Carbon-saving driving strategy for mixed traffic flow of connected vehicles on urban ecological roads

Abstract

The present invention proposes a carbon-saving driving strategy for urban ecological road vehicle network hybrid traffic flow, including the following steps: discretizing the driving space and car driving status of long sections of ecological roads; introducing the driver's risk coefficient, expanding the safety distance, and calculating the following safety distance; judging the vehicle's need and safety conditions for changing lanes; calculating the fuel consumption during the state transition in each stage; optimizing and evaluating the entire ecological road driving space, with the minimum fuel consumption as the objective function. Determine the constraints of the driving behavior, and limit the driving speed and acceleration; use the optimization objective function to evaluate the fuel consumption during each feasible state transition to realize the search for the optimal strategy. The strategy of the present invention has a good fit with the actual scene, improves the efficiency of solving the optimal vehicle driving decision, and solves the existing ecological road driving safety, energy saving, and carbon emission problems caused by the animal crossing problem.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent driving on ecological roads, and in particular relates to a carbon-saving driving strategy for mixed traffic flows on urban ecological roads connected by vehicle networks. Background Art

[0002] In recent years, the development of sensor technology and artificial intelligence technology has led to rapid development in the field of intelligent networked vehicles. Through the use of high-precision sensors and automatic driving technology, networked vehicles can obtain information such as road conditions ahead in advance and accurately perceive the driving behavior of surrounding vehicles. Then, according to certain algorithmic rules, they can formulate carbon-saving and reasonable driving behavior strategies to enhance the mobility and safety of traffic, while reducing carbon emissions and harmful gas emissions from energy consumption.

[0003] An ecological road is a road with ecological conservation functions. At this stage, ecological roads emphasize the importance of protecting the life safety of animals living on the road sections, species diversity and habitat conservation. When vehicles are driving on ecological roads, they must meet the safety of surrounding wild animals crossing the road, as well as the requirements of vehicle safety and energy-saving and emission-reduction driving. The main method currently used is to set up warning signs to remind drivers to slow down and serve as an early warning.

[0004] With the rapid progress of autonomous driving technology and vehicle networking technology, the traffic flow on the road has and will further transform into a mixed traffic flow combining manual driving and autonomous driving, and the traffic flow characteristics of the road will also change. Considering the mixed traffic flow model in a multi-lane environment, the driving characteristic models of manually driven vehicles, ACC vehicles, and CACC vehicles are established respectively. Considering the behaviors of intelligent vehicles such as following and changing lanes, a traffic flow model suitable for different types of intelligent vehicles can be established.

[0005] Therefore, there is an urgent need in relevant technologies for a safe, real-time, and energy-saving vehicle-to-vehicle mixed traffic flow driving strategy that can be applied to ecological road scenarios. Summary of the invention

[0006] The purpose of the present invention is to provide a carbon-saving driving strategy for mixed traffic flow on urban ecological roads and networked vehicles, and to use dynamic programming for discretization analysis and state division to optimize and establish a carbon-saving driving model for mixed traffic flow vehicles, so as to solve the existing problems of driving safety, energy saving and carbon emission on ecological roads caused by the problem of animal crossing. The technical solutions adopted are as follows:

[0007] A carbon-saving driving strategy for mixed traffic flow of urban ecological road vehicle network includes the following steps:

[0008] Step 1: Build the application framework of the Internet of Vehicles in the wildlife corridor of ecological roads;

[0009] Step 2: Divide the driving space of the long section of the ecological road into uniform discretized spaces and divide the driving states of the vehicles;

[0010] Step 3: Use the expected safe distance lane change model to determine the vehicle's need to change lanes and safety conditions, and ensure the safety of the vehicle's lane change behavior;

[0011] Step 4: Considering the actual driving situation, by introducing the driver risk factor, based on the Gipps safety distance following model, and expanding the safety distance to meet the requirements of car driving behavior, the following safety distance is calculated;

[0012] Step 5: Discretize the driving space according to the length of the front driving space, and calculate the fuel consumption at each stage of state transition by combining the calculation formula of the fuel consumption model;

[0013] Step 6: Determine the objective function and constraints of carbon-saving driving, and optimize and evaluate the entire ecological road driving space with the minimum fuel consumption as the goal to obtain the objective function. At the same time, it is necessary to consider the constraints on driving behavior. Vehicle performance, driving safety, driving comfort and other conditions will constrain driving behavior. In addition, due to the existence of wildlife passages, the road speed limit will also be in a dynamic process. Different driving scenarios on the ecological road will correspond to different road section speed limits, so the driving speed and acceleration will be restricted;

[0014] Step 7: By analyzing the stages and states of the controlled vehicle's driving space, the feasible state set of each stage is solved. The fuel consumption at each feasible state transition is evaluated using the optimization objective function to achieve the search for the optimal strategy.

[0015] Furthermore, the application framework of the Internet of Vehicles in the ecological road wildlife passage described in step 1 mainly includes three parts: roadside unit, Internet of Vehicles platform, and road vehicles. The roadside unit is composed of an animal detection and identification module, an information transmission module, and a roadside warning unit.

[0016] Furthermore, the uniform discretization of the driving space and the division of the vehicle state described in step 2 include:

[0017] Step 2.1: Based on the theory of dynamic programming, the driving space of the long section of the ecological road is evenly discretized according to the space to obtain multiple continuous stages. Each sub-interval corresponds to a driving state containing speed information in the optimal curve, so that the problem of solving the continuous operation strategy is transformed into a problem of solving the sub-interval state set;

[0018] Step 2.2: The state set corresponding to the interval is used to represent the position of the interval, the different speeds of the car in the interval, and the relative state with the front and rear vehicles. The state set S k :

[0019] S k ={s k,1 ,s k,2 ,…,s k,N}={(x k,1 ,v k,1 ,ω k,1 ),(x k,2 ,v k,2 ,ω k,2 ),…,(x k,N ,v k,N ,ω k,N )}

[0020] The driving space is equally divided into N sub-intervals, and each sub-interval corresponds to a set of vehicle driving states.

[0021] The driving space contains a total of N+1 car driving states.

[0022] The N+1 vehicle driving states include: an initial vehicle driving state, and N vehicle driving states corresponding to the N sub-intervals.

[0023] V k = {v k,1 ,v k,2 ,…,v k,N} represents the set of feasible speeds at stage k; where v k,N is the speed corresponding to stage N. Since the driving space is discrete enough, v k,N Is a constant value.

[0024] {ω k,1 ,ω k,2 ,…,ω k,N} indicates the relative position to the front and rear vehicles in the current situation.

[0025] Step 2.3: Divide the car driving status into three categories: distance, speed, and relative status.

[0026] Furthermore, the safety distance calculation described in step 4 is based on the speed, acceleration, reaction time of the current vehicle and the speed and acceleration data of the vehicle in front to deduce the minimum safety distance between the two vehicles. If the distance is less than this, the driving safety of the vehicle cannot be guaranteed. In actual driving, the safety distance needs to be expanded to define a moderate driving distance to meet the requirements of car driving behavior, that is, the current following distance can meet the safety requirements of the rear vehicle following at a constant speed. The expanded relative following distance calculation formula is as follows:

[0027]

[0028] Among them, Gap mid,nIt represents the moderate following distance of vehicle n, in meters;

[0029] Gap far,n Indicates the longer following distance of vehicle n, in meters;

[0030] v n (t) represents the speed of vehicle n at time t, in m·s -1 ;

[0031] v n+1 (t) represents the speed of vehicle n+1 at time t, in m·s -1 ;

[0032] a n Indicates the maximum acceleration of vehicle n, in m·s -2 ;

[0033] b n Indicates the maximum deceleration of vehicle n, in m·s -2 ;

[0034] b n+1 Indicates the maximum deceleration of vehicle n+1, in m·s -2 ;

[0035] β represents the risk coefficient of the autonomous vehicle and the manually driven vehicle.

[0036] Therefore, the following distance to the vehicle in front of the lane and the vehicle in front of the adjacent lane is expressed in the form of relative distance, which is divided into three categories: farther, moderate, and closer.

[0037] Furthermore, the lane change judgment in step 3 is based on the expected safe distance lane change model to determine whether a lane change demand is generated:

[0038] d n <min((v n +a n )t,v max t)

[0039] d n,other >d n

[0040] Among them, d n Indicates the distance between vehicle n and the vehicle in front, in meters;

[0041] d n,other Represents the distance between vehicle n and the vehicle ahead in the adjacent lane, in meters;

[0042] v n Indicates the current speed of vehicle n, in m·s -1 ;

[0043] vmax Indicates the maximum speed of the current section, in m·s -1 ;

[0044] a n Indicates the current acceleration of vehicle n, in m·s -2 ;

[0045] t represents the simulation time interval, in seconds.

[0046] Determine the safety conditions for changing lanes:

[0047] d n,back >Gap safe,n,back

[0048] d n,other >Gap safe,n,othrer

[0049] Among them, d n,back Indicates the distance between vehicle n and the vehicle behind it in the adjacent lane, in meters;

[0050] Gap safe,n,back It represents the minimum safe distance between vehicle n and the vehicle behind it in the adjacent lane, in meters;

[0051] Gap safe,n,other It represents the minimum safe distance between vehicle n and the vehicle ahead in the adjacent lane, in meters.

[0052] The following distance from the vehicle behind in the adjacent lane only needs to meet the minimum distance for safe lane change, so it is divided into two driving states: safe and unsafe.

[0053] If changing lanes, the vehicle will change lanes quickly, and the speed and acceleration will not change; if not changing lanes, continue to choose to follow the car, and calculate the acceleration according to the vehicle's movements at the next moment, and calculate or monitor the corresponding speed.

[0054] Furthermore, the calculation formula of the fuel consumption model in step 5 is expressed as follows through the multivariate linear regression method:

[0055]

[0056] Among them, MOE m Indicates the instantaneous emission rate or fuel consumption rate of the vehicle, in g / s or liters / s;

[0057] M ij represents the regression coefficient of the model during acceleration;

[0058] L ij represents the regression coefficient of the model during deceleration;

[0059] v represents instantaneous speed, unit is km·h -1, that is, the speed at time m, which is the output of step 3 or step 4;

[0060] a-instantaneous acceleration, unit km·h -1 ·s -1 , that is, the acceleration at time m, which is the output of step 3 or step 4.

[0061] m-moment, the corresponding m moment.

[0062] Where i, j = 0, 1, 2, 3v i is v raised to the power of i, a j is a raised to the power of j.

[0063] Furthermore, the objective function and constraints of carbon-saving driving are determined in step 6, including:

[0064] Step 6.1: The optimization object is a fuel-consuming connected vehicle. The goal of the energy-saving driving model is to minimize the total fuel consumption of the optimization object in the entire driving state. The optimization objective function is as follows:

[0065]

[0066] Where E represents the total fuel consumption of the optimization object, in L;

[0067] L represents the total length of the driving space, in meters;

[0068] MOE m (v,a) represents the instantaneous emission rate or fuel consumption rate of the vehicle, which is related to the current speed and acceleration, and the unit is g / s or liters / s.

[0069] After discretizing the optimization problem, the optimization objective function will also be transformed into the following formula:

[0070]

[0071] Among them, l gap Indicates the length of the subinterval, in m.

[0072] Step 6.2: The speed limit on roads with wildlife passages will be in a dynamic process. Different driving scenarios on ecological roads will correspond to different speed limits on sections. The driving speed and acceleration are as follows:

[0073]

[0074] 0 <v k ≤v k,max

[0075] v k ≤v safe,k

[0076] v k,0 =0;

[0077] Among them, v k Indicates the speed of the controlled vehicle (vehicle n) at stage k, in m·s -1 ;

[0078] V k,max Indicates the maximum speed allowed on the road section during stage k, in m·s -1 ;

[0079] v safe,k Indicates the maximum safe speed allowed by the current traffic flow at stage k, in m·s -1 ;

[0080] a min 、a max Respectively represent the maximum deceleration and maximum acceleration of the controlled vehicle, in m·s -2 ;

[0081] v k,0 =0 means the starting speed of the controlled vehicle is 0.

[0082] Furthermore, the step of solving the optimal control strategy of the controlled vehicle by using the dynamic programming method in step 7 includes:

[0083] Step 7.1: Define the length of the sub-interval. The length of the sub-interval will directly affect the frequency of strategy switching during driving. Therefore, it is necessary to try multiple distance schemes according to the actual situation and computing power, compare the results and reduce the amount of calculation as much as possible;

[0084] Step 7.2: Define the speed interval. As with the sub-interval length division, the speed interval will also have a greater impact on the amount of calculation, so it should be selected in comparison to improve efficiency;

[0085] Step 7.3: Starting from the speed, relative distance and other conditions, simplify the state space of each stage to reduce the amount of algorithm calculation;

[0086] Step 7.4: Starting from the initial stage, solve the feasible state transfer matrix between each stage in a forward order, calculate the amount of fuel consumed during the state transition, and record it in the corresponding matrix and list set;

[0087] Step 7.5: Starting from the final state, in reverse order, compare the state transfer effects by optimizing the objective function, so as to analyze the optimal control strategy for this problem and obtain the optimal speed, fuel consumption and other information of the whole process.

[0088] Compared with the prior art, the advantages of the present invention are:

[0089] (1) The present invention builds an application framework of the Internet of Vehicles in wildlife corridors on ecological roads, utilizes the characteristics and advantages of ecological roads in the Internet of Vehicles environment, analyzes the factors affecting carbon-saving driving issues, proposes carbon-saving driving strategies, and enhances the adaptability of strategies to actual scenarios.

[0090] (2) The present invention discretizes the driving space and the driving state of the vehicle to obtain multiple continuous stages, converts the traffic flow information into the state of relative position, and integrates the road condition information into the optimization objective function. The dynamic programming method uses the optimal actions of each stage to obtain the optimal decision of all states through step-by-step solution, thereby improving the efficiency of solving the optimal vehicle driving decision.

[0091] (3) The present invention introduces a driver risk coefficient through a following model based on a safe distance, describes and simulates the driving style of an artificial driver, and expands the calculation of a moderate relative following distance in combination with actual driving conditions, thereby defining a more comprehensive automobile driving behavior and making the driving state division of an ecological road more realistic. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 It is the overall flow chart of the present invention;

[0093] Figure 2 It is a schematic diagram of the application framework of the vehicle networking of the present invention on the ecological road;

[0094] Figure 3 It is a schematic diagram of the division of ecological road driving scenes of the present invention;

[0095] Figure 4 It is a schematic diagram of the vehicle following safety distance of the present invention;

[0096] Figure 5 It is a driving status update flow chart of the present invention. DETAILED DESCRIPTION

[0097] The following will be described in more detail with reference to the schematic diagram of the present invention for the urban ecological road vehicle network mixed traffic flow carbon saving driving strategy, wherein the preferred embodiment of the present invention is shown, and it should be understood that those skilled in the art can modify the present invention described herein while still achieving the beneficial effects of the present invention. Therefore, the following description should be understood as being widely known to those skilled in the art, and not as a limitation of the present invention.

[0098] The present invention provides a carbon-saving driving strategy for mixed traffic flow of urban ecological road vehicle networks. Figure 1 is a general flow chart of the carbon-saving driving strategy for mixed traffic flow under the conditions of ecological roads in a connected vehicle city according to the present invention, such as Figure 1 As shown, the process includes the following steps:

[0099] Step 1: Build the application framework of the Internet of Vehicles in the wildlife corridor of ecological roads;

[0100] Step 2: Discretely divide the driving space and vehicle driving state of the long section of the ecological road;

[0101] Step 3: Determine the vehicle's need to change lanes and safety conditions;

[0102] Step 4: Introduce the driver's risk factor, expand the safety distance, and calculate the following safety distance;

[0103] Step 5: Calculate the fuel consumption during each stage of state transition;

[0104] Step 6: Optimize and evaluate the entire ecological road driving space, taking the minimum fuel consumption as the objective function. Determine the constraints of driving behavior and limit driving speed and acceleration;

[0105] Step 7: Use the optimization objective function to evaluate the fuel consumption at each feasible state transition to search for the optimal strategy.

[0106] Through the above steps, the present invention builds an application framework for the ecological road Internet of Vehicles environment, analyzes the driving characteristics of autonomous driving vehicles and manually driven vehicles in the Internet of Vehicles environment, improves and constructs the vehicle driving model in the Internet of Vehicles environment based on the following model and the lane change model, and uses dynamic programming to perform discretization analysis and state division, and optimizes and establishes an energy-saving driving strategy for mixed traffic flow vehicles with the minimum fuel consumption as the objective function, so as to solve the existing ecological road driving safety, energy saving, and carbon emission problems caused by the animal crossing problem, and achieve carbon emission reduction.

[0107] Figure 2 This is a schematic diagram of the application framework of the Internet of Vehicles in ecological roads of the present invention. The application framework mainly includes three parts: roadside unit, Internet of Vehicles platform, and road vehicles. The roadside unit is composed of an animal detection and identification module, an information transmission module, and a roadside warning unit. The animal detection and identification module uses infrared camera technology and image recognition technology to identify and judge the presence of animals within the wildlife channel. When animals are found around the channel, the animal detection and identification module reports the location, number, and other information of the animals to the Internet of Vehicles platform through the information transmission module. After receiving the animal presence information, the Internet of Vehicles platform immediately conducts a risk assessment and publishes the temporary speed limit information and lane change prohibition information of the relevant area to the networked vehicles in the road section. At the same time, this information is sent to the roadside warning unit through the information transmission module, and the roadside warning unit publishes warning information such as temporary speed limit and lane change prohibition to warn non-networked vehicles in the road section. The specific information interaction details are shown in the table.

[0108] Table 1 List of information contents received and transmitted by each subject in the application framework

[0109]

[0110]

[0111] Figure 3 This is a schematic diagram of the driving scene division of the ecological road of the present invention. Compared with ordinary roads, ecological roads have more ecological protection responsibilities, so animal passages need to be established at certain locations on ecological roads. Wildlife passages can be combined with vehicle networking technology, using vehicle-to-road communication and vehicle-to-vehicle communication to establish a dynamic warning mechanism. When animals cross the road, vehicles in the wildlife passage section will slow down to avoid them, so as to reduce the risk of collision between vehicles and animals.

[0112] For energy-saving driving, eco-roads can be divided into two types: ordinary road sections and wildlife passage sections, with the following three driving scenarios:

[0113] (1) Ordinary road driving scenario. In this scenario, vehicles on the road can meet the road speed limit v 1 (For example, the speed limit on secondary roads is usually 60km / h) and you can drive freely under safe driving conditions;

[0114] (2) Low-risk driving scenario on wildlife passages. In this scenario, the roadside monitoring system detects animals crossing the road. The risk of collision between animals and vehicles is extremely low. There is no temporary speed limit on the road section, and vehicles can drive freely under the conditions of meeting the road speed limit and driving safety.

[0115] (3) High-risk driving scenario on wildlife crossing roads. In this scenario, there is a risk of collision due to animals crossing the road, so vehicles on the road need to meet the temporary speed limit v 2 (For example, the speed limit in animal passages is usually 30km / h), and changing lanes is strictly prohibited.

[0116] In step 2, the present invention is very critical in the discretization of the driving state of the car at each stage. Too little state quantity cannot describe the actual driving situation, and too much state quantity will make the model very complex and difficult to solve. Although the driving scene of the ecological road is different from the driving scene of the non-ecological road, in the driving state, this difference can be characterized by two parameters, distance and speed. Therefore, the present invention divides the driving state of the car into three categories: distance, speed, and relative state.

[0117] Step 4 is to calculate the relative following distance based on the expanded relative following distance calculation formula, consider calculating the relative following distance, and define the following state. The relative following distance calculation formula is as follows:

[0118]

[0119] Among them, Gap mid,n It represents the moderate following distance of vehicle n, in meters;

[0120] Gap far,n Indicates the longer following distance of vehicle n, in meters;

[0121] v n (t) represents the speed of vehicle n at time t, in m·s -1 ;

[0122] v n+1 (t) represents the speed of vehicle n+1 at time t, in m·s -1 ;

[0123] a n Indicates the maximum acceleration of vehicle n, in m·s -2 ;

[0124] b n Indicates the maximum deceleration of vehicle n, in m·s -2 ;

[0125] b n+1 Indicates the maximum deceleration of vehicle n+1, in m·s -2 ;

[0126] If the vehicle is an autonomous vehicle, it can obtain accurate required information and accurately estimate the braking distance of the vehicle ahead. Therefore, the risk factor of the autonomous vehicle can be set to a fixed value of β = 1.

[0127] If the vehicle is manually driven, since the driving style can be divided into three categories: aggressive, conservative, and cautious, the safety distances estimated by the three types of drivers are also different, so the risk coefficient β∈(0,1).

[0128] Gap calculated according to the formula mid,n , Gap far,n , if the following distance of the current vehicle is not less than Gap far,n , then the relative distance between the two vehicles is considered to be far; if the following distance of the current vehicle is less than Gap mid,n , then the relative distance between the two vehicles is considered to be low; if the following distance of the current vehicle is between the two, then the relative distance between the vehicles is considered to be moderate. Therefore, the following distances with the preceding vehicle in the lane and the preceding vehicle in the adjacent lane are both in the form of relative distances, which are divided into three categories: far, moderate, and close.

[0129] Step 3 is a lane change decision process. Based on the expected safe distance lane change model, according to the vehicle distance and speed information of the front and rear vehicles obtained by the vehicle at time t, combined with the required braking reaction time τ and risk coefficient β, the minimum safe distance Gap with the rear vehicle in the adjacent lane is calculated. safe,n,bcak , judge whether the lane change demand and safe lane change requirements are met at this time. If the requirements are not met, choose to follow the car, and the speed at the next moment will be calculated later; if the lane change requirements are met, choose to change lanes. If adopted, the speed at the next moment remains unchanged and the lane is changed. Otherwise, keep the existing lane unchanged, choose to follow the car, and the speed at the next moment will be calculated later.

[0130] Step 5: Calculate the instantaneous fuel consumption rate of the vehicle based on the instantaneous acceleration and speed of the vehicle. The formula is as follows:

[0131]

[0132] Among them, MOE m Indicates the instantaneous emission rate or fuel consumption rate of the vehicle, in g / s or liters / s;

[0133] M ij represents the regression coefficient of the model during acceleration;

[0134] L ij represents the regression coefficient of the model during deceleration;

[0135] v represents instantaneous speed, unit is km·h -1 ;

[0136] a represents instantaneous acceleration, unit is km·h -1 ·s -1 .

[0137] When using the regression coefficient to calculate the vehicle's fuel consumption per unit time, the suitable speed range is [0km / h, 120km / h], and the suitable acceleration range is [-6km / h / s, 16km / h / s]. When using the regression coefficient to calculate the vehicle's carbon dioxide emissions per unit time, the suitable speed range is [0km / h, 120km / h], and the suitable acceleration range is [-6km / h / s, 9km / h / s].

[0138] Assuming that the acceleration is constant within the sub-interval (stage), combined with the fuel consumption model calculation formula, the fuel consumption during the state transition of each stage can be obtained as follows:

[0139] E k =MOE m *T k

[0140] Among them, E kIndicates the emission rate or fuel consumption rate in stage K, in g or liters;

[0141] MOE m Indicates the instantaneous emission rate or fuel consumption rate of a vehicle in g / s or liters / s.

[0142] Step 6: After discretizing the optimization problem according to the dynamic programming method, the optimization objective function will also be transformed into the following formula:

[0143]

[0144] Among them, l gap Indicates the length of the k stage, in m.

[0145] The speed limit on roads with wildlife passages will be in a dynamic process. Different driving scenarios on ecological roads will correspond to different speed limits on sections. The driving speed and acceleration will be adjusted as follows:

[0146]

[0147] 0 <v k ≤v k,max

[0148] v k ≤v safe,k

[0149] v k,0 =0;

[0150] Among them, v k Indicates the speed of the controlled vehicle in stage k, in m·s -1 ;

[0151] V k,max Indicates the maximum speed allowed on the road section during stage k, in m·s -1 ;

[0152] v safe,k Indicates the maximum safe speed allowed by the current traffic flow at stage k, in m·s -1 ;

[0153] a min 、a max Respectively represent the maximum deceleration and maximum acceleration of the controlled vehicle, in m·s -2 ;

[0154] v k,0 =0 means the starting speed of the controlled vehicle is 0, which means stationary.

[0155] Step 7 solves the feasible state set of each stage by traversing and analyzing the stages and states of the controlled vehicle's driving space.

[0156] The fuel consumption at each feasible state transition is evaluated using the optimization objective function to search for the optimal strategy.

[0157]

[0158] The matrix is ​​the feasible state matrix from stage k to the next stage.

[0159] TR in the matrix k,m represents a non-empty set of all transferable states in the m-th state of stage k,

[0160] Indicates the pth phase in k+1 m status,

[0161] p m is the total number of transferable states in the mth state in stage k (a positive integer).

[0162] Through the states of the previous and next two stages, the fuel consumption calculation formula can be used to solve the fuel consumption during the state transfer process, thereby establishing the following fuel consumption matrix.

[0163]

[0164] Among them, E k,m represents a non-empty set of all transfer fuel consumptions in the m-th state of stage k,

[0165] It represents the fuel consumption from the nth state in stage k to the mth state in stage k+1. For two states that cannot establish a transfer relationship, a larger value is assigned to indicate that they cannot be transferred.

[0166] After the above steps are processed, the carbon-saving driving strategy for the mixed traffic flow of the ecological road vehicle network can be solved and the optimal search can be achieved.

[0167] The above is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any technician in the relevant technical field, without departing from the scope of the technical solution of the present invention, makes any form of equivalent replacement or modification to the technical solution and technical content disclosed in the present invention, which does not depart from the content of the technical solution of the present invention and still falls within the protection scope of the present invention.

Claims

1. A carbon-saving driving strategy for mixed traffic flow on urban ecological roads with vehicle networking, characterized in that: The following steps are involved: Step 1: Obtain the position, speed, acceleration, and relative distance between the vehicle and the front and rear vehicles at each moment on the urban ecological road; The relative distances to the front and rear vehicles include: the following distance d between vehicle n and the front vehicle in the lane n , the following distance d between vehicle n and the preceding vehicle in the adjacent lane n,other , the following distance between vehicle n and the vehicle behind it in the adjacent lane; Step 2: Based on the vehicle's position, speed, and relative distance to the front and rear vehicles at each moment, obtain the vehicle's driving state s k,N : Step 2A: Evenly discretize the urban ecological road with a length of L to obtain N sub-intervals; The set of vehicle states of all vehicles at stage k is S k , the car driving state corresponding to sub-interval N is s k,N ; S k ={s k,1 ,s k,2 ,…,s k,N } Step 2B: k,,N Characterized by position, speed, and relative distance; s k,N =(x k,N ,v k,N ,ω k,N ) Among them, ω k,N Contains the following distance d between vehicle n and the vehicle in front of it in the lane n , the following distance d between vehicle n and the preceding vehicle in the adjacent lane n,other , the following distance d between vehicle n and the vehicle behind it in the adjacent lane n,back ; Step 3: Based on the expected safe distance lane change model and the lane change safety condition, determine whether there is a lane change, and output the speed and acceleration of vehicle n at the next moment according to whether there is a lane change; The safety conditions for lane change are: d n,back >Gap safe,n,back d n,other >Gap safe,n,other Gap safe,n,back - the minimum safe distance between vehicle n and the vehicle behind it in the adjacent lane; Gap safe,n,other - the minimum safe distance between vehicle n and the vehicle ahead in the adjacent lane; If the lane is changed, the acceleration and speed of vehicle n at the next moment are the same as those at the previous moment, and the lane is changed; Otherwise, keep the current lane unchanged, select follow, and go to step 4; Step 4: Calculate the safe following distance Gap of vehicle n mid,n , to output the speed and acceleration of vehicle n at the next moment; Step 5: Introduce the instantaneous emission rate or fuel consumption rate MOE of vehicle n m : Among them, v k,n (t)-the speed of vehicle n at time t in subinterval k, obtained by the connected vehicle itself; M ij -Regression coefficients of the model when acceleration is non-negative; L ij -Regression coefficients of the model when acceleration is negative; v-instantaneous velocity; a-instantaneous acceleration; Step 6: Establish the objective function and constraints: Objective function: Among them, E-minimum fuel consumption; L-length of urban ecological road; l gap - length of the k phase; MOE k - Emission rate or fuel consumption rate; E k - Fuel consumption of vehicle n in stage k; T k - The travel time of vehicle n on stage k; v k - The speed of vehicle n in phase k; v k+1 - The speed of vehicle n in phase k+1; The maximum number of Nk stages; Constraints: 0<v k ≤v k,max in k ≤in safe,k v k,0 ＝0； Among them, v k,max -The maximum speed allowed in stage k; v safe,k -The maximum safe speed allowed by the traffic flow in stage k; a min - minimum acceleration of vehicle n; a max - maximum acceleration of vehicle n; Step 7: The optimizer solves and outputs E.

2. The carbon-saving driving strategy for mixed traffic flow on urban ecological roads and vehicle networks according to claim 1 is characterized in that: Based on the vehicle network application framework, the data in step 1 is obtained, which includes: a road test speed unit and an acceleration detection unit.

3. The carbon-saving driving strategy for mixed traffic flow on urban ecological roads and vehicle networks according to claim 2 is characterized in that: The Internet of Vehicles application framework also includes: Roadside units, connected vehicle platforms and connected vehicles; The roadside unit includes an animal detection and identification module, an information transmission module, and a roadside warning unit; The animal detection and identification module is used to identify and judge the presence of animals within the wildlife channel. When animals are found around the channel, the animal detection and identification module reports the location and number of animals to the Internet of Vehicles platform through the information transmission module; After receiving the information about animal presence, the Internet of Vehicles platform analyzes the information and publishes the temporary speed limit information and lane change prohibition information of the relevant area to the networked vehicles on the road section; at the same time, the information is sent to the roadside warning unit through the information transmission module.

4. The carbon-saving driving strategy for mixed traffic flow on urban ecological roads and vehicle networks according to claim 2 is characterized in that: The expected safe distance lane change model in step 3 is: d n <min((v n +a n )t,v k,max t) d n,other >d n Among them, v n represents the current speed of vehicle n; a n represents the current acceleration of vehicle n; t represents the simulation time interval.

5. The carbon-saving driving strategy for mixed traffic flow on urban ecological roads and vehicle networks according to claim 1 is characterized in that: In step 3, Gap safe,n,back , Gap safe,n,other All are calculated based on the car-following model.

6. The carbon-saving driving strategy for mixed traffic flow on urban ecological roads and vehicle networks according to claim 1 is characterized in that: In step 4, Gap mid,n The calculation model is: Among them, v n (t) represents the speed of vehicle n at time t; v n+1 (t) represents the speed of vehicle n+1 at time t; a n represents the maximum acceleration of vehicle n; b n represents the maximum deceleration of vehicle n; b n+1 Indicates the maximum deceleration of vehicle n+1; β - risk factor of non-connected vehicles.

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