Vehicle speed planning method, device and equipment and readable storage medium

Abstract

The invention discloses a vehicle speed planning method, device and equipment and a readable storage medium. The vehicle speed planning method comprises the steps of obtaining a preset number of multiple groups of weights; aiming at each group of weights in parallel, a multi-objective optimization function is used to solve the vehicle speed sequence of the travel to be planned, the vehicle speed sequence corresponding to each group of weights is obtained, and the multi-objective optimization function comprises an energy consumption objective function and an aging objective function; calculating the stroke energy consumption and the stroke duration corresponding to each vehicle speed sequence; if the travel duration less than the preset required duration exists, selecting a vehicle speed sequence corresponding to the minimum travel energy consumption from the travel duration less than the preset required duration as a recommended vehicle speed sequence of the to-be-planned travel; and if the travel duration less than the preset required duration does not exist, selecting the vehicle speed sequence corresponding to the minimum travel duration as a recommended vehicle speed sequence of the to-be-planned travel. According to the invention, the requirements of the logistics transportation scene of the commercial vehicle can be better met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle trip planning, and particularly relates to a vehicle speed planning method and device, equipment and a readable storage medium. BACKGROUND

[0002] The core goal of vehicle speed planning is to generate a vehicle speed curve meeting multiple constraint conditions for a to-be-planned trip by comprehensively considering multiple factors. For vehicle speed planning in a commercial vehicle logistics transportation scenario, the road length of the entire trip can be up to thousands of kilometers, and the solution efficiency of the vehicle speed planning is required to be high by the logistics transportation user, and the energy consumption requirement and the time efficiency requirement are also important considerations. The energy consumption requirement refers to the hope that the energy consumption of the entire trip is as low as possible, and the time efficiency requirement refers to the hope that the time length consumed by the entire trip does not exceed the required time length.

[0003] However, the solution efficiency of the current vehicle speed planning is low, and the balance between energy consumption and time efficiency is not good, which is difficult to meet the requirements of the commercial vehicle logistics transportation scenario. SUMMARY

[0004] The present application provides a vehicle speed planning method, device, equipment and readable storage medium, aiming at solving the technical problem that the solution efficiency of the current vehicle speed planning is low, and the balance between energy consumption and time efficiency is not good, which is difficult to meet the requirements of the commercial vehicle logistics transportation scenario.

[0005] In a first aspect, an embodiment of the present application provides a vehicle speed planning method, which comprises: obtaining a preset number of groups of weights, wherein each group of weights comprises an energy consumption weight and a time efficiency weight, the preset number is determined based on the computing power of parallel computing, and the sum of the energy consumption weight and the time efficiency weight is 1; parallelly solving a vehicle speed sequence of a to-be-planned trip using a multi-objective optimization function for each group of weights to obtain a vehicle speed sequence corresponding to each group of weights, wherein the multi-objective optimization function comprises an energy consumption objective function and a time efficiency objective function, and the energy consumption weight and the time efficiency weight are weights of the energy consumption objective function and the time efficiency objective function respectively; calculating a trip energy consumption and a trip time length corresponding to each vehicle speed sequence; if there is a trip time length less than a preset required time length, selecting a vehicle speed sequence corresponding to the minimum trip energy consumption from the trip time lengths less than the preset required time length as a recommended vehicle speed sequence of the to-be-planned trip; if there is no trip time length less than the preset required time length, selecting a vehicle speed sequence corresponding to the minimum trip time length as the recommended vehicle speed sequence of the to-be-planned trip.

[0006] Optionally, before the parallel solving of the vehicle speed sequence of the to-be-planned trip using the multi-objective optimization function for each group of weights to obtain the vehicle speed sequence corresponding to each group of weights, the method comprises: A multi-objective optimization function is established, which is: ; Wherein, , , The constraint condition of the multi-objective optimization function is: ; Wherein, minJ() is a minimum value function, k is the current stage, N is the predicted total step, is the instantaneous fuel consumption of the vehicle engine, △s(k) is the distance between adjacent two stages, v k and v k+1 are the vehicle speeds of the kth and k+1th stages, w1 is the energy consumption weight, w2 is the time efficiency weight, λ is the coefficient of the slope type, α is the slope type of the to-be-planned trip road, is the coefficient of the polynomial i term and j term, T k is the engine torque of the kth stage, n k is the engine speed of the kth stage, δ is the rotational mass conversion coefficient, m is the vehicle mass, r is the rolling radius of the vehicle wheel, i k is the vehicle transmission ratio of the kth stage, η is the mechanical efficiency, C D is the air resistance coefficient, A is the windward area, g is the gravity coefficient, f is the friction coefficient, σ k is the road slope of the kth stage, T min and T max are the minimum engine torque and the maximum engine torque, v min and v max are the minimum speed and the maximum speed, v min =0.9v average , v max =1.1v average , v average =L length / T req , v average is the average speed of the to-be-planned trip, L length is the total length of the to-be-planned trip, T req is the preset required time length, n min and n max are the minimum engine speed and the maximum engine speed, a min and a max are the minimum acceleration and the maximum acceleration.

[0007] Optionally, before the parallel solving, for each group of weights, using a multi-objective optimization function to solve a vehicle speed sequence of the to-be-planned trip to obtain a vehicle speed sequence corresponding to each group of weights, comprises: dividing a road of the to-be-planned trip into a plurality of segments according to slope and slope length; The parallel solving, for each group of weights, using a multi-objective optimization function to solve a vehicle speed sequence of the to-be-planned trip to obtain a vehicle speed sequence corresponding to each group of weights comprises: The parallel solving, for each group of weights, sequentially traversing each segment of the road of the to-be-planned trip, for each segment, using a multi-objective optimization function to solve to obtain a vehicle speed sequence of each segment; combining the vehicle speed sequence of each segment to obtain a vehicle speed sequence corresponding to each group of weights.

[0008] Optionally, the solving, for each segment, using a multi-objective optimization function to solve to obtain a vehicle speed sequence of each segment comprises: for each segment, determining an acceleration and deceleration strategy based on the slope and slope length of the current segment and the next segment; adjusting the constraint conditions of the multi-objective optimization function based on the determined acceleration and deceleration strategy, wherein the adjusting the constraint conditions of the multi-objective optimization function comprises adjusting the minimum acceleration, the maximum acceleration, the minimum vehicle speed and the maximum vehicle speed; using the multi-objective optimization function with adjusted constraint conditions to solve to obtain a vehicle speed sequence of each segment.

[0009] Optionally, after obtaining the recommended vehicle speed sequence of the to-be-planned trip, comprising: obtaining a vehicle transmission ratio sequence corresponding to the recommended vehicle speed sequence in the solving process; for each vehicle transmission ratio in the vehicle transmission ratio sequence, obtaining a vehicle gear corresponding to each vehicle transmission ratio by looking up a calibration relationship table of the vehicle transmission ratio and the vehicle gear, and taking a sequence composed of the vehicle gear corresponding to each vehicle transmission ratio as a recommended vehicle gear sequence.

[0010] In a second aspect, an embodiment of the present application provides a vehicle speed planning device, comprising: an acquisition module configured to acquire a preset number of groups of weights, wherein each group of weights comprises an energy consumption weight and a time efficiency weight, the preset number is determined based on the computing power of parallel computing, and the sum of the energy consumption weight and the time efficiency weight is 1; a parallel computing module configured to, in parallel, for each group of weights, use a multi-objective optimization function to solve a vehicle speed sequence of the to-be-planned trip to obtain a vehicle speed sequence corresponding to each group of weights, wherein the multi-objective optimization function comprises an energy consumption objective function and a time efficiency objective function, and the energy consumption weight and the time efficiency weight are weights of the energy consumption objective function and the time efficiency objective function, respectively; a trip calculation module configured to calculate a trip energy consumption and a trip duration corresponding to each vehicle speed sequence; The first selection module is configured to select a vehicle speed sequence corresponding to the minimum travel energy consumption from the travel time lengths less than the preset required travel time length as the recommended vehicle speed sequence of the to-be-planned travel if there is a travel time length less than the preset required travel time length. The second selection module is configured to select a vehicle speed sequence corresponding to the minimum travel time length as the recommended vehicle speed sequence of the to-be-planned travel if there is no travel time length less than the preset required travel time length.

[0011] Optionally, the vehicle speed planning device further comprises an establishment module configured to: establish a multi-objective optimization function, wherein the multi-objective optimization function is: ; wherein, , , the constraint condition of the multi-objective optimization function is: ; wherein, minJ() is a minimum value function, k is a current stage, N is a total predicted step length, is an instantaneous fuel consumption of a vehicle engine, △s(k) is a distance between adjacent two stages, v k and v k+1 are vehicle speeds of the kth and the k+1th stages respectively, w1 is an energy consumption weight, w2 is a time efficiency weight, λ is a coefficient of a slope type, α is a slope type of a to-be-planned travel road, are coefficients of polynomial i terms and j terms, T k is an engine torque of the kth stage, n k is an engine speed of the kth stage, δ is a rotational mass conversion coefficient, m is a vehicle mass, r is a rolling radius of a vehicle wheel, i k is a vehicle transmission ratio of the kth stage, η is a mechanical efficiency, C D is an air resistance coefficient, A is a windward area, g is a gravity coefficient, f is a friction coefficient, σ k is a road slope of the kth stage, T min and T max are minimum engine torque and maximum engine torque respectively, v min and v max are minimum vehicle speed and maximum vehicle speed respectively, v min =0.9v average , v max =1.1v average , v average =L length / T req , v average is an average vehicle speed of the to-be-planned travel, Llength T is a total length of the to-be-planned trip req n is a preset required time length min n and a max n and a min n and a max n and a

[0012] Optionally, the vehicle speed planning device further comprises a road division module, configured to: divide the road of the to-be-planned trip into a plurality of segments according to the slope and the slope length; the parallel computing module is configured to: parallelly traverse each segment of the road of the to-be-planned trip for each group of weights in sequence, and for each segment, use the multi-objective optimization function to obtain a vehicle speed sequence of each segment; combine the vehicle speed sequence of each segment to obtain a vehicle speed sequence corresponding to each group of weights.

[0013] In a third aspect, an embodiment of the present application provides a vehicle speed planning device, which comprises a processor, a memory, and a vehicle speed planning program stored in the memory and executable by the processor, wherein the vehicle speed planning program, when executed by the processor, implements the steps of the vehicle speed planning method described above.

[0014] In a fourth aspect, an embodiment of the present application provides a readable storage medium, which stores a vehicle speed planning program, wherein the vehicle speed planning program, when executed by a processor, implements the steps of the vehicle speed planning method described above.

[0015] The technical scheme provided by the embodiments of the present application has the following beneficial effects: In the embodiment of the present application, a preset number of groups of weights are obtained, wherein each group of weights includes an energy consumption weight and a time efficiency weight, the preset number is determined based on the computing power of parallel computing, and the sum of the energy consumption weight and the time efficiency weight is 1; for each group of weights, a multi-objective optimization function is used to solve the vehicle speed sequence of the to-be-planned trip to obtain a vehicle speed sequence corresponding to each group of weights, the multi-objective optimization function includes an energy consumption objective function and a time efficiency objective function, and the energy consumption weight and the time efficiency weight are weights of the energy consumption objective function and the time efficiency objective function respectively; the trip energy consumption and the trip time corresponding to each vehicle speed sequence are calculated; if there is a trip time less than the preset required time, the vehicle speed sequence corresponding to the minimum trip energy consumption is selected from the trip time less than the preset required time as the recommended vehicle speed sequence of the to-be-planned trip; if there is no trip time less than the preset required time, the vehicle speed sequence corresponding to the minimum trip time is selected as the recommended vehicle speed sequence of the to-be-planned trip. Through the embodiment of the present application, the specific values of the multiple groups of energy consumption weights and time efficiency weights can be flexibly set according to the importance requirements of the user on energy consumption and time efficiency. The preset number is determined based on the computing power of parallel computing. For example, if the computing power of parallel computing is higher, the preset number can be set larger, so that a better selection can be made from more solved vehicle speed sequences. Through parallel solving and calculation of vehicle speed planning for each group of weights, especially for vehicle speed planning of a road length of thousands of kilometers, multiple vehicle speed sequences can be quickly solved and obtained. Then, for the trip energy consumption and the trip time corresponding to the multiple vehicle speed sequences, the vehicle speed sequence corresponding to the minimum energy consumption is preferred when the trip time meets the requirements, and the vehicle speed sequence corresponding to the minimum trip time is preferred when the trip time does not meet the requirements. Therefore, based on the importance requirements of the user on energy consumption and time efficiency, multiple vehicle speed sequences are efficiently solved through parallel computing, and the recommended vehicle speed sequence of the to-be-planned trip is finally determined by combining the optimization rules of energy consumption and time efficiency, so that the requirements of commercial vehicle logistics transportation scenarios can be better met. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 Flowchart of an embodiment of the vehicle speed planning method of the present application; Figure 2 Road segment division schematic diagram of an embodiment of the vehicle speed planning method of the present application; Figure 3 Local schematic diagram of road segment division of an embodiment of the vehicle speed planning method of the present application; Figure 4 Vehicle speed and gear planning result schematic diagram of an embodiment of the vehicle speed planning method of the present application; Figure 5 Functional module schematic diagram of an embodiment of the vehicle speed planning device of the present application; Figure 6 Hardware structure schematic diagram of the vehicle speed planning device involved in the embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 this application.

[0018] In order to make the objectives, technical solutions and advantages of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.

[0019] In a first aspect, an embodiment of the present application provides a vehicle speed planning method.

[0020] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of an embodiment of the vehicle speed planning method of the present application. Figure 1 As shown, the vehicle speed planning method includes: Step S10, obtaining a preset number of multiple groups of weights, wherein each group of weights includes an energy consumption weight and a time efficiency weight, the preset number is determined based on the computing power of parallel computing, and the sum of the energy consumption weight and the time efficiency weight is 1.

[0021] In this embodiment, for commercial vehicle logistics and transportation scenarios, users have varying requirements for energy consumption and timeliness. For example, fresh produce orders prioritize timeliness, while general cargo orders prioritize economy. Therefore, through personalized weight settings, users are allowed to customize energy consumption and timeliness weights based on their actual needs. This allows the system to provide users with speed planning solutions that better meet their actual needs, both meeting timeliness requirements and further optimizing energy consumption based on meeting timeliness requirements. This improves the economic benefits and customer satisfaction of logistics and transportation. The number of preset values ​​is determined based on the computing power of parallel computing. If the system has high computing power (e.g., a multi-core CPU or GPU), more weight combinations (e.g., 12) can be set, providing more speed planning results for optimal selection. If computing power is limited, the number of weight combinations can be appropriately reduced. This design maximizes the accuracy of speed planning while maximizing computing resources.

[0022] In step S20, for each set of weights, a multi-objective optimization function is used to solve the vehicle speed sequence of the planned trip in parallel to obtain the vehicle speed sequence corresponding to each set of weights. The multi-objective optimization function includes an energy consumption objective function and a time efficiency objective function. The energy consumption weight and the time efficiency weight are the weights of the energy consumption objective function and the time efficiency objective function, respectively.

[0023] In this embodiment, the traditional vehicle speed planning method usually adopts serial calculation, that is, the vehicle speed sequence under different weight combinations is calculated one by one. This method is low in efficiency in long distance planning, and is difficult to meet the requirement of solution efficiency in the logistics transportation scenario. By using parallel computing technology, the vehicle speed sequence under multiple groups of weight combinations is calculated at the same time, which greatly improves the solution efficiency. Especially for the logistics transportation scenario in which the road length of the entire trip may be thousands of kilometers, this parallel computing method can significantly shorten the time of vehicle speed planning from dozens of minutes to a few minutes, so that the global speed planning in the commercial vehicle high-speed logistics scenario is more practical and has commercial value.

[0024] In step S30, the trip energy consumption and trip time corresponding to each vehicle speed sequence are calculated.

[0025] In this embodiment, the trip energy consumption can be calculated based on the vehicle speed sequence and the energy consumption model of the vehicle. The energy consumption model of the vehicle, such as the engine fuel consumption model, can be obtained by using a polynomial model to fit the instantaneous fuel consumption of the engine based on considering key parameters such as engine speed and torque. The trip time can be calculated based on the vehicle speed sequence and the length of the entire road of the trip to be planned. By accurately calculating the trip energy consumption and trip time corresponding to each vehicle speed sequence, the advantages and disadvantages of different vehicle speed sequences can be objectively evaluated, providing a reliable basis for subsequent selection, and ensuring that the finally recommended vehicle speed sequence achieves the optimal balance in terms of energy consumption and time efficiency.

[0026] In step S40, if there is a trip time less than the preset required time, the vehicle speed sequence corresponding to the minimum trip energy consumption is selected from the trip time less than the preset required time as the recommended vehicle speed sequence of the trip to be planned.

[0027] In this embodiment, in the logistics transportation scenario, the timeliness is usually a hard constraint, that is, the transportation must be completed within the specified time. Therefore, when there is a vehicle speed sequence that meets the timeliness requirement, the sequence with the lowest energy consumption should be selected first to reduce the transportation cost. This selection strategy optimizes the energy consumption to the greatest extent on the premise of ensuring timeliness. For example, when the preset required time is 15 hours, and there are multiple vehicle speed sequences whose trip time is between 13-14 hours in the planning result, the system will select the vehicle speed sequence with the lowest energy consumption as the recommendation, which can ensure that the planning result meets the customer's requirements and maximally reduces the operating cost, thereby improving the competitiveness of the logistics company. Especially in the highly competitive logistics market, this optimization can bring significant cost advantage to the company.

[0028] In step S50, if there is no trip time less than the preset required time, the vehicle speed sequence corresponding to the minimum trip time is selected as the recommended vehicle speed sequence of the trip to be planned.

[0029] In the embodiment, when the journey time of all vehicle speed sequences exceeds the preset required time, it indicates that the time limit requirement cannot be met under the current condition, at this time, the vehicle speed sequence with the shortest journey time should be selected to approach the time limit requirement as much as possible. For example, when the preset required time is 15 hours, and the journey time of all vehicle speed sequences is between 16-17 hours, the system will select the sequence with the shortest journey time (i.e. 16 hours) as the recommendation. Thus, in the case where the time limit requirement cannot be met, the optimal solution is provided, which maximizes the guarantee of the completion of the transportation task, and avoids customer complaints and economic losses caused by failure to deliver on time. The system provides the final recommended vehicle speed sequence to the logistics transportation user, which can be used as a reference for decision-making, as well as for vehicle speed control during actual driving, etc.

[0030] In this embodiment, for the logistics transportation scene of commercial vehicles, users have different degrees of demand for energy consumption and timeliness. Through personalized weight setting, users can customize energy consumption weight and timeliness weight according to actual demand, so that the system can provide a vehicle speed planning scheme that meets the actual demand, meets the timeliness requirement, and further optimizes the energy consumption on the basis of meeting the timeliness requirement, thereby improving the economic benefit and customer satisfaction of logistics transportation. The determination of the preset number is based on the computing power of parallel computing. For example, if the system computing power is high (such as multi-core CPU or GPU), more weight combinations (such as 12 groups) can be set, so that more vehicle speed planning results can be provided for optimal selection. If the computing power is limited, the number of weight combinations can be appropriately reduced. This design can maximize the computing resources and improve the accuracy of vehicle speed planning as much as possible. By using parallel computing technology, the vehicle speed sequence under multiple weight combinations is calculated simultaneously, which greatly improves the solving efficiency. Especially for the logistics transportation scene where the road length of the entire journey may be thousands of kilometers, this parallel computing method can significantly shorten the vehicle speed planning time from tens of minutes to a few minutes, thereby making the global speed planning of commercial vehicles in the high-speed logistics scene more practical and commercially valuable. By accurately calculating the journey energy consumption and journey time corresponding to each vehicle speed sequence, the advantages and disadvantages of different vehicle speed sequences can be objectively evaluated, providing a reliable basis for subsequent selection and ensuring that the finally recommended vehicle speed sequence achieves the optimal balance between energy consumption and timeliness. In the logistics transportation scene, timeliness is usually a hard constraint, that is, the transportation must be completed within the specified time. Therefore, when there is a vehicle speed sequence that meets the timeliness requirement, the sequence with the lowest energy consumption should be selected to reduce transportation costs. This selection strategy optimizes energy consumption to the greatest extent while ensuring timeliness. When the journey time of all vehicle speed sequences exceeds the preset requirement, it means that the timeliness requirement cannot be met under the current conditions. At this time, the vehicle speed sequence with the shortest journey time should be selected to approach the timeliness requirement as much as possible, thereby providing an optimal solution when the timeliness requirement cannot be met, maximizing the completion of the transportation task and avoiding customer complaints and economic losses due to failure to deliver on time.

[0031] Further, in an embodiment, before step S20, the following steps are included: A multi-objective optimization function is established, and the multi-objective optimization function is: ; Wherein, , , The constraint condition of the multi-objective optimization function is: ; wherein minJ() is a minimum value function, k is the current stage, N is the total predicted step length, is the instantaneous fuel consumption of the vehicle engine, △s(k) is the distance between the adjacent two stages, v k and v k+1 are the vehicle speeds of the kth and k+1th stages, respectively, w1 is the energy consumption weight, w2 is the time efficiency weight, λ is the slope type coefficient, α is the slope type of the road of the to-be-planned trip, are the coefficients of the i-th and j-th terms of the polynomial, T k is the engine torque of the kth stage, n k is the engine speed of the kth stage, δ is the rotational mass conversion coefficient, m is the vehicle mass, r is the rolling radius of the vehicle wheel, i k is the vehicle transmission ratio of the kth stage, η is the mechanical efficiency, C D is the air resistance coefficient, A is the windward area, g is the gravity coefficient, f is the friction coefficient, σ k is the road slope of the kth stage, T min and T max are the minimum engine torque and the maximum engine torque, respectively, v min and v max are the minimum vehicle speed and the maximum vehicle speed, respectively, v min = 0.9v average , v max = 1.1v average , v average = L length / T req , v average is the average vehicle speed of the to-be-planned trip, L length is the total length of the to-be-planned trip, T req is the preset required time length, n min and n max are the minimum engine speed and the maximum engine speed, respectively, a min and a max are the minimum acceleration and the maximum acceleration, respectively.

[0032] In this embodiment, the establishment of the multi-objective optimization function considers two key factors: energy consumption and time efficiency. The energy consumption objective function is based on the engine fuel consumption model, and the time efficiency objective function is based on the travel time. By introducing the energy consumption weight w1 and the time efficiency weight w2, the two objectives can be balanced. It should be noted that if it is a new energy vehicle, the engine fuel consumption model can be replaced by the corresponding energy consumption model. At the same time, the slope type a and the slope type coefficient l of the to-be-planned travel road are introduced, and the slope characteristics of the road are included in the multi-objective optimization model, which can make the result of the speed planning more consistent with the actual road conditions. This design enables the speed planning to consider energy consumption and time efficiency simultaneously, and can adapt to different road conditions, improving the accuracy and practicality of the speed planning. The engine speed of the kth stage , i o is the main reduction ratio of the vehicle, 3.6 2 in the above formula is used to convert the unit of vehicle speed from kilometers per hour to meters per second, and 21.5 is a comprehensive constant in the air resistance calculation, which integrates air density, resistance coefficient and unit conversion factor.

[0033] Further, in an embodiment, before step S20, the steps include: dividing the road of the to-be-planned travel into multiple segments according to the slope and slope length; Step S20 includes: sequentially traversing each segment of the road of the to-be-planned travel for each group of weights in parallel, and for each segment, using the multi-objective optimization function to solve the speed sequence of each segment; combining the speed sequence of each segment to obtain the speed sequence corresponding to each group of weights.

[0034] In this embodiment, the road of the to-be-planned travel is divided into multiple segments according to the slope and slope length, which can more accurately describe the slope characteristics of the road, so that these characteristics can be fully considered in the speed planning. For example, for an uphill road segment, the speed needs to be increased to overcome gravity; for a downhill road segment, the speed can be appropriately reduced to utilize gravity. By dividing the road into multiple segments, the system can make fine planning for the characteristics of each segment, avoiding the discontinuity problem caused by road changes in global planning. Dividing a long-distance road into multiple segments, each segment has its unique road characteristics, and solving the speed sequence for each segment separately can better adapt to local road conditions. At the same time, by combining the slope information of the current segment and the next segment, a more reasonable acceleration and deceleration strategy can be designed.

[0035] Specifically, the road of the to-be-planned trip is divided according to the slope and slope length, into three categories of uphill, downhill and no slope, and a total of 25 sub-types, and the specific division rules are as follows: 1) uphill: a) 1-small slope: slope length <= 500m (meters), 0.2 <= slope <= 0.5; b) 2-medium-small slope: 500m < slope length <= 1500m, 0.2 <= slope <= 0.5; c) 3-long-small slope: slope length > 1500m, 0.2 <= slope <= 0.5; d) 4-short-medium slope: slope length <= 500m, 0.5 < slope <= 1.5; e) 5-medium-medium slope: 500m < slope length <= 1500m, 0.5 < slope <= 1.5; f) 6-long-medium slope: 1500m < slope length, 0.5 < slope <= 1.5; g) 7-short-large slope: slope length <= 500m, 1.5 < slope <= 3; h) 8-medium-large slope: 500m < slope length <= 1500m, 1.5 < slope <= 3; i) 9-long-large slope: 1500m < slope length, 1.5 < slope <= 3; j) 10-short super slope: slope length <= 500m, 3 < slope; k) 11-medium super slope: 500m < slope length <= 1500, 3 < slope; l) 12-long super slope: 1500m < slope length, 3 < slope; 2) downhill: m) 1-small slope: slope length <= 500m, -0.5 <= slope <= 0.2; n) 2-medium-small slope: 500m < slope length, -0.5 <= slope <= 0.2; o) 3-long-small slope: slope length > 1500m, -0.5 <= slope <= 0.2; p) 4-short-medium slope: slope length <= 500m, -1.5 < slope <= -0.5; q) 5-medium-medium slope: 500m < slope length <= 1500m, -1.5 < slope <= -0.5; r) 6-long-medium slope: 1500 < slope length, -1.5 < slope <= -0.5; s) 7-short-large slope: slope length <= 500m, -3 < slope <= -1.5; t) 8-medium-large slope: 500m < slope length <= 1500m, -3 < slope <= -1.5; u) 9-long-large slope: 1500 < slope length, -3 < slope <= -1.5; v) 10-short super slope: slope length <= 500m, slope < -3; w) 11-medium super slope: 500m < slope length <= 1500m, slope < -3; x) 12-long super slope: 1500 < slope length, slope < -3; 3) no slope: -0.2 < slope < 0.2, and the length of such stage road points > 200m.

[0036] Referring to Figure 2 , Figure 2 is a schematic diagram of road segmentation of an embodiment of the vehicle speed planning method of the present application, as Figure 2As shown, the road of the 1000km planned trip is segmented according to the above rules, and the road segments are referred to as S1-S1000. Figure 3 , Figure 3 FIG. 2 is a partial schematic view of the road segments of an embodiment of the vehicle speed planning method of the present application, as shown, the local part of the road segments of 548km-568km is enlarged. Figure 3

[0037] It should be noted that when segmenting the road based on the slope data from the map, in order to prevent the slope data of the road from frequently jumping due to the existence of noise, when the same direction change of the slope data lasts for more than five frames, it is determined that the slope of the road has switched. For example, when the map module sends road data, if these data have small range time fluctuations (usually a frame of commercial vehicle is 0.1 seconds or shorter time, such as 2.1 seconds slope 3%, but 2.2 seconds slope 4%, 2.3 seconds slope becomes 2%), we think it is normal fluctuation caused by data noise, which does not belong to actual slope change, only when the slope data of the road changes in the same direction for 5 consecutive frames is considered as the real road has changed slope.

[0038] Further, in an embodiment, the using a multi-objective optimization function to solve the vehicle speed sequence of each segment includes: determining the acceleration and deceleration strategy based on the slope and slope length of the current segment and the next segment for each segment; adjusting the constraint conditions of the multi-objective optimization function based on the determined acceleration and deceleration strategy, wherein the adjusting the constraint conditions of the multi-objective optimization function includes adjusting the minimum acceleration, the maximum acceleration, the minimum vehicle speed and the maximum vehicle speed; solving the vehicle speed sequence of each segment using the multi-objective optimization function with adjusted constraint conditions.

[0039] ​In this embodiment, the acceleration and deceleration strategy combined with road slope segmentation is as follows: if the road is determined to be "current flat + subsequent uphill", the hill rushing strategy is adopted to appropriately increase the vehicle speed to overcome the subsequent uphill; if the current is in a long downhill, the coasting strategy is adopted to appropriately reduce the vehicle speed to utilize gravity and reduce energy consumption; if the slope is determined to be "current crest + subsequent downhill", the deceleration strategy is adopted to avoid increasing energy consumption caused by acceleration after the crest. For example, when the system detects that the current section is a short slope (slope length ≤ 500 m, slope 0.2 ≤ slope ≤ 0.5) and the next section is a long uphill (slope length > 1500 m, slope 0.2 ≤ slope ≤ 0.5), the system will adopt the hill rushing strategy to appropriately increase the vehicle speed of the current section to provide sufficient kinetic energy for the subsequent long uphill. For each segment, the acceleration and deceleration strategy is determined based on the slope information of the current segment and the next segment, which can more accurately adapt to road changes, make the vehicle speed change more smoothly, and reduce unnecessary rapid acceleration and rapid deceleration. By adjusting the constraint conditions (such as minimum / maximum vehicle speed, acceleration), the system can ensure that the acceleration and deceleration strategy is implemented within the allowable range of vehicle performance.

[0040] Further, in an embodiment, after obtaining the recommended vehicle speed sequence of the to-be-planned trip, the following steps are included: obtaining a vehicle transmission ratio sequence corresponding to the recommended vehicle speed sequence in the solving process; for each vehicle transmission ratio in the vehicle transmission ratio sequence, obtaining a vehicle gear corresponding to each vehicle transmission ratio by looking up a calibration relationship table of the vehicle transmission ratio and the vehicle gear, and taking the sequence composed of the vehicle gears corresponding to each vehicle transmission ratio as a recommended vehicle gear sequence.

[0041] In this embodiment, the vehicle speed and the gear are closely related, different vehicle speeds correspond to different optimal gears, and appropriate vehicle speed matching appropriate gear can reduce the energy consumption of the vehicle. Referring to the calculation formula of the vehicle speed v k of the k+1 stage in the above, when calculating the vehicle speed v k of the k+1 stage, the vehicle transmission ratio i k of the k stage is also calculated and used, therefore, when using the multi-objective optimization function to solve the vehicle speed sequence of the to-be-planned trip, the corresponding vehicle transmission ratio is also determined, therefore, after obtaining the recommended vehicle speed sequence of the to-be-planned trip, the vehicle transmission ratio sequence corresponding to the recommended vehicle speed sequence in the solving process is obtained, and by looking up the calibration relationship table of the vehicle transmission ratio and the vehicle gear, the recommended vehicle gear sequence can be obtained. Providing the recommended vehicle speed sequence and the recommended vehicle gear sequence to the logistics transportation user at the same time, combining the gear planning and the vehicle speed planning can make the vehicle always in the high-efficiency working interval in the actual driving, at the same time, improve the driving smoothness, reduce the operation burden of the driver, better realize the energy saving and performance optimization, and improve the overall transportation efficiency.Figure 4 , Figure 4 This is a schematic diagram of the speed and gear planning results for an embodiment of the speed planning method of this application. Table 1 shows the output of speed planning for a specific road trip. The total length of the planned route is 995.5 kilometers. Using parallel computing, the global speed and gear planning took 314.78 seconds (less than 6 minutes), significantly less than the tens of minutes required by traditional serial computing.

[0042] Table 1.

[0043] In a second aspect, an embodiment of the present application also provides a vehicle speed planning device.

[0044] In one embodiment, referring to Figure 5 , Figure 5 This is a functional module diagram of an embodiment of the vehicle speed planning device of the present application, as shown in FIG. Figure 5 As shown, the vehicle speed planning device includes: An acquisition module 10 is configured to acquire a preset number of weight groups, wherein each weight group includes an energy consumption weight and a time efficiency weight, wherein the preset number is determined based on the computing power of the parallel computing, and the sum of the energy consumption weight and the time efficiency weight is 1; A parallel computing module 20 is configured to solve, in parallel, for each set of weights, a speed sequence for the planned trip using a multi-objective optimization function to obtain a speed sequence corresponding to each set of weights, wherein the multi-objective optimization function includes an energy consumption objective function and a time efficiency objective function, wherein the energy consumption weight and the time efficiency weight are weights of the energy consumption objective function and the time efficiency objective function, respectively; A trip calculation module 30 is used to calculate the trip energy consumption and trip duration corresponding to each vehicle speed sequence; A first selection module 40 is configured to select, if there is a trip duration shorter than the preset required duration, a vehicle speed sequence corresponding to a minimum trip energy consumption from the trip durations shorter than the preset required duration as a recommended vehicle speed sequence for the trip to be planned; The second selection module 50 is configured to select a vehicle speed sequence corresponding to a minimum travel duration as a recommended vehicle speed sequence for the planned trip if there is no travel duration less than the preset required duration.

[0045] Furthermore, in one embodiment, the vehicle speed planning device further includes a building module for: A multi-objective optimization function is established, and the multi-objective optimization function is: ; in, , , The constraints of the multi-objective optimization function are: ; wherein minJ() is a minimum function, k is the current stage, N is the total predicted step, is the instantaneous fuel consumption of the vehicle engine, △s(k) is the distance between the adjacent two stages, v k and v k+1 are the vehicle speeds of the kth and k+1th stages respectively, w1 is the energy consumption weight, w2 is the time efficiency weight, λ is the coefficient of the slope type, α is the slope type of the road of the to-be-planned trip, are the coefficients of the i-th and j-th terms of the polynomial, T k is the engine torque of the kth stage, n k is the engine speed of the kth stage, δ is the rotational mass conversion coefficient, m is the vehicle mass, r is the rolling radius of the vehicle wheel, i k is the vehicle transmission ratio of the kth stage, η is the mechanical efficiency, C D is the air resistance coefficient, A is the windward area, g is the gravity coefficient, f is the friction coefficient, σ k is the road slope of the kth stage, T min and T max are the minimum engine torque and the maximum engine torque respectively, v min and v max are the minimum vehicle speed and the maximum vehicle speed respectively, v min = 0.9v average , v max = 1.1v average , v average = L length / T req , v average is the average vehicle speed of the to-be-planned trip, L length is the total length of the to-be-planned trip, T req is the preset required time length, n min and n max are the minimum engine speed and the maximum engine speed, a min and a max are the minimum acceleration and the maximum acceleration respectively.

[0046] Further, in an embodiment, the vehicle speed planning device further comprises a road division module, configured to: divide the road of the to-be-planned trip into a plurality of segments according to the slope and the slope length; the parallel computing module 20 is configured to: parallelly traverse each segment of the road of the to-be-planned trip for each group of weights in sequence, and for each segment, use the multi-objective optimization function to obtain a vehicle speed sequence of each segment; combine the vehicle speed sequence of each segment to obtain a vehicle speed sequence corresponding to each group of weights.

[0047] Further, in an embodiment, the vehicle speed sequence of each segment is obtained by solving the multi-objective optimization function for each segment, for: determining a speed-up or slow-down strategy for each segment based on the slope and length of the current segment and the next segment; adjusting the constraint conditions of the multi-objective optimization function based on the determined speed-up or slow-down strategy, wherein the adjusting the constraint conditions of the multi-objective optimization function comprises adjusting the minimum acceleration, the maximum acceleration, the minimum vehicle speed and the maximum vehicle speed; obtaining the vehicle speed sequence of each segment by solving the multi-objective optimization function with the adjusted constraint conditions.

[0048] Further, in an embodiment, the vehicle speed planning device further comprises a gear recommendation module, configured to: obtain a vehicle transmission ratio sequence corresponding to the recommended vehicle speed sequence in the solving process; for each vehicle transmission ratio in the vehicle transmission ratio sequence, obtain a vehicle gear corresponding to each vehicle transmission ratio by looking up a calibration relationship table of vehicle transmission ratio and vehicle gear, and group the vehicle gears corresponding to each vehicle transmission ratio to obtain a recommended vehicle gear sequence.

[0049] The functions of each module in the vehicle speed planning device correspond to the steps in the vehicle speed planning method embodiments, and the functions and implementation processes will not be repeated here.

[0050] In a third aspect, an embodiment of the present application provides a vehicle speed planning device.

[0051] Reference Figure 6 , Figure 6 is a schematic diagram of the hardware structure of the vehicle speed planning device involved in the embodiment of the present application. In the embodiment of the present application, the vehicle speed planning device can include a processor, a memory, a communication interface and a communication bus.

[0052] The communication bus can be of any type, used to interconnect the processor, the memory and the communication interface.

[0053] The communication interface includes an input / output (input / output, I / O) interface, a physical interface and a logical interface, etc. for realizing the interconnection of devices inside the vehicle speed planning device, and interfaces for realizing the interconnection of the vehicle speed planning device with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber interface, an ATM interface, etc.; the user device can be a display (Display), a keyboard (Keyboard), etc.

[0054] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0055] The processor can be a general-purpose processor, which can invoke a vehicle speed planning program stored in the memory and execute the vehicle speed planning method provided by the embodiments of the present application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed by the vehicle speed planning program when invoked can refer to various embodiments of the vehicle speed planning method of the present application, which will not be described here.

[0056] Those skilled in the art can understand that the hardware structure shown in the above-mentioned embodiments is not a limitation of the present application, and can include more or less components than the illustrated components, or combine certain components, or different component arrangements. Figure 6

[0057] In a fourth aspect, the embodiments of the present application further provide a readable storage medium.

[0058] The readable storage medium of the present application stores a vehicle speed planning program therein, wherein the vehicle speed planning program, when executed by a processor, implements the steps of the vehicle speed planning method as described above.

[0059] The method implemented by the vehicle speed planning program when executed can refer to various embodiments of the vehicle speed planning method of the present application, which will not be described here.

[0060] It should be noted that the above-mentioned sequence numbers of the embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0061] The terms "include" and "have" and any variations thereof in the specification and claims of the present application and the above-described drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include other steps or units not listed or can optionally include other steps or units inherent to the process, method, product or device. The terms "first", "second" and "third" and the like descriptions are used to distinguish different objects, etc., and do not represent the order or limit the types of "first", "second" and "third".​

[0062] In the description of the embodiments of the present application, "exemplary", "for example", "e.g." or "for instance" are used on the basis that a thing in the example is a thing of an example, illustration, or description. Any embodiment or design scheme described as "exemplary", "for example", or "for instance" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words "exemplary", "for example", or "for instance" are intended to present the relevant concept in a specific manner.

[0063] In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" in the text only describes the relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which means that there are three cases of A alone, A and B together, and B alone. In addition, in the description of the embodiments of the present application, "multiple" means two or more than two.

[0064] In some of the processes described in the embodiments of the present application, a plurality of operations or steps are included in a specific order, but it should be understood that these operations or steps can be executed or performed in parallel or in an order different from that in which they appear in the embodiments of the present application. The serial number of the operation is only used to distinguish different operations, and the serial number itself does not represent any execution order. In addition, these processes can include more or fewer operations, and these operations or steps can be executed in sequence or in parallel, and these operations or steps can be combined.

[0065] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment method can be realized by means of software and necessary general hardware platform, of course, it can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a ROM / RAM, a magnetic disk, an optical disk) as described above, and includes a plurality of instructions for causing a terminal device to execute the method described in each embodiment of the present application.

[0066] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation using the content of the specification and drawings of the present application, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A vehicle speed planning method, characterized in that: The vehicle speed planning method comprises: Obtain a preset number of multiple groups of weights, where each group of weights includes an energy consumption weight and a time efficiency weight, the preset number is determined based on the computing power of the parallel computing, and the sum of the energy consumption weight and the time efficiency weight is 1; For each set of weights, a multi-objective optimization function is used in parallel to solve the vehicle speed sequence of the planned trip to obtain the vehicle speed sequence corresponding to each set of weights, wherein the multi-objective optimization function includes an energy consumption objective function and a time efficiency objective function, and the energy consumption weight and the time efficiency weight are the weights of the energy consumption objective function and the time efficiency objective function respectively; Calculate the trip energy consumption and trip duration corresponding to each speed sequence; If there is a trip duration shorter than the preset required duration, then a speed sequence corresponding to the minimum trip energy consumption is selected from the trip durations shorter than the preset required duration as a recommended speed sequence for the trip to be planned; If there is no trip duration shorter than the preset required duration, the speed sequence corresponding to the minimum trip duration is selected as the recommended speed sequence for the trip to be planned.

2. The vehicle speed planning method according to claim 1, wherein: Before solving the vehicle speed sequence of the to-be-planned trip using the multi-objective optimization function for each set of weights in parallel to obtain the vehicle speed sequence corresponding to each set of weights, the method includes: A multi-objective optimization function is established, and the multi-objective optimization function is: ； in, , ， The constraints of the multi-objective optimization function are: ； Among them, minJ() is the minimum value function, k is the current stage, N is the total prediction step length, is the instantaneous fuel consumption of the vehicle engine, △s(k) is the distance between two adjacent stages, v k and v k+1 and are the vehicle speeds of the kth and k+1th stages respectively, w1 is the energy consumption weight, w2 is the time efficiency weight, λ is the coefficient of the slope type, α is the slope type of the road to be planned, are the coefficients of the i-term and j-term of the polynomial, T k is the engine torque at stage k, n k is the engine speed at the kth stage, δ is the rotation mass conversion coefficient, m is the vehicle mass, r is the rolling radius of the vehicle wheel, i k is the vehicle transmission ratio at stage k, η is the mechanical efficiency, C D is the air resistance coefficient, A is the windward area, g is the gravity coefficient, f is the friction coefficient, σ k is the road slope of the kth stage, T min and T max are the minimum engine torque and the maximum engine torque, respectively, v min and v max are the minimum and maximum speeds, respectively, v min =0.9v average , v max =1.1v average , v average =L length / T req , v average is the average speed of the trip to be planned, L length is the total length of the trip to be planned, T req is the preset required time, n min and n max is the minimum engine speed and the maximum engine speed, a min and a max are the minimum acceleration and the maximum acceleration respectively.

3. The vehicle speed planning method according to claim 2, wherein: Before solving the vehicle speed sequence of the to-be-planned trip using the multi-objective optimization function for each set of weights in parallel to obtain the vehicle speed sequence corresponding to each set of weights, the method includes: Divide the road to be planned into multiple sections according to slope and slope length; The multi-objective optimization function is used to solve the speed sequence of the planned trip for each set of weights in parallel, and the speed sequence corresponding to each set of weights is obtained, including: For each set of weights, each segment of the road to be planned is traversed in turn. For each segment, a multi-objective optimization function is used to solve the speed sequence of each segment. The vehicle speed sequences of each segment are combined to obtain the vehicle speed sequence corresponding to each group of weights.

4. The vehicle speed planning method according to claim 3, wherein: For each segment, the vehicle speed sequence of each segment is obtained by using a multi-objective optimization function, including: For each segment, the acceleration and deceleration strategy is determined based on the slope and length of the current segment and the next segment; Adjusting the constraints of the multi-objective optimization function based on the determined acceleration and deceleration strategy, wherein adjusting the constraints of the multi-objective optimization function includes adjusting the minimum acceleration, the maximum acceleration, the minimum vehicle speed, and the maximum vehicle speed; The vehicle speed sequence of each segment is obtained by solving the multi-objective optimization function after adjusting the constraints.

5. The vehicle speed planning method according to claim 2, wherein: After obtaining the recommended speed sequence for the planned trip, including: Obtain the vehicle transmission ratio sequence corresponding to the recommended vehicle speed sequence during the solution process; For each vehicle transmission ratio in the vehicle transmission ratio sequence, the vehicle gear corresponding to each vehicle transmission ratio is obtained by searching the calibration relationship table between the vehicle transmission ratio and the vehicle gear, and the sequence composed of the vehicle gears corresponding to each vehicle transmission ratio is used as the recommended vehicle gear sequence.

6. A vehicle speed planning device, characterized in that: The vehicle speed planning device comprises: An acquisition module is configured to acquire a preset number of sets of weights, wherein each set of weights includes an energy consumption weight and a time efficiency weight, the preset number being determined based on the computing power of the parallel computing, and the sum of the energy consumption weight and the time efficiency weight being 1; a parallel computing module for solving the vehicle speed sequence of the to-be-planned trip in parallel for each set of weights using a multi-objective optimization function to obtain a vehicle speed sequence corresponding to each set of weights, wherein the multi-objective optimization function includes an energy consumption objective function and a time efficiency objective function, and the energy consumption weight and the time efficiency weight are weights of the energy consumption objective function and the time efficiency objective function, respectively; The trip calculation module is used to calculate the trip energy consumption and trip duration corresponding to each vehicle speed sequence; a first selection module configured to select, if there is a trip duration shorter than a preset required duration, a vehicle speed sequence corresponding to a minimum trip energy consumption from the trip durations shorter than the preset required duration as a recommended vehicle speed sequence for the trip to be planned; The second selection module is configured to select a vehicle speed sequence corresponding to a minimum travel duration as a recommended vehicle speed sequence for the planned travel if there is no travel duration less than the preset required duration.

7. The vehicle speed planning device according to claim 6, characterized in that: The vehicle speed planning device further includes a building module, which is used to: A multi-objective optimization function is established, and the multi-objective optimization function is: ； in, , ， The constraints of the multi-objective optimization function are: ； Among them, minJ() is the minimum value function, k is the current stage, N is the total prediction step length, is the instantaneous fuel consumption of the vehicle engine, △s(k) is the distance between two adjacent stages, v k and v k+1 and are the vehicle speeds of the kth and k+1th stages respectively, w1 is the energy consumption weight, w2 is the time efficiency weight, λ is the coefficient of the slope type, α is the slope type of the road to be planned, are the coefficients of the i-term and j-term of the polynomial, T k is the engine torque at stage k, n k is the engine speed at the kth stage, δ is the rotation mass conversion coefficient, m is the vehicle mass, r is the rolling radius of the vehicle wheel, i k is the vehicle transmission ratio at stage k, η is the mechanical efficiency, C D is the air resistance coefficient, A is the windward area, g is the gravity coefficient, f is the friction coefficient, σ k is the road slope of the kth stage, T min and T max are the minimum engine torque and the maximum engine torque, respectively, v min and v max are the minimum and maximum speeds, respectively, v min =0.9v average , v max =1.1v average , v average =L length / T req , v average is the average speed of the trip to be planned, L length is the total length of the trip to be planned, T req is the preset required duration, n min and n max is the minimum engine speed and the maximum engine speed, a min and a max are the minimum acceleration and the maximum acceleration respectively.

8. The vehicle speed planning device according to claim 7, characterized in that: The vehicle speed planning device further includes a road division module, which is used to: Divide the road to be planned into multiple sections according to slope and slope length; Parallel computing module for: For each set of weights, each segment of the road to be planned is traversed in turn. For each segment, a multi-objective optimization function is used to solve the speed sequence of each segment. The vehicle speed sequences of each segment are combined to obtain the vehicle speed sequence corresponding to each group of weights.

9. A vehicle speed planning device, characterized in that: The vehicle speed planning device includes a processor, a memory, and a vehicle speed planning program stored in the memory and executable by the processor, wherein when the vehicle speed planning program is executed by the processor, the steps of the vehicle speed planning method according to any one of claims 1 to 5 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a vehicle speed planning program, wherein when the vehicle speed planning program is executed by a processor, the steps of the vehicle speed planning method according to any one of claims 1 to 5 are implemented.

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