Driving path optimization method and system based on autonomous vehicle

Abstract

The invention relates to the technical field of automatic driving, and particularly discloses a driving path optimization method and system based on an automatic driving vehicle, and the method comprises the steps: obtaining historical driving path information, and inputting preference standard data according to the historical driving path information; obtaining a destination position, determining a possible driving path according to the destination position, and determining an initial driving path according to the preference standard data; acquiring vehicle information on the possible driving path in real time according to the destination position; updating and optimizing the driving path in real time according to the preference standard data and the vehicle information on the possible driving path; according to the method, the preference standard data of the vehicle owner user is input, so that selection can be performed according to the preference standard data of the user when different possible paths can be selected in the driving process, and then, the path can be adjusted by obtaining the vehicle information on the path, so that higher passing efficiency can be obtained.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a driving path optimization method and system based on an autonomous driving vehicle. Background Art

[0002] With the continuous development of artificial intelligence, computer vision and sensor technology, autonomous driving is becoming more and more intelligent. Autonomous driving technology is an important part of future intelligent transportation, and its development will have a profound impact on people's travel methods, traffic patterns and urban development. Therefore, autonomous driving is the development trend of future intelligent transportation. Current autonomous driving systems usually plan routes based on pre-acquired map data and road condition data, such as traffic volume, distance, road speed limit and other factors to find the best route; however, the best route itself is relative, and users have different habits and preferences, so it cannot be set up specifically for different users. Secondly, the actual road traffic environment is dynamically changing. In this case, the route determined at the beginning cannot cope with sudden traffic accidents or congestion. The current autonomous driving system has limited adaptability and flexibility, and it is difficult to meet complex and dynamic traffic conditions, and it is impossible to make timely and effective adjustments and decisions. Summary of the invention

[0003] The purpose of the present invention is to provide a driving path optimization method and system based on an autonomous driving vehicle to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a driving path optimization method based on an autonomous driving vehicle, the method comprising: Obtain historical driving route information, generate questionnaire information based on the historical driving route information, and input preference standard data based on the questionnaire information; Obtaining the destination location, determining a possible driving path based on the destination location, and determining an initial driving path based on the preference standard data; Acquire vehicle information on the possible driving path in real time according to the destination location, the vehicle information including vehicle owner user information and current vehicle speed; Update and optimize the driving route in real time based on preference standard data and vehicle information on possible driving routes; As a further solution of the present invention, the step of obtaining historical driving path information and entering preference standard data according to the historical driving path information specifically includes: Obtain historical driving path information, and obtain the destination location based on the historical driving path information; Obtaining path comparison information based on the destination location, and generating questionnaire information based on the path comparison information; Obtain confirmation information input by the user based on the questionnaire information; Enter preference criteria data based on confirmation information.

[0005] As a further solution of the present invention, the step of acquiring vehicle information on a possible driving path in real time according to the destination location, wherein the vehicle information includes vehicle owner user information and the corresponding current vehicle speed, specifically comprises: Obtain possible driving paths in real time based on the vehicle's current location and destination location; Acquiring vehicle information on the possible driving path according to the possible driving path, wherein the vehicle information includes vehicle owner user information, current vehicle speed, and vehicle distance data to the vehicle ahead; The vehicle owner is marked based on the vehicle speed information and the distance to the vehicle ahead, and a marking label corresponding to a single user is generated.

[0006] As a further solution of the present invention, the step of marking the vehicle owner user based on the vehicle speed and the distance to the vehicle in front and generating a marking label corresponding to a single user specifically includes: Get the average speed of a single vehicle on a possible driving path on urban roads; When the difference between the average vehicle speed on the possible driving path and the current vehicle speed is equal to or less than the preset threshold, and the distance to the vehicle in front is greater than the preset threshold, the vehicle owner is marked as "delaying passage"; When the difference between the average vehicle speed on the possible driving path and the current vehicle speed is greater than a preset threshold, and the distance to the vehicle ahead is equal to or less than a preset threshold, the vehicle owner is marked as "normal driving"; When the difference between the average vehicle speed on the possible driving path and the current vehicle speed is greater than a preset threshold, and the distance to the vehicle in front is greater than a preset threshold, the vehicle owner will be marked as "delaying passage".

[0007] As a further solution of the present invention, the step of updating and optimizing the driving path in real time according to the preference standard data and the vehicle information on the possible driving path specifically includes: Obtain the proportion of "delayed passage" marking labels on the possible driving path; The degree of smoothness is determined based on the ratio, and the driving path of the next intersection is determined based on the degree of smoothness and preference standard data.

[0008] As a further embodiment of the present invention, the present invention further comprises Get the number of lanes on the current path of the intersection and the front view picture, and determine the pass lane based on the number of lanes on the current path and the front view picture.

[0009] As a further solution of the present invention, the step of obtaining the number of lanes of the current path of the intersection and the front view picture, and determining the pass lane based on the number of lanes of the current path and the front view picture specifically includes: Get the status of traffic lights at intersections, images of the front view, and the number and type of lanes on the current path; Estimate the number of vehicles on each lane of the current path based on the forward field of view image; When the lane types of the current path are exactly the same, the lane with the smallest number of vehicles is selected; When the lane types of the current path are not exactly the same, the lane is selected based on the traffic light status at the intersection.

[0010] The present invention also provides a driving path optimization system based on an autonomous driving vehicle, which is used to implement a driving path optimization method based on an autonomous driving vehicle, and the system includes: A preference determination module, used to obtain historical driving path information and input preference standard data according to the historical driving path information; An initial path determination module, used to obtain a destination location, determine a possible driving path according to the destination location, and determine an initial driving path according to the preference standard data; An information acquisition module is used to acquire vehicle information on a possible driving path in real time according to the destination location, wherein the vehicle information includes vehicle owner user information and current vehicle speed; An optimization module is used to update and optimize the driving route in real time based on the preference standard data and the vehicle information on the possible driving route; As a further solution of the present invention, the preference determination module includes: A location acquisition unit, used to acquire historical driving path information, and acquire a destination location based on the historical driving path information; A questionnaire survey unit, used for obtaining path comparison information based on the destination location, and generating questionnaire survey information based on the path comparison information; A confirmation unit, used to obtain confirmation information input by the user based on the questionnaire information; The input unit is used to input the preference standard data based on the confirmation information.

[0011] As a further solution of the present invention, the information acquisition module includes: A possible driving path acquisition unit, used to acquire a possible driving path in real time according to the current position of the vehicle and the destination position; A vehicle information determination unit, used to obtain vehicle information on a possible driving path according to the possible driving path, wherein the vehicle information includes vehicle owner user information, current vehicle speed, and vehicle distance data to the vehicle ahead; The marking unit is used to mark the vehicle owner user based on the vehicle speed information and the distance to the vehicle in front, and generate a marking label corresponding to a single user.

[0012] Compared with the prior art, the beneficial effects of the present invention are: by acquiring historical driving route data, the preference standard data of the vehicle owner can be entered, so that when different possible routes are available for selection during driving, the user can make a choice based on the user's preference standard data, without the need for the user to correct the route; secondly, by acquiring vehicle information on the route, the route can be adjusted, thereby achieving greater traffic efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0014] Figure 1 A flowchart of a method for optimizing a driving path of an autonomous vehicle provided in an embodiment of the present invention.

[0015] Figure 2 A flowchart of the steps of obtaining historical driving route information and entering preference standard data according to the historical driving route information provided by an embodiment of the present invention.

[0016] Figure 3 A flowchart of the steps of obtaining vehicle information on a possible driving path in real time according to a destination location provided in an embodiment of the present invention.

[0017] Figure 4 A flowchart of the steps of marking a vehicle owner based on vehicle speed information and the distance to the vehicle in front, and generating a marking label corresponding to a single user, provided in an embodiment of the present invention.

[0018] Figure 5 A structural block diagram of the components of a driving path optimization system based on an autonomous driving vehicle provided in an embodiment of the present invention.

[0019] Figure 6 This is a structural block diagram of the composition of the preference determination module provided in an embodiment of the present invention.

[0020] Figure 7 This is a structural block diagram of the information acquisition module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0022] Figure 1 The flowchart of a driving path optimization method based on an autonomous driving vehicle is as follows. In an embodiment of the present invention, a driving path optimization method based on an autonomous driving vehicle includes: Step S100, obtaining historical driving route information, generating questionnaire information according to the historical driving route information, and inputting preference standard data based on the questionnaire information; Step S200, obtaining a destination location, determining a possible driving path according to the destination location, and determining an initial driving path according to the preference standard data; Step S300, obtaining vehicle information on a possible driving path in real time according to the destination location, wherein the vehicle information includes vehicle owner user information and current vehicle speed; Step S400, updating and optimizing the driving route in real time according to the preference standard data and the vehicle information on the possible driving route; In this embodiment, by acquiring historical driving route data, the owner's preference standard data is entered, so that when different possible routes are available during driving, the user can choose according to the user's preference standard data, and the user does not need to correct the route. Secondly, by acquiring the vehicle information on the route, the route can be adjusted to achieve greater traffic efficiency.

[0023] like Figure 2 As shown, as a preferred embodiment of the present invention, the steps of obtaining historical driving path information, generating questionnaire information according to the historical driving path information, and entering preference standard data based on the questionnaire information specifically include: Step S101, obtaining historical driving path information, and obtaining a destination location according to the historical driving path information; Step S102, obtaining path comparison information based on the destination location, and generating questionnaire information based on the path comparison information; Step S103, obtaining confirmation information input by the user according to the questionnaire information; Step S104, inputting preference standard data based on the confirmation information.

[0024] In this embodiment, a historical path is obtained through historical driving path information, and the historical path includes a destination location and a driving path actually selected by a user. The driving path includes a selected path in a non-automatic driving state, for example, a small road with less traffic volume is selected between a wide main road and a small road. Path comparison information is generated through path information obtained from the historical path. For example, in the historical driving path, there are wide main roads with heavy traffic volume, single-lane small roads with light traffic volume, two-lane roads with short traffic lights, and wide main roads with bumpy roads. In the historical driving data, the above problems appear. Based on the above problems, questionnaire information is generated, and the questionnaire information is wirelessly transmitted to a mobile phone connected to the vehicle or to a display screen of the vehicle, and the user terminal makes a selection. The questionnaire information can be displayed as the following questions: The next intersection presents two options: one is a wide road with heavy traffic, and the other is a single-lane road with light traffic. The estimated travel time is similar. Which one would you choose? Considering that you are driving at night, the next intersection presents two options: one is a main road with street lights but heavy traffic, and the other is a small road without street lights and light traffic. Which one will you choose? The next intersection presents two options. One is that the road condition is good but the traffic volume is heavy. The other is that the road condition is bumpy but the traffic volume is light. The estimated travel time is similar. Which one will you choose? Based on the confirmation information input by the user, the priorities of multiple indicators are determined, including bumps, traffic volume, street lights at night, travel time, etc., and preference standard data is generated based on the priority results.

[0025] Car owners have certain personal habits when driving, but the automatic generation of driving routes in the autonomous driving system cannot provide personalized design for car owners.

[0026] like Figure 3 As shown, as a preferred embodiment of the present invention, the step of acquiring vehicle information on a possible driving path in real time according to the destination position, wherein the vehicle information includes vehicle owner user information and the corresponding current vehicle speed, specifically includes: Step S301, obtaining a possible driving path in real time according to the current position of the vehicle and the destination position; Step S302, obtaining vehicle information on the possible driving path according to the possible driving path, wherein the vehicle information includes vehicle owner user information, current vehicle speed, and vehicle distance data to the vehicle in front; Step S303 : marking the vehicle owner based on the vehicle speed information and the distance to the vehicle in front, and generating a marking tag corresponding to a single user.

[0027] In this embodiment, the possible driving path is updated in real time according to the current position. By acquiring the possible driving path in real time, it is possible to avoid sudden situations on the path, thereby selecting other routes with higher traffic efficiency. In addition, in this embodiment, by acquiring the vehicle information on the possible driving path, the vehicles on the possible driving path can be marked, that is, the vehicle owners can be marked.

[0028] like Figure 4 As shown, as a preferred embodiment of the present invention, the step of marking the vehicle owner based on the vehicle speed and the distance to the vehicle in front and generating a marking label corresponding to a single user specifically includes: Step S3031, obtaining the average speed of a single vehicle on a possible driving path on an urban road; Step S3032: when the difference between the average vehicle speed on the possible driving path and the current vehicle speed is equal to or less than a preset threshold, and the distance to the vehicle in front is greater than a preset threshold, the vehicle owner is marked as "delaying passage"; Step S3033: when the difference between the average vehicle speed on the possible driving path and the current vehicle speed is greater than a preset threshold, and the distance to the vehicle ahead is equal to or less than a preset threshold, the vehicle owner is marked as "normal driving"; Step S3034: When the difference between the average vehicle speed on the possible driving path and the current vehicle speed is greater than a preset threshold, and the distance to the vehicle in front is greater than a preset threshold, the vehicle owner is marked as "delaying passage".

[0029] In this embodiment, the average speed of a single vehicle on the possible driving path is obtained, that is, the average speed that characterizes the driving of the vehicle, so as to judge the driving state of the vehicle according to the speed of the vehicle at this time. When the driving speed at this time is substantially equal to the average speed, it means that the vehicle is driving normally within its own speed range. However, due to the large distance between it and the vehicle in front, it means that the vehicle behind will be decelerated, resulting in the concentration of vehicles at the next intersection, thereby increasing the traffic pressure in a short time. When the driving speed at this time is much lower than the average speed, there are generally two situations. One is that the vehicle is decelerated, which is characterized by a small distance between it and the vehicle in front. At this time, it is in a normal driving state. The other is that the owner user is absent-minded, which is characterized by a large distance between it and the vehicle in front. At this time, it is in a delayed driving state. On a road, a higher speed should be achieved, but some vehicles decelerate and block the road, resulting in a decrease in the vehicle's travel speed, and also increasing the traffic pressure. Therefore, the above-mentioned owner user is marked, so that the proportion of "delayed traffic" vehicles on the possible driving path can be identified, so as to facilitate the selection of the final driving path.

[0030] As a preferred embodiment of the present invention, the step of updating and optimizing the driving path in real time according to the preference standard data and the vehicle information on the possible driving path specifically includes: Obtain the proportion of "delayed passage" marking labels on the possible driving path; The degree of smoothness is determined based on the ratio, and the driving path of the next intersection is determined based on the degree of smoothness and preference standard data.

[0031] In this embodiment, by calculating the proportion of "delayed passage" mark labels on the possible driving path, the proportion value of the "delayed passage" mark labels is represented as the degree of smoothness. The degree of smoothness can be smooth, unsmooth, relatively smooth, very smooth, and not completely smooth. The different degrees of smoothness correspond to different proportion value ranges. In addition, traffic flow and traffic accident information are obtained in real time. The driving path of the next intersection is determined based on the above information and the generated preference standard data. The driving path of the next intersection includes straight going, left turn, and right turn. The driving path is composed of instructions for each intersection.

[0032] As a preferred embodiment of the present invention, it also includes Get the number of lanes on the current path of the intersection and the front view picture, and determine the pass lane based on the number of lanes on the current path and the front view picture.

[0033] In this embodiment, the lane with the fastest estimated speed is selected based on the vehicles parked on the lane passing through the traffic light. For example, the current path is a straight-ahead instruction, and the number of lanes on the current path refers to the number of straight-ahead lanes. For example, there are three straight-ahead lanes. The number of vehicles on the lane is judged through the front field of view picture, so as to select the lane with the least number of vehicles.

[0034] As a preferred embodiment of the present invention, the step of obtaining the number of lanes of the current path of the intersection and the front view picture, and determining the passing lane based on the number of lanes of the current path and the front view picture specifically includes: Get the status of traffic lights at intersections, images of the front view, and the number and type of lanes on the current path; Estimate the number of vehicles on each lane of the current path based on the forward field of view image; When the lane types of the current path are exactly the same, the lane with the smallest number of vehicles is selected; When the lane types of the current path are not exactly the same, the lane is selected based on the traffic light status at the intersection.

[0035] In this embodiment, the number and type of lanes of the current path are obtained, and the lanes of the current path are lanes that the vehicle can pass through at the current intersection. The number of vehicles on each lane of the current path is judged through the front field image to generate an estimated number of vehicles. When the number of lanes of the current path is 1, that is, only one lane is a passable lane, and no lane selection is made at this time. When the number of lanes of the current path is multiple, lane selection is performed, that is, the lane with the least number of vehicles on the lane is selected. When the number of lanes of the current path is multiple and the lane types are not exactly the same, it is necessary to select the lane according to the traffic light status. When the lane types are not exactly the same, for example, when the vehicle needs to turn left, there are two passable lanes, one of which is only for left turns, and the other is also for left turns. You can go straight. When the straight traffic light is red, when the number of vehicles on the straight + left turn lanes is equal to or less than the preset threshold, the time required to drive to the straight + left turn lanes is shorter, because when the straight traffic light turns green, the vehicle in front will pass the intersection or enter the waiting vehicle, and when the left turn light is green, you can pass smoothly. When the straight traffic light is green at this time, the number of vehicles on the straight + left turn lanes is 0, and the time to pass in the straight + left turn lanes is shorter. Otherwise, the time required to drive to the left turn lane is shorter, because, when the straight traffic light turns from green to red, straight traffic vehicles are likely to gather on the straight + left turn lanes, thereby blocking the vehicles. When the straight traffic light is left turn, the time required to drive to the left turn lane is shorter, because, straight traffic vehicles are likely to gather on the straight + left turn lanes, thereby blocking the lanes.

[0036] like Figure 5 As shown, an embodiment of the present invention further provides a driving path optimization system based on an autonomous driving vehicle, the system comprising: The preference determination module 100 is used to obtain historical driving path information and input the preference standard data according to the historical driving path information; An initial path determination module 200 is used to obtain a destination location, determine a possible driving path according to the destination location, and determine an initial driving path according to the preference standard data; The information acquisition module 300 is used to acquire vehicle information on a possible driving path in real time according to the destination location, wherein the vehicle information includes vehicle owner user information and current vehicle speed; An optimization module 400, for updating and optimizing the driving path in real time according to the preference standard data and the vehicle information on the possible driving path; like Figure 6 As shown, as a preferred embodiment of the present invention, the preference determination module 100 includes: The location acquisition unit 101 is used to acquire historical driving path information and acquire the destination location according to the historical driving path information; A questionnaire survey unit 102, configured to obtain path comparison information based on a destination location, and generate questionnaire survey information based on the path comparison information; The confirmation unit 103 is used to obtain the confirmation information input by the user according to the questionnaire information; The input unit 104 is used to input the preference standard data based on the confirmation information.

[0037] like Figure 7 As shown, as a preferred embodiment of the present invention, the information acquisition module 300 includes: A possible driving path acquisition unit 301 is used to acquire a possible driving path in real time according to the current position of the vehicle and the destination position; The vehicle information determination unit 302 is used to obtain vehicle information on the possible driving path according to the possible driving path, wherein the vehicle information includes vehicle owner user information, current vehicle speed, and vehicle distance data to the vehicle in front; The marking unit 303 is used to mark the vehicle owner user based on the vehicle speed information and the distance to the vehicle in front, and generate a marking label corresponding to a single user.

[0038] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A driving path optimization method based on an autonomous driving vehicle, characterized in that: The method comprises: Obtain historical driving route information, generate questionnaire information based on the historical driving route information, and input preference standard data based on the questionnaire information; Obtaining the destination location, determining a possible driving path based on the destination location, and determining an initial driving path based on the preference standard data; Acquire vehicle information on the possible driving path in real time according to the destination location, the vehicle information including vehicle owner user information and current vehicle speed; The driving route is updated and optimized in real time based on preference standard data and vehicle information on possible driving routes.

2. The driving path optimization method based on an autonomous driving vehicle according to claim 1, characterized in that: The steps of obtaining historical driving route information, generating questionnaire information according to the historical driving route information, and inputting preference standard data based on the questionnaire information specifically include: Obtain historical driving path information, and obtain the destination location based on the historical driving path information; Obtaining path comparison information based on the destination location, and generating questionnaire information based on the path comparison information; Obtain confirmation information input by the user based on the questionnaire information; Enter preference criteria data based on confirmation information.

3. The driving path optimization method based on an autonomous driving vehicle according to claim 1, characterized in that: The step of obtaining vehicle information on a possible driving path in real time according to the destination location, wherein the vehicle information includes vehicle owner user information and the corresponding current vehicle speed, specifically includes: Obtain possible driving paths in real time based on the vehicle's current location and destination location; Acquiring vehicle information on the possible driving path according to the possible driving path, wherein the vehicle information includes vehicle owner user information, current vehicle speed, and vehicle distance data to the vehicle ahead; The vehicle owner is marked based on the vehicle speed information and the distance to the vehicle ahead, and a marking label corresponding to a single user is generated.

4. The driving path optimization method based on an autonomous driving vehicle according to claim 3, characterized in that: The step of marking the vehicle owner user based on the vehicle speed and the distance to the vehicle ahead and generating a marking label corresponding to a single user specifically includes: Get the average speed of a single vehicle on a possible driving path on urban roads; When the difference between the average vehicle speed on the possible driving path and the current vehicle speed is equal to or less than the preset threshold, and the distance to the vehicle in front is greater than the preset threshold, the vehicle owner is marked as "delaying passage"; When the difference between the average vehicle speed on the possible driving path and the current vehicle speed is greater than a preset threshold, and the distance to the vehicle ahead is equal to or less than a preset threshold, the vehicle owner is marked as "normal driving"; When the difference between the average vehicle speed on the possible driving path and the current vehicle speed is greater than a preset threshold, and the distance to the vehicle in front is greater than a preset threshold, the vehicle owner will be marked as "delaying passage".

5. The driving path optimization method based on an automatic driving vehicle according to claim 4, characterized in that: The step of updating and optimizing the driving path in real time according to the preference standard data and the vehicle information on the possible driving path specifically includes: Get the proportion of "delayed passage" marks on the possible driving path; The degree of smoothness is determined based on the ratio, and the driving path of the next intersection is determined based on the degree of smoothness and preference standard data.

6. The driving path optimization method based on an autonomous driving vehicle according to claim 1, characterized in that: Also includes Get the number of lanes on the current path of the intersection and the front view picture, and determine the pass lane based on the number of lanes on the current path and the front view picture.

7. The driving path optimization method based on an automatic driving vehicle according to claim 6, characterized in that: The step of obtaining the number of lanes of the current path of the intersection and the front view picture, and determining the pass lane based on the number of lanes of the current path and the front view picture specifically includes: Get the status of traffic lights at intersections, images of the front view, and the number and type of lanes on the current path; Estimate the number of vehicles on each lane of the current path based on the forward field of view image; When the lane types of the current path are exactly the same, the lane with the smallest number of vehicles is selected; When the lane types of the current path are not exactly the same, the lane is selected based on the traffic light status at the intersection.

8. A driving path optimization system based on an autonomous driving vehicle, used to implement the driving path optimization method based on an autonomous driving vehicle according to any one of claims 1 to 7, characterized in that: The system comprises: A preference determination module, used to obtain historical driving path information and input preference standard data according to the historical driving path information; An initial path determination module, used to obtain a destination location, determine a possible driving path according to the destination location, and determine an initial driving path according to the preference standard data; An information acquisition module is used to acquire vehicle information on a possible driving path in real time according to the destination location, wherein the vehicle information includes vehicle owner user information and current vehicle speed; The optimization module is used to update and optimize the driving path in real time based on the preference standard data and the vehicle information on the possible driving path.

9. The driving path optimization system based on an autonomous driving vehicle according to claim 8, characterized in that: The preference determination module comprises: A location acquisition unit, used to acquire historical driving path information, and acquire a destination location based on the historical driving path information; A questionnaire survey unit, used for obtaining path comparison information based on the destination location, and generating questionnaire survey information based on the path comparison information; A confirmation unit, used to obtain confirmation information input by the user based on the questionnaire information; The input unit is used to input the preference standard data based on the confirmation information.

10. The driving path optimization system based on an autonomous driving vehicle according to claim 8, characterized in that: The information acquisition module includes: A possible driving path acquisition unit, used to acquire a possible driving path in real time according to the current position of the vehicle and the destination position; A vehicle information determination unit, used to obtain vehicle information on a possible driving path according to the possible driving path, wherein the vehicle information includes vehicle owner user information, current vehicle speed, and vehicle distance data to the vehicle ahead; The marking unit is used to mark the vehicle owner user based on the vehicle speed information and the distance to the vehicle in front, and generate a marking label corresponding to a single user.

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