CN117104272A - Intelligent driving method, system, vehicle and storage medium - Google Patents
Intelligent driving method, system, vehicle and storage medium Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 56
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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- B60W60/001—Planning or execution of driving tasks
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Abstract
The application relates to an intelligent driving method, an intelligent driving system, a vehicle and a storage medium, and relates to the technical field of intelligent driving. The method comprises the following steps: acquiring environment information, vehicle position information, barrier information and navigation information of a vehicle in the running process; determining a constraint condition and a plurality of reference paths based on the navigation information; the constraint condition at least comprises path constraint information and speed constraint information; determining a plurality of reference trajectories from a plurality of reference paths based on the constraint condition, the environmental information, the vehicle position information, the obstacle information, and the first decision planning algorithm; determining an optimal track from a plurality of reference tracks based on a second decision-making planning algorithm; and controlling the vehicle to run according to the optimal track. Therefore, the decision planning capability under the complex driving scene can be improved.
Description
Technical Field
The application relates to the technical field of intelligent driving, in particular to the technical field of intelligent driving decision planning, and specifically relates to an intelligent driving method, an intelligent driving system, a vehicle and a storage medium.
Background
With the development of artificial intelligence, unmanned technologies of intelligent vehicles have received a great deal of attention. The intelligent driving decision planning is one of key technologies for realizing unmanned driving of the intelligent vehicle, and is the planning of driving decisions by the vehicle according to perceived environmental information and preset targets through decision algorithms and models, wherein the intelligent driving decision planning comprises path planning, behavior decision, adherence of traffic rules and the like.
However, in the actual driving process, the intelligent vehicle is in various environments, and the simple decision plan cannot meet the safety and comfort in various driving scenes.
Therefore, how to design a safe and comfortable driving track for an intelligent vehicle in various driving scenes is a problem to be solved.
Disclosure of Invention
The application provides an intelligent driving method, an intelligent driving system, a vehicle and a storage medium, which at least solve the technical problem that simple decision planning in the related technology cannot cope with complex driving scenes. The technical scheme of the application is as follows:
according to a first aspect to which the present application relates, there is provided an intelligent driving method comprising:
acquiring environment information, vehicle position information, barrier information and navigation information of a vehicle in the running process;
determining a constraint condition and a plurality of reference paths based on the navigation information; the constraint condition at least comprises path constraint information and speed constraint information;
determining a plurality of reference trajectories from a plurality of reference paths based on the constraint condition, the environmental information, the vehicle position information, the obstacle information, and the first decision planning algorithm;
determining an optimal track from a plurality of reference tracks based on a second decision-making planning algorithm;
and controlling the vehicle to run according to the optimal track.
According to the technical means, the constraint condition and the multiple reference paths are determined based on the navigation information, then the multiple reference paths are selected from the multiple reference paths according to the first decision planning algorithm in combination with the constraint information, the environment information, the vehicle position information and the obstacle information, and further the optimal path is determined from the multiple reference paths based on the second decision planning algorithm, so that in various driving scenes, the safety and the comfort of driving can be guaranteed by controlling the vehicle to drive according to the optimal path.
In one possible embodiment, the method further comprises: the first decision-making algorithm is obtained by: identifying a driving scene of the vehicle based on the environmental information; a first decision-making planning algorithm is determined that matches the driving scenario.
Because the driving scene of the vehicle is complex and changeable, the safety and the comfort of various driving scenes can be influenced by the decision planning of the decision calculation using the rules, and in this way, the driving scene of the vehicle is identified according to the acquired environmental information, and the first decision planning algorithm matched with the driving scene is further determined, so that the reference track determined by different algorithms is safer and more comfortable, and the driving requirement can be better met.
In one possible implementation, when the driving scene is a curve overtaking scene, the first decision planning algorithm matched with the curve overtaking scene comprises a rule algorithm; when the driving scene is a through-intersection scene, the first decision-making planning algorithm matched with the through-intersection scene comprises a machine learning algorithm.
In one possible embodiment, the intelligent driving method further includes: determining an optimal track based on the second decision planning algorithm, the comprehensive evaluation index and the safety constraint index of each reference track; wherein, the comprehensive evaluation index includes: one or more of reference track length, longitudinal speed, and speed curvature, the safety constraint index comprises: one or more of a relative speed of the vehicle and the obstacle, a distance of the vehicle from the obstacle, a maximum acceleration, and a safety level.
According to the technical means, the track information with highest safety and comfort can be determined by comprehensively considering the factors of the reference track length, the longitudinal speed, the speed curvature, the relative speed of the vehicle and the obstacle, the distance between the vehicle and the obstacle, the maximum acceleration and the safety level.
According to a second aspect of the present application, there is provided an intelligent driving system comprising:
the acquisition module is used for acquiring environment information, vehicle position information, barrier information and navigation information of the vehicle in the running process;
the decision planning module is used for determining constraint conditions and a plurality of reference paths based on the navigation information; the constraint condition at least comprises path constraint information and speed constraint information;
the decision planning module is further used for determining a plurality of reference tracks from a plurality of reference paths based on constraint conditions, environment information, vehicle position information, barrier information and a first decision planning algorithm;
the decision planning module is further used for determining an optimal track from the plurality of reference tracks based on a second decision planning algorithm;
and the processing module is used for controlling the vehicle to run according to the optimal track.
In one possible implementation manner, the decision-making module is further configured to identify a driving scenario of the vehicle based on the environmental information;
a first decision-making planning algorithm is determined that matches the driving scenario.
In one possible implementation manner, when the driving scene is a curve overtaking scene, the first decision planning algorithm matched with the curve overtaking scene comprises a rule algorithm; when the driving scene is a through-crossroad scene, the first decision-making planning algorithm matched with the through-crossroad scene comprises a machine learning algorithm.
In one possible implementation, the decision-making module is further configured to: determining an optimal track based on the second decision planning algorithm, the comprehensive evaluation index and the safety constraint index of each reference track; wherein, the comprehensive evaluation index includes: one or more of reference track length, longitudinal speed, and speed curvature, the safety constraint index comprises: one or more of a relative speed of the vehicle and the obstacle, a distance of the vehicle from the obstacle, a maximum acceleration, and a safety level.
According to a third aspect of the present application, there is provided an electronic apparatus comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the method of the first aspect and any of its possible embodiments described above.
According to a fourth aspect of the present application there is provided a computer readable storage medium, which when executed by a processor of an electronic device, enables the electronic device to perform the method of the first aspect and any of its possible embodiments.
According to a fifth aspect of the present application there is provided a computer program product comprising computer instructions which, when run on a vehicle, cause the vehicle to perform the method of the first aspect and any one of its possible embodiments.
Therefore, the technical characteristics of the application have the following beneficial effects:
(1) According to the method, constraint conditions and a plurality of reference paths are determined based on navigation information, then the plurality of reference tracks are selected from the plurality of reference paths according to a first decision-making planning algorithm by combining constraint information, environment information, vehicle position information and barrier information, and further an optimal track is determined from the plurality of reference tracks based on a second decision-making planning algorithm, so that in various driving scenes, the safety and the comfort of driving can be guaranteed by controlling the vehicle to drive according to the optimal track.
(2) Because the driving scene of the vehicle is complex and changeable, the safety and the comfort of various driving scenes can be influenced by the decision planning by using the regular decision algorithm, and in this way, the driving scene of the vehicle is identified according to the acquired environmental information, and the first decision planning algorithm matched with the driving scene is further determined, so that the reference track determined by different algorithms is safer and more comfortable, and the driving requirement can be better met.
(3) The track information with highest safety and comfort can be determined by comprehensively considering the factors of the reference track length, the longitudinal speed, the speed curvature, the relative speed of the vehicle and the obstacle, the distance between the vehicle and the obstacle, the maximum acceleration and the safety level.
It should be noted that, the technical effects caused by any implementation manner of the second aspect to the fifth aspect may refer to the technical effects caused by the corresponding implementation manner in the first aspect, which are not described herein.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application and do not constitute a undue limitation on the application.
FIG. 1 is a schematic diagram of an intelligent driving system, according to an exemplary embodiment;
FIG. 2 is a schematic diagram of a further intelligent driving system, according to an exemplary embodiment;
FIG. 3 is a schematic diagram of a further intelligent driving system, according to an exemplary embodiment;
FIG. 4 is a flow chart illustrating a method of intelligent driving according to an exemplary embodiment;
FIG. 5 is a schematic illustration of an application scenario of an intelligent driving method, according to an exemplary embodiment;
FIG. 6 is a schematic diagram of a further intelligent driving system, according to an exemplary embodiment;
FIG. 7 is a block diagram of a vehicle, according to an example embodiment.
Detailed Description
In order to enable a person skilled in the art to better understand the technical solutions of the present application, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
For ease of understanding, the basic concepts of some terms or techniques involved in embodiments of the present application are first briefly described and illustrated.
Reference path: refers to an ideal path that is predefined during the planning phase for the vehicle to travel, typically an abstract, smooth curve or line segment, that guides the vehicle in selecting the appropriate path during the planning process.
Reference trajectory: the reference track refers to a real path of the vehicle in an actual running process, and can comprise position information, path information, speed information, acceleration information and the like of the vehicle, wherein the reference track is the real path of the vehicle in actual running. The reference path is used as input for planning decisions and the reference trajectory is used as output for controlling the vehicle to travel.
Rule algorithm: refers to algorithms that make decisions based on predefined rules and logic, typically made manually. For example, if an obstacle is detected in front of the vehicle, emergency braking is performed.
Machine learning algorithm: refers to algorithms that make decisions by learning and optimizing from a large amount of data. For example, through training data sets, the machine learning algorithm may learn how to predict optimal vehicle speed and steering angle based on road conditions and travel history.
The foregoing is a description of some concepts related to the embodiments of the present application, and is not repeated herein.
As described in the background, with the development of artificial intelligence, unmanned technology of intelligent automobiles has been paid attention to. Intelligent driving decision planning is one of key technologies for realizing unmanned driving of an intelligent automobile, and in an intelligent driving system, the intelligent driving system is generally divided into an acquisition unit, a planning unit and a control unit, wherein the acquisition unit is used for acquiring environmental information around a vehicle and position information of the vehicle. The planning unit needs to make corresponding decisions and plan the running track of the vehicle according to the acquired surrounding environment information and the position information of the vehicle, and in the planning unit, the planned running track needs to consider the following factors from the safety point of view: path constraint information and speed constraint information, the other is that the planned trajectory does not collide with other obstacles, including stationary obstacles and moving obstacles. The control unit is used for controlling the vehicle to run according to the track planned by the planning unit.
However, in the actual driving process, the environment where the intelligent vehicle is located is complex and changeable, and the simple decision plan cannot meet the safety and comfort in the complex driving scene.
Based on the intelligent driving method provided by the application, constraint conditions and a plurality of reference paths are determined based on navigation information, then the plurality of reference paths are selected from the plurality of reference paths according to a first decision-making planning algorithm in combination with constraint information, environment information, vehicle position information and barrier information, and further an optimal path is determined from the plurality of reference paths based on a second decision-making planning algorithm, so that the safety and the comfort of running can be ensured by controlling the vehicle to run according to the optimal path.
In order to further describe the technical scheme of the embodiment of the application, as shown in fig. 1, the intelligent running system provided by the embodiment of the application is provided.
Referring to fig. 1, the intelligent traveling system may include: the system comprises a main decision module, a post decision module and a planning module.
In some embodiments, the master decision module may include an environmental model construction module, a constraint information construction module, and a reference path generation module. Wherein, as shown in figure 2,
the environment model construction module is mainly used for receiving environment information of the vehicle in the running process after the data preprocessing sent by the perception module, vehicle pose information after the data preprocessing sent by the positioning module, obstacle information after the data preprocessing sent by the prediction module, road information after the data preprocessing provided by the map module, running scenes sent by the environment reconstruction module and navigation information sent by the navigation module. The environment model building module packages the information into data information required by decision making and provides combined information of candidate lanes.
The environment model construction module is used for acquiring environment information, vehicle position information, barrier information and navigation information of the vehicle in the running process and identifying the running scene of the vehicle based on the acquired environment information.
The constraint condition construction module is used for constructing constraints according to the environment information, the vehicle position information, the obstacle information and the navigation information, wherein the constraint condition construction module determines path constraint information according to the obstacle information and determines speed constraint information according to traffic rule information in the environment information.
And the reference path generation module is used for screening out a reference path and a boundary according to the lane candidate information and carrying out smoothing processing on the reference path.
In some embodiments, the decision-making planning module is configured to determine a first decision-making planning algorithm according to the identified driving scenario of the vehicle, determine a plurality of reference paths based on the first decision-making planning algorithm, the navigation information, and the constraint condition, and further determine a plurality of reference trajectories from the plurality of reference paths according to the constraint information and the navigation information.
In some embodiments, the post-decision module is configured to determine an optimal trajectory from a plurality of reference trajectories generated in the decision planning module.
As shown in fig. 3, the actual software data flow of the intelligent driving method of the present application is: the system perception module and the positioning module transmit obstacle information comprising the type, the size, the position and the like of the obstacle to the prediction module, the prediction module predicts the motion track of the obstacle and transmits the predicted motion track of the obstacle to the main decision module, the main decision module calculates path constraint information and speed constraint information of the reference track based on the obstacle information, the planning module determines a plurality of reference tracks according to the path constraint information and the speed constraint information, and the rear decision module determines an optimal track from the plurality of reference tracks.
For easy understanding, the following describes the intelligent driving method provided by the application with reference to the accompanying drawings.
As shown in fig. 4, the intelligent driving method provided by the embodiment of the application can be applied to an intelligent driving system, and the intelligent driving system can be an electronic device with calculation and processing capabilities and is embedded in a vehicle. Taking a control device of a vehicle as an example, the intelligent driving method comprises the following steps:
s101, acquiring environment information, vehicle position information, barrier information and navigation information of a vehicle in the running process.
Alternatively, the environmental information of the vehicle while traveling may include weather information or the like; the position information of the vehicle can comprise head orientation information, running speed information, acceleration information, type of the area where the vehicle is located (such as urban area, village and town, high speed) and the like; the obstacle information may include the kind, size, position, etc. of static obstacle information and dynamic obstacle information; the navigation information may include path length, path type, traffic light information of the start point and the destination of the vehicle.
S102, determining constraint conditions and a plurality of reference paths based on navigation information.
The constraint condition at least comprises path constraint information and speed constraint information. Exemplary path constraint information includes: a safe distance between the vehicle and the obstacle, a method for avoiding the obstacle, and the like; the speed constraint information includes: the current running speed of the vehicle, the current running acceleration of the vehicle, and speed information of each path point of the vehicle in the reference path.
Illustratively, a plurality of reference paths and safe distances between the vehicle and the obstacle, and speed information of each track point of the vehicle in the reference paths are determined based on path lengths of the start point and the destination of the vehicle in the navigation information, the path type, and traffic light information.
In some embodiments, the plurality of reference paths may also include a lane centerline and a sequence of boundary lines.
S103, determining a plurality of reference tracks from a plurality of reference paths based on constraint conditions, environment information, vehicle position information, obstacle information and a first decision planning algorithm.
In some embodiments, the first decision-making algorithm is obtained by: identifying a driving scene of the vehicle based on the environmental information; a first decision-making planning algorithm is determined that matches the driving scenario.
Because the driving scene of the vehicle is complex and changeable, the safety and the comfort of various driving scenes can be influenced by the decision planning by using the regular decision algorithm, and in this way, the driving scene of the vehicle is identified according to the acquired environmental information, and the first decision planning algorithm matched with the driving scene is further determined, so that the reference track determined by different algorithms is safer and more comfortable, and the driving requirement can be better met.
In some embodiments, when the driving scenario is a curve cut-in scenario, the first decision-making algorithm that matches the curve cut-in scenario comprises a rule algorithm; when the driving scene is a through-intersection scene, the first decision-making planning algorithm matched with the through-intersection scene comprises a machine learning algorithm.
In the rule algorithm, the driving behavior of the automatic driving vehicle is divided, and a driving behavior rule base is established based on information such as a perception environment and navigation information. In the running process of the automatic driving vehicle, the environment information, the navigation information and the like around the vehicle are acquired in real time, and then the next decision is determined. In the machine learning algorithm, a neural network model is trained by natural driving data acquired in advance when an old driver drives the vehicle, and then the trained algorithm model is deployed on the vehicle.
S104, determining an optimal track from a plurality of reference tracks based on a second decision planning algorithm.
In some embodiments, the optimal trajectory is determined based on the second decision-making algorithm and the comprehensive evaluation index and the safety constraint index of each reference trajectory.
Wherein, the comprehensive evaluation index includes: one or more of reference track length, longitudinal speed, and speed curvature, the safety constraint index comprises: one or more of a relative speed of the vehicle and the obstacle, a distance of the vehicle from the obstacle, a maximum acceleration, and a safety level.
In some embodiments, the score of the comprehensive evaluation index and the score of the safety constraint index of each reference track are calculated based on the second decision-making planning algorithm, and the reference track with the highest average score of the comprehensive evaluation index and the score of the safety constraint index in the plurality of reference tracks is taken as the optimal track.
In this way, the track information with highest safety and comfort can be determined by comprehensively considering the reference track length, the longitudinal speed, the speed curvature, the relative speed of the vehicle and the obstacle, the distance between the vehicle and the obstacle, the maximum acceleration and the safety level.
In other embodiments, as shown in FIG. 5, a plurality of reference trajectories are displayed to the user, and the user selects an optimal trajectory from the plurality of reference trajectories.
S105, controlling the vehicle to run according to the optimal track.
The embodiment shown in fig. 4 has at least the following advantages that based on navigation information, constraint conditions and a plurality of reference paths are determined, then according to a first decision planning algorithm, the constraint information, environment information, vehicle position information and obstacle information are combined, a plurality of reference paths are selected from the plurality of reference paths, and further, based on a second decision planning algorithm, an optimal track is determined from the plurality of reference paths, so that in various driving scenes, the safety and the comfort of driving can be guaranteed by controlling the vehicle to drive according to the optimal track.
The foregoing description of the solution provided by the embodiments of the present application has been mainly presented in terms of a method. To achieve the above functions, the intelligent driving system or the vehicle includes a hardware structure and/or a software module that performs the respective functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. 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.
The embodiment of the application can divide the functional modules of the intelligent driving system or the vehicle according to the method, for example, the intelligent driving system or the vehicle can comprise each functional module corresponding to each functional division, or two or more functions can be integrated in one processing module. The integrated modules may be implemented in hardware or in software functional modules. It should be noted that, in the embodiment of the present application, the division of the modules is schematic, which is merely a logic function division, and other division manners may be implemented in actual implementation.
FIG. 6 is a block diagram illustrating an intelligent driving system, according to an example embodiment. Referring to fig. 6, the intelligent driving system 10 includes: an acquisition module 101, a decision-making module 102 and a processing module 103.
The acquisition module 101 is configured to acquire environmental information, vehicle position information, obstacle information, and navigation information of the vehicle during traveling.
A decision-making planning module 102 for determining a constraint condition and a plurality of reference paths based on the navigation information; the constraint includes at least path constraint information and speed constraint information.
The decision-making planning module 102 is further configured to determine a plurality of reference trajectories from the plurality of reference paths based on the constraint condition, the environmental information, the vehicle location information, the obstacle information, and the first decision-making planning algorithm.
The decision planning module 102 is further configured to determine an optimal trajectory from the plurality of reference trajectories based on the second decision planning algorithm.
And the processing module 103 is used for controlling the vehicle to run according to the optimal track.
In some embodiments, the decision-making module 102 is further configured to identify a driving scenario of the vehicle based on the environmental information; a first decision-making planning algorithm is determined that matches the driving scenario.
In some embodiments, when the driving scenario is a curve cut-in scenario, the first decision-making algorithm that matches the curve cut-in scenario comprises a rule algorithm; when the driving scene is a through-intersection scene, the first decision-making planning algorithm matched with the through-intersection scene comprises a machine learning algorithm.
In some embodiments, the decision planning module 102 is further configured to determine an optimal track based on the second decision planning algorithm and the comprehensive evaluation index and the safety constraint index of each reference track; wherein, the comprehensive evaluation index includes: one or more of reference track length, longitudinal speed, and speed curvature, the safety constraint index comprises: one or more of a relative speed of the vehicle and the obstacle, a distance of the vehicle from the obstacle, a maximum acceleration, and a safety level.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
In the case that the intelligent driving system adopts the hardware form to realize the functions of the integrated modules, the embodiment of the application provides a structural schematic diagram of a vehicle. As shown in fig. 7, the vehicle 20 includes: a processor 202, a communication interface 203, a bus 204. Optionally, the vehicle 20 may also include a memory 201.
The processor 202 may be any means for implementing or executing the various exemplary logic blocks, modules and circuits described in connection with this disclosure. The processor 202 may be a central processing unit, a general purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various exemplary logic blocks, modules and circuits described in connection with this disclosure. The processor 202 may also be a combination that performs computing functions, such as including one or more microprocessors, a combination of a DSP and a microprocessor, or the like.
A communication interface 203 for connecting with other devices through a communication network. The communication network may be an ethernet, a radio access network, a wireless local area network (wireless local area networks, WLAN), etc.
Memory 201, which may be, but is not limited to, a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (electrically erasable programmable read-only memory, EEPROM), magnetic disk storage or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
As a possible implementation, the memory 201 may exist separately from the processor 202, and the memory 201 may be connected to the processor 202 through the bus 204 for storing instructions or program code. The intelligent driving method provided by the embodiment of the application can be realized when the processor 202 calls and executes the instructions or the program codes stored in the memory 201.
In another possible implementation, the memory 201 may also be integrated with the processor 202.
Bus 204 may be an extended industry standard architecture (extended industry standard architecture, EISA) bus or the like. The bus 204 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in fig. 7, but not only one bus or one type of bus.
In an exemplary embodiment, a computer readable storage medium is also provided, such as a memory 201, comprising instructions executable by the processor 202 of the vehicle 20 to implement the method of the above embodiments.
In actual implementation, the functions of the acquisition module 101, the decision planning module 102, and the processing module 103 in fig. 6 may be implemented by the processor 202 in fig. 7 invoking a computer program stored in the memory 201. For specific implementation, reference may be made to the description of the method in the above embodiment, and details are not repeated here.
Alternatively, the computer readable storage medium may be a non-transitory computer readable storage medium, for example, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
In an exemplary embodiment, embodiments of the application also provide a computer program product comprising one or more instructions executable by the processor 202 of the vehicle to perform the method of the above-described embodiments.
It should be noted that, when the instructions in the computer readable storage medium or one or more instructions in the computer program product are executed by the processor of the vehicle, the processes of the method embodiments are implemented, and the technical effects similar to those of the method are achieved, so that repetition is avoided, and no further description is given here.
From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules, so as to perform all the classification parts or part of the functions described above.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another apparatus, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and the parts shown as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed in a plurality of different places. The purpose of the embodiment scheme can be achieved by selecting part or all of the classification part units according to actual needs.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a readable storage medium. Based on such understanding, the technical solution of the embodiments of the present application, or the portion contributing to the prior art or the whole classification portion or portion of the technical solution, may be embodied in the form of a software product stored in a storage medium, where the software product includes several instructions to cause a device (may be a single-chip microcomputer, a chip or the like) or a processor (processor) to execute the whole classification portion or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
The present application is not limited to the above embodiments, and any changes or substitutions within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (10)
1. An intelligent driving method, characterized in that the method comprises:
acquiring environment information, vehicle position information, barrier information and navigation information of a vehicle in the running process;
determining a constraint condition and a plurality of reference paths based on the navigation information; the constraint condition at least comprises path constraint information and speed constraint information;
determining a plurality of reference trajectories from a plurality of the reference paths based on the constraint condition, the environmental information, the vehicle position information, the obstacle information, and a first decision planning algorithm;
determining an optimal track from a plurality of reference tracks based on a second decision planning algorithm;
and controlling the vehicle to run according to the optimal track.
2. The intelligent driving method according to claim 1, characterized in that the first decision-making algorithm is obtained by:
identifying a driving scene of the vehicle based on the environmental information;
a first decision-making planning algorithm is determined that matches the driving scenario.
3. The intelligent driving method according to claim 2, wherein,
when the driving scene is a curve overtaking scene, the first decision planning algorithm matched with the curve overtaking scene comprises a rule algorithm;
when the driving scene is a through-crossroad scene, the first decision-making planning algorithm matched with the through-crossroad scene comprises a machine learning algorithm.
4. The intelligent driving method according to claim 1, wherein the determining an optimal trajectory from a plurality of the reference trajectories based on the second decision planning algorithm comprises:
determining an optimal track based on the second decision planning algorithm, and comprehensive evaluation indexes and safety constraint indexes of each reference track; wherein, the comprehensive evaluation index comprises: one or more of a reference trajectory length, a longitudinal velocity, and a velocity curvature, the safety constraint index comprising: one or more of a relative speed of the vehicle and the obstacle, a distance of the vehicle from the obstacle, a maximum acceleration, and a safety level.
5. An intelligent driving system, comprising:
the acquisition module is used for acquiring environment information, vehicle position information, barrier information and navigation information of the vehicle in the running process;
the decision planning module is used for determining constraint conditions and a plurality of reference paths based on the navigation information; the constraint condition at least comprises path constraint information and speed constraint information;
the decision planning module is further configured to determine a plurality of reference trajectories from a plurality of the reference paths based on the constraint condition, the environmental information, the vehicle location information, the obstacle information, and a first decision planning algorithm;
the decision planning module is further used for determining an optimal track from a plurality of reference tracks based on a second decision planning algorithm;
and the processing module is used for controlling the vehicle to run according to the optimal track.
6. The intelligent driving system of claim 5, wherein the decision planning module is further configured to:
identifying a driving scene of the vehicle based on the environmental information;
a first decision-making planning algorithm is determined that matches the driving scenario.
7. The intelligent driving system according to claim 6, wherein,
when the driving scene is a curve overtaking scene, the first decision planning algorithm matched with the curve overtaking scene comprises a rule method;
when the driving scene is a through-crossroad scene, the first decision-making planning algorithm matched with the through-crossroad scene comprises a machine learning algorithm.
8. The intelligent driving system of claim 5, wherein the decision planning module is further configured to:
determining an optimal track based on the second decision planning algorithm, and comprehensive evaluation indexes and safety constraint indexes of each reference track; wherein, the comprehensive evaluation index comprises: one or more of a reference trajectory length, a longitudinal velocity, and a velocity curvature, the safety constraint index comprising: one or more of a relative speed of the vehicle and the obstacle, a distance of the vehicle from the obstacle, a maximum acceleration, and a safety level.
9. A vehicle comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the intelligent driving method of any of claims 1-4.
10. A computer readable storage medium storing computer executable instructions which, when run on a computer, cause the computer to perform the intelligent driving method of any one of claims 1-4.
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| CN117698769A (en) * | 2024-02-05 | 2024-03-15 | 上海鉴智其迹科技有限公司 | Automatic driving track planning method and device, electronic equipment and storage medium |
| CN117698769B (en) * | 2024-02-05 | 2024-04-26 | 上海鉴智其迹科技有限公司 | Automatic driving track planning method and device, electronic equipment and storage medium |
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