CN115027503A

CN115027503A - Control method and device for automatic driving vehicle and automatic driving vehicle

Info

Publication number: CN115027503A
Application number: CN202210814025.XA
Authority: CN
Inventors: 不公告发明人
Original assignee: Jiuzhizhixing Beijing Technology Co ltd; Jiuzhi Suzhou Intelligent Technology Co ltd
Current assignee: Jiuzhizhixing Beijing Technology Co ltd; Jiuzhi Suzhou Intelligent Technology Co ltd
Priority date: 2022-07-11
Filing date: 2022-07-11
Publication date: 2022-09-09

Abstract

A control method and device for an autonomous vehicle and the autonomous vehicle, the method comprising: predicting behaviors of surrounding obstacles in real time in the running process of the automatic driving vehicle, wherein the obstacles comprise first obstacles and second obstacles, each first obstacle corresponds to a unique behavior prediction result, and each second obstacle corresponds to at least two behavior prediction results; determining a decision result for each behavior prediction result; determining a first solution space based on the unique decision result; determining a plurality of second solution spaces based on the plurality of non-unique decision results; selecting an optimal second solution space from the plurality of second solution spaces; and obtaining a third solution space according to the optimal second solution space and the first solution space, and determining the driving strategy of the automatic driving vehicle based on the third solution space. The invention can enable the automatic driving vehicle to avoid the obstacles in time when the automatic driving vehicle runs under the scene with more obstacles, thereby ensuring the safety of the running process.

Description

Control method and device of automatic driving vehicle and automatic driving vehicle

Technical Field

The invention relates to the field of automatic driving vehicles, in particular to a control method and device of an automatic driving vehicle and the automatic driving vehicle.

Background

Interaction strategies correspond to the human driver's mind for the autonomous vehicle to direct the actions that the autonomous vehicle should take in the face of other traffic participants, such as braking, accelerating, detouring, etc. As the automatic driving technology is gradually matured, the application scenarios thereof are also subdivided, and the automatic driving vehicle should have different interaction strategies with the social vehicle according to different application scenarios. At present, most interaction strategies are concentrated on high-speed scenes and urban road scenes, and no effective solution is available for low-speed non-motor lane scenes. How to ensure the efficiency and safety of the automatic driving vehicle in the non-motor vehicle and pedestrian environment such as the non-motor lane becomes a problem to be solved urgently for the automatic driving vehicle.

Disclosure of Invention

A series of concepts in a simplified form are introduced in the summary section, which is described in further detail in the detailed description section. This summary of the invention is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

In view of the defects of the prior art, a first aspect of the embodiment of the present invention provides a control method for an autonomous vehicle, the method including:

predicting behaviors of obstacles around an automatic driving vehicle in real time in the running process of the automatic driving vehicle, wherein the obstacles comprise first obstacles and second obstacles, each first obstacle corresponds to a unique behavior prediction result, and each second obstacle corresponds to at least two behavior prediction results;

determining a decision result for each of the behavioral predictors, wherein each of the first obstacles corresponds to a unique decision result and each of the second obstacles corresponds to at least two non-unique decision results;

determining a first solution space by integrating the unique decision result;

determining a plurality of second solution spaces by integrating the non-unique decision results;

selecting an optimal second solution space from the plurality of second solution spaces;

and obtaining a third solution space according to the optimal second solution space and the first solution space, and determining the driving strategy of the automatic driving vehicle based on the third solution space.

In some embodiments, the method is used during travel of the autonomous vehicle in a non-motorized lane.

In some embodiments, the predicting, in real-time, behavior of obstacles around the autonomous vehicle during travel of the autonomous vehicle includes:

in the running process of an automatic driving vehicle, acquiring the position information of obstacles around the automatic driving vehicle in real time;

predicting behavior of the obstacle based on a change in the position information over time.

In some embodiments, said determining a plurality of second solution spaces based on a plurality of said non-unique decision results comprises:

and selecting any one decision result from the non-unique decision results corresponding to each second obstacle to perform permutation and combination, and determining the second solution space according to the combination of the non-unique decision results corresponding to different second obstacles.

In some embodiments, said selecting an optimal second solution space among said plurality of second solution spaces comprises:

and scoring each second solution space based on a scoring model, and taking the second solution space with the highest score as the optimal second solution space.

In some embodiments, the scoring model scores the second solution space based on at least one of: somatosensory factors and safety factors.

In some embodiments, the behavioral prediction results include at least one of: cutting, running side by side, running in reverse and following.

In some embodiments, the decision result comprises at least one of: yield, overtaking, neglect, alert, detour.

A second aspect of an embodiment of the present invention provides a control apparatus for an autonomous vehicle, the control apparatus comprising a memory and a processor, the memory having stored thereon a computer program for execution by the processor, the computer program, when executed by the processor, performing the control method for an autonomous vehicle as described above.

A third aspect of the embodiments of the present invention provides an autonomous vehicle, including a vehicle body, a driving device for driving the vehicle body to operate, and a sensor disposed on the vehicle body; the autonomous vehicle further includes a control device connecting the driving device and the sensor, the control device being configured to execute the control method of the autonomous vehicle as described above.

Embodiments of the present invention further provide a storage medium, where the storage medium stores a computer program, and the computer program executes the control method for an autonomous vehicle when running.

The control method of the automatic driving vehicle and the automatic driving vehicle can enable the automatic driving vehicle to avoid obstacles in time when the automatic driving vehicle runs under the scene with more obstacles, and ensure the safety of the running process.

Drawings

The above and other objects, features and advantages of the present invention will become more apparent from the following detailed description of the embodiments of the present invention when taken in conjunction with the accompanying drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings, like reference numbers generally indicate like parts or steps.

FIG. 1 is a schematic flow chart diagram of a control method of an autonomous vehicle according to one embodiment of the invention;

FIG. 2 is a schematic block diagram of a control apparatus of an autonomous vehicle according to one embodiment of the invention;

FIG. 3 is a schematic block diagram of an autonomous vehicle in accordance with one embodiment of the invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, exemplary embodiments according to the present application will be described in detail below with reference to the accompanying drawings. It should be understood that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and that the present application is not limited by the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the application described in the application without inventive step, shall fall within the scope of protection of the application.

In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present application. It will be apparent, however, to one skilled in the art, that the present application may be practiced without one or more of these specific details. In other instances, well-known features of the art have not been described in order to avoid obscuring the present application.

It is to be understood that the present application may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of the associated listed items.

In order to provide a thorough understanding of the present application, a detailed structure will be presented in the following description in order to explain the technical solutions presented in the present application. Alternative embodiments of the present application are described in detail below, however, the present application may have other implementations in addition to these detailed descriptions.

In the field of autonomous driving, more attention is being focused on interaction with motor vehicles and a small number of non-motor vehicles in an interaction strategy. Common interaction strategies include: 1. a line giving/line robbing strategy based on a game theory; 2. rule-based interaction strategies based on decision trees, Markov decision models and the like; 3. and based on the static characteristics of the road, the machine learning model of the dynamic characteristics of the traffic participants.

The technical scheme obtains certain practical achievements in high-speed scenes and urban road scenes. However, in low speed environments such as non-motorways, pedestrians and non-motorways occupy a great majority of them as traffic participants. Compared with urban roads, the interaction between non-motor vehicles and pedestrians and between non-motor vehicles and automatic driving vehicles is more frequent, the obstacles of the vehicles of the automatic driving system in the driving process are more, the passing space is narrower, the driving safety and efficiency of the vehicles are effectively guaranteed, and no clear and effective solution is available at present.

For example, if a line giving/line robbing strategy based on a game theory is adopted, game balance points are more difficult to reach than a regular motor vehicle road environment, the game failure result is more serious, and pedestrians are easily injured. If the interaction strategy based on the rules such as the decision tree and the Markov decision model is adopted, the overall decision link is too long, which may result in high time consumption, slow execution due to too high system delay, and difficulty in handling more scenes with emergencies. For the machine model method, as the behavior of the participants of the non-motor vehicle lane is more discrete, the long tail problem is more difficult to converge, and more problems can be left and cannot be solved.

In view of the above problems, an embodiment of the present invention provides a control method for an autonomous vehicle, which considers a plurality of possible behaviors of an obstacle in an environment with strong uncertainty such as a non-motor lane, divides a complex uncertainty problem into a plurality of simple sub-problems, and obtains an optimal result from the plurality of sub-problems, thereby obtaining an optimal driving strategy that meets an expected behavior, so that the autonomous vehicle can avoid the obstacle in time when driving in a scene with many non-motor vehicles and pedestrians such as the non-motor lane, and the safety of the driving process is effectively ensured. A control method of an autonomous vehicle and an autonomous vehicle proposed by an embodiment of the present invention are described below with reference to the accompanying drawings.

Referring initially to FIG. 1, FIG. 1 illustrates a schematic flow chart of a control method 100 for an autonomous vehicle of the autonomous vehicle in accordance with an embodiment of the invention. The control method 100 of the autonomous vehicle according to the embodiment of the present invention is applied to an autonomous vehicle, which may also be called an unmanned vehicle, and is an intelligent vehicle that can automatically complete a vehicle running task instead of a driver without the driver performing a driving operation. As shown in fig. 1, a control method 100 of an autonomous vehicle according to an embodiment of the present invention includes the steps of:

in step S110, predicting behaviors of obstacles around an autonomous vehicle in real time during a driving process of the autonomous vehicle, where the obstacles include first obstacles and second obstacles, each of the first obstacles corresponds to a unique behavior prediction result, and each of the second obstacles corresponds to at least two behavior prediction results;

in step S120, determining a decision result for each of the behavior prediction results, wherein each of the first obstacles corresponds to a unique decision result, and each of the second obstacles corresponds to at least two non-unique decision results;

determining a first solution space based on at least one of the unique decision results at step S130;

determining a plurality of second solution spaces based on a plurality of the non-unique decision results at step S140;

in step S150, selecting an optimal second solution space from the plurality of second solution spaces;

in step S160, a third solution space is obtained according to the optimal second solution space and the first solution space, and a driving strategy of the autonomous vehicle is determined based on the third solution space.

The control method 100 of the autonomous vehicle according to the embodiment of the present invention determines a first solution space according to a decision result corresponding to an explicit behavior prediction result, determines a second solution space according to a decision result corresponding to an implicit behavior prediction result, and finally selects an optimal second solution space from a plurality of second solution spaces to merge with the first solution space, thereby considering a plurality of possible behaviors of an obstacle in an environment with strong uncertainty, and finally obtaining an optimal driving strategy according with an expected behavior.

Illustratively, the control method 100 of the autonomous vehicle of the embodiment of the invention is implemented during the course of traveling of the autonomous vehicle in the non-motor vehicle lane. Compared with urban roads, the interaction between the automatic driving vehicle and the non-motor vehicles and pedestrians is more frequent in the environment of the non-motor vehicle lane, more obstacles are generated in the driving process, and the passing space is narrower. For example, the control method 100 of the autonomous vehicle of an embodiment of the invention may be enabled when it is determined that the autonomous vehicle is traveling in a non-motorized lane; other interaction strategies may be enabled while the autonomous vehicle is traveling in the lane of the vehicle, which is not limited in this embodiment of the invention. The interaction strategy of embodiments of the present invention may also be implemented in other similar scenarios, in addition to non-motorway lanes.

Specifically, in step S110, the behavior of obstacles around the autonomous vehicle is predicted in real time while the autonomous vehicle is running. The obstacle around the autonomous vehicle may refer to an obstacle within a preset range around the autonomous vehicle, including but not limited to a vehicle and a pedestrian around the autonomous vehicle. The behavior of obstacles around the automatic driving vehicle is predicted, namely the behavior of the obstacles in a future period of time is predicted according to the behavior of the obstacles in the past period of time, so that the behaviors of the obstacles are effectively responded in time.

Illustratively, the method of predicting obstacle behavior comprises: the method comprises the steps of obtaining position information of obstacles around the automatic driving vehicle, and predicting behavior of the obstacles according to change of the position information along with time. As one implementation, position information of an obstacle around the autonomous vehicle, which may be a relative positional relationship between the obstacle and the autonomous vehicle, may be acquired based on the radar. Specifically, the point cloud data of the surrounding environment is collected through a radar arranged on the automatic driving vehicle, the radar can be a laser radar, and the laser radar can be a laser radar with regular repeated scanning or a laser radar with a complex scanning track and a non-repeated scanning characteristic. The radar can sense external environmental information, such as range information, azimuth information, reflection intensity information, speed information, etc., of environmental targets. Then, point cloud clusters of the obstacles are extracted from the point cloud data, for example, the point cloud data may be clustered to identify point cloud clusters belonging to different obstacles therein. When applied in a non-motorway scenario, the obstacles primarily include non-motor vehicles and pedestrians. According to the distance information and the azimuth information contained in the point cloud cluster, the relative position of the obstacle and the automatic driving vehicle can be determined, and the change of the position information of the obstacle along with the time can be determined according to the position of the same obstacle in continuously acquired point cloud data.

It should be noted that the method for obtaining the position information of the obstacle according to the embodiment of the present invention is not limited to obtaining by radar, and may also obtain the position information of the obstacle by other sensors, including but not limited to a vision sensor, an infrared sensor, an ultrasonic sensor, and the like.

Thereafter, the behavior of the obstacle is recognized from the change over time of the position information of the obstacle. For example, the behavior of an obstacle may be labeled as cut, side-by-side, reverse, follow, etc. Specific methods for identifying the behavior of the obstacle include, but are not limited to, machine learning methods, that is, position information of the obstacle is input to a machine learning label trained in advance, and a result of identifying the behavior of the obstacle is output by a machine learning model. When the machine learning model is trained, the position information of the obstacle is used as input, and the model parameters are optimized, so that the prediction result of the obstacle behavior output by the machine learning model is close to the real behavior of the manually marked obstacle, and the trained machine learning model can be used for predicting the behavior of the obstacle. The method of recognizing the behavior of the obstacle is not limited to the machine learning method.

Obstacles around an autonomous vehicle may have multiple behaviors, some of which are relatively explicit and may result in a unique behavior prediction. For example, when an obstacle is stopped ahead in the traveling direction of the autonomous vehicle, it can be definitely determined that its behavior is a hindrance to traveling. While other behaviors are ambiguous and there may be multiple behavior predictions, for example, for a vehicle approaching an autonomous vehicle, it is unpredictable whether the behavior is a cut or a side-by-side run, and therefore at least two non-unique behavior predictions will result. For convenience of description, an obstacle from which a unique prediction result can be obtained is defined as a first obstacle, and an obstacle from which the unique prediction result cannot be obtained is defined as a second obstacle; the first obstacle and the second obstacle do not constitute a limitation on the type of the obstacle, and the first obstacle and the second obstacle may also be switched with each other over time.

Thereafter, in step S120, for each behavior prediction result for each obstacle, a decision result for the behavior prediction result is determined. Decision results include, but are not limited to, yield (yield), overtake (overake), ignore (ignore), vigilance (caution), nudge (nude), and the like. It will be appreciated that each first obstacle corresponds to a unique decision result, since each first obstacle corresponds to a unique behaviour predictor; each second obstacle corresponds to at least two non-unique decision outcomes, since each second obstacle corresponds to at least two non-unique prediction outcomes.

Next, in step S130, the unique decision result is integrated to determine a first solution space, i.e. a travelable space for each time point of the time series, which solution space is defined as safe for the driver. Specifically, a first solution space is divided by combining the unique decision results of all the first obstacles, and the first solution space is a solution space obtained by considering only decision results of definite behaviors. The unique decision result of each first obstacle forms a limit to a solution space, and after the unique decision results of a plurality of first obstacles are integrated, a solution space which can easily obtain an optimal solution based on methods such as optimization is obtained.

Then, in step S140, a plurality of second solution spaces are determined by integrating the non-unique decision results. The plurality of second solution spaces takes into account a plurality of decision results for a plurality of possible behaviors. Specifically, one of the non-unique decision results corresponding to each second obstacle may be selected for permutation and combination, and the second solution space may be determined according to a combination of the non-unique decision results corresponding to different second obstacles. For example, assuming that N second obstacles are recognized around the autonomous vehicle, each second obstacle may have M behavior prediction results, and each behavior prediction result has a corresponding decision result, so that N × M second solution spaces are obtained in total. Of course, the above is merely an example, and the number of behavior prediction results for different obstacles may be different.

Thereafter, in step S150, an optimal second solution space is selected from the plurality of second solution spaces. For example, each second solution space is scored based on a scoring model, and the highest scoring second solution space is selected as the optimal second solution space. When the scoring model scores the second solution space, the considered factors include but are not limited to safety factors, somatosensory factors and the like.

Finally, in step S160, a third solution space is obtained according to the optimal second solution space and the first solution space, and a driving strategy of the autonomous vehicle is determined based on the third solution space. Specifically, the intersection may be solved for the first solution space and the optimal second solution space to obtain the final solution space as the output result of the solution space

In summary, the control method 100 for the autonomous vehicle according to the embodiment of the present invention can enable the autonomous vehicle to avoid the obstacle in time when the autonomous vehicle runs in a scene with many obstacles, so as to ensure safety during the running process.

Referring to fig. 2, the control device 200 of the autonomous vehicle includes a memory 210 and a processor 220, the memory 210 stores a computer program executed by the processor 220, and the computer program executes the control method 100 of the autonomous vehicle when executed by the processor 220.

Illustratively, memory 210 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc.

Processor 220 may execute the program instructions stored by memory 210 to implement the functions of the embodiments of the invention described herein (as implemented by the processor) and/or other desired functions. Various applications and various data, such as various data used and/or generated by the applications, may also be stored in the computer-readable storage medium. Processor 220 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other form of processing unit having data processing capabilities and/or instruction execution capabilities.

Embodiments of the present invention also provide an autonomous vehicle, which may be used to implement the above-described control method 100 of the autonomous vehicle. The automatic driving vehicle is an intelligent vehicle which does not need a driver to execute driving operation and can replace the driver to automatically finish the vehicle driving task; autonomous vehicles may also have manual driving functionality. Referring to fig. 3, fig. 3 shows a schematic block diagram of an autonomous vehicle according to an embodiment of the invention.

As shown in fig. 3, the autonomous vehicle includes a vehicle body 300, a driving device 310, a sensor 320, and a control device 330, the control device 330 is connected to the driving device 310 and the sensor 320, and the control device 330 may execute the control method 100 of the autonomous vehicle as described above to control the driving device 310 to drive the vehicle body to operate. The sensor 320 includes a radar, an image sensor, an infrared sensor, and the like. It should be noted that the autonomous vehicle further includes other constituent structures, and the embodiment of the present invention is not limited thereto. The control method 100 of the autonomous driving vehicle executed by the control device 330 can refer to the above, and is not described in detail herein.

The automatic driving vehicle disclosed by the embodiment of the invention can avoid the obstacles in time when the automatic driving vehicle runs under the scene with more obstacles, and the safety of the running process is ensured.

Furthermore, according to an embodiment of the present invention, there is also provided a computer storage medium having stored thereon program instructions for executing the respective steps of the control method 100 of an autonomous vehicle of an embodiment of the present invention when the program instructions are executed by a computer or a processor, the specific details of which may be referred to above. The computer storage medium may include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a portable compact disc read only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

Although the example embodiments have been described herein with reference to the accompanying drawings, it is to be understood that the above-described example embodiments are merely illustrative and are not intended to limit the scope of the present application thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the scope or spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as claimed in the appended claims.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another device, or some features may be omitted, or not executed.

In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the description of exemplary embodiments of the present application, various features of the present application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the method of the present application should not be construed to reflect the intent: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.

It will be understood by those skilled in the art that all of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where such features are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Moreover, those of skill in the art will understand that although some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

The various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some of the modules according to embodiments of the present application. The present application may also be embodied as apparatus programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present application may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means can be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.

The above description is only for the specific embodiments of the present application or the description thereof, and the protection scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the protection scope of the present application. The protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A control method of an autonomous vehicle, the method comprising:

determining a first solution space by integrating the unique decision result;

2. The control method of an autonomous vehicle as claimed in claim 1, characterized in that the method is used during driving of the autonomous vehicle in a non-motorized lane.

3. The control method of an autonomous vehicle according to claim 1, wherein predicting, in real time, behavior of an obstacle around the autonomous vehicle while the autonomous vehicle is traveling, includes:

4. The method of controlling an autonomous-capable vehicle as recited in claim 1, wherein the determining a plurality of second solution spaces based on a plurality of the non-unique decision results comprises:

5. The control method of an autonomous vehicle as claimed in claim 1, wherein the selecting an optimal second solution space among the plurality of second solution spaces comprises:

6. The control method of an autonomous vehicle as claimed in claim 5, characterized in that the scoring model scores the second solution space according to at least one of the following factors: somatosensory factors and safety factors.

7. The control method of an autonomous vehicle as claimed in claim 1, characterized in that the behavior prediction result includes at least one of: cutting, running side by side, running in reverse and following.

8. The control method of an autonomous vehicle as claimed in claim 1, characterized in that the decision result comprises at least one of: yield, overtaking, ignoring, vigilance, detour.

9. A control apparatus of an autonomous vehicle, characterized in that the control apparatus comprises a memory and a processor, the memory having stored thereon a computer program to be run by the processor, the computer program, when being run by the processor, performing the control method of an autonomous vehicle according to any one of claims 1-8.

10. An automatic driving vehicle is characterized by comprising a vehicle body, a driving device for driving the vehicle body to operate, and a sensor arranged on the vehicle body;

the autonomous vehicle further includes a control device connecting the drive device and the sensor, the control device being configured to execute the control method of the autonomous vehicle according to any one of claims 1 to 8.