阅读说明：本技术 一种基于大数据的车辆行驶控制方法及系统 (Vehicle running control method and system based on big data ) 是由 刘洋 于 2021-04-26 设计创作，主要内容包括：本发明涉及一种基于大数据的车辆行驶控制方法及系统,所述方法包括以下步骤：对车辆行驶路径进行导航规划；获取车辆数据及行驶数据,以及道路通行数据；根据车辆数据及行驶数据控制车辆的行驶。本发明可以使得其在保证燃油经济性的情况下尽可能快地到达目的地,提高道路通行能力。(The invention relates to a vehicle running control method and system based on big data, wherein the method comprises the following steps: performing navigation planning on a vehicle running path; acquiring vehicle data, driving data and road traffic data; and controlling the running of the vehicle according to the vehicle data and the running data. The invention can make the vehicle reach the destination as fast as possible under the condition of ensuring the fuel economy, thereby improving the road traffic capacity.)

1. A vehicle travel control method based on big data, characterized by comprising the steps of:

performing navigation planning on a vehicle running path;

acquiring vehicle data, driving data and road traffic data;

and controlling the running of the vehicle according to the vehicle data and the running data.

2. The big data-based vehicle travel control method according to claim 1, wherein the navigation planning comprises obtaining a travel start point and an arrival end point of the vehicle, and recommending travel paths of the start point and the end point.

3. The big data-based vehicle travel control method according to claim 2, wherein the vehicle data includes a position of the vehicle, a travel start point and an arrival end point, a current travel speed, a travel planned path, and the road traffic data includes data of a traffic light or a traffic intersection on the vehicle travel path.

4. The big-data-based vehicle travel control method according to claim 3, wherein if there are N vehicles in the road area, where P represents the position of the vehicle, i.e. Pi represents the position Pi (x, y) where the ith vehicle is currently located, whose position can be represented as Pi (x, y, z), V represents the speed of the vehicle, Vi represents the speed of the ith vehicle, V is a vector with a belt direction, U represents the position of the vehicle at the next traffic intersection on the navigation path road thereof, Ui represents the position of the next traffic light intersection on the ith vehicle travel road, G represents the position of the vehicle navigation end point, and gi represents the position of the ith vehicle end point, for which road area the current positions of all vehicles can be represented as P ═ P (P1, P2, P3 ·, PN), and the current speeds of all vehicles can be represented as V ═ V1, v2, V3 · ·, VN), where the position of the next traffic light is denoted as U ═ U (U1, U2, U3 ·, UN), and the end position G ═ G (G1, G2, G3 ·, gN), then the average driving speed Vi' of the vehicle i driving to the next traffic light intersection is calculated in the following way: vi '═ α × Vi + β + χ, where α, β, χ are weighting coefficients, Vi and Vi' are both less than Vimax, which represents the highest speed limit for the road between the current location and the next traffic light.

5. The big data-based vehicle running control method according to claim 4, wherein α is a weighting coefficient related to economic fuel consumption, the economic fuel consumption of each vehicle is different, and the weighting coefficient of economic fuel consumption of the ith trolley is α i, where α i is a variable,wherein μ is a fuel consumption rate of the vehicle, k is a friction coefficient of an engine of the vehicle, ω (t) is a time t, i.e. a rotation speed of the engine of the vehicle at the current time, d is an engine displacement of the vehicle, ω (t) d is a current motor power of the engine of the vehicle, for a new energy vehicle powered by electric energy, the ω (t) d can be represented by a parameter, the ω (t) d is r (t), r (t) is a current driving resistance of the vehicle, the driving resistance is an external resistance, c1 and c2 are constants, and β is a weighted value related to road data.

6. The big data-based vehicle running control method according to claim 5, wherein the value range of c2 is 1500-2500, which is related to vehicle type, and c1 is C1I.e. the average value of the weighting factors for fuel economy of the vehicle, which can be obtained by averaging ai calculated during all previous driving of the vehicle.

7. The big data-based vehicle running control method according to claim 6, wherein the external resistance is wind resistance and friction resistance between the vehicle and a road surface, Ri is a fixed resistance constant of the vehicle, and the fixed resistance constant is a wind resistance coefficient and a cross-sectional area of the vehicle, wherein r (t) may include the wind resistance and the friction resistance.

8. The big data-based vehicle travel control method according to claim 7, characterized in that the big data-based vehicle travel control method is executed by the computer

In the above formula, ui-pi represents a road distance between ui and pi, | ui-pi | represents an absolute distance between ui and pi, that is, a straight-line distance, gi-pi represents a road distance between all traffic light intersections between pi and gi on the driving road of the i-th vehicle, and Mui represents the number of lanes on the intersection road where ui is located in accordance with the driving planned path.

9. The big data based vehicle running control method according to claim 8, wherein the big data based vehicle running control method is characterized in that ifThe road distance between ui and pi is long enough, so that the vehicle can be accelerated on the premise of ensuring safety; if it isIf the vehicle is driven according to the current speed, the traffic light of the road intersection where ui is located may change, if the vehicle can just catch up with the green light of the intersection, the vehicle is accelerated, otherwise, the vehicle is decelerated, and x is a weighting coefficient related to the road congestion condition.

10. A big data based vehicle travel control system, comprising:

the planning module is used for performing navigation planning on a vehicle driving path, wherein the navigation planning comprises the steps of obtaining a driving starting point and an arrival end point of the vehicle and recommending the driving path of the starting point and the end point;

the acquisition module is used for acquiring vehicle data, driving data and road traffic data;

the processing module is used for controlling the running of the vehicle according to vehicle data and running data, the vehicle data comprises the position of the vehicle, a running starting point, an arrival ending point, a current running speed and a running planned path, and the path data comprises data of traffic lights or traffic intersections on the vehicle running path.

Technical Field

The invention relates to the technical field of vehicle running control, in particular to a vehicle running control method and system based on big data.

Background

Road traffic capacity refers to the capacity of road facilities to dredge traffic flow. I.e., the ability of the infrastructure to pass through traffic flow particles over a period of time (typically 15min or lh) and under normal road, traffic, regulatory and operational quality requirements. Road capacity is a result of group behavior.

It is noted that the road traffic capacity is understood as the maximum amount of traffic that can pass per unit of time, and there are studies indicating a calculation method of the road traffic capacity, i.e. the basic traffic capacity is 1000 × the driving speed/the safety distance between vehicles. However, it is known that the light knowledge of the theoretical capacity of a road in one or more road zones is not sufficient, and it is not realistic for individual vehicles to enable the road to reach maximum traffic capacity only if the driver is fully consciously following the traffic regulations, and without robbing the road and without inking the road. On the other hand, each vehicle may travel to a different destination and may go straight, turn left, turn right or turn around when arriving at the intersection, and therefore, there are many factors to be considered for improving the traffic capacity of the road, for example, the individual driving demand of each vehicle. The traffic capacity of one or more roads depends on the behavior of all vehicles travelling on the road, which means that the traffic capacity of the road is increased if the traffic demand of each individual vehicle is met as much as possible, so that it reaches the destination in the shortest time or passes through the currently traveled road in the shortest time possible.

In the prior art, the traffic capacity of roads is considered from a macroscopic level, and the control of individual vehicles is neglected, so that the improvement is urgently needed.

Disclosure of Invention

In order to overcome the technical defects in the prior art, the invention provides a vehicle running control method and system based on big data, so that the vehicle can reach a destination as soon as possible under the condition of ensuring fuel economy, the road traffic capacity is improved, and the problems in the background art can be effectively solved.

In order to solve the technical problems, the technical scheme of the vehicle running control method and system based on big data provided by the invention is as follows:

in a first aspect, an embodiment of the present invention discloses a vehicle driving control method based on big data, which is characterized in that the method includes the following steps:

performing navigation planning on a vehicle running path;

acquiring vehicle data, driving data and road traffic data;

and controlling the running of the vehicle according to the vehicle data and the running data.

In any of the above schemes, preferably, the navigation planning includes obtaining a driving start point and a driving end point of the vehicle, and recommending a driving path of the start point and the driving end point.

In any of the above schemes, preferably, the vehicle data includes a position, a driving start point, a driving end point, a current driving speed, and a driving planned path, and the road traffic data includes data of a traffic light or a traffic intersection on the driving path of the vehicle.

In any of the above solutions, it is preferable that there are N vehicles in the road area, where P represents the position of the vehicle, i.e. Pi represents the position Pi (x, y) where the ith vehicle is currently located, the position of which can be represented as Pi (x, y, z), V represents the speed of the vehicle, Vi represents the speed of the ith vehicle, V is a vector with direction, U represents the position of the next traffic intersection on the navigation path road of the vehicle, Ui represents the position of the next traffic light intersection on the driving road of the ith vehicle, G represents the position of the navigation end point of the vehicle, and gi represents the position of the end point of the ith vehicle, and for the road area, the current positions of all vehicles can be represented as P (P1, P2, P3, PN), and the current speeds of all vehicles can be represented as V (V1, V2, V3), VN), the position of the next traffic light is represented as U ═ U1, U2, U3 · ·, UN), and the end position G ═ G · (G1, G2, G3 ·, gN), then the average driving speed Vi' of the vehicle i to the next traffic light intersection is calculated in the following manner: vi '═ α × Vi + β + χ, where α, β, χ are weighting coefficients, Vi and Vi' are both less than Vimax, which represents the highest speed limit for the road between the current location and the next traffic light.

In any of the above schemes, preferably, α is a weighting coefficient related to the economic fuel consumption, the economic fuel consumption of each vehicle is different, and the weighting coefficient of the economic fuel consumption of the ith trolley is α i, where α i is a variable,wherein μ is a fuel consumption rate of the vehicle, k is a friction coefficient of an engine of the vehicle, ω (t) is a time t, i.e. a rotation speed of the engine of the vehicle at the current time, d is an engine displacement of the vehicle, ω (t) d is a current motor power of the engine of the vehicle, for a new energy vehicle powered by electric energy, the ω (t) d can be represented by a parameter, the ω (t) d is r (t), r (t) is a current driving resistance of the vehicle, the driving resistance is an external resistance, c1 and c2 are constants, and β is a weighted value related to road data.

In any of the above-described aspects it is preferred that,the value range of c2 is 1500-2500, which is related to the vehicle type, and c1 isI.e. the average value of the weighting factors for fuel economy of the vehicle, which can be obtained by averaging ai calculated during all previous driving of the vehicle.

In any of the above aspects, preferably, the external resistance is wind resistance and friction resistance between the vehicle and a road surface, Ri is a fixed resistance constant of the vehicle, where the fixed resistance constant is a wind resistance coefficient and a cross-sectional area of the vehicle, and r (t) may include the wind resistance and the friction resistance.

In any of the above aspects, it is preferred that the

In the above formula, ui-pi represents a road distance between ui and pi, | ui-pi | represents an absolute distance between ui and pi, that is, a straight-line distance, gi-pi represents a road distance between all traffic light intersections between pi and gi on the driving road of the i-th vehicle, and Mui represents the number of lanes on the intersection road where ui is located in accordance with the driving planned path.

In any of the above embodiments, it is preferred ifThe road distance between ui and pi is long enough, so that the vehicle can be accelerated on the premise of ensuring safety; if it isIf the vehicle is driven according to the current speed, the traffic light of the road intersection where ui is located may change, if the vehicle can just catch up with the green light of the intersection, the vehicle is accelerated, otherwise, the vehicle is decelerated, and x is a weighting coefficient related to the road congestion condition.

Where J is the number of intersections between ui and gi, and it is noted that the number of intersections on the remaining route of the vehicle i is decreasing as the vehicle i travels on the travel route, J is changing, but at some point in time, J is fixed. u. of_i+1The position of the next intersection on the road where the vehicle i runs is shown, the ei shows the congestion coefficient of the intersection where the ui is located, the value is not 0, the congestion degree exceeds a certain threshold value, the ei is a negative number, otherwise, the ei is a positive number, the more congested, the larger the negative number of the ei is, the more uncongested, and the larger the positive number of the ei is. Note that χ is a sum value, i.e., a sum that takes into account the degree of congestion of each road on the remaining route.

As can be seen from the above, in the vi '═ α × vi + β + χ, α × vi has the largest influence factor on vi', which is also in accordance with the actual situation, and in the road region where the road condition and the traffic condition are good, the weighting factor α has the largest effect in order to improve the traffic performance and the fuel economy of the road. The beta value is the traffic efficiency of the traffic light closest to the current vehicle, and the chi factor has the significance of comprehensively considering the congestion degree of the road on the running path to balance the vehicle speed and achieve the optimal fuel economy. In a less preferred embodiment, β and x may be 0.

Compared with the prior art, the invention has the following beneficial effects: the vehicle can reach the destination as fast as possible under the condition of ensuring the fuel economy, and the road traffic capacity is improved.

In a second aspect, a big data-based vehicle travel control system includes:

the planning module is used for performing navigation planning on a vehicle driving path, wherein the navigation planning comprises the steps of obtaining a driving starting point and an arrival end point of the vehicle and recommending the driving path of the starting point and the end point;

the acquisition module is used for acquiring vehicle data, driving data and road traffic data;

the processing module is used for controlling the running of the vehicle according to vehicle data and running data, the vehicle data comprises the position of the vehicle, a running starting point, an arrival ending point, a current running speed and a running planned path, and the path data comprises data of traffic lights or traffic intersections on the vehicle running path.

The advantageous effects of the second aspect are the same as those of the first aspect, and therefore, the description thereof is omitted.

Drawings

The drawings are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification.

FIG. 1 is a schematic diagram of a big data based vehicle travel control method according to the present invention;

fig. 2 is a schematic length diagram of a road between pi and ui according to the big data based vehicle travel control method of the present invention.

Fig. 3 is a schematic diagram of a big data based vehicle travel control system according to the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or be indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or be indirectly connected to the other element.

In the description of the present invention, it is to be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships illustrated in the drawings, and are used merely for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.

For better understanding of the above technical solutions, the technical solutions of the present invention will be described in detail below with reference to the drawings and the detailed description of the present invention.

Example (b):

in a first aspect, as shown in fig. 1, an embodiment of the present invention discloses a vehicle driving control method based on big data, the method including the following steps:

step 1: performing navigation planning on a vehicle running path, wherein the navigation planning comprises the steps of acquiring a running starting point and an arrival end point of a vehicle, and recommending the starting point and the end point running path, and the recommending method can be a shortest path method;

step 2: and acquiring vehicle data, driving data and road traffic data. The vehicle data comprises the position, the driving starting point, the arrival end point, the current driving speed and the driving planned path of the vehicle, and the road data comprises the data of traffic lights or traffic intersections on the driving path of the vehicle and the like;

and step 3: and controlling the running of the vehicle according to the vehicle data and the running data. The step includes controlling the running speed of the vehicle.

Suppose that there are N vehicles in a road area where large data can be collected, where P represents the position of the vehicle, i.e. pi represents the current position pi (x, y) of the ith vehicle, and more generally, the position can be represented as pi (x, y, z), V represents the speed of the ith vehicle, vi represents the speed of the ith vehicle, note that V is a vector with a direction, U represents the position of the next traffic intersection on the navigation path road of the vehicle, ui represents the position of the next traffic light intersection on the driving road of the ith vehicle, G represents the position of the navigation end point of the vehicle, and gi represents the position of the end point of the ith vehicle. Then for the road region, the current position of all vehicles may be expressed as P ═ P1, P2, P3 … …, pN, and the current speed of all vehicles may be expressed as:

v ═ V1, V2, V3 … …, vN, and the position of its next traffic light can be expressed as: u ═ U1, U2, U3 … …, uN, and the end position G ═ G (G1, G2, G3 … …, gN).

The average traveling speed vi' at which the vehicle i travels to the next traffic light intersection can be calculated in the following manner:

vi' ═ α × vi + β + χ in the above formula, α, β, x are weighting coefficients. Vi and Vi' are both smaller than Vi max, which represents the highest speed limit for the road between the current location and the next traffic light.

Where α is a weighting coefficient related to the economic fuel consumption, and more generally, the economic fuel consumption of each vehicle is different, and the weighting coefficient of the economic fuel consumption of the ith trolley is α i, and preferably, α i is a variable rather than a constant. Experiments have shown that, with respect to a fixed weighted constant value,

is the most reasonable weighting scheme. Where μ is the fuel consumption rate of the vehicle, k is the friction coefficient of the engine of the vehicle, ω (t) is time t, i.e. the rotation speed of the engine of the vehicle at the current time, d is the engine displacement of the vehicle, ω (t) d is the current motor power of the engine of the vehicle, which can be expressed by a parameter, e.g. θ (t), r (t) is the current driving resistance of the vehicle, which can be understood as the external resistance, e.g. the wind resistance and the friction resistance of the vehicle to the road surface, Ri is a fixed resistance constant of the vehicle, e.g. calculated by the wind resistance coefficient x the cross-sectional area of the vehicle, and r (t) can include the wind resistance and the friction resistance, for a vehicle powered by fuel or cng.

C1 and C2 are constants, C2 generally takes a value between 1500 + 2500, and can be related to vehicle types, for example, for a large vehicle, C2 can be 1500,1600, for suv vehicle type, the value can be 1800, for a car can be 2000, for a hybrid vehicle can be 2200, and for an electric vehicle can be 2400.

The above values are exemplary only and not limiting. C1 may beThat is, the average value of the weighting coefficients for the economic fuel consumption of the vehicle, which can be obtained by averaging α i calculated in all previous driving processes of the vehicle, in practice, a fixed value may also be taken, for example, the value of c1 is between 220 and 300, for example, 220,250,280,300, etc. The vehicle model has little effect on c1 and the power consumption factor of the engine has a greater effect on c1, so c1 may be a constant value related to the engine power, since the engine power is fixed for a particular vehicle, and therefore c1 may be a constant related to the engine power.

The β value is a weighted value associated with the road data, such as:

in the above equation, ui-pi does not represent a subtraction of an absolute distance, but a road distance between ui and pi, and it will be understood by those skilled in the art that the road distance is not less than the absolute distance between ui and pi because the absolute distance is a straight line segment, and a road may have a slope or a curve. Similarly, gi-pi does not represent the absolute distance between gi and pi, but represents the road distance between all the traffic light intersections between pi and gi on the traveling road of the ith vehicle, and pi-ui on the way represents the length of the road between pi and ui, i.e., the length of the curve shown by the broken line, as shown in fig. 2. And gi-pi represents the road length along the navigation path shown by the dotted line, i.e., the sum of the lengths of all the curves, and | gi-ui | represents the straight-line distance between gi and ui.

When the vehicle i reaches ui, ui at this time becomes pi, and the position of the next traffic light intersection becomes ui, so that pi and ui are updated. Tui, the periods of the change of the traffic lights at the intersection ui are different, for example, 70 seconds, 90 seconds, etc. Mui denotes the number of lanes on the intersection where ui is located that are consistent with the planned path of travel.

The road distance between ui and pi is long enough, so that the vehicle can be accelerated on the premise of ensuring safety; whileIt means that the traffic light at the road intersection where ui is located may change if driven at the current speed. Accelerating the vehicle if it is just able to catch up to the green light at the intersection, and decelerating it otherwise is a weighting factor related to the road congestion condition.

Where J is the number of intersections between ui and gi, and it is noted that the number of intersections on the remaining route of the vehicle i is decreasing as the vehicle i travels on the travel route, J is changing, but at some point in time, J is fixed. u. of_i+1The position of the next intersection on the road where the vehicle i runs is shown, the ei shows the congestion coefficient of the intersection where the ui is located, the value is not 0, the congestion degree exceeds a certain threshold value, the ei is a negative number, otherwise, the ei is a positive number, the more congested, the larger the negative number of the ei is, the more uncongested, and the larger the positive number of the ei is. Note that χ is a sum value, i.e., a sum that takes into account the degree of congestion of each road on the remaining route.

As can be seen from the above, in the vi '═ α × vi + β + χ, α × vi has the largest influence factor on vi', which is also in accordance with the actual situation, and in the road region where the road condition and the traffic condition are good, the weighting factor α has the largest effect in order to improve the traffic performance and the fuel economy of the road. The beta value is the traffic efficiency of the traffic light closest to the current vehicle, and the chi factor has the significance of comprehensively considering the congestion degree of the road on the running path to balance the vehicle speed and achieve the optimal fuel economy. In a less preferred embodiment, β and x may be 0.

It should be noted that the above control of the vehicle speed is an optimal solution, and on a specific intersection and a specific road, because of different driving environments, a driver needs to actively control the vehicle to ensure driving safety, which belongs to the field of active safety of automobiles and is out of the discussion range of the present invention.

As shown in fig. 3, in a second aspect, a big data-based vehicle travel control system includes:

the planning module is used for performing navigation planning on a vehicle driving path, wherein the navigation planning comprises the steps of obtaining a driving starting point and an arrival end point of the vehicle and recommending the driving path of the starting point and the end point;

the acquisition module is used for acquiring vehicle data, driving data and road traffic data;

the processing module is used for controlling the running of the vehicle according to vehicle data and running data, the vehicle data comprises the position of the vehicle, a running starting point, an arrival ending point, a current running speed and a running planned path, and the path data comprises data of traffic lights or traffic intersections on the vehicle running path.

The advantageous effects of the second aspect are the same as those of the first aspect, and therefore, the description thereof is omitted.

Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that various changes, modifications and substitutions can be made without departing from the spirit and scope of the invention as defined by the appended claims. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.