically, the intelligent networked vehicle is an organic combination of the Internet of Vehicles and smart cars, equipped with advanced on-board sensors, controllers, actuators and other devices, and integrated with modern communication and network technologies, which can realize intelligent information exchange and sharing between the vehicle and the cloud, and drive completely according to the instructions of the cloud. Therefore, it will not be affected by the driver's operating errors and different driving styles, and can drive completely according to the optimal trajectory; therefore, when the vehicle entering the intelligent networked vehicle control area is an intelligent networked vehicle, there is no need to consider whether there is a vehicle queue in the current lane. The intelligent networked vehicle can be directly used as the head vehicle of a new vehicle queue, and the intelligent networked vehicle can be directly optimized and controlled in the cloud according to the optimal trajectory obtained by the preset trajectory optimization algorithm to achieve fuel economy.

[0036] As an optional implementation of the embodiment of the present invention, the method further includes: when the connected vehicle vehicle cannot be included in the vehicle queue or when the vehicle queue does not exist in front of the lane where the connected vehicle vehicle is located, optimizing the driving speed of the connected vehicle vehicle according to the drivable speed range of the connected vehicle vehicle. Specifically, the absence of a vehicle queue in front of the lane where the connected vehicle vehicle is located includes the following situations:

[0037] (1) There is no vehicle queue in front of the lane where the connected vehicle is located;

[0038] (2) There is a vehicle queue that has been formed in front of the lane where the connected vehicle vehicle is located, but there is a single vehicle between the vehicle queue and the connected vehicle vehicle (which can be any one of an intelligent connected vehicle, a connected vehicle, or a non-connected vehicle).

[0039] When the connected vehicle cannot be included in the vehicle queue, or there is no vehicle queue in front of the lane where the connected vehicle is located, only the driving speed of the connected vehicle is optimized and controlled. Specifically, it is achieved according to the minimum and maximum drivable speeds in the drivable speed range of the connected vehicle.

[0040] First, the current driving speed of the connected vehicle is obtained, and secondly, the driving speed is compared with the drivable speed range. Finally, the driving speed of the connected vehicle is optimized and controlled according to the comparison result.

[0041] Specifically, when the driving speed is lower than the minimum drivable speed, the connected vehicle needs to accelerate, that is, the cloud sends an acceleration control instruction to the connected vehicle and controls the connected vehicle to accelerate; when the driving speed is higher than the maximum drivable speed, the connected vehicle needs to decelerate, that is, the cloud sends a deceleration control instruction to the connected vehicle and controls the connected vehicle to decelerate.

[0042] As an optional implementation of an embodiment of the present invention, step S103 includes: judging whether the connected vehicle vehicle can pass the stop line within the same green light phase as other vehicles in the vehicle queue according to the drivable speed range of the connected vehicle vehicle; calculating the maximum queue length of the vehicle queue according to the drivable speed range of the connected vehicle vehicle and comparing the queue length of the vehicle queue with the maximum queue length of the vehicle queue; judging whether the connected vehicle vehicle can be included in the vehicle queue according to the comparison result and the judgment result.

[0043] First, the drivable speed range of the connected vehicle is calculated using the following formula:

[0044]

[0045]

[0046] in, represents the maximum drivable speed of vehicle i in the connected vehicle network; represents the minimum drivable speed of vehicle i in the connected vehicle network; d i(t) represents the distance between the vehicle at the head of the queue and the stop line of the intersection corresponding to the current lane; C represents the count of the number of signal light cycles at the intersection corresponding to the current lane; k represents the time corresponding to time t in the current signal light cycle; T all Indicates the duration of a cycle corresponding to the signal light; T green Indicates the duration of the green light in a cycle of the signal light; T red Indicates the duration of the red light in one cycle of the traffic light.

[0047] Secondly, use C in formulas (1) and (2) to verify whether the connected vehicle can pass the stop line in the same green light phase as other vehicles in the vehicle queue. Specifically, when the speed limit calculated with the same C value is greater than v max When , it means that the connected vehicle cannot pass the current intersection with the vehicle queue, that is, it cannot pass the stop line in the same green light phase. Otherwise, it can pass the stop line in the same green light phase. Among them, the value of C should make the calculated drivable speed range satisfy [0,v max ].

[0048] Then, the maximum queue length of the vehicle queue is calculated.

[0049] Specifically, first, the expected spacing within the vehicle queue is calculated by the following formula:

[0050]

[0051] In the formula, g exp Represents the expected spacing; l represents the average length of the vehicles in the vehicle queue; V1, V2, C1, and C2 represent calibration parameters in the optimal speed model. For example, in one example, they can be: V1=6.75, V2=7.91, C1=0.13, and C2=1.57; wherein the optimal speed model is a type of following vehicle model, which mainly calculates the expected speed of the vehicle based on the relative speed and relative distance between the vehicle and the preceding vehicle. In the present invention, a deformation of the optimal speed model is used to calculate the expected vehicle spacing within the vehicle queue based on the drivable vehicle speed range.

[0052] Then, the maximum queue length in the vehicle queue is calculated according to the expected spacing by the following formula:

[0053]

[0054] In the formula, Q max Indicates the maximum queue length.

[0055] Finally, the queue length of the vehicle queue is compared with the maximum queue length, and based on the comparison result and the judgment result corresponding to whether the above-mentioned Internet of Vehicles vehicle can pass the stop line within the same green light phase as other vehicles in the vehicle queue, it is determined whether the Internet of Vehicles vehicle can be included in the vehicle queue.

[0056] Specifically, when the queue length of the vehicle queue is greater than the maximum queue length, the connected vehicle vehicle cannot be included in the vehicle queue;

[0057] When the queue length of the vehicle queue is less than the maximum queue length, if the connected vehicle vehicle can pass the stop line within the same green light phase as the vehicle queue, the connected vehicle vehicle can be included in the vehicle queue; if the connected vehicle vehicle cannot pass the stop line within the same green light phase as the vehicle queue, the connected vehicle vehicle cannot be included in the vehicle queue.

[0058] As an optional implementation of an embodiment of the present invention, step S104 includes: obtaining a preset longitudinal dynamics model corresponding to the connected vehicle vehicle; determining the actual acceleration of the connected vehicle vehicle according to the preset longitudinal dynamics model; constructing a corresponding target control model according to the actual acceleration of the connected vehicle vehicle; and optimizing and controlling the connected vehicle vehicle according to the target control model.

[0059] Specifically, within the current intersection control range, the vehicle does not perform lane change behavior, so only the longitudinal behavior of the vehicle is considered.

[0060] First, the corresponding longitudinal dynamics model is constructed for the vehicles in the vehicle platoon according to the following formula:

[0061]

[0062] Where: x i (t) represents the longitudinal position of the connected vehicle i in the lane at time t; v i (t) represents the speed of the connected vehicle i at time t; α = 0.5ρ a C D A f , ρ a Indicates air density; C D Indicates the air resistance coefficient; A f represents the frontal area of ​​the connected vehicle; f represents the rolling resistance coefficient of the connected vehicle; M i represents the mass of the connected vehicle i; g represents the gravitational acceleration of the connected vehicle; θ represents the road slope of the lane; a i (t) represents the actual acceleration of the connected vehicle i after considering the driver's operating error at time t; a i,exp (t) represents the expected acceleration of the connected vehicle i during the optimization control process; ai,err (t) represents the control error of the driver in controlling the connected vehicle vehicle i, which is the error between the acceleration taken by the driver according to the optimal speed sequence and the optimal control amount calculated by the model, and can be obtained using actual data statistics using methods such as a driving simulator.

[0063] Specifically, the error has a Markov property, that is, the error at the next moment is only related to the error at the current moment. After the driver's manipulation error is discretized, the state transfer matrix can be used to describe the change law of the driver's manipulation error, as shown in the following formula (6). e When the value is 9, the corresponding driver control errors of levels 1 to 9 are: -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4. The number of levels can be modified according to the actual situation.

[0064]

[0065] Where: τ ij represents the probability of driving behavior transferring from state i to state j; N e Indicates the grading level of driver control errors.

[0066] Secondly, consider the instantaneous power of the vehicle during driving. The specific calculation method is shown in the following formula (7):

[0067]

[0068] Where: P i (t) represents instantaneous power; b represents bearing resistance coefficient; R t represents the radius of the vehicle tire; r represents the armature resistance; K = K a φ d , K a represents the armature constant, φ d represents magnetic flux; η represents the conversion efficiency of the braking capacity recovery system; P e Indicates the power of other electronic devices in the vehicle, such as air conditioning, etc.

[0069] The energy consumption of vehicles passing through intersections can be reduced by optimizing the control of the vehicles. Therefore, the optimization goal is to minimize the total energy consumption of the fleet, as shown in the following formula (8):

[0070]

[0071] Where: N Q represents the queue length of the vehicle queue; t f represents the time when the vehicle leaves the intelligent connected vehicle control area; Δt represents the time interval for the longitudinal dynamics model calculation; v minIndicates the minimum speed limit of the current lane; v max Indicates the maximum speed limit of the current lane; a min Indicates the actual minimum acceleration; a max Indicates the actual maximum acceleration.

[0072] Finally, based on the above optimization objectives, the corresponding target control model is constructed according to the actual acceleration of the connected vehicle.

[0073] Specifically, first, the longitudinal dynamics model is discretized according to the following formula (9):

[0074] Since the connected vehicle is actually controlled by the driver, there is a certain manipulation error in tracking the optimal speed sequence. It is difficult to model the driver's manipulation error using the general form of MPC control, resulting in a certain deviation in the results of the optimal control sequence calculated by the longitudinal dynamics model in actual use. Therefore, the stochastic MPC (SMPC) model is used in the present invention, which can consider random factors and their probability distribution in a finite time domain, thereby improving the control effect of the system with random chance constraint input.

[0075] Specifically, the target control model is shown as follows:

[0076]

[0077] Where: N = N e ×l, represents the quantity path; p s represents the probability of each path appearing; l represents the prediction duration; represents the reference speed of vehicle i at time k; α, β, γ represent weight coefficients;

[0078] The constraints of the model are:

[0079]

[0080] Then, according to formula (6), it can be calculated that within a certain period of time (for example, from t to t+1), the driver's control error level changes from e0 to e l The probability (p s ). The specific calculation formula is shown in the following formula (12):

[0081]

[0082] Among them, e i It represents the corresponding classification of the driver's control error after discretization at time t+i.

[0083] The optimal trajectory can be obtained based on the calculated probability and sent to the connected vehicle through the cloud to complete the optimized control of the connected vehicle in the vehicle queue.

[0084] As an optional implementation of an embodiment of the present invention, the method also includes: when there is a vehicle that is not included in the vehicle queue in front of the lane where the vehicle queue is located or when there is a non-networked vehicle in front of the lane where the vehicle queue is located, using a preset vehicle following model to calculate the maximum following speed of the networked vehicle in the vehicle queue within the same green light phase interval.

[0085] Specifically, when there is a vehicle that is not included in the vehicle queue in front of the lane where the vehicle queue is located, or when there is a non-connected vehicle in front of the lane where the vehicle queue is located, at this time, when optimizing the control of the vehicles in the vehicle queue, it is also necessary to control the corresponding driving speed to be less than the maximum safe following speed.

[0086] The maximum safe following speed is calculated using the preset vehicle following model (Krauss model):

[0087]

[0088] Where: v f Indicates the maximum safe following speed; v l represents the speed of the vehicle in front; τ represents the driver's reaction time; g represents the distance between the current vehicle and the vehicle in front.

[0089] The embodiment of the present invention also provides a vehicle driving optimization control device, such as Figure 2 As shown, the device comprises:

[0090] The first judgment module 201 is used to judge the type of the vehicle when a vehicle is detected entering the intelligent connected vehicle control area; for details, please refer to the relevant description of step S101 in the above method embodiment.

[0091] The second judgment module 202 is used to judge whether there is a vehicle queue in front of the lane where the connected vehicle vehicle is located when the vehicle is a connected vehicle vehicle with manual assisted driving; for details, please refer to the relevant description of step S102 in the above method embodiment.

[0092] The third judgment module 203 is used to judge whether the connected vehicle vehicle can be included in the vehicle queue when there is a vehicle queue in front of the lane where the connected vehicle vehicle is located according to the queue length of the vehicle queue and the drivable speed range of the connected vehicle vehicle; for details, please refer to the relevant description of step S103 in the above method embodiment.

[0093] The control module 204 is used to optimize and control the connected vehicle in the new vehicle queue using a preset trajectory optimization algorithm when the connected vehicle can be included in the vehicle queue; for details, please refer to the relevant description of step S104 in the above method embodiment.

[0094] The vehicle driving optimization control device provided by the embodiment of the present invention realizes the vehicle driving optimization control under mixed traffic flow, and reduces the energy consumption of vehicles passing through intersections through optimization control.

[0095] As an optional implementation of an embodiment of the present invention, the device also includes: a first control module, which is used to, when the vehicle is an intelligent connected vehicle, use the intelligent connected vehicle as the first vehicle in a new vehicle queue and use the preset trajectory optimization algorithm to optimize the control of the intelligent connected vehicle.

[0096] As an optional implementation of the embodiment of the present invention, the device also includes: a second control module, which is used to optimize the driving speed of the connected vehicle vehicle according to the drivable speed range of the connected vehicle vehicle when the connected vehicle vehicle cannot be included in the vehicle queue or when the vehicle queue does not exist in front of the lane where the connected vehicle vehicle is located.

[0097] As an optional implementation manner of the embodiment of the present invention, the third judgment module includes: a first judgment submodule, used to judge whether the connected vehicle vehicle can pass the stop line within the same green light phase as other vehicles in the vehicle queue according to the drivable speed range of the connected vehicle vehicle; a first processing submodule, used to calculate the maximum queue length of the vehicle queue according to the drivable speed range of the connected vehicle vehicle and compare the queue length of the vehicle queue with the maximum queue length of the vehicle queue; a second judgment submodule, used to judge whether the connected vehicle vehicle can be included in the vehicle queue according to the queue length comparison result and the judgment result of whether the connected vehicle vehicle can pass the stop line within the same green light phase.

[0098] As an optional implementation of an embodiment of the present invention, the control module includes: a first acquisition submodule, used to acquire a preset longitudinal dynamic model corresponding to the connected vehicle; a first determination submodule, used to determine the actual acceleration of the connected vehicle according to the preset longitudinal dynamic model; a first construction submodule, used to construct a corresponding target control model according to the actual acceleration of the connected vehicle; and a first control submodule, used to optimize the control of the connected vehicle according to the target control model.

[0099] As an optional implementation of the embodiment of the present invention, the preset longitudinal dynamics model is shown in the following formula:

[0100]

[0101] Where: x i (t) represents the longitudinal position of the connected vehicle i in the lane at time t; v i (t) represents the speed of the connected vehicle i at time t; α = 0.5ρ a C D A f , ρ a Indicates air density; C D Indicates the air resistance coefficient; A f represents the frontal area of ​​the connected vehicle; f represents the rolling resistance coefficient of the connected vehicle; M i represents the mass of the connected vehicle i; g represents the gravitational acceleration of the connected vehicle; θ represents the road slope of the lane; a i (t) represents the actual acceleration of the connected vehicle i after considering the driver's operating error at time t; a i,exp (t) represents the expected acceleration of the connected vehicle i during the optimization control process; a i,err (t) represents the driver's control error in controlling the connected vehicle i.

[0102] As an optional implementation of the embodiment of the present invention, the device also includes: a calculation module, which is used to calculate the maximum following speed of the connected vehicle vehicles in the vehicle queue within the same green light phase interval using a preset vehicle following model when there is a vehicle that is not included in the vehicle queue in front of the lane where the vehicle queue is located or when there is a non-networked vehicle in front of the lane where the vehicle queue is located.

[0103] As an optional implementation manner of the embodiment of the present invention, the second control module includes: a second acquisition submodule, used to acquire the current driving speed of the connected vehicle; a first comparison submodule, used to compare the current driving speed with the drivable speed range of the connected vehicle; a second control submodule, used to send an acceleration control instruction to the connected vehicle and control the connected vehicle to accelerate when the current driving speed is less than the minimum drivable speed corresponding to the drivable speed range; a third control submodule, used to send a deceleration control instruction to the connected vehicle and control the connected vehicle to decelerate when the current driving speed is greater than the maximum drivable speed corresponding to the drivable speed range.

[0104] For a detailed description of the functions of the vehicle driving optimization control device provided in an embodiment of the present invention, please refer to the description of the vehicle driving optimization control method in the above embodiment.

[0105] The embodiment of the present invention also provides a storage medium, such as Figure 3As shown, a computer program 301 is stored thereon, and when the instruction is executed by the processor, the steps of the vehicle driving optimization control method in the above embodiment are implemented. The storage medium also stores audio and video stream data, feature frame data, interaction request signaling, encrypted data, and preset data size, etc. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive, abbreviated: HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memory.

[0106] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above-mentioned types of memory.

[0107] The embodiment of the present invention further provides an electronic device, such as Figure 4 As shown, the electronic device may include a processor 41 and a memory 42, wherein the processor 41 and the memory 42 may be connected via a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0108] The processor 41 may be a central processing unit (CPU). The processor 41 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0109] The memory 42 is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as the corresponding program instructions / modules in the embodiment of the present invention. The processor 41 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory 42, that is, the vehicle driving optimization control method in the above method embodiment is implemented.

[0110] The memory 42 may include a program storage area and a data storage area, wherein the program storage area may store an application required for operating the device and at least one function; the data storage area may store data created by the processor 41, etc. In addition, the memory 42 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 42 may optionally include a memory remotely arranged relative to the processor 41, and these remote memories may be connected to the processor 41 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0111] The one or more modules are stored in the memory 42 and when executed by the processor 41, perform the following steps: Figure 1 The vehicle driving optimization control method in the illustrated embodiment.

[0112] For details of the above electronic equipment, please refer to Figure 1 The corresponding related descriptions and effects in the illustrated embodiments can be understood and will not be repeated here.

[0113] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A vehicle driving optimization control method, characterized in that: The steps include: When a vehicle is detected to enter a smart connected vehicle control area, determining the type of the vehicle; When the vehicle is a connected vehicle with manual assisted driving, determining whether there is a vehicle queue in front of the lane where the connected vehicle is located; When there is a vehicle queue in front of the lane where the connected vehicle vehicle is located, judging whether the connected vehicle vehicle can be included in the vehicle queue according to the queue length of the vehicle queue and the drivable speed range of the connected vehicle vehicle; When the Internet of Vehicles vehicle can be incorporated into the vehicle queue, optimizing control of the Internet of Vehicles vehicle in the new vehicle queue is performed using a preset trajectory optimization algorithm; Wherein, when there is a vehicle queue in front of the lane where the connected vehicle vehicle is located, judging whether the connected vehicle vehicle can be included in the vehicle queue according to the queue length of the vehicle queue and the drivable speed range of the connected vehicle vehicle includes: Determining whether the connected vehicle vehicle can pass the stop line within the same green light phase as other vehicles in the vehicle queue according to the drivable vehicle speed range of the connected vehicle vehicle; Calculating the maximum queue length of the vehicle queue according to the drivable speed range of the connected vehicle vehicles and comparing the queue length of the vehicle queue with the maximum queue length of the vehicle queue; Determining whether the vehicle network vehicle can be included in the vehicle queue according to the comparison result of the queue length and the determination result of whether the vehicle can pass the stop line within the same green light phase; The method also includes: when the vehicle is an intelligent networked vehicle, taking the intelligent networked vehicle as the first vehicle in a new vehicle queue and optimizing and controlling the intelligent networked vehicle using the preset trajectory optimization algorithm.

2. The method according to claim 1, characterized in that The method further comprises: When the connected vehicle vehicle cannot be included in the vehicle queue or when the vehicle queue does not exist in front of the lane where the connected vehicle vehicle is located, the driving speed of the connected vehicle vehicle is optimized and controlled according to the drivable speed range of the connected vehicle vehicle.

3. The method according to claim 1, characterized in that When the Internet of Vehicles vehicle can be incorporated into the vehicle queue, the Internet of Vehicles vehicle in the new vehicle queue is optimized and controlled using a preset trajectory optimization algorithm, including: Obtaining a preset longitudinal dynamics model corresponding to the connected vehicle vehicle; Determining the actual acceleration of the connected vehicle vehicle according to the preset longitudinal dynamics model; A corresponding target control model is constructed according to the actual acceleration of the connected vehicle; The connected vehicle vehicle is optimally controlled according to the target control model.

4. The method according to claim 3, characterized in that The preset longitudinal dynamics model is shown in the following formula: Where: x i (t) represents the longitudinal position of the connected vehicle i in the lane at time t; v i (t) represents the speed of the connected vehicle i at time t; α = 0.5ρ a C D A f , ρ a Indicates air density; C D Indicates the air resistance coefficient; A f represents the frontal area of ​​the connected vehicle; f represents the rolling resistance coefficient of the connected vehicle; M i represents the mass of the connected vehicle i; g represents the gravitational acceleration of the connected vehicle; θ represents the road slope of the lane; a i (t) represents the actual acceleration of the connected vehicle i after considering the driver's operating error at time t; a i,exp (t) represents the expected acceleration of the connected vehicle i during the optimization control process; a i,err (t) represents the driver's control error in controlling the connected vehicle i.

5. The method according to claim 1, characterized in that The method further comprises: When there is a vehicle that is not included in the vehicle queue in front of the lane where the vehicle queue is located or when there is a non-networked vehicle in front of the lane where the vehicle queue is located, a preset vehicle following model is used to calculate the maximum following speed of the networked vehicle in the vehicle queue within the same green light phase interval.

6. The method according to claim 2, characterized in that When the connected vehicle vehicle cannot be incorporated into the vehicle queue or when the vehicle queue does not exist in front of the lane where the connected vehicle vehicle is located, optimizing the driving speed of the connected vehicle vehicle according to the drivable speed range of the connected vehicle vehicle, including: Obtaining the current speed of the connected vehicle; Comparing the current driving speed with the drivable speed range of the connected vehicle vehicle; When the current driving speed is less than the minimum drivable speed corresponding to the drivable speed range, an acceleration driving control instruction is sent to the connected vehicle vehicle to control the connected vehicle vehicle to accelerate; When the current driving speed is greater than the maximum drivable speed corresponding to the drivable speed range, a deceleration driving control instruction is sent to the connected vehicle vehicle to control the connected vehicle vehicle to decelerate.

7. A vehicle driving optimization control device, characterized in that: Used to execute the vehicle driving optimization control method according to any one of claims 1 to 6; comprising: A first judgment module is used to judge the type of the vehicle when a vehicle is detected to enter the intelligent connected vehicle control area; A second judgment module is used to judge whether there is a vehicle queue in front of the lane where the connected vehicle vehicle is located when the vehicle is a connected vehicle vehicle with manual assisted driving; A third judgment module is used to judge whether the connected vehicle vehicle can be included in the vehicle queue according to the queue length of the vehicle queue and the drivable speed range of the connected vehicle vehicle when there is a vehicle queue in front of the lane where the connected vehicle vehicle is located; The control module is used to optimize and control the connected vehicle in the new vehicle queue by using a preset trajectory optimization algorithm when the connected vehicle vehicle can be included in the vehicle queue.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the vehicle driving optimization control method as described in any one of claims 1 to 6.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the vehicle driving optimization control method as described in any one of claims 1 to 6 by executing the computer instructions.

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