CN114620060A - Automatic driving mode selection method, automatic driving automobile and control method thereof - Google Patents
Automatic driving mode selection method, automatic driving automobile and control method thereof Download PDFInfo
- Publication number
- CN114620060A CN114620060A CN202011475270.XA CN202011475270A CN114620060A CN 114620060 A CN114620060 A CN 114620060A CN 202011475270 A CN202011475270 A CN 202011475270A CN 114620060 A CN114620060 A CN 114620060A
- Authority
- CN
- China
- Prior art keywords
- automatic driving
- driving mode
- driving
- autonomous
- vehicle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- 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
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
-
- 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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/082—Selecting or switching between different modes of propelling
Abstract
The invention discloses an automatic driving mode selection method, an automatic driving automobile and a control method thereof. The automatic driving mode selection method comprises the following steps: acquiring manual driving data of a user driving an autonomous vehicle, wherein the autonomous vehicle is provided with a plurality of autonomous driving modes; acquiring environment information, navigation information and a planning track of an automatic driving vehicle in respective automatic driving modes; selecting corresponding manual driving data according to the environment information and the navigation information, and obtaining an actual track corresponding to the planned track according to the selected manual driving data; respectively comparing each planned track with the corresponding actual track; an optimal automatic driving mode suitable for the user is selected from a plurality of automatic driving modes according to the comparison result. The automatic driving mode selection method can match the automatic driving mode for the user according to the driving habits of the user, so that the driving experience of the user is improved.
Description
Technical Field
The invention relates to the technical field of automobiles, in particular to an automatic driving mode selection method, an automatic driving automobile and a control method thereof.
Background
One important factor for passengers to assess the safety of an automated driving system is to determine whether the driving behavior of the vehicle meets the expectations of the passengers. The driving behavior evaluated differently by different passengers due to individual differences, for example, the safe driving behavior considered by the passenger a may be a driving behavior that is too aggressive/too conservative in the view of the passenger B, and thus the safe driving behavior considered by the passenger a may cause an unsafe feeling or a sense of discomfort to the passenger B.
Because the driving habits of each person are different, the automatic driving system is required to support various driving modes, so that the passengers can adjust the automatic driving strategy according to the personal habits, and the riding experience of the passengers is improved. However, the driving habits are not well quantified, and even if the automatic driving system has a plurality of modes for adapting to different users (such as 4 modes of lane-invariant, conservative, ordinary and aggressive driving of a tesla automobile), passengers have difficulty in determining the automatic driving mode suitable for the passengers.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. Therefore, a first objective of the present invention is to provide an automatic driving mode selection method, so as to match an automatic driving mode for a user according to the driving habit of the user, thereby improving the driving experience of the user.
A second object of the invention is to propose a control method for an autonomous vehicle.
A third object of the invention is to propose a computer-readable storage medium.
A fourth object of the invention is to propose an electronic device.
A fifth object of the invention is to propose an autonomous vehicle.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides an automatic driving mode selection method, including: acquiring manual driving data of a user driving an autonomous vehicle, wherein the autonomous vehicle is provided with a plurality of autonomous driving modes; acquiring environmental information, navigation information and a planned track of the automatic driving vehicle in the respective automatic driving mode; selecting corresponding manual driving data according to the environment information and the navigation information, and obtaining an actual track corresponding to the planned track according to the selected manual driving data; respectively comparing each planned track with the corresponding actual track; selecting an optimal automatic driving mode suitable for the user from the plurality of automatic driving modes according to the comparison result.
According to the automatic driving mode selection method, manual driving data of a user for driving the automatic driving vehicle are obtained, wherein the automatic driving vehicle is provided with a plurality of automatic driving modes; acquiring environment information, navigation information and a planning track of the automatic driving vehicle in respective automatic driving modes; selecting corresponding manual driving data according to the environment information and the navigation information, and obtaining an actual track corresponding to the planned track according to the selected manual driving data; respectively comparing each planned track with the corresponding actual track; thereby selecting an optimal automatic driving mode suitable for the user from the plurality of automatic driving modes according to the comparison result. Therefore, the automatic driving mode can be matched for the user according to the driving habits of the user, and the driving experience of the user is improved.
In order to achieve the above object, an embodiment of a second aspect of the present invention provides a control method for an autonomous vehicle, the method including the steps of: selecting an optimal automatic driving mode suitable for a current user by using the automatic driving mode selection method; controlling the autonomous vehicle according to the optimal autonomous driving mode.
According to the control method of the automatic driving vehicle, the optimal automatic driving mode suitable for the current user is selected through the automatic driving mode selection method; and then controlling the autonomous vehicle according to the optimal autonomous driving mode. Therefore, the automatic driving mode can be matched for the user according to the driving habits of the user, and the driving experience of the user is improved.
To achieve the above object, a third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described automatic driving mode selection method, or the above-described control method of an automatically-driven vehicle.
The computer readable storage medium of the embodiment of the present invention, when the computer program is executed by the processor, may match the automatic driving mode for the user according to the driving habit of the user, thereby improving the driving experience of the user.
To achieve the above object, a fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the computer program, when executed by the processor, implements the above automatic driving mode selection method or the above control method for an automatic driving vehicle.
According to the electronic equipment provided by the embodiment of the invention, the automatic driving mode selection method is realized, or the automatic driving vehicle control method is realized, so that the automatic driving mode can be matched with the electronic equipment according to the driving habits of the user, and the driving experience of the user is improved.
In order to achieve the above object, a fifth aspect of the present invention provides an autonomous vehicle, including the electronic device.
According to the automatic driving vehicle provided by the embodiment of the invention, the automatic driving mode can be matched for the automatic driving vehicle according to the driving habit of the user through the electronic equipment, so that the driving experience of the user is improved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flow chart of an automatic driving mode selection method of one embodiment of the present invention;
FIG. 2 is a flow chart of a control method of an autonomous vehicle according to one embodiment of the invention;
fig. 3 is a block diagram of the structure of an autonomous vehicle according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present invention and should not be construed as limiting the present invention.
An autonomous driving mode selection method, an autonomous driving vehicle, and a control method thereof according to embodiments of the present invention are described below with reference to the accompanying drawings.
Fig. 1 is a flow chart of an automatic driving mode selection method according to an embodiment of the present invention.
As shown in fig. 1, the automatic driving mode selection method includes the steps of:
and S11, acquiring manual driving data of the user driving the automatic driving vehicle, wherein the automatic driving vehicle is provided with a plurality of automatic driving modes.
The manual driving data includes driving data of a user driving an autonomous vehicle in a plurality of scenes, and the driving data in each scene is continuous with a time sequence.
Specifically, the autonomous driving vehicle is provided with a planning control module and a vehicle body control module, and various different scenes (such as scene setting according to a plurality of autonomous driving modes of the autonomous driving vehicle) can be preset, for example, urban road scenes, rural road scenes and the like, and also for example, snow scenes, sand scenes and the like. In the process of driving the automatic driving vehicle by a user, the manual driving data can be obtained through sensors (such as a vehicle speed sensor, a steering wheel angle sensor, a camera, a vehicle-mounted radar, a vehicle-mounted navigation and the like) installed on the automatic driving vehicle. The manual driving data may include obstacle information, vehicle state information, and the like, wherein the vehicle state information may include a position, a vehicle speed, and a driving direction of the autonomous vehicle.
Further, the obtained manual driving data may be grouped according to a certain rule. For example, it is possible to group the data by scenes, with manual driving data acquired by driving an autonomous vehicle in one scene as a group; the information may be further grouped according to the environmental information and navigation information in each scene. Therefore, the grouped driving data can be stored in a memory of the automatic driving vehicle or in the cloud, and the reference data collection of the automatic driving vehicle is completed.
The number of groups of manual driving data is larger than the number of autonomous driving modes of the autonomous vehicle. For example, the autonomous vehicle has 10 autonomous driving modes, and the number of groups of manual driving data is larger than 10, and may be 100, 150, 200, or the like. Therefore, the matching accuracy of the manual driving data and the automatic driving mode is improved.
And S12, acquiring environment information, navigation information and a planned track when the automatic driving vehicle runs in the respective automatic driving modes.
The environment information (which may include obstacle information around the autonomous vehicle, surrounding vehicle information, signal light information, and the like), and the navigation information (which may include a vehicle speed, a position, a driving direction, and the like of the autonomous vehicle) are obtained from any time T when the autonomous vehicle operates in the autonomous driving mode, and the planned trajectory corresponding to the any time T includes a planned trajectory within at least one preset time period after the time T. For example, when the vehicle body control device controls the autonomous vehicle to stably run in the autonomous driving mode a1, the environmental information and the navigation information at a certain time T can be acquired, and then the planned trajectory of T +1s, the planned trajectory of T +2s, the planned trajectory of T +3s, and the like are analyzed and acquired by the planning control module according to the environmental information and the navigation information.
It should be noted that, since the environmental data is constantly changing, the longer the preset time period is, the more inaccurate the obtained planned trajectory is, so the value range of the preset time period may be 0 to 10s, and the multiple time periods may be three time periods, such as 1s, 2s, 3s, and the like. Alternatively, the time period may also be set according to the vehicle speed of the autonomous vehicle, and the larger the vehicle speed, the smaller the preset time period may be.
And S13, selecting corresponding manual driving data according to the environment information and the navigation information, and obtaining an actual track corresponding to the planned track according to the selected manual driving data.
Specifically, the environmental information and the navigation information acquired at the time T may be substituted into the stored sets of manual driving data to obtain manual driving data with the best matching degree with the time T, and then a plurality of actual tracks within at least one preset time period after the time T are determined from the obtained manual driving data. For example, if the planned trajectory is a trajectory within 3s after the time T, the obtained actual trajectory is also taken within 3s correspondingly; if the planned trajectory is within 2s after the time T, the obtained actual trajectory is also taken within 2s correspondingly.
And S14, comparing each planned track with the corresponding actual track.
Specifically, the two tracks can be directly compared from a geometric perspective, that is, the coincidence degree of the two tracks is compared, and the average distance difference of the tracks is obtained according to the comparison result, and the higher the coincidence degree is, the more the tracks are matched. In order to ensure the matching reliability, a plurality of groups of planning tracks obtained by driving the automatic driving vehicle in different scenes in respective dynamic driving modes can be taken, the planning tracks are compared with manual driving data to obtain a plurality of groups of comparison results, and the comprehensive comparison result of the respective dynamic driving modes is obtained based on the plurality of groups of comparison results.
And S15, selecting the optimal automatic driving mode suitable for the user from a plurality of automatic driving modes according to the comparison result.
Specifically, the automatic driving mode with the best comparison result, i.e., the smallest difference in the average distance of the trajectories, may be used as the automatic driving mode that best matches the user, i.e., the best automatic driving mode. After the optimal automatic driving mode is obtained, the user selects the optimal automatic driving mode to control the automatic driving vehicle to drive on the road. Therefore, the riding experience of the user on the automatic driving vehicle can be improved.
It should be noted that the above-mentioned pattern matching (i.e., pattern selection) process may be performed when the autonomous vehicle is idle/off-line. Of course, for each autonomous vehicle, an autonomous driving mode closest to the current user behavior can be learned through the above steps and used as a default driving mode for autonomous driving for the user to select. Therefore, automatic personalized automatic driving parameter selection can be provided for each user, and better user experience can be provided.
As an example, each manual driving data may be analyzed to obtain parameters such as a following distance, an acceleration/deceleration capability, and a lane change timing when the user drives the autonomous vehicle, and the parameters may be compared with the following distance, the acceleration/deceleration capability, the lane change timing, and the like corresponding to each autonomous driving mode to obtain a plurality of comparison distances, and further, a weighted calculation may be performed according to the plurality of distances to obtain an average distance difference, and the autonomous driving mode corresponding to the minimum average distance difference may be used as the optimal autonomous driving mode.
It should be noted that the automatic driving mode is usually a set of parameters in the automatic driving system, and the parameters may directly or indirectly reflect the following distance, acceleration and deceleration capability, lane change timing, etc., even if the matching essence of the mode is the matching of the parameters, the above-mentioned obtaining of the best mode essence is the automatic driving parameters most close to the current user habits.
The above steps S12-S15 are described below in conjunction with one of two specific examples.
In one particular example, the autonomous vehicle a has 3 autonomous driving modes: conservative, common, aggressive; the automatic driving vehicle stores manual driving data of a user B; and acquiring environmental information and navigation information of the automatic driving vehicle A at the moment T when the automatic driving vehicle A stably runs in three automatic driving modes of conservation, common and aggressive under the same scene, and planning the track in the next 3s to obtain corresponding planned tracks G1, G2 and G3. Meanwhile, data similar to or identical to the environmental information and the navigation information at the time T are acquired from the manual driving data, and an actual trajectory S within the next 3S is obtained. And comparing the planned trajectories G1, G2 and G3 with the actual trajectory S to obtain corresponding average distance differences C1, C2 and C3. Comparing C1, C2, C3, if C1< C2< C3, then the conservative mode is selected as the best autopilot mode.
In another specific example, the autonomous vehicle a has 3 autonomous driving modes: conservative, common, aggressive; the automatic driving vehicle stores manual driving data of a user B; and acquiring environmental information and navigation information at the moment T when the automatic driving vehicle A stably runs in three automatic driving modes of conservation, common and aggressive under the same scene, and planning the tracks in the next 1s, 2s and 3s to obtain corresponding planned tracks G11, G12, G13, G21, G22, G23, G31, G32 and G33. Meanwhile, data similar to or identical to the environmental information and the navigation information at the time T are acquired from the manual driving data, and actual trajectories S1, S2, S3 in the next 1S, 2S, 3S are obtained. Comparing the planned trajectories G11, G21 and G31 with the actual trajectory S1 to obtain corresponding distance differences C11, C21 and C31; comparing the planned trajectories G12, G22 and G32 with the actual trajectory S2 to obtain corresponding distance differences C12, C22 and C32; the planned trajectories G13, G23, G33 are compared with the actual trajectory S3 to obtain corresponding distance differences C13, C23, C33. Calculating the weighted distances of C11, C12 and C13 to obtain an average distance difference C1; in the same way, C2 and C3 are obtained. Comparing C1, C2, C3, if C1< C2< C3, then the conservative mode is selected as the best autopilot mode.
In summary, the automatic driving mode selection method provided by the embodiment of the invention can match the automatic driving mode according to the driving habit of the user, thereby improving the driving experience of the user.
Fig. 2 is a flowchart of a control method of an autonomous vehicle according to an embodiment of the present invention.
As shown in fig. 2, the control method of the autonomous vehicle includes the steps of:
s21, selecting an optimal auto-driving mode suitable for the current user using an auto-driving mode selection method.
And S22, controlling the automatic driving vehicle according to the optimal automatic driving mode.
According to the control method of the automatic driving vehicle, the automatic driving mode can be matched according to the driving habits of the user, so that the driving experience of the user is improved.
Further, the present invention proposes a computer-readable storage medium.
In an embodiment of the present invention, a computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the above-described automated driving mode selection method or the above-described control method of an automated driving vehicle.
The computer readable storage medium of the embodiment of the present invention, when the computer program is executed by the processor, may match the automatic driving mode for the user according to the driving habit of the user, thereby improving the driving experience of the user.
Further, the invention provides an electronic device.
In an embodiment of the invention, the electronic device comprises a memory, a processor and a computer program stored on the memory, which computer program, when executed by the processor, implements the above-mentioned automatic driving mode selection method or the above-mentioned control method of an automatic driving vehicle.
According to the electronic equipment provided by the embodiment of the invention, the automatic driving mode selection method is realized, or the automatic driving vehicle control method is realized, so that the automatic driving mode can be matched with the electronic equipment according to the driving habits of the user, and the driving experience of the user is improved.
Fig. 3 is a block diagram of the structure of an autonomous vehicle according to an embodiment of the present invention.
As shown in fig. 3, the autonomous vehicle 1000 includes the electronic device 100 described above.
According to the automatic driving vehicle provided by the embodiment of the invention, the automatic driving mode can be matched for the automatic driving vehicle according to the driving habit of the user through the electronic equipment, so that the driving experience of the user is improved.
It should be noted that the logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following technologies, which are well known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature "under," "beneath," and "under" a second feature may be directly under or obliquely under the second feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. An automatic driving mode selection method, characterized in that it comprises the steps of:
acquiring manual driving data of a user driving an autonomous vehicle, wherein the autonomous vehicle is provided with a plurality of autonomous driving modes;
acquiring environment information, navigation information and a planning track of the automatic driving vehicle in respective automatic driving modes;
selecting corresponding manual driving data according to the environment information and the navigation information, and obtaining an actual track corresponding to the planned track according to the selected manual driving data;
respectively comparing each planned track with the corresponding actual track;
selecting an optimal automatic driving mode suitable for the user from the plurality of automatic driving modes according to the comparison result.
2. The autonomous driving mode selection method of claim 1, wherein the manual driving data comprises driving data of the user driving the autonomous vehicle in a plurality of scenarios, and the driving data in each scenario is continuous and time-sequenced.
3. The autonomous driving mode selection method of claim 2 wherein the manual driving data includes obstacle information, vehicle status information, wherein the vehicle status information includes a position, a speed, and a direction of travel of the autonomous vehicle.
4. The automated driving mode selection method of claim 1, wherein the environmental information and the navigation information are obtained from an arbitrary time T when the automated driving vehicle operates in the automated driving mode, and the planned trajectory corresponding to the arbitrary time T includes a planned trajectory within at least one preset time period after the time T.
5. The automatic driving mode selection method of claim 1, wherein comparing a planned trajectory with a corresponding actual trajectory comprises:
comparing the coincidence degree of the planned track and the corresponding actual track;
obtaining the average distance difference of the tracks according to the comparison result;
and taking the automatic driving mode corresponding to the minimum track average distance difference as the optimal automatic driving mode.
6. The automatic driving mode selection method of claim 4, wherein the preset time period is in a range of 0 to 10 seconds.
7. A control method of an autonomous vehicle, characterized by comprising the steps of:
selecting an optimal autonomous driving mode suitable for a current user using an autonomous driving mode selection method according to any one of claims 1-6;
controlling the autonomous vehicle according to the optimal autonomous driving mode.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the autonomous driving mode selection method of any one of claims 1-6 or the control method of an autonomous vehicle of claim 7.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory, wherein the computer program, when executed by the processor, implements an autonomous driving mode selection method according to any of claims 1-6, or a control method of an autonomous vehicle according to claim 7.
10. An autonomous vehicle comprising the electronic device of claim 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011475270.XA CN114620060A (en) | 2020-12-14 | 2020-12-14 | Automatic driving mode selection method, automatic driving automobile and control method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011475270.XA CN114620060A (en) | 2020-12-14 | 2020-12-14 | Automatic driving mode selection method, automatic driving automobile and control method thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114620060A true CN114620060A (en) | 2022-06-14 |
Family
ID=81897066
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011475270.XA Pending CN114620060A (en) | 2020-12-14 | 2020-12-14 | Automatic driving mode selection method, automatic driving automobile and control method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114620060A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115675493A (en) * | 2023-01-04 | 2023-02-03 | 北京易控智驾科技有限公司 | Method and device for unmanned driving by utilizing information of manual driving track layer |
-
2020
- 2020-12-14 CN CN202011475270.XA patent/CN114620060A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115675493A (en) * | 2023-01-04 | 2023-02-03 | 北京易控智驾科技有限公司 | Method and device for unmanned driving by utilizing information of manual driving track layer |
| CN115675493B (en) * | 2023-01-04 | 2023-08-11 | 北京易控智驾科技有限公司 | Unmanned method and device using manual driving track layer information |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111434554B (en) | Controlling an autonomous vehicle based on passenger and context aware driving style profiles | |
| CN109085837B (en) | Vehicle control method, vehicle control device, computer equipment and storage medium | |
| CN107121979B (en) | Autonomous confidence control | |
| CN109426261B (en) | Automatic driving device | |
| US7177750B2 (en) | System for influencing the speed of a motor vehicle | |
| JP4445926B2 (en) | How to find a parking space | |
| CN111559388B (en) | Target vehicle screening method, device, equipment and storage medium | |
| CN112046501A (en) | Automatic driving device and method | |
| CN108149990B (en) | Automatic parking space detection method and system and vehicle | |
| EP2741268B1 (en) | Vehicle information processing apparatus and vehicle information processing method | |
| US20050216137A1 (en) | System for influencing the speed of a motor vehicle | |
| CN112046481B (en) | Automatic driving device and method | |
| CN112799384A (en) | Vehicle and driving track planning method and planning device thereof | |
| CN114620060A (en) | Automatic driving mode selection method, automatic driving automobile and control method thereof | |
| CN114954632A (en) | Vehicle control method, vehicle control system and vehicle | |
| CN113460084A (en) | Method and device for determining vehicle driving intention, vehicle and storage medium | |
| CN110654373A (en) | Automatic parking method and device and vehicle | |
| US20050209764A1 (en) | System for recognizing left-hand or right-hand driving conditions | |
| CN115973164A (en) | Vehicle navigation auxiliary driving method, medium and device | |
| CN114604235A (en) | Automatic driving assistance system | |
| CN116588078B (en) | Vehicle control method, device, electronic equipment and computer readable storage medium | |
| KR20200133857A (en) | Autonomous driving apparatus and method | |
| CN111469841B (en) | Curve target selection method, vehicle-mounted equipment and storage medium | |
| CN115123252B (en) | Vehicle control method, vehicle control device, vehicle and storage medium | |
| JP5056192B2 (en) | Vehicle control device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |