CN118960774A - Trip planning method based on vehicle historical navigation data - Google Patents
Trip planning method based on vehicle historical navigation data Download PDFInfo
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Abstract
本发明公开了基于车辆历史导航数据的行程路况规划方法，属于车辆导航技术领域，包括如下步骤：S1：对车辆的历史导航数据进行获取及确认，训练基于车辆历史导航数据的行程路况规划模型，并确定出最优的行程路况规划模型；S2：获取车辆的实时路况数据，基于最优的行程路况规划模型对车辆的实时路况数据进行路况分析预测，生成一个或多个行程路况规划方案。本发明解决了现有的不能基于车辆历史导航数据对车辆行程路况进行最优规划，不能为用户提供高效舒适的行程路况规划路线，降低了车辆行驶安全性的问题。本发明可基于车辆历史导航数据对车辆行程路况进行最优规划，能为用户提供高效舒适的行程路况规划路线，可提升车辆行驶安全性。
The present invention discloses a method for itinerary traffic condition planning based on vehicle historical navigation data, which belongs to the field of vehicle navigation technology and includes the following steps: S1: acquiring and confirming the vehicle's historical navigation data, training a itinerary traffic condition planning model based on the vehicle's historical navigation data, and determining the optimal itinerary traffic condition planning model; S2: acquiring the vehicle's real-time traffic condition data, performing traffic condition analysis and prediction on the vehicle's real-time traffic condition data based on the optimal itinerary traffic condition planning model, and generating one or more itinerary traffic condition planning schemes. The present invention solves the existing problem that the vehicle's itinerary traffic condition cannot be optimally planned based on the vehicle's historical navigation data, and that an efficient and comfortable itinerary traffic condition planning route cannot be provided to users, thereby reducing the vehicle's driving safety. The present invention can optimally plan the vehicle's itinerary traffic condition based on the vehicle's historical navigation data, can provide users with an efficient and comfortable itinerary traffic condition planning route, and can improve the vehicle's driving safety.
Description
Technical Field
The invention relates to the technical field of vehicle navigation, in particular to a journey road condition planning method based on vehicle history navigation data.
Background
With the construction and popularization of intelligent traffic systems, traffic safety becomes an increasingly focused problem. In the traveling process, the vehicle and the driver are affected by various factors, such as traffic jams, road disasters and the like, which can cause the running speed of the vehicle to be reduced, the running time to be prolonged and even traffic accidents to be possibly caused. Therefore, in order to improve driving safety, the driving route of the vehicle needs to be optimized to avoid encountering poor road conditions.
Chinese patent publication No. CN115638801a discloses a system and method for suggesting charging stations for an electric vehicle, the method comprising determining a destination of the electric vehicle and accessing a calendar of a user of the electric vehicle, identifying a calendar scheduled event in the calendar of the user during a travel period to the destination, and selecting the suggested charging station based at least on the destination and the identified calendar scheduled event, generating the suggested charging station at a display for presentation; but this patent suffers from the following drawbacks:
The existing method can not optimally plan the road conditions of the vehicle travel based on the historical navigation data of the vehicle, can not provide efficient and comfortable route planning for the road conditions of the travel for the user, and reduces the running safety of the vehicle.
Disclosure of Invention
The invention aims to provide a journey road condition planning method based on vehicle history navigation data, which can optimally plan vehicle journey road conditions based on the vehicle history navigation data, can provide a journey road condition planning route with high efficiency and comfort for a user, can improve vehicle driving safety and solves the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a journey road condition planning method based on vehicle history navigation data comprises the following steps:
S1: acquiring historical navigation data of a vehicle, confirming the historical navigation data of the vehicle, training a journey road condition planning model based on the historical navigation data of the vehicle according to journey road condition planning requirements based on the historical navigation data of the vehicle, testing and optimizing the journey road condition planning model, and determining an optimal journey road condition planning model;
s2: analyzing the running conditions of the vehicle on different routes at different times, acquiring real-time road condition data of the vehicle, and carrying out road condition analysis and prediction on the real-time road condition data of the vehicle based on an optimal journey road condition planning model to generate one or more journey road condition planning schemes;
s3: and selecting an optimal journey road condition planning scheme, guiding the vehicle to travel based on the optimal journey road condition planning scheme, and monitoring the traveling vehicle in real time so that the vehicle is always on an optimal journey road condition planning route.
Preferably, in the step S1, historical navigation data of the vehicle is acquired, and the following operations are performed:
Acquiring a driving route of a vehicle based on vehicle-mounted equipment, and determining driving route data of the vehicle;
acquiring the running time of the vehicle based on the vehicle-mounted equipment, and determining the running time data of the vehicle;
Acquiring the running speed of the vehicle based on the vehicle-mounted equipment, and determining the running speed data of the vehicle;
acquiring parking points of the vehicle based on the vehicle-mounted equipment, and determining parking point data of the vehicle;
acquiring the passing points of the vehicle based on the vehicle-mounted equipment, and determining the data of the passing points of the vehicle;
Wherein, based on the driving route data, the driving time data, the driving speed data, the parking spot data and the passing point data of the vehicle, the historical navigation data of the vehicle is determined.
Preferably, in the step S1, the following operations are performed to confirm the historical navigation data of the vehicle:
acquiring historical navigation data of a vehicle;
cleaning historical navigation data of a vehicle, including:
consistency checking is carried out on the historical navigation data of the vehicle;
Checking whether the historical navigation data of the vehicle is satisfactory or not according to the reasonable value range and the correlation of each parameter in the historical navigation data of the vehicle;
Removing inconsistent data which exceeds a normal range, is unreasonable in logic or contradicts in the historical navigation data of the vehicle;
processing invalid values and missing values of historical navigation data of the vehicle;
Removing invalid data and missing data which are not valuable for planning the road conditions of the vehicle journey in the historical navigation data;
historical navigation data valuable for planning the road conditions of the vehicle journey is identified.
Preferably, in the step S1, training a route road condition planning model based on vehicle history navigation data, the following operations are performed:
acquiring historical navigation data of a vehicle;
dividing historical navigation data of the vehicle;
determining a journey road condition planning training set and a journey road condition planning testing set;
selecting a neural network model frame suitable for vehicle journey road condition planning according to journey road condition planning requirements based on vehicle historical navigation data;
training the selected neural network model framework suitable for vehicle journey road condition planning based on the journey road condition planning training set;
and training a journey road condition planning model based on the vehicle history navigation data.
Preferably, in the step S1, the test optimization is performed on the road condition planning model, and the following operations are performed:
Acquiring a journey road condition planning model based on vehicle history navigation data;
performing performance test evaluation on the journey road condition planning model based on the vehicle history navigation data based on the journey road condition planning test set;
determining a performance test evaluation report based on the journey road condition planning model;
performing mining analysis on a performance test evaluation report based on the journey road condition planning model;
determining a parameter adjustment optimization scheme based on a journey road condition planning model;
carrying out parameter adjustment optimization on the journey road condition planning model according to a parameter adjustment optimization scheme based on the journey road condition planning model;
And determining an optimal journey road condition planning model.
Preferably, in the step S2, the real-time road condition data of the vehicle is obtained, and the following operations are performed:
The method comprises the steps that road congestion conditions of vehicles on different routes at different times are obtained in real time based on vehicle-mounted equipment, and vehicle road congestion data are determined;
the method comprises the steps that road accident conditions of vehicles on different routes at different times are obtained in real time based on vehicle-mounted equipment, and vehicle road accident data are determined;
the method comprises the steps that road construction conditions of vehicles on different routes at different times are obtained in real time based on vehicle-mounted equipment, and vehicle road construction data are determined;
the real-time road condition data of the vehicle is determined based on road congestion data, road accident data and road construction data of the vehicle.
Preferably, in the step S2, the real-time road condition data of the vehicle is analyzed and predicted, and the following operations are executed:
Acquiring real-time road condition data of a vehicle;
Inputting real-time road condition data of the vehicle into an optimal journey road condition planning model;
Carrying out road condition analysis and prediction on real-time road condition data of the vehicle based on an optimal journey road condition planning model;
and generating one or more journey road condition planning schemes based on the user preference, and displaying the one or more journey road condition planning schemes to the navigation terminal for display.
Preferably, in the step S3, an optimal route road condition planning scheme is selected, and the following operations are executed:
Acquiring one or more travel road condition planning schemes;
The user selects an optimal journey road condition planning scheme from one or more journey road condition planning schemes according to the self requirements;
The navigation terminal performs journey road condition planning based on the optimal journey road condition planning scheme, and guides the vehicle to travel based on the optimal journey road condition planning scheme.
Preferably, in the step S3, the running vehicle is monitored in real time, and the following operations are performed:
When the vehicle runs based on the optimal journey road condition planning scheme, the running vehicle is monitored in real time, and whether the vehicle is always on the optimal journey road condition planning route is judged;
when the vehicle deviates from the optimal route planning route of the journey road condition, real-time early warning is carried out on the vehicle, and journey road condition regulation measures are timely adopted to guide the vehicle to run.
Preferably, when the vehicle deviates from the optimal route planning route of the road condition of journey, the vehicle is pre-warned in real time, and the vehicle is guided to run by timely taking the regulation and control measures of the road condition of journey, comprising:
When a plurality of acquired travel road condition planning schemes are adopted, after an optimal travel road condition planning scheme is selected, carrying out relevance judgment on the travel road condition planning schemes except the optimal travel road condition planning scheme, and acquiring a first relevance value between each travel road condition planning scheme and the optimal travel road condition planning scheme;
When a vehicle deviates from an optimal journey road condition planning route, monitoring deviation parameters between the vehicle and the optimal journey road condition planning route in real time; the deviation parameters comprise a direction deviation angle between the vehicle and the optimal road condition planning route, a linear distance between the vehicle and the optimal road condition planning route and a deviation driving speed;
acquiring a vehicle deviation evaluation parameter according to the deviation parameter between the vehicle and the optimal journey road condition planning route; wherein the vehicle deviation evaluation parameter is obtained by the following formula:
；
Wherein P represents a vehicle deviation evaluation parameter; w 01、w02 and w 03 respectively represent a preset first weight value, a preset second weight value and a preset third weight value; θ represents the direction deviation angle between the current vehicle and the optimal road condition planning route; d represents the linear distance between the vehicle and the optimal road condition planning route; alpha represents a preset basic proportionality constant, and the value range is 0.02-0.08; beta represents a preset speed index for representing the influence of speed, and the value range of the speed index is (-1, 0); gamma represents a preset road condition complexity parameter, and the value range of the road condition complexity parameter is 0.27-2.38; l represents the rate of change of the linear distance deviation; v represents the deviated running speed of the current vehicle;
comparing the vehicle deviation evaluation parameter with a preset vehicle deviation evaluation parameter threshold;
When the vehicle deviation evaluation parameter exceeds a preset vehicle deviation evaluation parameter threshold, judging that the road condition of the journey needs to be regulated and controlled, and acquiring an alternative path for recommendation of a user.
Preferably, the determining of relevance between the plurality of route road condition planning schemes except the optimal route road condition planning scheme, and obtaining a first relevance value between each route road condition planning scheme and the optimal route road condition planning scheme, includes:
When a plurality of acquired travel road condition planning schemes are adopted, after the optimal travel road condition planning scheme is selected, the plurality of travel road condition planning schemes except the optimal travel road condition planning scheme are called to serve as candidate travel road condition planning schemes;
Extracting path parameters of a path corresponding to the candidate journey road condition planning scheme and a path corresponding to the optimal journey road condition planning scheme; the path parameters comprise path travel distance, coincident path distance and traffic light quantity;
Obtaining a first association degree value between each travel road condition planning scheme and the optimal travel road condition planning scheme by using path parameters of a path corresponding to the candidate travel road condition planning scheme and a path corresponding to the optimal travel road condition planning scheme;
the first association degree value between each travel road condition planning scheme and the optimal travel road condition planning scheme is obtained through the following formula:
；
Wherein G 01 represents a first relevancy value; x 01 represents a preset first weight parameter value; x 02 represents a preset second weight parameter value; d c represents the overlapping path distance between the path corresponding to the candidate route road condition planning scheme and the path corresponding to the optimal route road condition planning scheme; d z represents the path travel distance of the path corresponding to the candidate travel road condition planning scheme; d yz represents the path travel distance of the path corresponding to the optimal path road condition planning scheme; n z represents the number of traffic lights of the route corresponding to the candidate journey road condition planning scheme; n yz represents the number of traffic lights of the route corresponding to the optimal route road condition planning scheme.
Preferably, when the vehicle deviation estimation parameter exceeds a preset vehicle deviation estimation parameter threshold, determining that the road condition of the journey needs to be regulated and controlled, and obtaining an alternative path for the user to recommend includes:
When the vehicle deviation evaluation parameter exceeds a preset vehicle deviation evaluation parameter threshold, retrieving a path association parameter between the current vehicle driving path and the candidate journey road condition planning scheme;
The path association parameters comprise a coincidence path distance and a straight line distance between a current vehicle driving path and a path corresponding to the candidate journey road condition planning scheme, and a path journey distance between the current vehicle driving path and the path corresponding to the candidate journey road condition planning scheme;
Obtaining a second association degree value between the current vehicle driving path and the candidate journey road condition planning scheme by using the path association parameter between the current vehicle driving path and the candidate journey road condition planning scheme;
The second association degree value is obtained through the following formula:
；
Wherein G 02 represents a second relevancy value; x 01 represents a preset first weight parameter value; x 02 represents a preset second weight parameter value; d cs represents the superposition path distance between the current vehicle driving path and the path corresponding to the candidate journey road condition planning scheme; d z represents the path travel distance of the path corresponding to the candidate travel road condition planning scheme; d yz represents the path travel distance of the path corresponding to the optimal path road condition planning scheme; d f represents the linear distance between the current vehicle driving path and the path corresponding to the candidate journey road condition planning scheme;
acquiring a comprehensive relevance value corresponding to each candidate journey road condition planning scheme by using the first relevance value and the second relevance value; the comprehensive relevance value is obtained through the following formula:
；
Wherein G represents a comprehensive relevance value, and G 01 represents a first relevance value; g 02 denotes a second relevancy value;
Taking the candidate journey road condition planning scheme corresponding to the maximum value of the comprehensive association degree value as an alternative path;
And recommending the alternative path to a user.
Compared with the prior art, the invention has the beneficial effects that:
According to the invention, the historical navigation data of the vehicle is confirmed by acquiring the historical navigation data of the vehicle, the journey road condition planning model based on the historical navigation data of the vehicle is trained according to the journey road condition planning requirement based on the historical navigation data of the vehicle, the journey road condition planning model is tested and optimized, the optimal journey road condition planning model is determined, the driving condition of the vehicle on different time and different routes is analyzed, the real-time road condition data of the vehicle is acquired, the real-time road condition data of the vehicle is analyzed and predicted based on the optimal journey road condition planning model, one or more journey road condition planning schemes are generated and displayed on a navigation terminal, the optimal journey road condition planning scheme is selected by a user, the vehicle is guided to drive based on the optimal journey road condition planning scheme, the driving vehicle is monitored in real time, the vehicle is always on the optimal journey road condition planning route, the journey road condition of the vehicle can be planned based on the historical navigation data of the vehicle, the efficient and comfortable journey road condition planning route can be provided for a user, and the driving safety of the vehicle can be improved.
Drawings
Fig. 1 is a flowchart of a journey road condition planning method based on vehicle history navigation data according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to solve the problem that the existing vehicle travel road condition cannot be optimally planned based on the vehicle history navigation data, and a high-efficiency and comfortable travel road condition planning route cannot be provided for a user, and the vehicle driving safety is reduced, referring to fig. 1, the following technical scheme is provided in this embodiment:
a journey road condition planning method based on vehicle history navigation data comprises the following steps:
S1: acquiring historical navigation data of a vehicle, confirming the historical navigation data of the vehicle, training a journey road condition planning model based on the historical navigation data of the vehicle according to journey road condition planning requirements based on the historical navigation data of the vehicle, testing and optimizing the journey road condition planning model, and determining an optimal journey road condition planning model;
In the present embodiment, history navigation data of a vehicle is acquired, and the following operations are performed:
Acquiring a driving route of a vehicle based on vehicle-mounted equipment, and determining driving route data of the vehicle;
acquiring the running time of the vehicle based on the vehicle-mounted equipment, and determining the running time data of the vehicle;
Acquiring the running speed of the vehicle based on the vehicle-mounted equipment, and determining the running speed data of the vehicle;
acquiring parking points of the vehicle based on the vehicle-mounted equipment, and determining parking point data of the vehicle;
acquiring the passing points of the vehicle based on the vehicle-mounted equipment, and determining the data of the passing points of the vehicle;
Wherein, based on the driving route data, the driving time data, the driving speed data, the parking spot data and the passing point data of the vehicle, the historical navigation data of the vehicle is determined.
It should be noted that, by collecting the driving route data, the driving time data, the driving speed data, the parking spot data and the passing point data of the vehicle, the history navigation data of the vehicle can be determined, so that the road condition planning model of the journey based on the history navigation data of the vehicle can be conveniently constructed.
In the present embodiment, the historical navigation data of the vehicle is confirmed, and the following operations are performed:
acquiring historical navigation data of a vehicle;
cleaning historical navigation data of a vehicle, including:
consistency checking is carried out on the historical navigation data of the vehicle;
Checking whether the historical navigation data of the vehicle is satisfactory or not according to the reasonable value range and the correlation of each parameter in the historical navigation data of the vehicle;
Removing inconsistent data which exceeds a normal range, is unreasonable in logic or contradicts in the historical navigation data of the vehicle;
processing invalid values and missing values of historical navigation data of the vehicle;
Removing invalid data and missing data which are not valuable for planning the road conditions of the vehicle journey in the historical navigation data;
historical navigation data valuable for planning the road conditions of the vehicle journey is identified.
By confirming the historical navigation data of the vehicle, inconsistent data, invalid data and missing data in the historical navigation data of the vehicle can be removed, further historical navigation data valuable for vehicle journey road condition planning is determined, and a journey road condition planning model based on the historical navigation data of the vehicle is convenient to construct.
In this embodiment, a journey road condition planning model based on vehicle history navigation data is trained, and the following operations are performed:
acquiring historical navigation data of a vehicle;
dividing historical navigation data of the vehicle;
determining a journey road condition planning training set and a journey road condition planning testing set;
selecting a neural network model frame suitable for vehicle journey road condition planning according to journey road condition planning requirements based on vehicle historical navigation data;
training the selected neural network model framework suitable for vehicle journey road condition planning based on the journey road condition planning training set;
and training a journey road condition planning model based on the vehicle history navigation data.
It should be noted that, by dividing the historical navigation data of the vehicle, the journey road condition planning training set is based on the journey road condition planning training set, and the journey road condition planning model based on the historical navigation data of the vehicle is trained.
In this embodiment, the test optimization is performed on the road condition planning model of the journey, and the following operations are performed:
Acquiring a journey road condition planning model based on vehicle history navigation data;
performing performance test evaluation on the journey road condition planning model based on the vehicle history navigation data based on the journey road condition planning test set;
determining a performance test evaluation report based on the journey road condition planning model;
performing mining analysis on a performance test evaluation report based on the journey road condition planning model;
determining a parameter adjustment optimization scheme based on a journey road condition planning model;
carrying out parameter adjustment optimization on the journey road condition planning model according to a parameter adjustment optimization scheme based on the journey road condition planning model;
And determining an optimal journey road condition planning model.
It should be noted that, by performing performance test evaluation and parameter adjustment optimization on the road condition planning model, an optimal road condition planning model can be determined.
S2: analyzing the running conditions of the vehicle on different routes at different times, acquiring real-time road condition data of the vehicle, and carrying out road condition analysis and prediction on the real-time road condition data of the vehicle based on an optimal journey road condition planning model to generate one or more journey road condition planning schemes;
in this embodiment, real-time road condition data of a vehicle is acquired, and the following operations are performed:
The method comprises the steps that road congestion conditions of vehicles on different routes at different times are obtained in real time based on vehicle-mounted equipment, and vehicle road congestion data are determined;
the method comprises the steps that road accident conditions of vehicles on different routes at different times are obtained in real time based on vehicle-mounted equipment, and vehicle road accident data are determined;
the method comprises the steps that road construction conditions of vehicles on different routes at different times are obtained in real time based on vehicle-mounted equipment, and vehicle road construction data are determined;
the real-time road condition data of the vehicle is determined based on road congestion data, road accident data and road construction data of the vehicle.
In this embodiment, the real-time road condition data of the vehicle is analyzed and predicted, and the following operations are performed:
Acquiring real-time road condition data of a vehicle;
Inputting real-time road condition data of the vehicle into an optimal journey road condition planning model;
Carrying out road condition analysis and prediction on real-time road condition data of the vehicle based on an optimal journey road condition planning model;
and generating one or more journey road condition planning schemes based on the user preference, and displaying the one or more journey road condition planning schemes to the navigation terminal for display.
It should be noted that, acquiring real-time road condition data of the vehicle, performing road condition analysis and prediction on the real-time road condition data of the vehicle based on an optimal journey road condition planning model, generating one or more journey road condition planning schemes for selection by a user, and selecting the optimal journey road condition planning scheme from the one or more journey road condition planning schemes according to self requirements by the user, so as to guide the vehicle to travel based on the optimal journey road condition planning scheme.
S3: and selecting an optimal journey road condition planning scheme, guiding the vehicle to travel based on the optimal journey road condition planning scheme, and monitoring the traveling vehicle in real time so that the vehicle is always on an optimal journey road condition planning route.
In this embodiment, an optimal route road condition planning scheme is selected, and the following operations are performed:
Acquiring one or more travel road condition planning schemes;
The user selects an optimal journey road condition planning scheme from one or more journey road condition planning schemes according to the self requirements;
The navigation terminal performs journey road condition planning based on the optimal journey road condition planning scheme, and guides the vehicle to travel based on the optimal journey road condition planning scheme.
In the present embodiment, the traveling vehicle is monitored in real time, and the following operations are performed:
When the vehicle runs based on the optimal journey road condition planning scheme, the running vehicle is monitored in real time, and whether the vehicle is always on the optimal journey road condition planning route is judged;
when the vehicle deviates from the optimal route planning route of the journey road condition, real-time early warning is carried out on the vehicle, and journey road condition regulation measures are timely adopted to guide the vehicle to run.
Therefore, the historical navigation data of the vehicle is confirmed by acquiring the historical navigation data of the vehicle, the journey road condition planning model based on the historical navigation data of the vehicle is trained according to the journey road condition planning requirement based on the historical navigation data of the vehicle, the journey road condition planning model is tested and optimized, the optimal journey road condition planning model is determined, the driving condition of the vehicle on different time and different routes is analyzed, the real-time road condition data of the vehicle is acquired, the real-time road condition data of the vehicle is analyzed and predicted based on the optimal journey road condition planning model, one or more journey road condition planning schemes are generated and displayed to a navigation terminal, the optimal journey road condition planning scheme is selected by a user, the vehicle is guided to drive based on the optimal journey road condition planning scheme, the driving vehicle is monitored in real time, the vehicle is always on the optimal journey road condition planning route, the journey road condition of the vehicle can be planned based on the historical navigation data of the vehicle, the efficient and comfortable journey road condition planning route can be provided for a user, and the driving safety of the vehicle can be improved.
Specifically, when the vehicle deviates from the optimal route planning route of the road condition of journey, the vehicle is pre-warned in real time, and the vehicle is guided to run by timely taking the regulation and control measures of the road condition of journey, comprising:
When a plurality of acquired travel road condition planning schemes are adopted, after an optimal travel road condition planning scheme is selected, carrying out relevance judgment on the travel road condition planning schemes except the optimal travel road condition planning scheme, and acquiring a first relevance value between each travel road condition planning scheme and the optimal travel road condition planning scheme;
When a vehicle deviates from an optimal journey road condition planning route, monitoring deviation parameters between the vehicle and the optimal journey road condition planning route in real time; the deviation parameters comprise a direction deviation angle between the vehicle and the optimal road condition planning route, a linear distance between the vehicle and the optimal road condition planning route and a deviation driving speed;
acquiring a vehicle deviation evaluation parameter according to the deviation parameter between the vehicle and the optimal journey road condition planning route; wherein the vehicle deviation evaluation parameter is obtained by the following formula:
；
Wherein P represents a vehicle deviation evaluation parameter; w 01、w02 and w 03 respectively represent a preset first weight value, a preset second weight value and a preset third weight value; θ represents the direction deviation angle between the current vehicle and the optimal road condition planning route; d represents the linear distance between the vehicle and the optimal road condition planning route; alpha represents a preset basic proportionality constant, and the value range is 0.02-0.08; beta represents a preset speed index for representing the influence of speed, and the value range of the speed index is (-1, 0); gamma represents a preset road condition complexity parameter, and the value range of the road condition complexity parameter is 0.27-2.38; l represents the rate of change of the linear distance deviation; v represents the deviated running speed of the current vehicle;
comparing the vehicle deviation evaluation parameter with a preset vehicle deviation evaluation parameter threshold;
When the vehicle deviation evaluation parameter exceeds a preset vehicle deviation evaluation parameter threshold, judging that the road condition of the journey needs to be regulated and controlled, and acquiring an alternative path for recommendation of a user.
The technical effects of the technical scheme are as follows: by monitoring the deviation situation of the vehicle and the planned route of the optimal journey road condition in real time, the system can rapidly find the deviation behavior of the vehicle and immediately trigger an early warning mechanism. The real-time performance ensures that a driver or an automatic driving system can quickly obtain feedback, thereby having the opportunity to correct the driving route in time and avoiding further deviation or entering an unfavorable road condition area. A plurality of parameters such as a direction deviation angle, a linear distance, a deviation driving speed and the like between the vehicle and the optimal route are comprehensively considered, and a comprehensive vehicle deviation evaluation parameter is calculated by combining a preset weight value, a basic proportionality constant, a speed index and road condition complexity parameters. This multi-dimensional evaluation improves the accuracy and scientificity of the deviation determination, enabling the system to more accurately determine the severity and potential risk of vehicle deviation.
When the system judges that the road condition of the journey is required to be regulated, the system can automatically acquire and recommend alternative paths to the user. The intelligent path recommending function is not only beneficial to users to quickly find alternative routes, but also capable of relieving the driving inconvenience caused by road congestion, construction or other emergencies to a certain extent, and improving the user experience and the travel efficiency. Through real-time early warning and timely regulation, the technical scheme can avoid safety accidents caused by vehicles entering dangerous road sections by mistake or encountering unfavorable traffic conditions to a great extent. Meanwhile, the intelligent path recommending function also enhances the flexibility of driving and the capability of coping with emergency, and further improves the safety and stability of driving. The optimal scheme is selected from the plurality of road condition planning schemes, relevance judgment is carried out on other schemes, and the system is helped to more comprehensively know the current traffic condition, so that more scientific and reasonable scheduling decisions are made. The utilization of traffic resources is optimized, congestion and waste are reduced, and the operation efficiency of the whole traffic system is improved.
In summary, according to the technical scheme, the safety, stability and efficiency of vehicle running are effectively improved through means of real-time early warning, multidimensional deviation evaluation, intelligent path recommendation and the like, and meanwhile, the utilization of traffic resources is optimized.
Specifically, performing relevance determination on a plurality of travel road condition planning schemes except for an optimal travel road condition planning scheme, and obtaining a first relevance value between each travel road condition planning scheme and the optimal travel road condition planning scheme includes:
When a plurality of acquired travel road condition planning schemes are adopted, after the optimal travel road condition planning scheme is selected, the plurality of travel road condition planning schemes except the optimal travel road condition planning scheme are called to serve as candidate travel road condition planning schemes;
Extracting path parameters of a path corresponding to the candidate journey road condition planning scheme and a path corresponding to the optimal journey road condition planning scheme; the path parameters comprise path travel distance, coincident path distance and traffic light quantity;
Obtaining a first association degree value between each travel road condition planning scheme and the optimal travel road condition planning scheme by using path parameters of a path corresponding to the candidate travel road condition planning scheme and a path corresponding to the optimal travel road condition planning scheme;
the first association degree value between each travel road condition planning scheme and the optimal travel road condition planning scheme is obtained through the following formula:
；
Wherein G 01 represents a first relevancy value; x 01 represents a preset first weight parameter value; x 02 represents a preset second weight parameter value; d c represents the overlapping path distance between the path corresponding to the candidate route road condition planning scheme and the path corresponding to the optimal route road condition planning scheme; d z represents the path travel distance of the path corresponding to the candidate travel road condition planning scheme; d yz represents the path travel distance of the path corresponding to the optimal path road condition planning scheme; n z represents the number of traffic lights of the route corresponding to the candidate journey road condition planning scheme; n yz represents the number of traffic lights of the route corresponding to the optimal route road condition planning scheme.
The technical effects of the technical scheme are as follows: by performing relevance determination on a plurality of travel road condition planning schemes other than the optimal travel road condition planning scheme, the system can more comprehensively understand the characteristics of the alternative scheme and the similarity or difference between the alternative scheme and the optimal scheme. When the driving route needs to be adjusted, an alternative scheme which has higher association degree with the optimal scheme and is relatively better is quickly selected, so that the continuity and the efficiency of the driving route are maintained. By extracting the path parameters (such as the path travel distance, the overlapping path distance and the number of traffic lights) between the candidate trip road condition planning scheme and the optimal scheme and calculating the first relevance value by using the parameters, the system can evaluate the advantages and disadvantages of the alternative scheme based on the quantized index. The data-based decision mode improves the scientificity and accuracy of decisions and reduces uncertainty caused by subjective judgment.
In a real-time traffic environment, the road condition change is normal. Through carrying out relevance judgment on a plurality of travel road condition planning schemes in advance and storing corresponding first relevance values, the system can quickly call the information when needed, and a flexible path adjustment scheme is provided for vehicle running. This enhances the adaptability and the coping ability of the system to complex traffic environments. When the vehicle needs to deviate from the optimal driving route for some reason, the system can rapidly recommend an alternative route with higher association degree with the optimal scheme. The intelligent path recommending function not only reduces the decision burden of the user, but also improves the running stability and continuity, thereby improving the overall travel experience of the user. By comprehensively considering factors such as path travel distance, overlapping path distance, traffic light quantity and the like, the system can evaluate the advantages and disadvantages of different road condition planning schemes accurately. The vehicles are guided to select a more efficient and smooth driving path, so that the congestion and waiting time are reduced, and the reasonable utilization of traffic resources and the improvement of the overall traffic efficiency are promoted.
In summary, according to the technical scheme, relevance judgment is carried out on the plurality of road condition planning schemes, and the advantages and disadvantages of the alternative schemes are evaluated based on the quantized indexes, so that the scientificity and the accuracy of decision making are improved, the flexibility and the adaptability of the system are enhanced, the user experience is improved, and the reasonable utilization of traffic resources is promoted.
Specifically, when the vehicle deviation evaluation parameter exceeds a preset vehicle deviation evaluation parameter threshold, determining that the road condition of the journey needs to be regulated and controlled, and acquiring an alternative path for recommending to the user comprises:
When the vehicle deviation evaluation parameter exceeds a preset vehicle deviation evaluation parameter threshold, retrieving a path association parameter between the current vehicle driving path and the candidate journey road condition planning scheme;
The path association parameters comprise a coincidence path distance and a straight line distance between a current vehicle driving path and a path corresponding to the candidate journey road condition planning scheme, and a path journey distance between the current vehicle driving path and the path corresponding to the candidate journey road condition planning scheme;
Obtaining a second association degree value between the current vehicle driving path and the candidate journey road condition planning scheme by using the path association parameter between the current vehicle driving path and the candidate journey road condition planning scheme;
The second association degree value is obtained through the following formula:
；
Wherein G 02 represents a second relevancy value; x 01 represents a preset first weight parameter value; x 02 represents a preset second weight parameter value; d cs represents the superposition path distance between the current vehicle driving path and the path corresponding to the candidate journey road condition planning scheme; d z represents the path travel distance of the path corresponding to the candidate travel road condition planning scheme; d yz represents the path travel distance of the path corresponding to the optimal path road condition planning scheme; d f represents the linear distance between the current vehicle driving path and the path corresponding to the candidate journey road condition planning scheme;
acquiring a comprehensive relevance value corresponding to each candidate journey road condition planning scheme by using the first relevance value and the second relevance value; the comprehensive relevance value is obtained through the following formula:
；
Wherein G represents a comprehensive relevance value, and G 01 represents a first relevance value; g 02 denotes a second relevancy value;
Taking the candidate journey road condition planning scheme corresponding to the maximum value of the comprehensive association degree value as an alternative path;
And recommending the alternative path to a user.
The technical effects of the technical scheme are as follows: by monitoring whether the vehicle deviates from a preset evaluation parameter threshold in real time, once the deviation is detected, a travel road condition regulation mechanism is immediately triggered. And the potential driving risks such as false entering dangerous road sections, traffic jam areas and the like are found and corrected in time, so that the safety of drivers and passengers is improved. After the deviation of the vehicle is detected, the system not only considers the coincident path distance and the straight line distance between the current vehicle driving path and the candidate journey road condition planning scheme, but also introduces the path journey distance as an evaluation factor, and the advantages and disadvantages of the candidate paths can be evaluated more comprehensively by comprehensively calculating the second association degree value. And a more reasonable and efficient alternative path is provided for the driver, and the driving route is optimized.
The system not only recommends alternative paths based on the current position and the driving state, but also comprehensively evaluates the first relevance value (although not described in detail in the original text, the multi-dimensional evaluation such as road condition, time, oil consumption and the like is usually involved) and the second relevance value, so that the recommended paths are ensured to be in line with the user preference and have practical feasibility. The personalized recommendation mode can remarkably improve the driving experience and satisfaction of the user. The whole scheme relies on advanced navigation, positioning and data processing technologies, and can analyze the running state of the vehicle and road condition information in real time and make intelligent decisions according to the running state of the vehicle and the road condition information. The intelligent level of the vehicle is improved, and the driving process is more convenient and efficient. Meanwhile, by optimizing the driving route, unnecessary detouring and waiting time are reduced, the fuel consumption and emission of the vehicle can be reduced to a certain extent, and the method has positive significance for environmental protection, energy conservation and emission reduction.
Meanwhile, the system can evaluate the matching degree of the candidate path and the current vehicle running state more accurately by comprehensively considering the multi-path correlation parameters (such as the coincident path distance, the straight line distance and the path travel distance) between the current vehicle running path and the candidate travel road condition planning scheme and calculating the second correlation value according to the multi-path correlation parameters. Then, the candidate route closest to the current vehicle running state is selected as the candidate route for recommendation by comprehensively processing the first association degree value (based on the association degree of the candidate route and the optimal route) and the second association degree value. The accurate recommendation mechanism can remarkably improve the acceptance and satisfaction of users.
When the vehicle deviation evaluation parameter exceeds a preset threshold, the system can quickly respond and immediately start the screening and recommending process of the alternative path. The timely travel regulation and control can effectively prevent the vehicle from deviating from the optimal path, reduce unnecessary travel time and distance and improve the overall travel efficiency. By intelligently recommending alternative paths which are highly matched with the current running state of the user, the system can provide more personalized and convenient navigation service for the user. The user can obtain the optimal running proposal without searching for the alternative route by himself, thereby greatly improving the travel experience and satisfaction of the user. By guiding the vehicle to select a more reasonable and efficient driving path, the system can relieve traffic jam to a certain extent and optimize traffic flow distribution. The running efficiency of the whole traffic system is improved, and the energy consumption and the environmental pollution are reduced. According to the technical scheme, intelligent means such as relevance calculation and path relevance parameter extraction are introduced, so that real-time monitoring and accurate regulation and control of the running state of the vehicle are realized. The response speed and the accuracy of the system are improved, and the intelligent level and the autonomous learning ability of the system are enhanced.
In summary, according to the technical scheme, through real-time monitoring, intelligent evaluation and personalized recommendation, driving safety and user experience are effectively improved, and meanwhile, realization of vehicle intellectualization and energy conservation and emission reduction targets is promoted. According to the technical scheme, intelligent management and regulation of the running state of the vehicle are realized by accurately recommending alternative paths, improving timeliness of travel regulation and control, enhancing user experience, optimizing traffic flow, improving the intelligent level of a system and the like.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations may be made to the foregoing embodiments without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents.
Claims (8)
1. The road condition planning method based on the vehicle history navigation data is characterized by comprising the following steps:
S1: acquiring historical navigation data of a vehicle, confirming the historical navigation data of the vehicle, training a journey road condition planning model based on the historical navigation data of the vehicle according to journey road condition planning requirements based on the historical navigation data of the vehicle, testing and optimizing the journey road condition planning model, and determining an optimal journey road condition planning model;
s2: analyzing the running conditions of the vehicle on different routes at different times, acquiring real-time road condition data of the vehicle, and carrying out road condition analysis and prediction on the real-time road condition data of the vehicle based on an optimal journey road condition planning model to generate one or more journey road condition planning schemes;
S3: selecting an optimal journey road condition planning scheme, guiding the vehicle to travel based on the optimal journey road condition planning scheme, and monitoring the traveling vehicle in real time so that the vehicle is always on an optimal journey road condition planning route;
in the step S3, the running vehicle is monitored in real time, and the following operations are executed:
When the vehicle runs based on the optimal journey road condition planning scheme, the running vehicle is monitored in real time, and whether the vehicle is always on the optimal journey road condition planning route is judged;
when the vehicle deviates from the optimal route planning route of the road condition of the journey, the vehicle is pre-warned in real time, and the road condition regulation and control measures of the journey are timely adopted to guide the vehicle to run;
When the vehicle deviates from the optimal route planning route of the road condition of journey, the vehicle is pre-warned in real time, and journey road condition regulation measures are timely taken to guide the vehicle to run, comprising:
When a plurality of acquired travel road condition planning schemes are adopted, after an optimal travel road condition planning scheme is selected, carrying out relevance judgment on the travel road condition planning schemes except the optimal travel road condition planning scheme, and acquiring a first relevance value between each travel road condition planning scheme and the optimal travel road condition planning scheme;
When a vehicle deviates from an optimal journey road condition planning route, monitoring deviation parameters between the vehicle and the optimal journey road condition planning route in real time; the deviation parameters comprise a direction deviation angle between the vehicle and the optimal road condition planning route, a linear distance between the vehicle and the optimal road condition planning route and a deviation driving speed;
acquiring a vehicle deviation evaluation parameter according to the deviation parameter between the vehicle and the optimal journey road condition planning route; wherein the vehicle deviation evaluation parameter is obtained by the following formula:
；
Wherein P represents a vehicle deviation evaluation parameter; w 01、w02 and w 03 respectively represent a preset first weight value, a preset second weight value and a preset third weight value; θ represents the direction deviation angle between the current vehicle and the optimal road condition planning route; d represents the linear distance between the vehicle and the optimal road condition planning route; alpha represents a preset basic proportionality constant, and the value range is 0.02-0.08; beta represents a preset speed index for representing the influence of speed, and the value range of the speed index is (-1, 0); gamma represents a preset road condition complexity parameter, and the value range of the road condition complexity parameter is 0.27-2.38; l represents the rate of change of the linear distance deviation; v represents the deviated running speed of the current vehicle;
comparing the vehicle deviation evaluation parameter with a preset vehicle deviation evaluation parameter threshold;
When the vehicle deviation evaluation parameter exceeds a preset vehicle deviation evaluation parameter threshold, judging that the road condition of the journey needs to be regulated and controlled, and acquiring an alternative path for recommendation of a user.
2. The method for planning a road condition of a journey based on historical navigation data of a vehicle according to claim 1, wherein in S1, the historical navigation data of the vehicle is obtained, and the following operations are performed:
Acquiring a driving route of a vehicle based on vehicle-mounted equipment, and determining driving route data of the vehicle;
acquiring the running time of the vehicle based on the vehicle-mounted equipment, and determining the running time data of the vehicle;
Acquiring the running speed of the vehicle based on the vehicle-mounted equipment, and determining the running speed data of the vehicle;
acquiring parking points of the vehicle based on the vehicle-mounted equipment, and determining parking point data of the vehicle;
acquiring the passing points of the vehicle based on the vehicle-mounted equipment, and determining the data of the passing points of the vehicle;
Wherein, based on the driving route data, the driving time data, the driving speed data, the parking spot data and the passing point data of the vehicle, the historical navigation data of the vehicle is determined.
3. The method for planning a road condition of a journey based on historical navigation data of a vehicle according to claim 2, wherein in S1, the historical navigation data of the vehicle is confirmed, and the following operations are performed:
acquiring historical navigation data of a vehicle;
cleaning historical navigation data of a vehicle, including:
consistency checking is carried out on the historical navigation data of the vehicle;
Checking whether the historical navigation data of the vehicle is satisfactory or not according to the reasonable value range and the correlation of each parameter in the historical navigation data of the vehicle;
Removing inconsistent data which exceeds a normal range, is unreasonable in logic or contradicts in the historical navigation data of the vehicle;
processing invalid values and missing values of historical navigation data of the vehicle;
Removing invalid data and missing data which are not valuable for planning the road conditions of the vehicle journey in the historical navigation data;
historical navigation data valuable for planning the road conditions of the vehicle journey is identified.
4. The method for planning road conditions on a journey based on vehicle history navigation data according to claim 3, wherein in S1, training a road condition planning model on a journey based on vehicle history navigation data, performing test optimization on the road condition planning model, and performing the following operations:
acquiring historical navigation data of a vehicle;
dividing historical navigation data of the vehicle;
determining a journey road condition planning training set and a journey road condition planning testing set;
selecting a neural network model frame suitable for vehicle journey road condition planning according to journey road condition planning requirements based on vehicle historical navigation data;
training the selected neural network model framework suitable for vehicle journey road condition planning based on the journey road condition planning training set;
Training a journey road condition planning model based on vehicle historical navigation data;
performing performance test evaluation on the journey road condition planning model based on the vehicle history navigation data based on the journey road condition planning test set;
determining a performance test evaluation report based on the journey road condition planning model;
performing mining analysis on a performance test evaluation report based on the journey road condition planning model;
determining a parameter adjustment optimization scheme based on a journey road condition planning model;
carrying out parameter adjustment optimization on the journey road condition planning model according to a parameter adjustment optimization scheme based on the journey road condition planning model;
And determining an optimal journey road condition planning model.
5. The method for planning road conditions on a journey based on vehicle history navigation data according to claim 4, wherein in S2, real-time road condition data of a vehicle is obtained, road condition analysis and prediction are performed on the real-time road condition data of the vehicle, and the following operations are performed:
The method comprises the steps that road congestion conditions of vehicles on different routes at different times are obtained in real time based on vehicle-mounted equipment, and vehicle road congestion data are determined;
the method comprises the steps that road accident conditions of vehicles on different routes at different times are obtained in real time based on vehicle-mounted equipment, and vehicle road accident data are determined;
the method comprises the steps that road construction conditions of vehicles on different routes at different times are obtained in real time based on vehicle-mounted equipment, and vehicle road construction data are determined;
The method comprises the steps of determining real-time road condition data of a vehicle based on road congestion data, road accident data and road construction data of the vehicle;
Inputting real-time road condition data of the vehicle into an optimal journey road condition planning model;
Carrying out road condition analysis and prediction on real-time road condition data of the vehicle based on an optimal journey road condition planning model;
and generating one or more journey road condition planning schemes based on the user preference, and displaying the one or more journey road condition planning schemes to the navigation terminal for display.
6. The route road condition planning method based on vehicle history navigation data according to claim 5, wherein in S3, an optimal route road condition planning scheme is selected, and the following operations are performed:
Acquiring one or more travel road condition planning schemes;
The user selects an optimal journey road condition planning scheme from one or more journey road condition planning schemes according to the self requirements;
The navigation terminal performs journey road condition planning based on the optimal journey road condition planning scheme, and guides the vehicle to travel based on the optimal journey road condition planning scheme.
7. The method for planning road conditions on a journey route based on vehicle history navigation data according to claim 6, wherein performing relevance determination on a plurality of road conditions on the journey route other than the optimal road conditions on the journey route, obtaining a first relevance value between each road conditions on the journey route and the optimal road conditions on the journey route, comprises:
When a plurality of acquired travel road condition planning schemes are adopted, after the optimal travel road condition planning scheme is selected, the plurality of travel road condition planning schemes except the optimal travel road condition planning scheme are called to serve as candidate travel road condition planning schemes;
Extracting path parameters of a path corresponding to the candidate journey road condition planning scheme and a path corresponding to the optimal journey road condition planning scheme; the path parameters comprise path travel distance, coincident path distance and traffic light quantity;
Obtaining a first association degree value between each travel road condition planning scheme and the optimal travel road condition planning scheme by using path parameters of a path corresponding to the candidate travel road condition planning scheme and a path corresponding to the optimal travel road condition planning scheme;
the first association degree value between each travel road condition planning scheme and the optimal travel road condition planning scheme is obtained through the following formula:
；
Wherein G 01 represents a first relevancy value; x 01 represents a preset first weight parameter value; x 02 represents a preset second weight parameter value; d c represents the overlapping path distance between the path corresponding to the candidate route road condition planning scheme and the path corresponding to the optimal route road condition planning scheme; d z represents the path travel distance of the path corresponding to the candidate travel road condition planning scheme; d yz represents the path travel distance of the path corresponding to the optimal path road condition planning scheme; n z represents the number of traffic lights of the route corresponding to the candidate journey road condition planning scheme; n yz represents the number of traffic lights of the route corresponding to the optimal route road condition planning scheme.
8. The method for planning road conditions on a journey based on vehicle history navigation data according to claim 7, wherein when the vehicle deviation evaluation parameter exceeds a preset vehicle deviation evaluation parameter threshold, determining that road conditions on journey are required to be regulated, and acquiring an alternative path for recommendation of a user comprises:
When the vehicle deviation evaluation parameter exceeds a preset vehicle deviation evaluation parameter threshold, retrieving a path association parameter between the current vehicle driving path and the candidate journey road condition planning scheme;
The path association parameters comprise a coincidence path distance and a straight line distance between a current vehicle driving path and a path corresponding to the candidate journey road condition planning scheme, and a path journey distance between the current vehicle driving path and the path corresponding to the candidate journey road condition planning scheme;
Obtaining a second association degree value between the current vehicle driving path and the candidate journey road condition planning scheme by using the path association parameter between the current vehicle driving path and the candidate journey road condition planning scheme;
The second association degree value is obtained through the following formula:
；
Wherein G 02 represents a second relevancy value; x 01 represents a preset first weight parameter value; x 02 represents a preset second weight parameter value; d cs represents the superposition path distance between the current vehicle driving path and the path corresponding to the candidate journey road condition planning scheme; d z represents the path travel distance of the path corresponding to the candidate travel road condition planning scheme; d yz represents the path travel distance of the path corresponding to the optimal path road condition planning scheme; d f represents the linear distance between the current vehicle driving path and the path corresponding to the candidate journey road condition planning scheme;
acquiring a comprehensive relevance value corresponding to each candidate journey road condition planning scheme by using the first relevance value and the second relevance value; the comprehensive relevance value is obtained through the following formula:
；
Wherein G represents a comprehensive relevance value, and G 01 represents a first relevance value; g 02 denotes a second relevancy value;
Taking the candidate journey road condition planning scheme corresponding to the maximum value of the comprehensive association degree value as an alternative path;
And recommending the alternative path to a user.
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