CN113654568A - Navigation method, device, medium and equipment based on vehicle braking times - Google Patents
Navigation method, device, medium and equipment based on vehicle braking times Download PDFInfo
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- G01C21/34—Route searching; Route guidance
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
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- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
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Abstract
The invention discloses a navigation method, a navigation device, a navigation medium and navigation equipment based on vehicle braking times, which are characterized by firstly acquiring the driving habit characteristics of a user, and constructing a braking time calculation model by a machine learning method based on the driving habit characteristics of the user, the road condition of a path traveled by the user and the braking times on the traveled path; receiving a navigation path request, and determining a path to be selected according to the navigation path request; respectively taking characteristic vectors formed by the driving habit characteristics of the user and the road real-time condition characteristics of each to-be-selected route as input, and calculating the braking times of each to-be-selected route through a braking time calculation model; and finally, selecting the path to be selected with the least braking times for recommendation. Therefore, the method and the device can combine the personalized driving habits of the user and the real-time road condition characteristics, recommend the navigation path with more balanced and smooth traffic condition and smaller braking times, help the user to reduce the braking times, and meet the requirements of safe and green driving.
Description
Technical Field
The invention belongs to the technical field of vehicle navigation, and particularly relates to a navigation method, a navigation device, a navigation medium and navigation equipment based on vehicle braking times.
Background
At present, a navigation system searches and matches a plurality of corresponding paths, such as paths with different navigation strategies of 'congestion avoidance', 'high-speed priority', 'shortest time', 'charge avoidance' and the like, according to a starting point and an ending point of a user navigation request, so that the user can select the paths.
However, the existing navigation strategy only recommends the path with the shortest distance and the shortest time to the user, and the navigation path recommendation mainly has two problems:
firstly, the fastest navigation path is not necessarily the best, and in fact, the fastest path is often the fastest path, but the traffic flow is larger, the traffic condition is more complex, and a most critical problem is that a driver often encounters a 'sudden braking' condition, and the relevance of the sudden braking, the traffic safety and the green travel is higher.
Secondly, the navigation path recommendation does not consider the personalized driving habits of the users, and the recommended navigation path may cause the users to need frequent braking, which results in poor user experience.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a navigation method based on the vehicle braking times, which realizes the recommendation of a navigation path with more balanced and smooth traffic condition and smaller braking times, helps a user to reduce the braking times and meets the requirements of safe and green driving.
A second object of the present invention is to provide a navigation device based on a braking condition of a vehicle.
A third object of the present invention is to provide a storage medium.
It is a fourth object of the invention to provide a computing device.
The first purpose of the invention is realized by the following technical scheme: a navigation method based on the braking times of a vehicle comprises the following steps:
determining the driving habits of the user based on the historical driving information of the user to obtain the driving habit characteristics of the user;
constructing a braking frequency calculation model by a machine learning method based on the driving habit characteristics of the user, the road condition of a path traveled by the user and the braking frequency on the traveled path;
receiving a navigation path request, and determining a path to be selected according to the navigation path request;
respectively taking characteristic vectors formed by the driving habit characteristics of the user and the road real-time condition characteristics of each to-be-selected route as input, and calculating the braking times of each to-be-selected route through a braking time calculation model;
and selecting the path to be selected with the least braking times for recommendation.
Preferably, the driving habit characteristics of the user comprise driving speed per hour information and braking frequency information of the user in various time periods, driving speed per hour information and braking frequency information of the user in various road sections, and a corresponding relation between the speed of the user and the braking force.
Preferably, the road condition characteristics include road static information and road dynamic information;
the static road information comprises the number of lanes of the road, the speed limiting speed, the number of left-turn/right-turn/straight-going lanes, the length of a straight path, the length and the bending radius of a bent path and the number of signal lamps;
the road dynamic information comprises pedestrian flow and pedestrian flow speed of pedestrians in various time periods, traffic flow and traffic flow speed of pedestrians in various time periods, the length of a congested road section, the length of a non-congested road section and the non-congested time period.
Preferably, the specific process of constructing the braking frequency calculation model is as follows:
acquiring driving habit characteristics of a user, and acquiring road conditions of a driving path of the user and braking times on the driving path;
and training the neural network model by taking a feature vector formed by the driving habit features of the user and the road condition of the path traveled by the user as the input of the neural network model and taking the braking times on the path traveled by the corresponding user as a label to obtain a braking time calculation model.
Further, the neural network model may be a convolutional neural network model, a BP neural network model, or a multi-layer perceptron.
Furthermore, the method also comprises the steps of collecting the braking position, the braking force and the braking duration of the user during each braking on the driving path;
when the braking frequency calculation model is constructed, the driving habit characteristics of a user and the road condition of a path traveled by the user are used as input, the braking frequency of the user on the path traveled, the braking position, the braking force and the braking duration of each braking are used as labels, and the neural network model is trained to obtain the braking frequency calculation model.
Furthermore, aiming at each candidate route, the driving habit characteristics of the user and the road conditions of the candidate route are input into the braking frequency calculation model, and the recommended braking position, the recommended braking force and the recommended braking duration of each braking are calculated while the braking frequency is calculated through the braking frequency calculation model.
The second purpose of the invention is realized by the following technical scheme: a navigation device based on a number of vehicle brakes, comprising:
the acquisition module is used for determining the driving habits of the user based on the historical driving information of the user to obtain the driving habit characteristics of the user;
the model building module is used for building a braking frequency calculation model through a machine learning method based on the driving habit characteristics of the user, the road condition of a path traveled by the user and the braking frequency on the traveled path;
the candidate path determining module is used for receiving the navigation path request and determining a candidate path according to the navigation path request;
the braking frequency calculation module is used for respectively taking characteristic vectors formed by the driving habit characteristics of the user and the road real-time condition characteristics of each to-be-selected route as input, and calculating the braking frequency of each to-be-selected route through the braking frequency calculation model;
and the path recommendation module is used for selecting the path to be selected with the least braking times for recommendation.
The third purpose of the invention is realized by the following technical scheme: a storage medium stores a program that, when executed by a processor, implements a vehicle braking count-based navigation method according to a first object of the present invention.
The fourth purpose of the invention is realized by the following technical scheme: a computing device comprising a processor and a memory for storing a program executable by the processor, wherein the processor executes the program stored in the memory to implement the navigation method based on the braking times of the vehicle according to the first object of the present invention.
Compared with the prior art, the invention has the following advantages and effects:
(1) the navigation method based on the vehicle braking times comprises the steps of firstly obtaining driving habit characteristics of a user, and constructing a braking time calculation model through a machine learning method based on the driving habit characteristics of the user, road conditions of a path traveled by the user and the braking times on the traveled path; receiving a navigation path request, and determining a path to be selected according to the navigation path request; respectively taking characteristic vectors formed by the driving habit characteristics of the user and the road real-time condition characteristics of each to-be-selected route as input, and calculating the braking times of each to-be-selected route through a braking time calculation model; and finally, selecting the path to be selected with the least braking times for recommendation. Therefore, the method and the device can combine the personalized driving habits of the user and the real-time road condition characteristics, recommend the navigation path with more balanced and smooth traffic condition and smaller braking times, help the user to reduce the braking times, and meet the requirements of safe and green driving.
(2) According to the navigation method based on the vehicle braking times, when a braking time calculation model is constructed, the braking times of a user on a driving path, the braking position, the braking force and the braking duration of each braking are used as labels, the neural network model is trained, the obtained braking time calculation model not only can calculate the braking times of a path to be selected, but also can calculate the suggested braking position, the suggested braking force and the suggested braking duration of each braking, and a better reference is provided for the operation of the user on the braking when the user drives on the path.
Drawings
FIG. 1 is a flow chart of a navigation method based on the braking times of a vehicle according to the present invention.
FIG. 2 is a flow chart of the braking number calculation model training and use of the present invention.
FIG. 3 is a schematic diagram of the multi-layered sensor of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
Example 1
At present, the conventional navigation method only recommends paths with shortest distance and least time to a user, and the recommended navigation paths in the modes possibly need frequent braking of the user, so that the driving experience of the user is poor, and safe and green driving is difficult to meet. Based on the above, the embodiment discloses a navigation method based on vehicle braking times, which can accurately calculate the braking times, provide a navigation path with the minimum personalized braking times for a user, and meet the requirements of safe and green driving.
For the convenience of understanding the present embodiment, a detailed description will be given to a navigation method based on the braking times of a vehicle disclosed in the embodiments of the present application.
Referring to fig. 1, a flow chart of a navigation method based on the braking times of a vehicle, which is executed by a server such as a computer, includes the following steps:
s101, determining the driving habits of the user based on the historical driving information of the user, and obtaining the driving habit characteristics of the user.
In this embodiment, the driving habit characteristics of the user mainly refer to some driving habits of the user under different time periods, different road conditions and other conditions, including but not limited to driving speed per hour information and braking frequency information of the user in various time periods, driving speed per hour information and braking frequency information of the user in various road sections such as straight roads, curved roads, ramps, urban roads, expressways, main roads, branches and the like, and a corresponding relationship between the speed of the user and the braking force. For example, the driving speed information and the braking frequency information (within a certain distance, for example, within one hundred meters) of the user during the peak time period of the commute, and the driving speed information and the braking frequency information (within a certain distance) of the user during two lanes and four lanes. The driving habit characteristic information is obtained by arranging the driving habit characteristic information based on the historical driving information of the user. In this embodiment, the driving information of the user and the corresponding road information are collected periodically (e.g., every 1 second), where: the driving information of the user comprises whether the user steps on the brake, the brake position, the brake force and the brake duration when the user steps on the brake, the corresponding road information comprises road dynamic information and road static information, and according to the information, the driving habit characteristics of the user can be determined, for example: the speed of the bicycle is 80 km/h, the lane is 4 lanes and bends, a traffic light is arranged in front of the bicycle at 200 m, the traffic flow in front is 40 cars/min, the traffic flow speed is 60 km/h, when the distance is 60 m from the front car, the brake is stepped on for 1 time, the duration is 0.3s, and the brake force is moderately reduced to 70 km/h; when the distance is 40 meters from the front vehicle, the brake is stepped on for 1 time, the duration is 0.5s, and the brake force is moderately reduced to 65 kilometers per hour; when the distance is 20 meters from the front vehicle, the brake is stepped on for 1 time, the duration is 0.5s, and the braking force is moderately reduced to 60 kilometers per hour.
In the embodiment, based on the historical driving information of the user, a user driving habit feature library is established for each user and used for storing the driving habit features of the user, and the driving habit features can be directly called from the library when used later.
S102, building a braking frequency calculation model through a machine learning method based on the driving habit characteristics of the user, the road condition of the path traveled by the user and the braking frequency on the traveled path.
In this embodiment, the road condition features include road static information and road dynamic information; wherein:
the static road information includes, but is not limited to, the number of lanes of the road, the speed limit speed, the number of left-turn/right-turn/straight lanes, the length of a straight path, the length and the bending radius of a bent path, and the number of signal lamps;
the road dynamic information includes, but is not limited to, pedestrian flow and pedestrian flow speed of pedestrians in various time periods, traffic flow and traffic flow speed in various time periods, congested road segment length and congested time period, and non-congested road segment length and non-congested time period.
In this embodiment, as shown in fig. 2, a specific process of constructing the braking frequency calculation model by the machine learning method is as follows:
s1021, acquiring driving habit characteristics of the user, collecting road conditions of a driving path of the user and braking times of the driving path of the user, and obtaining training sample data. Specifically, the method comprises the following steps:
in the embodiment, for each user, the driving habit characteristics of the user are acquired through the driving habit characteristic library of the user; acquiring road conditions of a plurality of paths traveled by a user in the past and corresponding brake times; aiming at each user, combining the driving habit characteristics of the user and the road conditions of each path driven by the user to form a characteristic vector as training sample data; one of the training samples is formed by combining the driving habit characteristics of the user and the road condition of one of the paths traveled, and a plurality of training samples can be formed based on a plurality of paths traveled by the user.
And S1022, performing data preprocessing aiming at the training sample data, including data missing and normalization processing.
And S1023, taking training sample data after data preprocessing as input of the neural network model, taking the braking times on a driving path of a corresponding user as a label, and training the neural network model to obtain a braking time calculation model.
Further, in the above step S1021 in this embodiment, for a previous driving route of the user, while acquiring a road condition and the braking frequency of the user in the route, a braking position, a braking force and a braking duration of the user during each braking on the driving route are acquired at the same time; further, when the braking frequency calculation model is constructed in the step S1023, the driving habit characteristics of the user and the road condition of the route traveled by the user are used as inputs, the corresponding braking frequency on the route traveled by the user, the braking position, the braking force and the braking duration of each braking are used as labels, and the neural network model is trained to obtain the braking frequency calculation model.
In this embodiment, the neural network model may be a convolutional neural network model, a BP neural network model, or a multi-layer perceptron. As shown in fig. 3, the model for calculating the braking times based on the multi-layer perceptron provided in this embodiment is shown, wherein: the input number of the multilayer perceptron is as follows: the feature vectors { T1, T2., Tn } (defined as braking frequency calculation feature vectors) formed by combining the driving habit features of the user and the road conditions of the route traveled by the user are dimension numbers, the output number is 1 (corresponding to a braking frequency calculation result), the number of layers is 4, and each hidden layer comprises (the dimension number of the braking frequency calculation feature vectors is-1) hidden units. In addition, when the neural network model is used, if the label comprises the brake times, the brake position, the brake force and the brake duration when the brake is performed each time, the output number of the multilayer sensing machine is 4.
S103, receiving the navigation path request, and determining a path to be selected according to the navigation path request. In this embodiment, one or more candidate routes may be determined based on the departure place and the destination selected by the user.
And S104, preprocessing the data of the feature vectors formed by the driving habit features of the user and the road real-time condition features of each candidate route, and then respectively using the preprocessed data as input, and calculating the braking times of each candidate route driven by the user through the braking time calculation model obtained in the step S102. For example, if the candidate routes A, B and C exist, a feature vector formed by the driving habit features of the user and the road real-time condition features of the candidate route A is subjected to data preprocessing and then is input into a braking frequency calculation model to obtain the braking frequency of the user when the user drives the candidate route A, and the braking frequency of the user when the user drives the candidate route B, C is obtained by the same method.
In the step S102, in the training process of the model, if the label includes the brake position, the brake force and the brake duration in each braking process in addition to the brake frequency, the model can also obtain the recommended brake position, the recommended brake force and the recommended brake duration in each braking process based on the brake frequency calculation model.
In this embodiment, the road real-time status of the candidate route includes road static information and road dynamic information; the static road information includes, but is not limited to, the number of lanes of the road, the speed limit speed, the number of left-turn/right-turn/straight lanes, the length of a straight path, the length and the bending radius of a bent path, and the number of signal lamps; the road dynamic information includes, but is not limited to, pedestrian flow and pedestrian flow speed of pedestrians in various time periods, traffic flow and traffic flow speed in various time periods, congested road segment length and congested time period, and non-congested road segment length and non-congested time period.
And S105, selecting the path to be selected with the minimum braking frequency for recommendation, recommending the recommended braking position, the recommended braking force and the recommended braking duration of each braking obtained under the corresponding braking frequency to the user, and providing the navigation strategy with the minimum braking frequency matched with the personalized navigation for the user.
In this embodiment, a braking frequency calculation model is trained in the above steps based on the driving habits of the user, the road condition information of the driving path and the braking frequency of the user corresponding to the driving path. In the to-be-selected navigation path of the user, the braking frequency calculation model is combined with the personalized driving habits of the user and the real-time road condition characteristics, so that the navigation path with more balanced and smooth traffic condition and smaller braking frequency is recommended, the user is helped to reduce the braking frequency, and the requirements of safe and green driving are met.
Those skilled in the art will appreciate that all or part of the steps in the method according to the present embodiment may be implemented by a program to instruct the relevant hardware, and the corresponding program may be stored in a computer-readable storage medium. It should be noted that although the method operations of embodiment 1 are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Rather, the depicted steps may change the order of execution, some steps may be performed concurrently, some steps may additionally or alternatively be omitted, multiple steps may be combined into one step execution, and/or one step may be broken down into multiple step executions.
Example 2
The embodiment discloses a navigation device based on vehicle braking times, which comprises an acquisition module, a model construction module, a candidate route determination module, a braking time calculation module and a route recommendation module, wherein the functions of the modules are as follows:
the acquisition module is used for determining the driving habits of the user based on the historical driving information of the user to obtain the driving habit characteristics of the user; the driving habit characteristics of the user include, but are not limited to, driving speed per hour information and braking frequency information of the user in various time periods, driving speed per hour information and braking frequency information of the user in various road sections, and a corresponding relation between the speed of the user and the braking force.
The model building module is used for building a braking frequency calculation model through a machine learning method based on the driving habit characteristics of the user, the road condition of a path traveled by the user and the braking frequency on the traveled path; in the embodiment, the road condition of the path traveled by the user comprises road static information and road dynamic information; wherein: the static road information includes, but is not limited to, the number of lanes of the road, the speed limit speed, the number of left-turn/right-turn/straight lanes, the length of a straight path, the length and the bending radius of a bent path, and the number of signal lamps; the road dynamic information includes, but is not limited to, pedestrian flow and pedestrian flow speed of pedestrians in various time periods, traffic flow and traffic flow speed in various time periods, congested road segment length and congested time period, and non-congested road segment length and non-congested time period.
And the candidate path determining module is used for receiving the navigation path request and determining a candidate path according to the navigation path request. The candidate route refers to a route obtained based on the departure place and the destination of the user, and the number of the routes may be multiple.
And the braking frequency calculation module is used for respectively taking the characteristic vectors formed by the driving habit characteristics of the user and the road real-time condition characteristics of each to-be-selected route as input, and calculating the braking frequency of each to-be-selected route through the braking frequency calculation model.
And the path recommendation module is used for selecting the path to be selected with the least braking times for recommendation.
For specific implementation of each module in this embodiment, reference may be made to embodiment 1, and details are not described here. It should be noted that, the apparatus provided in this embodiment is only illustrated by dividing the functional modules, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the internal structure is divided into different functional modules to complete all or part of the functions described above.
Example 3
The embodiment discloses a storage medium storing a program, wherein the program is executed by a processor to implement the navigation method based on the vehicle braking times, and the method comprises the following steps:
determining the driving habits of the user based on the historical driving information of the user to obtain the driving habit characteristics of the user;
constructing a braking frequency calculation model by a machine learning method based on the driving habit characteristics of the user, the road condition of a path traveled by the user and the braking frequency on the traveled path;
receiving a navigation path request, and determining a path to be selected according to the navigation path request;
respectively taking characteristic vectors formed by the driving habit characteristics of the user and the road real-time condition characteristics of each to-be-selected route as input, and calculating the braking times of each to-be-selected route through a braking time calculation model;
and selecting the path to be selected with the least braking times for recommendation.
In this embodiment, specific implementation of each process may be referred to in embodiment 1, which is not described herein again.
In this embodiment, the storage medium may be a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a Random Access Memory (RAM), a usb disk, a removable hard disk, or other media.
Example 4
The embodiment discloses a computing device, which includes a processor and a memory for storing an executable program of the processor, and when the processor executes the program stored in the memory, the navigation method based on the vehicle braking times, which is described in embodiment 1, is implemented, and includes the steps of:
determining the driving habits of the user based on the historical driving information of the user to obtain the driving habit characteristics of the user;
constructing a braking frequency calculation model by a machine learning method based on the driving habit characteristics of the user, the road condition of a path traveled by the user and the braking frequency on the traveled path;
receiving a navigation path request, and determining a path to be selected according to the navigation path request;
respectively taking characteristic vectors formed by the driving habit characteristics of the user and the road real-time condition characteristics of each to-be-selected route as input, and calculating the braking times of each to-be-selected route through a braking time calculation model;
and selecting the path to be selected with the least braking times for recommendation.
In this embodiment, specific implementation of each process may be referred to in embodiment 1, which is not described herein again.
In this embodiment, the computing device may be a desktop computer, a notebook computer, a PDA handheld terminal, a tablet computer, or other terminal devices.
In this embodiment, the computing device includes: the system comprises a processor, a memory, a bus and a communication interface, wherein the processor, the communication interface and the memory are connected through the bus; the processor is configured to execute an executable module, such as a computer program, stored in the memory.
The Memory may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network and the like can be used.
The bus may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, and the like.
The storage is configured to store a program, and the processor executes the program after receiving an execution instruction, and the method performed by the apparatus defined by the flow program disclosed in the foregoing embodiments of the present application may be applied to or implemented by the processor.
The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSP), Application Specific Integrated Circuits (ASIC), Field-Programmable Gate arrays (FPGA) or other Programmable logic devices, discrete Gate or transistor logic devices, and discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium that is mature in the art, such as a random access memory, a flash memory and/or a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory and/or a register, and the storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware thereof.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (10)
1. A navigation method based on the braking times of a vehicle is characterized by comprising the following steps:
determining the driving habits of the user based on the historical driving information of the user to obtain the driving habit characteristics of the user;
constructing a braking frequency calculation model by a machine learning method based on the driving habit characteristics of the user, the road condition of a path traveled by the user and the braking frequency on the traveled path;
receiving a navigation path request, and determining a path to be selected according to the navigation path request;
respectively taking characteristic vectors formed by the driving habit characteristics of the user and the road real-time condition characteristics of each to-be-selected route as input, and calculating the braking times of each to-be-selected route through a braking time calculation model;
and selecting the path to be selected with the least braking times for recommendation.
2. The vehicle braking frequency-based navigation method according to claim 1, wherein the driving habit characteristics of the user comprise driving speed per hour information and braking frequency information of the user in various time periods, driving speed per hour information and braking frequency information of the user in various road sections, and a corresponding relation between the speed of the user and the braking force.
3. The vehicle braking number based navigation method according to claim 1, wherein the road condition characteristics include road static information and road dynamic information;
the static road information comprises the number of lanes of the road, the speed limiting speed, the number of left-turn/right-turn/straight-going lanes, the length of a straight path, the length and the bending radius of a bent path and the number of signal lamps;
the road dynamic information comprises pedestrian flow and pedestrian flow speed of pedestrians in various time periods, traffic flow and traffic flow speed of pedestrians in various time periods, the length of a congested road section, the length of a non-congested road section and the non-congested time period.
4. The vehicle braking number-based navigation method according to claim 1, wherein the specific process of constructing the braking number calculation model is as follows:
acquiring driving habit characteristics of a user, and acquiring road conditions of a driving path of the user and braking times on the driving path;
and training the neural network model by taking a feature vector formed by the driving habit features of the user and the road condition of the path traveled by the user as the input of the neural network model and taking the braking times on the path traveled by the corresponding user as a label to obtain a braking time calculation model.
5. The vehicle braking number based navigation method according to claim 4, wherein the neural network model can be a convolutional neural network model, a BP neural network model or a multi-layer perceptron.
6. The vehicle braking number-based navigation method according to claim 4, further comprising collecting a braking position, a braking force and a braking duration of a user at each braking on a driving path;
when the braking frequency calculation model is constructed, the driving habit characteristics of a user and the road condition of a path traveled by the user are used as input, the braking frequency of the user on the path traveled, the braking position, the braking force and the braking duration of each braking are used as labels, and the neural network model is trained to obtain the braking frequency calculation model.
7. The vehicle braking frequency-based navigation method according to claim 6, wherein the driving habit characteristics of the user and the road conditions of the candidate route are input into the braking frequency calculation model for each candidate route, and the recommended braking position, the recommended braking force and the recommended braking duration of each braking are calculated while the braking frequency is calculated through the braking frequency calculation model.
8. A navigation device based on the number of times of braking of a vehicle, comprising:
the acquisition module is used for determining the driving habits of the user based on the historical driving information of the user to obtain the driving habit characteristics of the user;
the model building module is used for building a braking frequency calculation model through a machine learning method based on the driving habit characteristics of the user, the road condition of a path traveled by the user and the braking frequency on the traveled path;
the candidate path determining module is used for receiving the navigation path request and determining a candidate path according to the navigation path request;
the braking frequency calculation module is used for respectively taking characteristic vectors formed by the driving habit characteristics of the user and the road real-time condition characteristics of each to-be-selected route as input, and calculating the braking frequency of each to-be-selected route through the braking frequency calculation model;
and the path recommendation module is used for selecting the path to be selected with the least braking times for recommendation.
9. A storage medium storing a program, wherein the program, when executed by a processor, implements the vehicle braking number-based navigation method according to any one of claims 1 to 7.
10. A computing device comprising a processor and a memory for storing a program executable by the processor, wherein the processor, when executing the program stored in the memory, implements the vehicle braking number based navigation method of any one of claims 1 to 7.
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