Intelligent data analysis system and method applied to automatic driving technology

Abstract

The invention discloses an intelligent data analysis system and method applied to an automatic driving technology, and relates to the technical field of automatic driving analysis, and the system comprises an automatic driving module, a highway departure analysis module, a feature data collection module and a vehicle automatic driving decision module. The output end of the automatic driving module is connected with the input end of the highway leaving analysis module; the output end of the departure high-speed analysis module is connected with the input end of the characteristic data acquisition module; the output end of the characteristic data acquisition module is connected with the input end of the vehicle automatic driving decision module; the output end of the vehicle automatic driving decision module is connected with the input end of the automatic driving module. A brand new solution is provided based on part of defects of the automatic driving technology in practical application, and the intelligent degree of the automatic driving technology can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving analysis technology, and specifically to an intelligent data analysis system and method applied to autonomous driving technology. Background Art

[0002] Autonomous driving technology has been a hot topic in the current automotive industry for more than a decade, and new technologies related to autonomous driving are emerging in an endless stream. At the current level of technology, the autonomous driving technology of some vehicles can be identified as being at the L3 level, that is, conditional automation. It has the ability to drive autonomously in certain environments, but in case of emergency, the driver is still needed to supervise.

[0003] Under the assistance of L3 level autonomous driving, there is still a big problem. The driving style of autonomous driving itself is relatively conservative and not decisive enough. On the highway, when exiting, for example, when the autonomous driving is leaving the highway and preparing to exit a certain exit, it often slows down to avoid vehicles on the right ramp driving side by side on the highway. Once there are continuous vehicles, it will cause the window time for exiting the highway to be lost. It will drive across the expressway exit grid line without being able to stop, resulting in the need to leave the highway at the next exit. Therefore, for the problem of autonomous driving at the entrance and exit of the highway, the current technology gives priority to ensuring avoidance, and lacks judgment on the timing of active acceleration. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent data analysis system and method applied to autonomous driving technology to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: an intelligent data analysis method applied to autonomous driving technology, the method comprising the following steps:

[0006] S1. The autonomous driving system connects to the navigation system, obtains the highway exit location, sets a distance threshold, and drives to the rightmost lane when the distance between the current location and the highway exit location meets the distance threshold;

[0007] S2. Constructing a designated position, wherein the designated position refers to the first time that the front of the vehicle reaches the dashed line of the highway exit on the rightmost lane, and starting the highway exit analysis system at the designated position;

[0008] S3, collecting feature data of vehicles traveling in the right lane adjacent to the rightmost lane in real time, setting a time period, and generating feature data according to the time period and inputting it into the exit high-speed analysis system;

[0009] S4. The leaving highway analysis system calls the data judgment model to analyze the characteristic data of the vehicles traveling in the adjacent right lane, and forms a vehicle autonomous driving decision to feed back to the autonomous driving system.

[0010] According to the above technical solution, the characteristic data includes:

[0011] Set a time period T0, collect the speed of the autonomous driving vehicle at both ends of the time period T0, the speed of the vehicle in the right lane adjacent to the rightmost lane, the position of the vehicle in the right lane adjacent to the rightmost lane, and the deviation degree of the vehicle in the right lane adjacent to the rightmost lane;

[0012] The vehicle in the right lane adjacent to the rightmost lane refers to the first vehicle behind the autonomous driving vehicle and satisfies the requirement of being in the right lane adjacent to the rightmost lane;

[0013] The degree of deviation refers to the distance between the two end points of the time period T0 and the left lane line of the lane.

[0014] According to the above technical solution, the data judgment model includes:

[0015] Data collection devices are deployed at several highway exits to obtain historical driving characteristics data;

[0016] The driving characteristic data includes:

[0017] Setting a basic scenario, wherein the basic scenario refers to a vehicle entering the expressway from the adjacent right lane of the rightmost lane;

[0018] A multiple-occurrence scenario is set, wherein the multiple-occurrence scenario includes two situations in the basic scenario, including a vehicle in the rightmost lane in the basic scenario and a vehicle in the rightmost lane in the basic scenario; the vehicle in the multiple-occurrence scenario refers to a vehicle in front of a vehicle in the adjacent right lane of the rightmost lane;

[0019] Collect characteristic data of frequent scenarios under historical data to form several independent driving characteristic data groups. Any driving characteristic data group is written as: [A0, V0, S0, K0, L0, Y0];

[0020] Among them, A0 refers to the change value of the vehicle speed in the rightmost lane within the time period T0, if there is no vehicle in the rightmost lane, it is recorded as 0; V0 refers to the change value of the speed of the vehicle traveling in the adjacent right lane of the rightmost lane during the time period T0; S0 refers to the position change value of the vehicle traveling in the adjacent right lane of the rightmost lane during the time period T0; K0 refers to the change value of the offset degree of the vehicle traveling in the adjacent right lane of the rightmost lane during the time period T0; L0 refers to the distance value between the vehicle in the rightmost lane and the vehicle traveling in the adjacent right lane of the rightmost lane in the initial situation, and the distance value is the distance between the horizontal line of the rear of the front vehicle and the horizontal line of the front of the rear vehicle; the initial situation refers to the time when the front of the vehicle traveling in the adjacent right lane of the rightmost lane reaches the dotted line on the left side of the current lane for the first time; Y0 refers to the driving mode selected by the vehicle traveling in the adjacent right lane of the rightmost lane, and the driving mode includes entering the highway from the rear of the vehicle in the rightmost lane and entering the highway from the front of the vehicle in the rightmost lane.

[0021] According to the above technical solution, it also includes:

[0022] Construct the independence hypothesis, set the driving feature data to be unrelated, and construct the probability equation F0:

[0023]

[0024] Among them, ci represents c1 and c2, which refer to the two options of entering the highway from behind the vehicle in the rightmost lane and entering the highway from in front of the vehicle in the rightmost lane respectively; X(j) represents the five features A0, V0, S0, K0, and L0 in the driving feature data set, and j refers to the sequence number; b(j) represents the specific values ​​of the five features A0, V0, S0, K0, and L0 in the driving feature data set; p represents the probability;

[0025] Based on F0, the driving mode selected by the vehicles in the adjacent right lane of the rightmost lane under different driving characteristic data groups is obtained to form the data judgment model result.

[0026] According to the above technical solution, the vehicle automatic driving decision is formed based on the data judgment model results;

[0027] The vehicle's automatic driving decision includes giving priority to avoidance and accelerating lane change;

[0028] When the data judgment model result shows that the rear side of the vehicle in the rightmost lane enters the highway, choose to accelerate and change lanes to leave; when the data judgment model result shows that the front side of the vehicle in the rightmost lane enters the highway, choose to give priority to avoidance.

[0029] An intelligent data analysis system applied to autonomous driving technology, the system comprising an autonomous driving module, a highway leaving analysis module, a feature data collection module and a vehicle autonomous driving decision module;

[0030] The automatic driving module is used to connect to the navigation system, obtain the highway exit position, set the distance threshold, and drive to the rightmost lane when the distance between the current position and the highway exit position meets the distance threshold; the leaving highway analysis module is used to construct a designated position and start the leaving highway analysis system at the designated position; the characteristic data acquisition module is used to collect the characteristic data of the vehicles driving in the adjacent right lane of the rightmost lane in real time, set a time period, and form characteristic data according to the time period and input it into the leaving highway analysis module; the vehicle automatic driving decision module analyzes the characteristic data of the vehicles driving in the adjacent right lane based on the data judgment model, and forms a vehicle automatic driving decision and feeds it back to the automatic driving module;

[0031] The output end of the autonomous driving module is connected to the input end of the leaving high-speed analysis module; the output end of the leaving high-speed analysis module is connected to the input end of the feature data acquisition module; the output end of the feature data acquisition module is connected to the input end of the vehicle autonomous driving decision module; the output end of the vehicle autonomous driving decision module is connected to the input end of the autonomous driving module.

[0032] According to the above technical solution, the autonomous driving module includes a navigation and positioning unit and a control unit;

[0033] The navigation positioning unit is used to connect to the navigation system to obtain the location of the highway exit; the control unit is used to set a distance threshold, and when the distance between the current position and the highway exit location meets the distance threshold, drive to the rightmost lane;

[0034] The output end of the navigation and positioning unit is connected to the input end of the control unit.

[0035] According to the above technical solution, the designated position refers to the first time the front of the vehicle reaches the dashed line of the highway exit on the rightmost lane.

[0036] According to the above technical solution, the characteristic data acquisition module includes a characteristic data acquisition unit and a time period setting unit;

[0037] The characteristic data collection unit is used to collect characteristic data of vehicles traveling in the right lane adjacent to the rightmost lane in real time; the time period setting unit is used to set the time period, and the characteristic data is formed according to the time period and input into the exit high-speed analysis module;

[0038] The output end of the characteristic data acquisition unit is connected to the input end of the time period setting unit.

[0039] According to the above technical solution, the vehicle automatic driving decision module includes a data judgment unit and an automatic feedback unit;

[0040] The data judgment unit is used to deploy data collection devices at several highway exits, obtain historical driving feature data, and build a data judgment model. The automatic feedback unit analyzes the feature data of vehicles traveling in the adjacent right lane based on the data judgment model, and forms a vehicle automatic driving decision and feeds it back to the automatic driving module;

[0041] The output end of the data judgment unit is connected to the input end of the automatic feedback unit.

[0042] Compared with the prior art, the beneficial effects of the present invention are: the present invention proposes a new solution based on some drawbacks of autonomous driving technology in practical applications, which can effectively improve the intelligence level of autonomous driving technology, form more efficient and accurate autonomous driving decision-making processing based on big data analysis, and provide the optimal solution when vehicles enter and exit highway exits, thereby further improving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of a road scene of an intelligent data analysis method applied to autonomous driving technology in the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Example: Figure 1 As shown, the present invention provides an intelligent data analysis method applied to autonomous driving technology, the method comprising: the autonomous driving system connects to the navigation system, obtains the highway exit position, sets a distance threshold, and drives to the rightmost lane when the distance between the current position and the highway exit position meets the distance threshold;

[0046] In this embodiment, taking 2KM as an example, when the distance between the current position and the highway exit position meets 2KM, the vehicle is controlled to change lanes to the right lane until it reaches the rightmost lane; a designated position is constructed, and the designated position refers to the first time the vehicle head reaches the highway exit dashed line on the rightmost lane, and the highway exit analysis system is started at the designated position; the designated position is as follows: Figure 1 As shown; the characteristic data of the vehicles in the right lane adjacent to the rightmost lane are collected in real time, a time period is set, and characteristic data is formed according to the time period and input into the high-speed analysis system;

[0047] Set a time period T0, collect the speed of the autonomous driving vehicle at both ends of the time period T0, the speed of the vehicle in the right lane adjacent to the rightmost lane, the position of the vehicle in the right lane adjacent to the rightmost lane, and the deviation degree of the vehicle in the right lane adjacent to the rightmost lane;

[0048] The vehicle in the right lane adjacent to the rightmost lane refers to the first vehicle behind the autonomous driving vehicle and satisfies the requirement of being in the right lane adjacent to the rightmost lane;

[0049] The degree of deviation refers to the distance between the two end points of the time period T0 and the left lane line of the lane.

[0050] The data judgment model includes:

[0051] Data collection devices are deployed at several highway exits to obtain historical driving characteristics data;

[0052] The driving characteristic data includes:

[0053] Setting a basic scenario, wherein the basic scenario refers to a vehicle entering the expressway from the adjacent right lane of the rightmost lane;

[0054] A multiple-occurrence scenario is set, wherein the multiple-occurrence scenario includes two situations in the basic scenario, including a vehicle in the rightmost lane in the basic scenario and a vehicle in the rightmost lane in the basic scenario; the vehicle in the multiple-occurrence scenario refers to a vehicle in front of a vehicle in the adjacent right lane of the rightmost lane;

[0055] like Figure 1 As shown, in this embodiment, the situation where there is a vehicle in the rightmost lane in a frequent scene is selected, where the solid line vehicle position is the designated position, and the high-speed analysis system is turned on; the dashed line vehicle position is the time when the vehicle in the adjacent right lane of the rightmost lane starts to collect feature data;

[0056] Collect characteristic data of frequent scenarios under historical data to form several independent driving characteristic data groups. Any driving characteristic data group is written as: [A0, V0, S0, K0, L0, Y0];

[0057] Among them, A0 refers to the change value of the vehicle speed in the rightmost lane within the time period T0, if there is no vehicle in the rightmost lane, it is recorded as 0; V0 refers to the change value of the speed of the vehicle traveling in the adjacent right lane of the rightmost lane during the time period T0; S0 refers to the position change value of the vehicle traveling in the adjacent right lane of the rightmost lane during the time period T0; K0 refers to the change value of the offset degree of the vehicle traveling in the adjacent right lane of the rightmost lane during the time period T0; L0 refers to the distance value between the vehicle in the rightmost lane and the vehicle traveling in the adjacent right lane of the rightmost lane in the initial situation, and the distance value is the distance between the horizontal line of the rear of the front vehicle and the horizontal line of the front of the rear vehicle; the initial situation refers to the time when the front of the vehicle traveling in the adjacent right lane of the rightmost lane reaches the dotted line on the left side of the current lane for the first time; Y0 refers to the driving mode selected by the vehicle traveling in the adjacent right lane of the rightmost lane, and the driving mode includes entering the highway from the rear of the vehicle in the rightmost lane and entering the highway from the front of the vehicle in the rightmost lane.

[0058] Construct the independence hypothesis, set the driving feature data to be unrelated, and construct the probability equation F0:

[0059]

[0060] Among them, ci represents c1 and c2, which refer to the two options of entering the highway from behind the vehicle in the rightmost lane and entering the highway from in front of the vehicle in the rightmost lane respectively; X(j) represents the five features A0, V0, S0, K0, and L0 in the driving feature data set, and j refers to the sequence number; b(j) represents the specific values ​​of the five features A0, V0, S0, K0, and L0 in the driving feature data set; p represents the probability;

[0061] Based on F0, the driving mode selected by the vehicles in the adjacent right lane of the rightmost lane under different driving characteristic data groups is obtained to form the data judgment model result.

[0062] Make decisions on autonomous driving based on data-based judgment model results;

[0063] The vehicle's automatic driving decision includes giving priority to avoidance and accelerating lane change;

[0064] When the data judgment model result shows that the rear side of the vehicle in the rightmost lane enters the highway, choose to accelerate and change lanes to leave; when the data judgment model result shows that the front side of the vehicle in the rightmost lane enters the highway, choose to give priority to avoidance.

[0065] In this embodiment, an intelligent data analysis system applied to autonomous driving technology is also provided, the system comprising an autonomous driving module, a highway leaving analysis module, a feature data collection module and a vehicle autonomous driving decision module;

[0066] The automatic driving module is used to connect to the navigation system, obtain the highway exit position, set the distance threshold, and drive to the rightmost lane when the distance between the current position and the highway exit position meets the distance threshold; the leaving highway analysis module is used to construct a designated position and start the leaving highway analysis system at the designated position; the characteristic data acquisition module is used to collect the characteristic data of the vehicles driving in the adjacent right lane of the rightmost lane in real time, set a time period, and form characteristic data according to the time period and input it into the leaving highway analysis module; the vehicle automatic driving decision module analyzes the characteristic data of the vehicles driving in the adjacent right lane based on the data judgment model, and forms a vehicle automatic driving decision and feeds it back to the automatic driving module;

[0067] The output end of the autonomous driving module is connected to the input end of the leaving high-speed analysis module; the output end of the leaving high-speed analysis module is connected to the input end of the feature data acquisition module; the output end of the feature data acquisition module is connected to the input end of the vehicle autonomous driving decision module; the output end of the vehicle autonomous driving decision module is connected to the input end of the autonomous driving module.

[0068] The autonomous driving module includes a navigation and positioning unit and a control unit;

[0069] The navigation positioning unit is used to connect to the navigation system to obtain the location of the highway exit; the control unit is used to set a distance threshold, and when the distance between the current position and the highway exit location meets the distance threshold, drive to the rightmost lane;

[0070] The output end of the navigation and positioning unit is connected to the input end of the control unit.

[0071] The designated position refers to the first time the front of the vehicle reaches the dashed line of the highway exit on the rightmost lane.

[0072] The characteristic data collection module includes a characteristic data collection unit and a time period setting unit;

[0073] The characteristic data collection unit is used to collect characteristic data of vehicles traveling in the right lane adjacent to the rightmost lane in real time; the time period setting unit is used to set the time period, and the characteristic data is formed according to the time period and input into the exit high-speed analysis module;

[0074] The output end of the characteristic data acquisition unit is connected to the input end of the time period setting unit.

[0075] The vehicle automatic driving decision module includes a data judgment unit and an automatic feedback unit;

[0076] The data judgment unit is used to deploy data collection devices at several highway exits, obtain historical driving feature data, and build a data judgment model. The automatic feedback unit analyzes the feature data of vehicles traveling in the adjacent right lane based on the data judgment model, and forms a vehicle automatic driving decision and feeds it back to the automatic driving module;

[0077] The output end of the data judgment unit is connected to the input end of the automatic feedback unit.

[0078] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An intelligent data analysis method applied to autonomous driving technology, characterized in that: The method comprises the following steps: S1. The autonomous driving system connects to the navigation system, obtains the highway exit location, sets a distance threshold, and drives to the rightmost lane when the distance between the current location and the highway exit location meets the distance threshold; S2. Constructing a designated position, wherein the designated position refers to the first time that the front of the vehicle reaches the dashed line of the highway exit on the rightmost lane, and starting the highway exit analysis system at the designated position; S3, collecting feature data of vehicles traveling in the right lane adjacent to the rightmost lane in real time, setting a time period, and generating feature data according to the time period and inputting it into the exit high-speed analysis system; S4. The leaving highway analysis system calls the data judgment model to analyze the characteristic data of the vehicles traveling in the adjacent right lane, and forms a vehicle autonomous driving decision to feed back to the autonomous driving system.

2. The intelligent data analysis method for autonomous driving technology according to claim 1, characterized in that: The characteristic data includes: Set a time period T0, collect the speed of the autonomous driving vehicle at both ends of the time period T0, the speed of the vehicle in the right lane adjacent to the rightmost lane, the position of the vehicle in the right lane adjacent to the rightmost lane, and the deviation degree of the vehicle in the right lane adjacent to the rightmost lane; The vehicle in the right lane adjacent to the rightmost lane refers to the first vehicle behind the autonomous driving vehicle and satisfies the requirement of being in the right lane adjacent to the rightmost lane; The degree of deviation refers to the distance between the two end points of the time period T0 and the left lane line of the lane.

3. The intelligent data analysis method applied to autonomous driving technology according to claim 2, characterized in that: The data judgment model includes: Data collection devices are deployed at several highway exits to obtain historical driving characteristics data; The driving characteristic data includes: Setting a basic scenario, wherein the basic scenario refers to a vehicle entering the expressway from the adjacent right lane of the rightmost lane; A multiple-occurrence scenario is set, wherein the multiple-occurrence scenario includes two situations under the basic scenario, including a vehicle existing in the rightmost lane under the basic scenario and a vehicle not existing in the rightmost lane under the basic scenario; the vehicle existing in the multiple-occurrence scenario means that the vehicle is in front of a vehicle in the adjacent right lane of the rightmost lane; Collect characteristic data of frequent scenarios under historical data to form several independent driving characteristic data groups. Any driving characteristic data group is written as: [A0, V0, S0, K0, L0, Y0]; Among them, A0 refers to the change value of the vehicle speed in the rightmost lane within the time period T0, if there is no vehicle in the rightmost lane, it is recorded as 0; V0 refers to the change value of the speed of the vehicle traveling in the adjacent right lane of the rightmost lane during the time period T0; S0 refers to the position change value of the vehicle traveling in the adjacent right lane of the rightmost lane during the time period T0; K0 refers to the change value of the offset degree of the vehicle traveling in the adjacent right lane of the rightmost lane during the time period T0; L0 refers to the distance value between the vehicle in the rightmost lane and the vehicle traveling in the adjacent right lane of the rightmost lane in the initial situation, and the distance value is the distance between the horizontal line of the rear of the front vehicle and the horizontal line of the front of the rear vehicle; the initial situation refers to the time when the front of the vehicle traveling in the adjacent right lane of the rightmost lane reaches the dotted line on the left side of the current lane for the first time; Y0 refers to the driving mode selected by the vehicle traveling in the adjacent right lane of the rightmost lane, and the driving mode includes entering the highway from the rear of the vehicle in the rightmost lane and entering the highway from the front of the vehicle in the rightmost lane.

4. The intelligent data analysis method for autonomous driving technology according to claim 3, characterized in that: Also includes: Construct the independence hypothesis, set the driving feature data to be unrelated, and construct the probability equation F0: Among them, ci represents c1 and c2, which refer to the two options of entering the highway from behind the vehicle in the rightmost lane and entering the highway from in front of the vehicle in the rightmost lane respectively; X(j) represents the five features A0, V0, S0, K0, and L0 in the driving feature data set, and j refers to the sequence number; b(j) represents the specific values ​​of the five features A0, V0, S0, K0, and L0 in the driving feature data set; p represents the probability; Based on F0, the driving mode selected by the vehicles in the adjacent right lane of the rightmost lane under different driving characteristic data groups is obtained to form the data judgment model result.

5. The intelligent data analysis method for autonomous driving technology according to claim 4, characterized in that: Make decisions on autonomous driving based on data-based judgment model results; The vehicle's automatic driving decision includes giving priority to avoidance and accelerating lane change; When the data judgment model result shows that the rear side of the vehicle in the rightmost lane enters the highway, choose to accelerate and change lanes to leave; when the data judgment model result shows that the front side of the vehicle in the rightmost lane enters the highway, choose to give priority to avoidance.

6. An intelligent data analysis system applied to autonomous driving technology, characterized in that: The system includes an autonomous driving module, a highway departure analysis module, a feature data collection module, and a vehicle autonomous driving decision module; The autonomous driving module is used to connect to the navigation system, obtain the highway exit location, set a distance threshold, and drive to the rightmost lane when the distance between the current location and the highway exit location meets the distance threshold; the highway exit analysis module is used to construct a designated location and start the highway exit analysis system at the designated location; The characteristic data acquisition module is used to collect the characteristic data of the vehicles in the adjacent right lane of the rightmost lane in real time, set a time period, and form characteristic data according to the time period to input into the leaving high-speed analysis module; the vehicle automatic driving decision module analyzes the characteristic data of the vehicles in the adjacent right lane based on the data judgment model, forms a vehicle automatic driving decision and feeds it back to the automatic driving module; The output end of the automatic driving module is connected to the input end of the high-speed analysis module; the output end of the high-speed analysis module is connected to the input end of the characteristic data acquisition module; The output end of the feature data acquisition module is connected to the input end of the vehicle automatic driving decision module; the output end of the vehicle automatic driving decision module is connected to the input end of the automatic driving module.

7. The intelligent data analysis system for autonomous driving technology according to claim 6, characterized in that: The autonomous driving module includes a navigation and positioning unit and a control unit; The navigation positioning unit is used to connect to the navigation system to obtain the location of the highway exit; the control unit is used to set a distance threshold, and when the distance between the current position and the highway exit location meets the distance threshold, drive to the rightmost lane; The output end of the navigation and positioning unit is connected to the input end of the control unit.

8. The intelligent data analysis system for autonomous driving technology according to claim 6, characterized in that: The designated position refers to the first time the front of the vehicle reaches the dashed line of the highway exit on the rightmost lane.

9. The intelligent data analysis system for autonomous driving technology according to claim 6, characterized in that: The characteristic data collection module includes a characteristic data collection unit and a time period setting unit; The characteristic data collection unit is used to collect characteristic data of vehicles traveling in the right lane adjacent to the rightmost lane in real time; the time period setting unit is used to set the time period, and the characteristic data is formed according to the time period and input into the exit high-speed analysis module; The output end of the characteristic data acquisition unit is connected to the input end of the time period setting unit.

10. The intelligent data analysis system for autonomous driving technology according to claim 6, characterized in that: The vehicle automatic driving decision module includes a data judgment unit and an automatic feedback unit; The data judgment unit is used to deploy data collection devices at several highway exits, obtain historical driving feature data, and build a data judgment model. The automatic feedback unit analyzes the feature data of vehicles traveling in the adjacent right lane based on the data judgment model, and forms a vehicle automatic driving decision and feeds it back to the automatic driving module; The output end of the data judgment unit is connected to the input end of the automatic feedback unit.

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