JP2022000376A - Automatic drive function control method, device, electronic apparatus and storage medium - Google Patents

Abstract

To disclose an automatic drive function control method, a device, an electronic apparatus, and a storage medium that improve the accuracy and rationality of automatic drive function control.SOLUTION: The method includes the steps of: obtaining drive-related data; determining a type of a forward traveling scene of a vehicle, based on the drive-related data; and determining a target automatic drive function, based on the type of the forward traveling scene and the drive-related data.SELECTED DRAWING: Figure 1

Description

This application relates to the field of computer technology, specifically, to autonomous driving technology.

Self-driving vehicles can realize unmanned driving by using automatic driving technology by a computer system. Currently, the autonomous driving technology used in the autonomous driving vehicle can be divided into five stages shown as L1 level-L5 level, and the higher the stage, the higher the smartness of the automatic driving function. Currently, in the market, self-driving vehicles that are beginning to take shape are generally equipped with advanced driver assistance systems (Advanced Driver Assistance System, ADAS) of L1 level and L2 level, and will be equipped with L3 level self-driving function in the future. Self-driving vehicles will be gradually introduced to the global market. Since all of the L1 level to L3 level automatic driving functions can be used normally only in some driving environments, the automatic driving function is used for the automatic driving vehicle equipped with the L1 level to L3 level automatic driving functions. How to control is extremely important.

The embodiments of the present application provide automated driving function control methods, devices, electronic devices and storage media for improving the accuracy and rationality of automated driving function control.

A first aspect of the present invention provides an automated driving function control method, a step of acquiring driving-related data, a step of determining the type of a vehicle's forward driving scene based on the driving-related data, and the forward driving. Includes a scene type and a step of determining a target automated driving function based on said driving-related data.

A second aspect of the present invention is a driving-related data acquisition module configured to provide an automatic driving function control device and acquire driving-related data, and a type of a vehicle's forward driving scene based on the driving-related data. A driving scene type determination module configured to determine, a target automatic driving function determination module configured to determine a target automatic driving function based on the type of the forward driving scene and the driving-related data. And prepare.

A third aspect of the present application provides an electronic device comprising at least one processor and a memory communicably connected to the at least one processor, wherein the memory is by the at least one processor. When the executable instruction is stored and the instruction is executed by the at least one processor, the at least one processor can execute the automatic operation function control method according to the embodiment of the first aspect.

A fourth aspect of the present application is a non-temporary computer-readable storage medium in which computer instructions are stored, wherein the computer instructions are stored in a computer according to an embodiment of the first aspect. Provided is a computer-readable storage medium for executing a function control method.

The fifth embodiment of the present application is a computer program, and when the computer program operates on the computer, the computer executes the automatic driving function control method according to the first embodiment. Provides a computer program used to make it.

In the embodiment of the present application, the type of the vehicle's forward driving scene is determined based on the acquired driving-related data, and the target automatic driving function is determined based on the determined forward driving scene type and the acquired driving-related data. By doing so, it solves the irrational problem of the automatic driving function control by ignoring the influence of multiple factors in the conventional technique, and improves the accuracy and rationality of the automatic driving function control.

It should be noted that the content described in the outline of the present invention is not intended to limit the essential features or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will be facilitated by the following description.

The drawings are intended to better understand the proposed technology and are not intended to limit the application.
It is a flowchart of the automatic operation function control method which concerns on embodiment of this application. It is a flowchart of the automatic operation function control method which concerns on embodiment of this application. It is a block diagram of the automatic operation function control apparatus which concerns on embodiment of this application. It is a schematic block diagram of the electronic device for realizing the automatic operation function control method of the Example of this application.

Exemplary examples of the present application are described below with reference to the drawings, which include various details of the embodiments of the present application for ease of understanding, which are merely exemplary. Should be considered as a thing. Accordingly, one of ordinary skill in the art should understand that various changes and amendments can be made to the embodiments described herein without departing from the scope and intent of this application. Similarly, for clarity and brevity, the following description omits the description of well-known functions and configurations.

Since all of the L1 level to L3 level automatic driving functions can be used normally only in some driving environments, all L1 level to L3 level automatic driving functions are automatically installed by the driver. It is necessary to control the on and off of the driving function. As a subject for detecting vehicle control and driving environment safety, it is necessary for the driver to clearly recognize the usage conditions of various automatic driving functions and use them rationally, but this cannot be realized for general users. When a general user controls and uses the automatic driving function, he / she depends too much on the automatic driving function, so that unsafe driving behavior or an accident is likely to occur due to the use of the automatic driving function in an inappropriate scene. On the other hand, since some users are not familiar with the automatic driving function and its usage scene, the use of the automatic driving function is completely abandoned, resources are wasted, and the user experience is deteriorated.

In one embodiment, FIG. 1 is a flowchart of an automated driving function control method according to an embodiment of the present application. In the embodiment of the present application, in order to realize rational control of the automatic driving function, a rational target automatic driving function is determined based on the type of the vehicle's forward driving scene and the driving-related data acquired by the vehicle. It may be applied when The method can be performed by an automated driving function controller. The device may be implemented in a manner that employs software and / or hardware and may generally be configured within an electronic device. The electronic device may be a device such as a computer, or may be mounted on an autonomous driving vehicle as a vehicle control system. Accordingly, as shown in FIG. 1, the method comprises the following steps 110-10.

In step 110, operation-related data is acquired.

Here, the driving-related data may be all data types related to the vehicle driving situation.
In the embodiment of the present application, the vehicle must first acquire driving-related data of the vehicle before determining the automatic driving function applied to the road ahead. Driving-related data can be used to determine the type of road ahead of the vehicle and can also provide a reference basis for controlling the vehicle's automated driving capabilities. In addition, the vehicle can send the acquired driving-related data to the autonomous driving system, and the autonomous driving function determines the most rational and reliable autonomous driving function for the vehicle. Achieve rational and efficient control over.

In one embodiment of the present application, the driving-related data may include vehicle state data, vehicle-mounted map navigation data, cloud real-time data, and vehicle behavior history data.

Here, even if the vehicle state data is data for reflecting the vehicle state and related parameters such as the hardware state, video data, driving state, and automatic driving ability parameter transmitted from the vehicle sensor, the vehicle control system, and the like. good. For example, it suffices if the state of the vehicle and related parameters such as the sensor state, the driving mode of the vehicle, and the automatic driving function can be reflected. The vehicle-mounted map navigation data may be map data of the navigation route of the vehicle-mounted map or the traveling section ahead, such as detailed navigation route data, information about the road ahead, and environmental data around the road ahead. The cloud real-time data may be other dynamic information on the vehicle's forward navigation route acquired from the cloud, such as real-time road conditions and weather data for the road ahead. The vehicle behavior history data may be driving behavior data stored in the history of the vehicle, such as the vehicle's own driving behavior data and driving data returned when passing through a front section using the automatic driving function. Included, but not limited to these. In the embodiment of the present application, the specific data types of the vehicle state data, the vehicle-mounted map navigation data, the cloud real-time data, and the vehicle behavior history data are not particularly limited.

Currently, autonomous driving systems simply determine autonomous driving functions based on data from vehicle sensors and basic data such as navigation routes, ignore the effects of external environmental factors, and determine the inaccuracy of autonomous driving functions. It is easy to invite, and there is a risk of submitting an unreasonable proposal to use the automatic operation mode to the user. For example, in the smart navigation driving support mode of a city road, when the traffic policeman improves the flow of vehicles on the road ahead, the automatic driving module of the vehicle cannot obtain the information to improve the flow of vehicles by the traffic policeman in a timely manner. , The camera recognizes the signal of the traffic signal light and uses it as the basis for traffic, which may cause a serious traffic accident.

In the aspect, by acquiring various driving-related data, the influence status of each factor such as the internal and external environment of the vehicle is determined, and a complete, accurate and reliable evaluation of the current vehicle is realized. Therefore, it is possible to obtain full multidimensional data such as the current vehicle status of the vehicle, map navigation data, and environmental factors.

In step 120, the type of vehicle's forward driving scene is determined based on the driving-related data.

Here, the type of the forward traveling scene is, that is, the type corresponding to the road ahead of the vehicle. Selectable, forward driving scene types include, but are not limited to, highway scenes, city road scenes, municipal road scenes, parking scenes, and the like.

After the autonomous driving system has acquired the driving-related data of the vehicle, the type of the vehicle's forward driving scene can be determined based on the driving-related data. Optionally, the autonomous driving system can directly determine the type of forward driving scene based on the navigation data of the vehicle-mounted map. For example, if it is determined that the vehicle is traveling on the highway based on the navigation data of the vehicle-mounted map, it can be determined that the type of the forward traveling scene is the highway scene. Optionally, the autonomous driving system can also determine the road type ahead based on the navigation data on the vehicle-mounted map and combine it with the vehicle condition data to determine the vehicle's current driving condition, thereby determining the road ahead. The type and the current driving state of the vehicle are combined to determine the forward driving scene type. For example, based on the navigation data of the in-vehicle map, it is determined that the vehicle is currently traveling in a parking lot in an urban area, and the vehicle is currently parked in combination with vehicle condition data such as the traveling speed and driving mode of the vehicle. If it is determined that, it can be determined that the type of the forward traveling scene is the parking scene.

In step 130, the target automatic driving function is determined based on the type of the forward driving scene and the driving-related data.
Here, the target automatic driving function may be an automatic driving function finally determined by the automatic driving system.

Accordingly, after determining the type of the vehicle's forward driving scene, the target automated driving function can be determined in combination with driving-related data based on the type of the forward driving scene. Comprehensive analysis based on the type of driving scene in front of the vehicle combined with driving-related data to combine multidimensional data such as vehicle conditions, map navigation data, and environmental factors for complete analysis. It can, thereby, in the navigation process, determine the automatic driving function applicable to the front section of the vehicle and improve the accuracy and rationality of the automatic driving function control.

Once the autonomous driving system determines the target autonomous driving function, the goal is to provide rational usage guidance for the driver and to realize a seamless connection between autonomous driving and artificial driving during the entire driving process. It should be understood that the autonomous driving function can be pushed to the in-vehicle terminal.

In the embodiment of the present application, in order to determine the target automatic driving function based on the determined type of the forward driving scene and the acquired driving-related data, the type of the vehicle's forward driving scene based on the acquired driving-related data. By deciding, the irrational problem of the automatic driving function control by ignoring the influence of multiple factors in the prior art is solved, and the accuracy and rationality of the automatic driving function control are improved.

In one embodiment, FIG. 2 is a flowchart of an automated driving function control method according to an embodiment of the present application. This example is optimized and modified based on each of the above-mentioned examples. The present embodiment determines a plurality of specific examples in which the type of the vehicle's forward driving scene is determined based on the driving-related data, and the target automatic driving function is determined based on the type of the forward driving scene and the driving-related data. Embodiment is provided.

As shown in FIG. 2, the automatic operation function control method includes the following steps 210 to 237.

In step 210, operation-related data is acquired.

In step 220, the type of vehicle's forward driving scene is determined based on the driving-related data.

In step 230, the target automatic driving function is determined based on the type of the forward driving scene and the driving-related data.

Accordingly, steps 220 and 230 may specifically include the following steps:

In step 221 to acquire a sign of the road ahead based on the navigation data of the vehicle-mounted map.
Here, the sign of the road ahead may be a sign that identifies the type of road in the electronic map. It should be understood that different types of electronic maps may have different signs used for different road types.

In the embodiment of the present application, the autonomous driving system can acquire the sign of the forward driving road based on the navigation data of the vehicle-mounted map. For example, if "GS" is adopted as a highway sign on a certain map, among the navigation data of the in-vehicle map acquired by the autonomous driving system, the sign of the forward driving road for specifying the forward road type is "GS". If it is recognized, it indicates that the type of the road ahead is a highway.

It should be understood that since the automatic driving function of a vehicle is affected by various external factors in different road environments, it is necessary to specifically determine the applicable automatic driving function based on the different influential factors.

In step 222, it is determined whether or not the sign of the forward driving road is a sign of the expressway. If "YES", step 223 is executed, and if "NO", step 224 is executed.

In step 223, it is determined that the type of the forward traveling scene is the highway scene.

Optionally, the autonomous driving system determines the type of forward driving scene as the highway scene if it determines that the forward driving sign is a freeway sign. In general, the highway scene is applied to many highways, urban highways, and other road types, and in the highway scene, oncoming vehicles are running at intervals, and the road surface condition is good. Therefore, there is no traffic signal light, there is a possibility that a large curve exists in the entrance / exit lamp, and traffic accidents are likely to occur in the tunnel section.

In step 231 the target automatic driving section on the forward driving road is predicted.

Here, the target automatic driving section may be a section predicted by automatic driving and the automatic driving function can be used. For example, the target automatic driving section may be a high-speed running section or an entrance / exit ramp section. The number of target automatic driving sections may be one or more, and specifically, it needs to be determined based on the actual driving route, and may be applied to the automatic driving function. In the embodiment of the present application, the number of target automatic driving sections and the type of sections are not particularly limited.

Correspondingly, if the autonomous driving system determines that the type of driving scene ahead is a highway scene, it will further determine a target autonomous driving section in which the autonomous driving function can be used on the driving road ahead based on the navigation data of the in-vehicle map. Can be predicted.

In step 232, the target automated driving function corresponding to different automated driving levels is determined based on the section type of the target automated driving section.
Here, the target automatic driving function may be an automatic driving function applicable to the target automatic driving section at a specific automatic driving level.

Since different target autonomous driving sections can be applied to different automatic driving functions, for each target automatic driving section, the automatic driving system corresponds to different automatic driving levels based on the section type of the target automatic driving section. It is possible to determine the target automatic driving function to be achieved.

In one embodiment of the application, the automated driving level can include a first automated driving level, a second automated driving level, and a third automated driving level. Here, the first automatic operation level may be the L1 level automatic operation level, the second automatic operation level may be the L2 level automatic operation level, and the third automatic operation level may be L3. Level It may be an automatic operation level. A higher level of automated driving level can autonomously realize a fully automated driving function, and therefore, the automated driving function control method according to the embodiment of the present application is mainly an L1 level-L3 level automated driving function. Please understand that it is applicable to the control operation of.

Illustratively, in the case of a high-speed driving section in the highway scene, the target automatic driving is the high-speed automatic driving support function among the L3 level automatic driving levels and the smart automatic cruise control function among the L2 level automatic driving levels. It can be determined as a function. Upon entering the speed measurement section, the automatic speed limiting function among the L1 level automatic driving levels can be determined as the target automatic driving function.

According to the draft technology, by determining the target autonomous driving function corresponding to different automatic driving levels for different types of target autonomous driving sections in the highway scene, the driver can drive through the determined target autonomous driving function. It is possible to reduce the problem of overworked driving during long-distance driving.

In step 224, it is determined whether or not the sign of the forward driving road is a sign of the city road. If "YES", step 225 is executed, and if "NO", step 226 is executed.

In step 225, it is determined that the type of the forward traveling scene is an urban road scene.

Optionally, the autonomous driving system determines the type of forward driving scene as the urban road scene if it determines that the forward driving sign is a city road sign. In general, urban road scenes have been applied to many types of urban roads, where road conditions are more complex, traffic management such as traffic light and reversible lanes is more complex, and street pedestrians are also light vehicles. There are many, and traffic congestion is likely to occur.

In step 233, the real-time road condition of the road ahead is acquired in real time.
Here, the real-time road condition may be a real-time condition on the front road such as a vehicle traveling condition on the road, a traffic regulation condition, and various latest events (accidents, construction works, etc.). In the examples of the present application, the details of the real-time situation are not particularly limited.

Considering that urban roads have the characteristics of uncertainty and complexity of road surface conditions and that sudden events are likely to occur, the issue of driving safety should be emphasized on urban roads. Please understand that it must be. Therefore, the autonomous driving system needs to acquire the real-time road condition of the forward driving road in real time when it is determined that the type of the forward driving scene is the urban road scene.

In step 234, a target automated driving function corresponding to a different automated driving level is determined based on the real-time road condition state of the preceding driving road.

Correspondingly, after acquiring the real-time road condition of the forward road, the autonomous driving system may determine the target autonomous driving function corresponding to different automatic driving levels based on the real-time road condition of the forward road. can.

Illustratively, when the road ahead is a plurality of motorways in the urban road scene, the lane automatic change function among the L2 level automatic driving levels can be determined as the target automatic driving function. When a traffic jam occurs on the road ahead, the automatic tracking function among the L1 level automatic driving levels can be determined as the target automatic driving function. When the road is smooth, there are few light vehicles and pedestrians, and there are no artificial traffic restrictions, etc., the city automatic driving support function among the L3 level automatic driving levels is determined as the target automatic driving function. be able to.

According to the draft technology, driving safety issues are maximized by determining target autonomous driving functions that correspond to different autonomous driving levels based on the real-time road conditions of the road ahead in the urban road scene. be able to.

In step 226, it is determined whether or not the sign of the forward driving road is a sign of a municipal road. If "YES", step 227 is executed, and if "NO", step 228 is executed.

In step 227, it is determined that the type of the forward traveling scene is a municipal road scene.

Optionally, the autonomous driving system determines the type of forward travel scene as a municipal road scene if it determines that the forward road sign is a municipal road sign. In general, municipal road scenes are applied to many types of municipal roads, and in municipal road scenes, some municipal roads have fewer signal traffic lights than city roads, and some road signs such as lanes. Is irregular or incomplete, has many curves on mountain roads, and is prone to the danger of sudden intrusion of pedestrians and vehicles.

In step 235, the road instruction information of the road ahead is acquired in real time.

In step 236, the target automatic driving function corresponding to the different automatic driving level is determined based on the information completeness of the road instruction information of the preceding driving road.
Here, the road instruction information may be traffic sign instruction information on the road or the like.

Correspondingly, if the autonomous driving system determines that the type of forward driving scene is a municipal road scene, the autonomous driving will correspond to different autonomous driving levels based on the information completeness of the road instruction information of the forward driving road. In order to determine the function, the road instruction information of the driving road ahead can be acquired in real time.

In one embodiment of the present application, the step of determining the target automatic driving function corresponding to different automatic driving levels based on the information completeness of the road instruction information of the road ahead is the information completeness of the road instruction information. If the first completeness condition is satisfied, the step of determining the target automatic driving function corresponding to the target automatic driving level, and if the information completeness of the road instruction information does not satisfy the first completeness condition, the non-target automatic driving level Can include steps to determine the target autonomous driving function corresponding to.

Here, the first complete condition may be a value in which the information completeness of the road instruction information is equal to or higher than a predetermined threshold value. However, the predetermined threshold value can be set according to actual needs such as 80% and 90%, and in the embodiment of the present application, the specific numerical value of the predetermined threshold value is not particularly limited. The information completeness, that is, the ratio of the number of road instruction information to all the instruction quantities required for road instruction.

Here, the target automated driving level may be a high level automated driving level such as L2 level or L3 level, and may include only the L3 level automated driving level. The non-target automatic driving level is an automatic driving level other than the target automatic driving level. For example, when the target automatic operation level includes the L2 level or the L3 level automatic operation level, the non-target automatic operation level becomes the L1 level automatic operation level. When the target automatic operation level includes the L3 level automatic operation level, the non-target automatic operation level becomes the L1 level or the L2 level automatic operation level.

Considering that there are many cases where road instruction information is incomplete on municipal roads, in order to maintain driving safety, the autonomous driving system is based on the completeness of road instruction information on municipal roads acquired by the in-vehicle map. To determine the target autonomous driving function corresponding to different autonomous driving levels. Illustratively, when the information completeness of the road instruction information is high, the target automatic driving function corresponding to the target automatic driving level such as the automatic navigation driving function among the L3 level automatic driving levels can be determined. When the information completeness of the road instruction information is low, it is possible to determine the target automatic driving function corresponding to the non-target automatic driving level such as the smart cruising control function among the L2 level automatic driving levels. In addition, if the automatic driving system finally determines the target automatic driving function corresponding to the high level automatic driving level, the automatic driving level of L1 level or L2 level is to improve the driving safety in the municipal road scene. At the same time, it can be emphasized to present the use of the auxiliary driving function.

According to the technical proposal, by determining the target automatic driving function corresponding to different automatic driving levels based on the completeness of the road instruction information of the road ahead, the maximum effect of the automatic driving function is exhibited and the driving is performed. Safety issues can be guaranteed.

In step 228, it is determined whether or not the sign on the front road is a parking sign. If "YES", step 229 is executed, and if "NO", it is determined as another type of forward driving scene.

In step 229, it is determined that the type of the forward traveling scene is a parking scene.

Optionally, the autonomous driving system determines the type of forward driving scene as a parking scene if it determines that the sign on the road ahead is a parking sign. In general, the parking scene is applied to many outdoor parking lots and underground parking lots, and in the parking scene, in the case of outdoor parking, it is easy to encounter the danger of being hit by a car from the side, and indoor parking. In the case of, problems such as poor GPS (Global Positioning System) sensitivity are likely to occur.

In step 237, the target automatic driving function corresponding to different automatic driving levels is determined based on the vehicle behavior history data and the vehicle-mounted map navigation data.

Correspondingly, the autonomous driving system has a target autonomous driving function corresponding to different autonomous driving levels based on the vehicle behavior history data and the in-vehicle map navigation data when the type of the forward driving scene is determined to be the parking scene. Can be determined.

In one embodiment of the present application, the step of determining the target autonomous driving function corresponding to different automatic driving levels based on the vehicle behavior history data and the vehicle-mounted map navigation data is based on the vehicle behavior history data. The step of determining the operation success probability of the target automatic driving function, the step of determining the safety probability of the roadside zone parking of the vehicle based on the in-vehicle map navigation data, and the operation success probability and / or the safety probability are parking. If it is determined that the scene conditions are met, it can include a step of determining a target autonomous driving function corresponding to different autonomous driving levels.

Here, the parking scene condition may have an operation success probability of a value equal to or higher than the first probability threshold value, and / or may have a safety probability of a value of a value equal to or higher than the second probability threshold value. However, the first probability threshold value and the second probability threshold value can be set according to actual needs, and both may be the same or different. For example, the first probability threshold may be 98% and the second probability threshold may be 95%, and the examples of the present application are not limited thereto.

When determining the target autonomous driving function corresponding to different autonomous driving levels based on the vehicle behavior history data and the in-vehicle map navigation data, considering the special requirements of the parking scene, if it is an indoor parking scene, the signal quality In order to avoid the influence, the operation success probability of the target automatic driving function can be determined based on the vehicle behavior history data. For example, based on the behavior data such as vehicle history automatic parking and smart call, the operation success probability of the automatic driving function such as automatic parking and smart call is determined, and the operation success probability is a preset number of the automatic driving system. When it is larger than the probability threshold value of 1, the automatic driving function such as smart call or automatic parking among the automatic driving levels of the L3 level can be set as the target automatic driving function. In the case of an outdoor parking scene, it is possible to determine the safety probability of vehicle roadside parking based on in-vehicle map navigation data in consideration of driving safety factors. If the autonomous driving system determines that the safety probability of the vehicle's roadside parking is greater than the preset second probability threshold of the autonomous driving system, the autonomous driving system will automatically operate, such as remote-controlled parking, among the L2 level autonomous driving levels. The driving function can be the target automatic driving function.

According to the technical proposal, smart parking is realized and parking safety problems are solved by determining the target automatic driving function corresponding to different automatic driving levels based on the vehicle behavior history data and the in-vehicle map navigation data. Can be guaranteed.

Correspondingly, the autonomous driving system may not be able to obtain a valid forward road sign based on vehicle-mounted map navigation data, or may obtain a highway sign based on vehicle-mounted map navigation data. If it cannot be recognized as one of a scene, city road scene, municipal road scene or parking scene, it can be determined as the type of other forward driving scene. In this case, in order to guarantee the driving safety by determining that the automatic driving system can determine the corresponding target automatic driving function based on the result of the comprehensive analysis or cannot determine the corresponding target automatic driving function. To show the driver to use the artificial driving mode.

In one embodiment of the present application, the step of determining the target autonomous driving function based on the type of the forward driving scene and the driving-related data is based on the type of the forward driving scene and the driving-related data. It can include a step of determining a plurality of candidate automatic driving functions and a step of evaluating each candidate automatic driving function and determining the target automatic driving function from each candidate automatic driving function based on the evaluation result. ..

Here, a candidate automated driving function, i.e., a plurality of available automated driving features determined based on the type of forward driving scene and driving-related data.

In certain types of forward driving scenes, the autonomous driving system may determine multiple autonomous driving functions currently available to the vehicle, such as smart autonomous cruising control features and autonomous driving features such as automatic speed limits. Please understand that it is decided at the same time. In this case, the automatic driving system determines each candidate automatic driving function as a candidate automatic driving function, and determines the target automatic driving function from each candidate automatic driving function based on the evaluation result. Evaluation can be performed for the automatic driving function. For example, factors such as reliability, energy consumption, and safety are calculated for each candidate automatic driving function, and multiple factors are integrated to set the optimum candidate automatic driving function for the type of forward driving scene as the target automatic driving function. By making a decision, the rationality and accuracy of the target automatic driving function can be further ensured.

In one embodiment of the present application, the steps to determine the target automated driving function corresponding to different automated driving levels are the step of calculating the reliability of the target automated driving function and the confirmation of the driver via the human machine interface module. In order to receive the operation and determine the target automatic driving function based on the confirmation operation, the automatic driving level corresponding to the target automatic driving function, the reliability of the target automatic driving function, and the target automatic driving function It can include a step of transmitting the usage interval range to the human machine interface module.

Here, it is the range of used sections, that is, the section distance at which the target automatic driving function can be maintained.

The target autonomous driving function determined by the autonomous driving system by the automatic driving function control method has relatively high accuracy and rationality, but there may still be a certain risk. For this reason, the autonomous driving system calculates the reliability for the finally determined target autonomous driving function in order to determine whether the vehicle or driver activates or switches the target automatic driving function, and the target automatic driving function. It is possible to transmit the automatic driving level corresponding to the driving function, the reliability of the target automatic driving function, and the range of use of the target automatic driving function to the human machine interface module.

It should be understood that the smartening of the automatic driving function at the L3 level automatic driving level has reached a certain level and has high reliability. Therefore, if the automatic driving level corresponding to the target automatic driving function determined by the automatic driving system belongs to the L1 level automatic driving level and / or the L2 level automatic driving level, the automatic driving system will refer to the target automatic driving function for reference. Can also be sent to the human machine interface module.

According to the technical proposal, vehicles and drivers can drive the target autonomously by outputting data such as the automatic driving level corresponding to the target automatic driving function, the reliability of the target automatic driving function, and the range of used sections of the target automatic driving function. Allows you to decide in real time whether to activate or switch features.

In one embodiment of the present application, after determining the target automatic driving function based on the type of the forward driving scene and the driving-related data, a step of determining the target vehicle sensor of the target automatic driving function, and a target vehicle. It can include a step of transmitting sensor information to a human-machine interface module.

Here, the target vehicle sensor may be a necessary sensor for implementing the target automatic driving function. The target vehicle sensor information may be information such as a name or a sign of the target vehicle sensor.

In the embodiment of the present application, the automatic driving system further determines the necessary sensor for the target automatic driving function as the target vehicle sensor after determining the target automatic driving function, and transmits the target vehicle sensor information to the human machine interface module. be able to. In this case, the vehicle can determine whether or not the target vehicle sensor has an abnormal state based on the target vehicle sensor information. When the vehicle determines that the target vehicle sensor has an abnormal state, it generates abnormal caution information and sends the abnormal caution information to the human-machine interface module, so that the abnormal caution information causes an abnormality on a specific target vehicle sensor. , Remind you that it may not be used normally. The driver can finally determine whether to enable or switch the target automatic driving function based on the abnormality caution information pushed from the vehicle. If the vehicle determines that there is no anomalous condition in the target vehicle sensor, it may not operate in any way. As can be seen from this, by outputting the target vehicle sensor information, the vehicle can detect the state of the target vehicle sensor in a timely manner, and it is used as a reference for the driver's decision making, and the safety of automatic driving. Sex can be guaranteed.

The above-mentioned automatic driving function control method can adopt different automatic driving function evaluation policies and evaluation viewpoints for different scenes, and after determining the type of the forward driving scene, the detailed details of each forward driving scene type. Based on the scene data, determine the corresponding target autonomous driving function, instruct the driver to use the autonomous driving function installed in the vehicle rationally, and promote the high feasibility and safety of the autonomous driving function. Guaranteed. As a result, safe driving is realized and the utilization rate of the automatic driving function is improved.

In one embodiment, FIG. 3 is a configuration diagram of an automated driving function control device according to an embodiment of the present application. In the embodiment of the present application, a rational target automatic driving function is determined in order to realize rational control of the automatic driving function based on the type of the vehicle's forward driving scene and the driving-related data acquired by the vehicle. It may be applied when The device is realized by a method that employs software and / or hardware, and is specifically arranged in an electronic device. The electronic device may be a device such as a computer, or may be mounted on an autonomous driving vehicle as a vehicle control system.

The automatic driving function control device 300 as shown in FIG. 3 includes a driving-related data acquisition module 310, a driving scene type determination module 320, and a target automatic driving function determination module 330.

Here, the operation-related data acquisition module 310 is configured to acquire operation-related data.

The driving scene type determination module 320 is configured to determine the type of the vehicle's forward driving scene based on the driving-related data.

The target automatic driving function determination module 330 is configured to determine the target automatic driving function based on the type of the forward driving scene and the driving-related data.

In the embodiment of the present application, the type of the vehicle's forward driving scene is determined based on the acquired driving-related data, and the target automatic driving function is determined based on the determined forward driving scene type and the acquired driving-related data. By doing so, it solves the irrational problem of the automatic driving function control by ignoring the influence of multiple factors in the conventional technique, and improves the accuracy and rationality of the automatic driving function control.

Selectably, the driving-related data includes vehicle state data, vehicle-mounted map navigation data, cloud real-time data, and vehicle behavior history data.

Selectably, the driving scene type determination module 320 acquires the sign of the forward driving road based on the vehicle-mounted map navigation data, and when it is determined that the sign of the front driving road is a sign of the highway, the front is described. When the type of the driving scene is determined as the highway scene and the sign of the front driving road is determined to be the sign of the city road, the type of the driving scene is determined as the city road scene and the sign of the front driving road is determined. If it is determined that is a sign for a municipal road, the type of the forward driving scene is determined as a municipal road scene, and if it is determined that the sign for the forward driving road is a parking sign, the type of the forward driving scene is parked. It is configured to be determined as a scene.

To be selectable, the target automatic driving function determination module 330 predicts the target automatic driving section on the forward driving road when the type of the forward driving scene is determined to be the highway scene, and the section of the target automatic driving section. Based on the type, it is configured to determine the target autonomous driving function corresponding to different autonomous driving levels.

Selectably, the target automatic driving function determination module 330 acquires the real-time road condition of the forward driving road in real time when the type of the forward driving scene is determined to be the urban road scene, and the real-time of the front driving road. It is configured to determine the target autonomous driving function corresponding to different autonomous driving levels based on road conditions.

Selectably, when the target automatic driving function determination module 330 determines that the type of the forward driving scene is a municipal road scene, the target automatic driving function determination module 330 acquires the road instruction information of the forward driving road in real time, and obtains the road instruction information of the forward driving road in real time. It is configured to determine the target autonomous driving function corresponding to different autonomous driving levels based on the information completeness of the instruction information.

Selectably, the target automatic driving function determination module 330 determines the target automatic driving function corresponding to the target automatic driving level when the information completeness of the road instruction information satisfies the first completeness condition, and the road instruction. If the information completeness of the information does not meet the first completeness condition, it is configured to determine the target automated driving function corresponding to the non-targeted automated driving level.

Selectably, when the target automatic driving function determination module 330 determines that the type of the forward driving scene is a parking scene, the target automatic driving function determination module 330 is set to a different automatic driving level based on the vehicle behavior history data and the in-vehicle map navigation data. It is configured to determine the corresponding target autonomous driving function.

To be selectable, the target automatic driving function determination module 330 determines the operation success probability of the target automatic driving function based on the vehicle behavior history data, and parks the vehicle on the roadside zone based on the in-vehicle map navigation data. The safety probability of the vehicle is determined, and if it is determined that the operation success probability and / or the safety probability satisfies the parking scene condition, the target automatic driving function corresponding to a different automatic driving level is determined.

To be selectable, the target automatic driving function determination module 330 determines a plurality of candidate automatic driving functions based on the type of the forward driving scene and the driving-related data, and evaluates each candidate automatic driving function. It is configured to determine the target automatic driving function from each candidate automatic driving function based on the evaluation result.

To be selectable, the target automatic driving function determination module 330 calculates the reliability of the target automatic driving function, receives the confirmation operation of the driver via the human machine interface module, and the target based on the confirmation operation. In order to determine the automatic driving function, the automatic driving level corresponding to the target automatic driving function, the reliability of the target automatic driving function, and the range of use of the target automatic driving function are transmitted to the human machine interface module. It is composed.

Optionally, the autonomous driving function controller is configured to transmit the target vehicle sensor determination module configured to determine the target vehicle sensor of the target automated driving function and the target vehicle sensor information to the human machine interface module. It is further equipped with a configured target vehicle sensor information transmission module.

The automated driving function control device can execute the automated driving function control method according to any embodiment of the present application, has a functional module capable of executing the method, and has a beneficial effect. For other technical details not described in detail in this example, the description of the automated driving function control method in any of the embodiments of the present application may be referred to.

Since the above-mentioned automatic driving function control device is a device capable of executing the automatic driving function control method in the embodiment of the present application, those skilled in the art will use the present embodiment based on the automatic driving function control method described in the present specification. Since it is possible to grasp the specific embodiment of the automatic driving function control device and its various modified embodiments, how the automatic driving function control device relates to the automatic driving function control method according to the embodiment of the present application. As for the details of whether or not it is realized, detailed description is omitted here. Any device used by one of ordinary skill in the art to implement the automated driving function control method according to an embodiment of the present application should be included in the scope of protection of the present application.

In one embodiment, the present application further provides electronic devices and readable storage media.
As shown in FIG. 4, it is a schematic block diagram of the electronic device of the automatic driving function control method according to the embodiment of this application. Electronic devices are intended to represent various types of digital computers such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, large computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices such as personal digital processing, mobile phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the description of this specification and / or the realization of the required application.

As shown in FIG. 4, the electronic device includes one or more processors 401, a memory 402, and an interface for connecting each component having a high speed interface and a low speed interface. The components are interconnected by different buses and can be mounted on a common motherboard or, if desired, in other ways. The processor processes instructions executed within an electronic device, including instructions stored in memory to display GUI graphic information on an external input / output device (such as a display device coupled to an interface). Can be done. In other embodiments, a plurality of processors and / or a plurality of buses can be used with a plurality of memories, if desired. Similarly, a plurality of electronic devices can be connected, and each device can provide a partial necessary operation (eg, a server array, a blade server, or a multiprocessor system). In FIG. 4, one processor 401 is taken as an example.

The memory 402 is a non-temporary computer-readable storage medium according to the present application. Here, by storing instructions executed by at least one processor in the memory, the at least one processor can execute the automatic operation function control method according to the present application. The non-temporary computer-readable storage medium of the present application stores computer instructions for causing a computer to execute the automatic operation function control method according to the present application.

As a non-temporary computer-readable storage medium, the memory 402 is a program instruction / module (for example, the operation-related data acquisition module 310 shown in FIG. 3) corresponding to the automatic operation function control method according to the embodiment of the present application. It is configured to store non-temporary software programs, non-temporary computer-executable programs and modules such as scene type determination module 320, target automatic operation function determination module 330). The processor 401 executes various functional applications and data processing of the server by executing non-temporary software programs, instructions and modules stored in the memory 402, i.e., an automated operation function according to an embodiment of the method. Realize the control method.

The memory 402 can include a storage program area and a storage data area, where the storage program area can store an operating system, an application program required for at least one function, and the storage data area is a storage data area. Data created by using electronic devices can be stored. The memory 402 can also include high speed random access memory and can further include non-temporary memory, eg, at least one disk storage device, flash memory device, or other non-temporary solid state. It is a storage device. In some embodiments, the memory 402 may include memory remotely installed with respect to the processor 401, which connects the automated driving function control method to the performing electronic device via the network. be able to. Examples of networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The electronic device that executes the automatic operation function control method can further include an input device 403 and an output device 404. The processor 401, the memory 402, the input device 403, and the output device 404 can be connected via a bus or other method, and in FIG. 4, the connection via the bus is taken as an example.

The input device 403 can receive the input numerical or character information and generate a key signal input relating to user setting and function control of the electronic device for the automatic operation function control method, for example, a touch screen, a keypad. , Mouse, trackpad, touchpad, indicator stick, one or more mouse buttons, trackball, joystick and other input devices. The output device 404 can include a display device, an auxiliary lighting device (eg, an LED), a haptic feedback device (eg, a vibration motor), and the like. Display devices include, but are not limited to, liquid crystal displays (LCDs), light emitting diode (LED) displays, and plasma displays. In some embodiments, the display device may be a touch screen.

Various embodiments of the systems and techniques described herein include digital electronic circuit systems, integrated circuit systems, application specific ASICs (ASICs), computer hardware, firmware, software, and / or them. It can be realized by the combination of. These various embodiments may include being implemented in one or more computer programs, wherein the one or more computer programs are executed and executed in a programmable system with at least one programmable processor. / Or can be interpreted, the programmable processor may be a specific purpose or general purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device. Data and instructions can be transmitted to the storage system, the at least one input device, and the at least one output device.

These computing programs (also called programs, software, software applications, or code) are these computing in programmable processor machine instructions, high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Includes implementing the program. As used herein, the terms "machine readable medium" and "computer readable medium" are any computer program products used to provide machine instructions and / or data to programmable processors. , And / or equipment (eg, magnetic disks, optical disks, memories, programmable logic devices (PLDs)), including machine-readable media that receive machine instructions, which are machine-readable signals. The term "machine readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

To provide interaction with the user, the systems and techniques described herein can be implemented on a computer, which computer is a display device for displaying information to the user (eg, a CRT (cathode line tube)). ) Or LCD (LCD) monitor) and a keyboard and pointing device (eg, mouse or trackball), the user can provide input to the computer by the keyboard and the pointing device. Other types of devices may be configured to provide interaction with the user. For example, the feedback provided to the user may be of any form of sensing feedback (eg, visual feedback, auditory feedback, or tactile feedback) and of any form (acoustic input, audio input, and tactile input). Can receive input from the user.

The systems and techniques described herein are computing systems with back-end components (eg, data servers), computing systems with middleware components (eg, application servers), or computing with front-end components. A system (eg, a user computer having a graphical user interface or web browser, the user interacts with the graphical user interface or embodiments of the systems and techniques described herein by the web browser), or such backend components. And can be implemented in computing systems with any combination of middleware components and front-end components. The components of the system may be interconnected by digital data communication of any form or medium (eg, a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

A computer system can include a client side and a server.
The client side may be, but is not limited to, a smartphone, a laptop computer, a desktop computer, a tablet, a smart speaker, or the like. The server may be an independent physical server or a cluster of servers consisting of multiple physical servers, and may be a cloud that provides basic cloud computing services such as cloud computing, cloud services, cloud databases, and cloud storage. It may be a server. The client side and the server are generally separated from each other and usually interact with each other via a communication network. A computer program that runs on the corresponding computer and has a client-server relationship with each other creates a client-server relationship.

In the embodiment of the present application, the type of the vehicle's forward driving scene is determined based on the acquired driving-related data, and the target automatic driving function is determined based on the determined forward driving scene type and the acquired driving-related data. By doing so, it solves the irrational problem of the automatic driving function control by ignoring the influence of multiple factors in the conventional technique, and improves the accuracy and rationality of the automatic driving function control.

It should be understood that steps can be sorted, added or deleted using the various forms of flow shown above. For example, the steps described in this application may be performed in parallel, sequentially, or in a different order, but the techniques disclosed in this application. The present specification is not limited as long as the proposal can achieve the desired result.

Specific embodiments do not limit the scope of protection of this application. One of ordinary skill in the art can make various modifications, combinations, sub-combinations, and replacements, depending on design requirements and other factors. Any amendments, equivalent replacements, and improvements made within the spirit and principles of this application should be within the scope of this application's protection.

Claims (27)

Steps to get driving related data and
Steps to determine the type of vehicle's forward driving scene based on the driving-related data, and
A step of determining the target automatic driving function based on the type of the forward driving scene and the driving-related data, and
Automatic driving function control method including. The automatic driving function control method according to claim 1, wherein the driving-related data includes vehicle state data, vehicle-mounted map navigation data, cloud real-time data, and vehicle behavior history data. The step of determining the type of vehicle's forward driving scene based on the driving-related data is
Steps to get the sign of the road ahead based on the navigation data of the in-vehicle map,
When it is determined that the sign of the front driving road is a sign of a highway, a step of determining the type of the front driving scene as a highway scene and a step of determining the type of the front driving scene.
When it is determined that the sign of the forward driving road is a sign of a city road, a step of determining the type of the front driving scene as a city road scene and
When it is determined that the sign of the forward driving road is a sign of a municipal road, the step of determining the type of the forward driving scene as a municipal road scene and
When it is determined that the sign of the front driving road is a parking sign, the step of determining the type of the front driving scene as a parking scene and
The automatic operation function control method according to claim 2. The step of determining the target automatic driving function based on the type of the forward driving scene and the driving-related data is
When it is determined that the type of the forward driving scene is a highway scene, a step of predicting a target automatic driving section on the forward driving road and a step of predicting the target automatic driving section,
A step of determining a target automatic driving function corresponding to a different automatic driving level based on the section type of the target automatic driving section, and
The automatic operation function control method according to claim 1. The step of determining the target automatic driving function based on the type of the forward driving scene and the driving-related data is
When it is determined that the type of the forward driving scene is an urban road scene, the step of acquiring the real-time road condition of the forward driving road in real time and
Based on the real-time road conditions of the road ahead, the steps to determine the target autonomous driving function corresponding to different autonomous driving levels, and
The automatic operation function control method according to claim 1. The step of determining the target automatic driving function based on the type of the forward driving scene and the driving-related data is
When it is determined that the type of the forward driving scene is a municipal road scene, the step of acquiring the road instruction information of the forward driving road in real time and
Based on the completeness of the road instruction information of the preceding driving road, the step of determining the target automatic driving function corresponding to the different automatic driving level, and
The automatic operation function control method according to claim 1. The step of determining the target autonomous driving function corresponding to different autonomous driving levels based on the information completeness of the road instruction information of the preceding driving road is
When the information completeness of the road instruction information satisfies the first completeness condition, the step of determining the target automatic driving function corresponding to the target automatic driving level and the step.
When the information completeness of the road instruction information does not satisfy the first completeness condition, the step of determining the target automatic driving function corresponding to the non-target automatic driving level, and
The automatic operation function control method according to claim 6. The step of determining the target automatic driving function based on the type of the forward driving scene and the driving-related data is
A claim comprising the step of determining a target autonomous driving function corresponding to different autonomous driving levels based on vehicle behavior history data and vehicle-mounted map navigation data when the type of forward driving scene is determined to be a parking scene. The automatic operation function control method according to 1. The step of determining the target autonomous driving function corresponding to different autonomous driving levels based on the vehicle behavior history data and the in-vehicle map navigation data is
A step of determining the operation success probability of the target automatic driving function based on the vehicle behavior history data, and
Based on the in-vehicle map navigation data, the step of determining the safety probability of the roadside parking of the vehicle and
When it is determined that the operation success probability and / or the safety probability satisfy the parking scene condition, a step of determining a target automatic driving function corresponding to a different automatic driving level, and a step of determining the target automatic driving function.
The automatic operation function control method according to claim 8. The step of determining the target automatic driving function based on the type of the forward driving scene and the driving-related data is
A step of determining a plurality of candidate automatic driving functions based on the type of the forward driving scene and the driving-related data, and
A step of evaluating each candidate automatic driving function and determining the target automatic driving function from each candidate automatic driving function based on the evaluation result.
The automatic operation function control method according to claim 1. The steps to determine the target autonomous driving function for different autonomous driving levels are:
The step of calculating the reliability of the target automatic driving function and
In order to receive the driver's confirmation operation via the human-machine interface module and determine the target automatic operation function based on the confirmation operation, the automatic operation level corresponding to the target automatic operation function and the target automatic operation function And the step of transmitting the usage section range of the target automatic driving function to the human-machine interface module.
The automatic driving function control method according to any one of claims 4 to 9. The step of determining the target vehicle sensor of the target automatic driving function and
Steps to send target vehicle sensor information to the human-machine interface module,
The automatic operation function control method according to claim 1. A driving-related data acquisition module configured to acquire driving-related data,
A driving scene type determination module configured to determine the type of vehicle's forward driving scene based on the driving-related data.
A target automatic driving function determination module configured to determine a target automatic driving function based on the type of the forward driving scene and the driving-related data.
Automatic operation function control device equipped with. The automatic driving function control device according to claim 13, wherein the driving-related data includes vehicle state data, vehicle-mounted map navigation data, cloud real-time data, and vehicle behavior history data. The driving scene type determination module
Get the sign of the road ahead based on the in-vehicle map navigation data,
When it is determined that the sign of the front driving road is a sign of a highway, the type of the front driving scene is determined as a highway scene.
When it is determined that the sign of the front driving road is a sign of a city road, the type of the front driving scene is determined as the city road scene.
When it is determined that the sign of the front driving road is a sign of a municipal road, the type of the front driving scene is determined as a municipal road scene.
The automatic driving function control device according to claim 14, wherein when the sign of the front driving road is determined to be a parking sign, the type of the front driving scene is determined as a parking scene. The target automatic operation function determination module
When it is determined that the type of the forward driving scene is a highway scene, the target automatic driving section on the forward driving road is predicted, and the target automatic driving section is predicted.
The automatic driving function control device according to claim 13, wherein the target automatic driving function corresponding to a different automatic driving level is determined based on the section type of the target automatic driving section. The target automatic operation function determination module
When it is determined that the type of the forward driving scene is an urban road scene, the real-time road condition of the forward driving road is acquired in real time.
The automatic driving function control device according to claim 13, wherein the target automatic driving function corresponding to a different automatic driving level is determined based on the real-time road condition state of the preceding driving road. The target automatic operation function determination module
When it is determined that the type of the forward driving scene is a municipal road scene, the road instruction information of the forward driving road is acquired in real time.
The automatic driving function control device according to claim 13, wherein the target automatic driving function corresponding to a different automatic driving level is determined based on the information completeness of the road instruction information of the preceding driving road. The target automatic operation function determination module
When the information completeness of the road instruction information satisfies the first completeness condition, the target automatic driving function corresponding to the target automatic driving level is determined.
The automatic driving function control device according to claim 18, wherein when the information completeness of the road instruction information does not satisfy the first completeness condition, the target automatic driving function corresponding to the non-target automatic driving level is determined. .. The target automatic operation function determination module
When it is determined that the type of the forward driving scene is a parking scene, it is configured to determine the target automatic driving function corresponding to different automatic driving levels based on the vehicle behavior history data and the in-vehicle map navigation data. The automatic operation function control device according to claim 13. The target automatic operation function determination module
Based on the vehicle behavior history data, the operation success probability of the target automatic driving function is determined.
Based on the in-vehicle map navigation data, the safety probability of roadside parking of the vehicle is determined.
20. The automated driving function control according to claim 20, wherein if it is determined that the motion success probability and / or the safety probability satisfies the parking scene condition, the target automated driving function corresponding to the different automated driving level is determined. Device. The target automatic operation function determination module
A plurality of candidate automatic driving functions are determined based on the type of the forward driving scene and the driving-related data.
The automatic driving function control device according to claim 13, wherein each candidate automatic driving function is evaluated, and the target automatic driving function is determined from each candidate automatic driving function based on the evaluation result. The target automatic operation function determination module
Calculate the reliability of the target automatic driving function and
In order to receive the driver's confirmation operation via the human machine interface module and determine the target automatic operation function based on the confirmation operation, the automatic operation level corresponding to the target automatic operation function and the target automatic operation function The automatic driving function control device according to any one of claims 16 to 21, which is configured to transmit the reliability of the above and the range of used sections of the target automatic driving function to the human machine interface module. The target vehicle sensor determination module configured to determine the target vehicle sensor of the target automatic driving function, and the target vehicle sensor determination module.
A target vehicle sensor information transmission module configured to transmit target vehicle sensor information to a human-machine interface module, and a target vehicle sensor information transmission module.
13. The automated driving function control device according to claim 13. With at least one processor,
A memory communicably connected to the at least one processor and
Equipped with
The memory stores an instruction that can be executed by the at least one processor, and when the instruction is executed by the at least one processor, the at least one processor is any one of claims 1 to 12. An electronic device that can execute the automatic operation function control method described in. A non-temporary computer-readable storage medium that stores computer instructions.
A non-temporary computer-readable storage medium in which the computer instruction causes a computer to execute the automatic driving function control method according to any one of claims 1 to 12. A computer program that causes the computer to execute the automatic driving function control method according to any one of claims 1 to 12 while operating on the computer.

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