CN111080018A - Intelligent internet automobile speed prediction method based on road traffic environment - Google Patents
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Abstract
An intelligent networking automobile speed prediction method based on a road traffic environment. The invention relates to the field of intelligent networked automobiles. The long-time vehicle speed prediction method based on the road traffic environment characteristic quantification is provided, and the predicted vehicle speed obtained by the method can be used in a driving energy and driving time demand prediction scene, so that the robustness and generalization capability of the conventional vehicle speed prediction model are greatly improved, and data support and basis are provided for realizing intelligent management and optimization of vehicle-mounted energy and reasonable planning of paths. The prediction method comprises the following steps: s1, data acquisition; s2, data extraction; s3, establishing a model; and S4, consumption prediction. The invention greatly improves the robustness and generalization capability of the existing vehicle speed prediction model and provides data support and basis for realizing intelligent management and optimization of vehicle-mounted energy and reasonable planning of paths.
Description
Technical Field
The invention relates to the field of intelligent networked automobiles, in particular to an intelligent networked automobile speed prediction method based on road traffic environment characteristic quantization.
Background
The long-term vehicle speed prediction means that the future driving vehicle speed on the whole planned path is predicted according to the road traffic information on the whole planned path and by combining the behavior habits of the driver and the vehicle condition before the vehicle does not drive. Effective and accurate long-term vehicle speed prediction is a necessary requirement for the development of intelligent internet vehicles (ICV) and Intelligent Transportation Systems (ITS), has important research value and application prospect, and can be widely used for improving traffic efficiency, improving safe driving control, intelligently optimizing and controlling vehicle-mounted energy, improving fuel economy, estimating remaining driving mileage and the like. However, due to the time-varying property, complexity and coupling of the driving environment under the combined action of human-vehicle-road-traffic, the long-term vehicle speed sequence is influenced and restricted by various environmental factors, driver factors and random factors, the uncertainty is high, accurate prediction is difficult only by a pure theoretical model, and particularly, the prediction difficulty of the longer-term vehicle speed on a planned path is higher. However, the development of vehicle intelligent networking environment and artificial intelligence algorithm has actively promoted the research of long-term vehicle speed prediction based on data driving, and the vehicle-mounted intelligent sensing and communication equipment can provide required data including driver state, driver habit, vehicle dynamics, road condition, surrounding vehicle state, traffic state and the like.
Compared with the short-time vehicle speed prediction, the method of predicting by combining road characteristic data, the current vehicle speed and a vehicle dynamics theory is mainly adopted, and the long-time vehicle speed prediction mainly takes the historical vehicle speed or vehicle speed characteristic data with date or time period as an index and the average traffic flow speed of a road section as input quantities, wherein the influence of weather or holidays can be considered. However, most long-term vehicle speed prediction methods predict the vehicle speed based on time-position historical vehicle speed data, and essentially predict the future vehicle speed based on the historical vehicle speed, which cannot realize more accurate vehicle speed prediction for a new driving road section lacking historical data.
Disclosure of Invention
Aiming at the problems, the invention provides a long-time vehicle speed prediction method by quantizing the road traffic environment characteristics, and the predicted vehicle speed obtained by the method can be used in the scene of the prediction of the driving energy and the driving time requirements, so that the robustness and the generalization capability of the conventional vehicle speed prediction model are greatly improved, and data support and basis are provided for realizing the intelligent management and optimization of vehicle-mounted energy and the reasonable planning of paths.
The technical scheme of the invention is as follows: the prediction method comprises the following steps:
s1, data acquisition;
s1.1, acquiring big data of human-vehicle-road-traffic;
s1.2, describing road types by using speed limit, and dividing roads into urban roads, suburban roads and expressway roads;
s1.3, carrying out data analysis on the big data of human-vehicle-road-traffic, respectively extracting road information and traffic information, and storing the road information and the traffic information in a memory;
s2, data extraction;
s2.1, extracting traffic flow and traffic density in the traffic information;
s2.2, extracting speed limit, number of lanes, intersection setting, signal lamp arrangement and position information of vehicles in the road information;
s3, establishing a model: the method comprises the steps of taking speed limit, lane number, intersection setting, signal lamp arrangement, vehicle position information, traffic flow and traffic density as the input of a vehicle speed prediction model, taking vehicle speed as the output, and establishing a mapping relation model between road traffic environment characteristic parameters and the vehicle speed;
s4, consumption prediction;
s4.1, predicting the speed of the historical driving path or the new driving path to be driven through the model established in the step S3, so as to obtain a predicted speed;
s4.2, calculating by means of distance divided by speed according to the prediction result of the long-time vehicle speed to obtain the time consumed by each unit length; adding the time consumed by all unit lengths to obtain the total time consumed by a new driving path;
s4.3, combining the results of S4.1 and S4.2, respectively calculating the predicted consumed power and the predicted energy consumption of the vehicle by using the following two formulas;
wherein i is the road gradient, m is the mass of the vehicle, f is the rolling resistance coefficient, u is the vehicle speed, i is the road gradient, CD is the wind resistance coefficient, A is the windward area, delta is the vehicle rotating mass conversion coefficient, and η is the transmission efficiency.
The speed limit of each road type can be obtained based on the human-vehicle-road-traffic historical big data, and the roads can be divided into urban roads, suburban roads and expressway roads according to the speed limit information.
According to a GPS arranged on a vehicle, the position information, time, longitude and latitude, speed and acceleration information of the vehicle can be acquired; meanwhile, the starting point and the end point of the traffic route are input in the high-grade map, and the speed limit, the number of lanes, the intersection setting, the signal lamp arrangement and the position information of the vehicle in the road information are obtained.
The traffic flow and the traffic density of the road section can be acquired through the GIS/ITS of the big data cloud server and the road section camera.
In step S3, the speed limit, the number of lanes, the intersection setting, the signal light arrangement, the vehicle position information, the traffic flow and the traffic density characteristics are parameterized and then used as the input of a long-term vehicle speed prediction model, the vehicle speed is used as the output, a three-dimensional BP neural network optimized by a genetic algorithm is used to establish a vehicle speed prediction model, the vehicle speed is predicted, and the vehicle speed of a historical travel path or a new travel path to be traveled is predicted by the vehicle speed prediction model.
The invention has the beneficial effects that: the method comprises the steps of describing road types by speed limit, dividing roads into urban roads, suburban roads and expressways, extracting road traffic environment characteristic parameters according to main factors influencing speed under historical driving paths, parametrizing the characteristics of the road traffic environment characteristic parameters, and establishing a mapping relation model between the road traffic environment characteristic parameters and the speed based on an artificial intelligence algorithm. Meanwhile, the predicted vehicle speed obtained by the method is applied to a scene of predicting the requirements of the running energy and the running time, so that the robustness and the generalization capability of the conventional vehicle speed prediction model are greatly improved, and data support and basis are provided for realizing intelligent management and optimization of vehicle-mounted energy and reasonable planning of a path.
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FIG. 1 is a general embodiment of the present invention;
FIG. 2 is a diagram of a three-dimensional BP neural network structure;
FIG. 3 is a flowchart of a BP neural network optimized by a genetic algorithm.
Detailed Description
The present invention will now be described more clearly and fully hereinafter with reference to the accompanying drawings, in which figures 1-3 show some, but not all, of the embodiments of the invention.
The speed prediction is an indispensable precondition for intelligent transportation and intelligent energy management of automobiles, effective and accurate long-time speed prediction is an inevitable requirement for development of intelligent internet vehicles (ICV) and Intelligent Transportation Systems (ITS), however, the current research is more short-time speed prediction, and the long-time speed prediction is influenced by micro and random factors, so the research is less. Aiming at the current situation, the invention provides a vehicle speed prediction method of an intelligent networked automobile based on road traffic environment characteristic quantization. For a given human-vehicle system, the driving habits of humans and the dynamics of the vehicle remain stable for a long time, and therefore, in the case where the driving habits of humans and the dynamics of the vehicle are determined, the traveling speed is mainly affected by different road traffic environments. Therefore, based on the human-vehicle-road-traffic historical big data, the characteristic quantitative description is carried out on the road traffic environment aiming at different road types such as cities, suburbs, expressways and the like, and a mapping relation model between the characteristic parameters of the road traffic environment and the vehicle speed is established based on an artificial intelligence algorithm. The predicted vehicle speed obtained by the method can be used in a driving energy and driving time demand prediction scene, the robustness and generalization capability of the existing vehicle speed prediction model are greatly improved, and data support and basis are provided for realizing intelligent management and optimization of vehicle-mounted energy and reasonable planning of paths.
The prediction method comprises the following steps:
s1, data acquisition;
s1.1, for a given man-vehicle system, the driving habits of people and the dynamic characteristics of vehicles can be kept unchanged for a long time, the change of the driving speed characteristics is mainly influenced by different road traffic environments, and man-vehicle-road-traffic big data is obtained through a high-grade map, a GPS (global positioning system) and a GIS/ITS (geographic information system/information technology system) of a big data cloud service end, a road section camera and the like on the vehicle;
s1.2, describing road types by using speed limit, and dividing roads into urban roads, suburban roads and expressway roads; when the road speed limit is greater than or equal to 90km/h in the known path, dividing the road into high-speed roads; when the road speed limit is less than or equal to 60km/h, dividing the road into urban roads; and under the condition of limiting the speed of other roads, dividing the roads into suburban roads.
S1.3, carrying out data analysis on the big data of human-vehicle-road-traffic, respectively extracting road information and traffic information, and storing the road information and the traffic information in a memory;
s2, data extraction;
s2.1, extracting traffic flow and traffic density in the traffic information; the GIS/ITS of the big data cloud server and the road section camera can acquire traffic information such as average traffic flow speed, traffic density, traffic flow and the like of the road section, and the traffic flow and the density can reflect the characteristic better due to the time-varying property of the traffic state, so that the traffic flow and the density are selected as the input of the vehicle speed prediction model.
S2.2, extracting speed limit, number of lanes, intersection setting, signal lamp arrangement and position information of vehicles in the traffic information; selecting the influence factors as characteristic parameters of the vehicle speed, and adding the characteristic parameters into the input of a vehicle speed prediction model; in consideration of the influence of the signal lamp on the vehicle speed, the first 50 meters of the position of the traffic light is taken as a characteristic parameter. The definition of the intersection and the traffic light is 1 and 0, namely the position of the intersection is marked as 1, and the road section without the intersection is marked as 0; the positions of the traffic light and the front 50m are marked as 1, and no traffic light is marked as 0.
S3, establishing a model: the method comprises the steps of taking speed limit, lane number, intersection setting, signal lamp arrangement, vehicle position information, traffic flow and traffic density as the input of a vehicle speed prediction model, taking vehicle speed as the output, and establishing a mapping relation model between road traffic environment characteristic parameters and the vehicle speed; the method takes a three-dimensional BP neural network optimized by a genetic algorithm as an example, a mapping relation model between road traffic environment characteristic parameters and vehicle speed is established, the structure diagram of the three-dimensional BP neural network is shown as figure 2, the flow diagram of the three-dimensional BP neural network optimized by the genetic algorithm is shown as figure 3, the vehicle speed is predicted, and the vehicle speed prediction model is used for predicting the vehicle speed of a historical driving path to be driven (namely a frequently-used path which is historically traveled) or a new driving path.
The main steps of the genetic algorithm for optimizing the three-dimensional BP neural network are as follows:
(1) initializing a population
The individual coding adopts a real number coding method, and each individual is a real number string. The real number string is composed of 4 parts from the weight from the input layer to the hidden layer, the threshold value of the hidden layer, the weight from the hidden layer to the output layer and the threshold value of the output layer. The individual comprises all weights and thresholds of the neural network, and the neural network with the determined structure, weight and threshold can be formed on the premise of knowing the structure of the neural network.
(2) Fitness function
According to the initial weight and the threshold of the BP neural network obtained by individuals, taking the absolute value of the error between the prediction output and the actual output after the training of the BP neural network as a fitness function, namely
Wherein F is an individual fitness function, a is a coefficient, k is the number of network output nodes, and yiAnd oiRespectively, the expected output and the predicted output of the ith node.
(3) Selecting
And selecting a roulette method according to the individual fitness function, and inheriting the good individuals to the next group. Then, the selection probability for each individual i is:
fi＝b/Fi
wherein f isiIs the individual fitness of the ith individual, b is a coefficient, FiAs a fitness function of the ith individual, piT is the selection probability of the ith individual and the number of population individuals.
(4) Crossing
Since individuals are encoded by real numbers, the crossover operation method also adopts a real number crossover method, the x-th chromosome axiAnd the y-th chromosome ayiThe method of interleaving at j bits is as follows:
axi＝axi(1-c)+ayic
ayi＝ayi(1-c)+axic
wherein, axiAnd ayiJ positions of the x chromosome and the y chromosome respectively, and c is [0, 1 ]]A random number in between.
(5) Variation of
The operation method for selecting the jth gene of the ith individual to carry out mutation comprises the following steps:
wherein, amaxIs gene aijUpper bound of aminIs gene aijLower boundary of (f), (g) r2(1-g/Gmax)2G is the current iteration number, GmaxTo maximize the number of evolutions, r2Is a random number, r is [0, 1 ]]A random number in between.
(6) And replacing the initial value of the BP neural network training with the optimal individual obtained after GA optimization, and using the optimal individual as a new weight and a new threshold value during the BP neural network training for the BP neural network training and further for prediction to obtain an optimal result.
S4, consumption prediction;
s4.1, predicting the vehicle speed of the historical driving path to be driven (namely the historical driving path) or the new driving path through the model established in the step S3, so as to obtain a predicted vehicle speed, namely a variation curve of the vehicle speed along with the driving distance;
s4.2, calculating by means of distance divided by speed according to the prediction result of the long-time vehicle speed to obtain the time consumed by each unit length; adding the time consumed by all unit lengths to obtain the total time consumed by a new driving path;
s4.3, combining the results of S4.1 and S4.2, respectively calculating the predicted consumed power and the predicted energy consumption of the vehicle by using the following two formulas;
wherein i is the road gradient, m is the mass of the vehicle, f is the rolling resistance coefficient, u is the vehicle speed, i is the road gradient, CD is the wind resistance coefficient, A is the windward area, delta is the vehicle rotating mass conversion coefficient, and η is the transmission efficiency.
Finally, the energy consumption of the vehicle in the running process is calculated according to the predicted vehicle speed, the predicted energy consumption value and the actual energy consumption value can be obtained, the driving strategy is adjusted in time in the actual running process, the robustness and the generalization capability of the existing vehicle speed prediction model are greatly improved, and data support and basis are provided for realizing intelligent management and optimization of vehicle-mounted energy and reasonable planning of paths.
Claims (5)
1. An intelligent networking automobile speed prediction method based on a road traffic environment is characterized by comprising the following steps:
s1, data acquisition;
s1.1, acquiring big data of human-vehicle-road-traffic;
s1.2, describing road types by using speed limit, and dividing roads into urban roads, suburban roads and expressway roads;
s1.3, carrying out data analysis on the big data of human-vehicle-road-traffic, respectively extracting road information and traffic information, and storing the road information and the traffic information in a memory;
s2, data extraction;
s2.1, extracting traffic flow and traffic density in the traffic information;
s2.2, extracting speed limit, number of lanes, intersection setting, signal lamp arrangement and position information of vehicles in the road information;
s3, establishing a model: the method comprises the steps of taking speed limit, lane number, intersection setting, signal lamp arrangement, vehicle position information, traffic flow and traffic density as the input of a vehicle speed prediction model, taking vehicle speed as the output, and establishing a mapping relation model between road traffic environment characteristic parameters and the vehicle speed;
s4, consumption prediction;
s4.1, predicting the speed of the historical driving path or the new driving path to be driven through the model established in the step S3, so as to obtain a predicted speed;
s4.2, calculating by means of distance divided by speed according to the prediction result of the long-time vehicle speed to obtain the time consumed by each unit length; adding the time consumed by all unit lengths to obtain the total time consumed by a new driving path;
s4.3, combining the results of S4.1 and S4.2, respectively calculating the predicted consumed power and the predicted energy consumption of the vehicle by using the following two formulas;
wherein i is the road gradient, m is the mass of the vehicle, f is the rolling resistance coefficient, u is the vehicle speed, i is the road gradient, CD is the wind resistance coefficient, A is the windward area, delta is the vehicle rotating mass conversion coefficient, and η is the transmission efficiency.
2. The method as claimed in claim 1, wherein the speed limit of each road type is obtained based on the big data of the human-vehicle-road-traffic history, and the road is divided into an urban road, a suburban road and an expressway according to the speed limit information.
3. The intelligent internet automobile speed prediction method based on the road traffic environment as claimed in claim 1, wherein the position information, time, longitude and latitude, speed and acceleration information of the vehicle can be obtained according to a GPS installed on the vehicle; meanwhile, the starting point and the end point of the traffic route are input in the high-grade map, and the speed limit, the number of lanes, the intersection setting, the signal lamp arrangement and the position information of the vehicle in the road information are obtained.
4. The method for predicting the vehicle speed of the intelligent internet automobile based on the road traffic environment as claimed in claim 1, wherein the traffic flow and the traffic density of the road section can be obtained through a GIS/ITS of a big data cloud service end and a road section camera.
5. The method of claim 1, wherein in step S3, the speed limit, the number of lanes, the intersection setting, the signal light arrangement, the vehicle location information, the traffic flow and the traffic density characteristics are parameterized and used as the input of a long-term vehicle speed prediction model, the vehicle speed is used as the output, a three-dimensional BP neural network optimized by a genetic algorithm is used to establish a vehicle speed prediction model, the vehicle speed is predicted, and the vehicle speed of the historical driving path or the new driving path to be driven is predicted by the vehicle speed prediction model.
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| CN113327430A (en) * | 2021-05-06 | 2021-08-31 | 天地（常州）自动化股份有限公司 | Method and device for predicting speed of underground trackless rubber-tyred vehicle based on LSTM |
| CN113450564A (en) * | 2021-05-21 | 2021-09-28 | 江苏大学 | Intersection passing method based on NARX neural network and C-V2X technology |
| CN113642768A (en) * | 2021-07-12 | 2021-11-12 | 南京航空航天大学 | Vehicle running energy consumption prediction method based on working condition reconstruction |
| CN113879182A (en) * | 2021-11-11 | 2022-01-04 | 广东汉合汽车有限公司 | Vehicle energy management control method, system, device and medium |
| WO2024073938A1 (en) * | 2022-10-08 | 2024-04-11 | 合众新能源汽车股份有限公司 | Vehicle speed prediction method and device |
| CN117325875A (en) * | 2023-12-01 | 2024-01-02 | 北京航空航天大学 | Vehicle long-term speed prediction method based on individual driving characteristics |
| CN117325875B (en) * | 2023-12-01 | 2024-02-02 | 北京航空航天大学 | Vehicle long-term speed prediction method based on individual driving characteristics |
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