CN114264312A - Path planning method, device and autonomous vehicle for autonomous vehicle - Google Patents
Path planning method, device and autonomous vehicle for autonomous vehicle Download PDFInfo
- Publication number
- CN114264312A CN114264312A CN202111447650.7A CN202111447650A CN114264312A CN 114264312 A CN114264312 A CN 114264312A CN 202111447650 A CN202111447650 A CN 202111447650A CN 114264312 A CN114264312 A CN 114264312A
- Authority
- CN
- China
- Prior art keywords
- path
- data
- candidate
- route
- escape
- 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
Landscapes
- Traffic Control Systems (AREA)
Abstract
本公开提供了一种自动驾驶车辆的路径规划方法、装置、设备、介质、程序产品和自动驾驶车辆，涉及计算机技术领域，具体为智能交通、自动驾驶技术领域。自动驾驶车辆的路径规划方法包括：响应于检测到自动驾驶车辆行驶过程中在预定范围内存在障碍物，获取障碍物数据和道路边缘数据；基于障碍物数据和道路边缘数据，规划脱困路径；响应于脱困路径的行驶条件满足预设行驶条件，控制自动驾驶车辆基于脱困路径行驶。
The present disclosure provides a path planning method, device, device, medium, program product and automatic driving vehicle for an automatic driving vehicle, and relates to the field of computer technology, in particular to the technical fields of intelligent transportation and automatic driving. A path planning method for an autonomous vehicle includes: in response to detecting that an obstacle exists within a predetermined range during the driving of the autonomous vehicle, obtaining obstacle data and road edge data; planning an escape route based on the obstacle data and road edge data; responding to When the driving conditions of the escape route meet the preset driving conditions, the autonomous vehicle is controlled to drive based on the escape route.
Description
Technical Field
The present disclosure relates to the field of computer technologies, particularly to the field of intelligent transportation and automatic driving technologies, and more particularly, to a method and an apparatus for path planning for an automatic driving vehicle, an electronic device, a medium, a program product, and an automatic driving vehicle.
Background
When a vehicle encounters an obstacle during travel, it is often necessary to re-plan the travel path, the vehicle including, for example, an autonomous vehicle. However, the planned route of the related art autonomous vehicle is inevitably impassable, resulting in traffic congestion.
Disclosure of Invention
The present disclosure provides a path planning method, apparatus, electronic device, storage medium, program product, and autonomous vehicle of an autonomous vehicle.
According to an aspect of the present disclosure, there is provided a path planning method of an autonomous vehicle, including: acquiring obstacle data and road edge data in response to detecting that an obstacle exists in a preset range in the driving process of the automatic driving vehicle; planning a escaping route based on the obstacle data and the road edge data; and controlling the automatic driving vehicle to run based on the escaping path in response to the running condition of the escaping path meeting the preset running condition.
According to another aspect of the present disclosure, there is provided a path planning apparatus of an autonomous vehicle, including: the device comprises a first acquisition module, a first planning module and a control module. The automatic driving vehicle comprises a first acquisition module, a second acquisition module and a control module, wherein the first acquisition module is used for responding to the fact that an obstacle exists in a preset range in the driving process of the automatic driving vehicle and acquiring obstacle data and road edge data; a first planning module for planning a escaping path based on the obstacle data and the road edge data; and the control module is used for responding to the condition that the driving condition of the escaping path meets the preset driving condition and controlling the automatic driving vehicle to drive based on the escaping path.
According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor and a memory communicatively coupled to the at least one processor. Wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of path planning for an autonomous vehicle described above.
According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to execute the above-described method of path planning for an autonomous vehicle.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method of path planning for an autonomous vehicle as described above.
According to another aspect of the present disclosure, there is provided an autonomous vehicle including the above-described electronic apparatus.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 schematically illustrates an application scenario of a path planning and apparatus for an autonomous vehicle;
FIG. 2 schematically illustrates a flow chart of a method of path planning for an autonomous vehicle according to an embodiment of the disclosure;
FIG. 3 schematically illustrates a schematic diagram of a path planning method for an autonomous vehicle according to an embodiment of the disclosure;
FIG. 4 schematically illustrates a schematic diagram of a path planning method for an autonomous vehicle according to another embodiment of the disclosure;
FIG. 5 schematically illustrates a block diagram of a path planning apparatus of an autonomous vehicle in accordance with an embodiment of the disclosure; and
FIG. 6 is a block diagram of an electronic device for performing path planning for an autonomous vehicle used to implement an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
An embodiment of the present disclosure provides a path planning method for an autonomous vehicle, including: in response to detecting the presence of an obstacle within a predetermined range during travel of the autonomous vehicle, obstacle data and road edge data are acquired. Then, based on the obstacle data and the road edge data, a escaping path is planned. Next, in response to the traveling condition of the escaping path satisfying a preset traveling condition, the autonomous vehicle is controlled to travel based on the escaping path.
Fig. 1 schematically shows an application scenario of a path planning and apparatus for an autonomous vehicle. It should be noted that fig. 1 is only an example of an application scenario in which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, an application scenario 100 according to this embodiment may include an autonomous vehicle 101.
Illustratively, if it is detected that there is an obstacle 102 in front while the autonomous vehicle 101 is traveling, the autonomous vehicle 101 needs to bypass the obstacle 102.
For example, the autonomous vehicle 101 may re-plan a path based on the data of the obstacle 102, the planned path 103 avoids the obstacle 102, for example, and the autonomous vehicle 101 may travel based on the planned path 103.
The embodiment of the present disclosure provides a path planning method for an autonomous vehicle, and the path planning method for an autonomous vehicle according to an exemplary embodiment of the present disclosure is described below with reference to fig. 2 to 4 in conjunction with the application scenario of fig. 1.
FIG. 2 schematically illustrates a flow chart of a method of path planning for an autonomous vehicle according to an embodiment of the disclosure.
As shown in fig. 2, the method 200 for planning a path of an autonomous vehicle according to an embodiment of the present disclosure may include, for example, operations S210 to S230.
In operation S210, in response to detecting the presence of an obstacle within a predetermined range while the autonomous vehicle is traveling, obstacle data and road edge data are acquired.
In operation S220, a escaping path is planned based on the obstacle data and the road edge data.
In operation S230, the autonomous vehicle is controlled to travel based on the escape route in response to the travel condition of the escape route satisfying a preset travel condition.
For example, an autonomous vehicle may travel based on a planned travel path. In the process of driving the automatic driving vehicle, the traffic condition needs to be detected in real time. When it is detected that there is an obstacle in a predetermined range around the autonomous vehicle, including an area in front of the vehicle, causing a no-pass or a difficult-to-pass, the autonomous vehicle needs to re-plan a travel path. The presence of obstacles includes, for example, road construction ahead, the site of an accident, the presence of foreign objects in the road, the presence of illegal parking in the road, etc.
For example, when an obstacle is detected, the autonomous vehicle may acquire obstacle data and road edge data. The obstacle data includes, for example, position data of an obstacle, and the road edge data includes, for example, position data of a road edge. Then, a trapped-free path, which is bounded by road edges and bypasses the obstacles, is planned with the obstacle data and the road data as references.
Because the escaping path is limited by the edge of the road, the range of the escaping path across the road is possibly large, and in order to ensure the driving safety of the automatic driving vehicle and accord with the traffic specification, the escaping path can be evaluated to determine whether the driving condition of the escaping path meets the preset driving condition, and the meeting of the preset driving condition represents that the escaping path meets the safety condition and accords with the traffic specification.
After determining that the driving condition of the escaping path satisfies the preset driving condition, the autonomous vehicle may be controlled to drive based on the escaping path.
According to the embodiment of the disclosure, when an obstacle is encountered, in order to ensure that the automatic driving vehicle safely and efficiently bypasses the obstacle to continue driving, a high-freedom escape route can be planned based on the obstacle and the edge of the road, and the driving condition of the escape route is evaluated so as to control the automatic driving vehicle to drive based on the escape route under the condition that the driving condition is determined to meet the preset driving condition, so that the automatic driving vehicle is ensured to safely bypass the obstacle, the problem of traffic jam caused by stopping of the automatic driving vehicle is reduced as much as possible, and the trafficability and the safety of the vehicle are improved.
Fig. 3 schematically illustrates a schematic diagram of a path planning method for an autonomous vehicle according to an embodiment of the disclosure.
As shown in fig. 3, when an autonomous vehicle 301 encounters an obstacle 302 during driving, obstacle data and road edge data may be acquired, and a trapped-free path may be planned based on the obstacle data and the road edge data. The obstacle data includes, for example, position data of the obstacle 302. The road edge is indicated by a bold solid line in fig. 3, and the road edge data includes, for example, position data of the road edge.
In one example, the autonomous vehicle 301 plans a trapped road 303A associated with the current lane, with the obstacle data and road edge data as constraints. For example, the road includes a plurality of lanes, the boundaries of which are indicated, for example, by dashed lines in the figure. The current position P of the autonomous vehicle 301 is located in the current lane (center lane), for example, and the planned escape route 303A returns to the current lane again after bypassing the obstacle 302, and the vehicle can continue to be planned to the target position Q subsequently after returning to the current lane. The escaping route 303A is associated with the current lane, for example, representing that the escaping route 303A is generated by taking the center line of the current lane as a reference line.
In another example, the autonomous vehicle 301 plans the escape route 303B based on the current position P and the target position Q of the autonomous vehicle 301 with the obstacle data and the road edge data as constraints. The start point of the escape route 303B is, for example, the current position P, and the end point is, for example, the target position Q.
In the embodiment of the disclosure, the automatic driving vehicle takes the obstacle data and the road edge data as the limitation, and plans the escaping route based on the current lane or the current position and the target position of the automatic driving vehicle, so that the range of the escaping route is wider, the flexibility of the escaping route is improved, and the passing efficiency and the safety of the vehicle when encountering the obstacle are improved.
Fig. 4 schematically shows a schematic diagram of a path planning method for an autonomous vehicle according to another embodiment of the disclosure.
As shown in fig. 4, when an autonomous vehicle 401 encounters an obstacle 402 during traveling, a candidate route can be planned in addition to a trapped-free route 403.
For the candidate route, when it is detected that the autonomous vehicle 401 has an obstacle 402 within a predetermined range during traveling, lane line data is acquired, and the lane line is represented by a dotted line in the drawing. The lane line data includes, for example, position data of a lane line or a position of a center line of a lane indicated by the lane line data.
Candidate paths are then planned based on the obstacle data and the lane line data. The candidate paths include, for example, at least one of a straight path 404, a left lane change path 405, and a right lane change path 406.
For the straight-ahead path 404, the straight-ahead path 404 may be planned with the obstacle data and the lane line data of the current lane in which the autonomous vehicle 401 is located as restrictions. The straight path 404 is referenced to, for example, a lane center line of the current lane.
For the left lane change path 405, the left lane change path 405 is planned with the obstacle data and the lane line data of the left lane as restrictions. The left lane change path 405 is referenced to, for example, a lane center line of a left lane.
For the right lane-changing path 406, the right lane-changing path 406 is planned with the obstacle data and the lane line data of the right lane as restrictions. The right lane change path 406 is referenced to, for example, a lane center line of the right lane.
Since the candidate route is limited by the lane line data, the planned candidate route is difficult to pass in some cases, and the automatic driving vehicle is required to stop, which is easy to cause traffic jam.
In order to improve the traffic efficiency of the autonomous vehicle 401, after the escape route 403 and the candidate routes (the straight route 404, the left lane change route 405, and the right lane change route 406) are obtained, the autonomous vehicle 401 may determine whether the running condition of the escape route 403 satisfies the preset running condition based on the evaluation data of the escape route 403 and the evaluation data of the candidate routes.
For example, based on the evaluation data of the escape route 403 and the evaluation data of the candidate route, it is determined whether the travel condition of the escape route 403 is better than the travel condition of the candidate route. If it is determined that the travel condition of the escape route 403 is better than the travel condition of the candidate route, it is determined that the travel condition of the escape route 403 satisfies the preset travel condition, and the autonomous vehicle 401 is controlled to travel on the basis of the escape route 403 to bypass the obstacle 402.
In other words, after the trapped road 403, the straight road 404, the left lane-changing road 405, and the right lane-changing road 406 are obtained, the trapped road 403, the straight road 404, the left lane-changing road 405, and the right lane-changing road 406 are evaluated, respectively, to select an optimal road.
Illustratively, the evaluation data of the escaping path 403 includes at least one of: curvature of the escaping route 403, deviation degree of the escaping route 403, smoothness degree between the escaping route 403 and the historical planning route, safety of the escaping route 403, and trafficability of the escaping route 403. The starting point of the escaping route 403 is connected to the end point of the historical planned route, for example, and the smoothness between the escaping route 403 and the historical planned route is the smoothness of the connection. The historical planned route is, for example, a newly planned escaping route or a candidate route.
Illustratively, the evaluation data of the candidate path includes at least one of: curvature of the candidate path, deviation degree of the candidate path, smoothness degree between the candidate path and the historical planning path, safety of the candidate path and trafficability of the candidate path. The starting point of the candidate path is connected with the end point of the historical planning path, and the smoothness between the candidate path and the historical planning path is the smoothness of the connection part. The historical planned path is a newly planned escaping path or a candidate path.
According to the embodiment of the disclosure, the optimal path is obtained by planning the escaping path and the candidate path and evaluating each path, the automatic driving vehicle is controlled to run based on the optimal path, the passing rate of the automatic driving vehicle is improved, and the possibility of traffic jam is reduced.
According to embodiments of the present disclosure, autonomous vehicles perform path planning, for example, based on high-precision map data. One road in the high-precision map data is composed of, for example, a plurality of segments each including a plurality of lanes. During driving of the autonomous vehicle, a reference line may be generated based on the current position of the vehicle, the target position, and the high-precision map data, the reference line being determined, for example, from a lane line, the reference line being located, for example, at the center of a lane.
For the candidate path, a plurality of planning tasks may be generated based on the reference line, the plurality of planning tasks including, for example, a straight-path planning task, a left lane-changing path planning task, and a right lane-changing path planning task. Path planning and speed planning are independently performed on the basis of each task to generate candidate paths, wherein the candidate paths comprise an execution path, a left lane changing path and a right lane changing path. The speed plan generates time information on the candidate path, e.g., based on the speed of the vehicle, the time information characterizing, for example, a suggested speed at each location of the candidate path, a time required to complete the candidate path, etc.
Then, each candidate path is evaluated based on the evaluation function to obtain evaluation data for each subsequent path, and a final path is selected from the plurality of candidate paths based on the evaluation data.
Due to the complex road structure, the road comprises special structures such as bifurcations and convergence, and the lane lines comprise white dotted lines, yellow dotted lines, white solid lines, yellow solid lines, double yellow solid lines and the like. Planning of candidate paths according to lane lines is often restricted, so that vehicles cannot detour across lane lines or change lanes because the lane lines are solid lines. For obstacles in front, when the autonomous vehicle executes decision of whether to detour across lane lines or detour in a lane-changing manner, the autonomous vehicle depends too much on perception, prediction accuracy and stability, and often causes the problem that the decision is unreasonable and the autonomous vehicle is not expected to be in a standstill. In addition, since the width of the cross lane detour is too small due to the path planning based on the lane line, if the autonomous vehicle has an obstacle in front of the autonomous vehicle, the passability of the planned candidate path is reduced.
In view of this, the embodiments of the present disclosure may simultaneously perform the planning of the stranded-free path and the candidate path, and select the optimal path from the stranded-free path and the candidate path.
For example, a navigation path is planned based on the start and end points of the autonomous vehicle. Then, a plurality of reference lines are generated based on the current position of the vehicle, the front position (target position) on the navigation path that is 100 meters away from the current position, road edge data, lane line data, and the like, the reference lines being located at the center of the lane, for example.
And generating a straight-going path planning task, a left lane-changing path planning task, a right lane-changing path planning task and a trapped-free path planning task based on the plurality of reference lines.
And then, executing each task respectively, and independently planning to obtain a straight path, a left lane changing path, a right lane changing path and a trapped-escaping path.
For example, a straight-going path may be planned based on the lane center of a straight-going lane, a left lane-changing path may be planned based on a reference line of a left lane, and a right lane-changing path may be planned based on a reference line of a right lane.
For example, the escape route may be planned based on the lane center of the straight lane. Alternatively, the escaping path may be planned according to the current position and the target position, for example, based on the current position (or current pose) and the target position (or target pose), an initial escaping trajectory is dynamically generated, and the initial escaping trajectory is smoothed to obtain a final escaping trajectory.
After the straight-going path, the left lane-changing path, the right lane-changing path and the escaping path are obtained, each path is evaluated based on the evaluation function to obtain evaluation data, and the optimal path is determined from the straight-going path, the left lane-changing path, the right lane-changing path and the escaping path as the driving path of the automatic driving vehicle based on the evaluation data.
In other words, the embodiment of the disclosure adds a newly-planned escaping route in real time on the basis of planning the candidate route, and the escaping route is planned by taking the current position and the target position of the automatic driving vehicle as constraints, is not limited by a lane line, but is limited by a road edge, that is, the whole road space is considered to be accessible when the escaping route is planned, and the constraints of obstacles are added, so that the escaping route in the whole range of the road is generated. The escape route satisfies the constraints of the vehicle kinematics model. In addition, speed planning can be performed based on the escaping route to generate a suggested speed of each position on the escaping route, time required for walking the escaping route, and the like.
The stranded-out paths participate in the evaluation of the optimal paths together with the candidate paths, and then the optimal paths are selected based on the evaluation data of each path. The escape route generally coincides with any one of the straight travel route, the left lane change route, and the right lane change route without an obstacle. When the situation that obstacles such as a construction scene, an accident scene or parking violations are blocked is met, the trapped-escaping path is usually not superposed with any one of the straight-going path, the left lane-changing path and the right lane-changing path, the evaluation data usually shows that the three paths of the straight-going path, the left lane-changing path and the right lane-changing path are difficult to ensure that the vehicle can pass efficiently, and the vehicle can try to pass by taking the trapped-escaping path as the optimal path.
According to the embodiment of the disclosure, the automatic driving vehicle can bypass the obstacle through the escaping path under the condition that the obstacle such as a construction scene, an accident scene or illegal parking blocks, so that the problem that the automatic driving vehicle is unreasonably and unsmooth is avoided as much as possible, and the passing performance and the safety of the vehicle are improved.
Fig. 5 schematically illustrates a block diagram of a path planning apparatus of an autonomous vehicle according to an embodiment of the disclosure.
As shown in fig. 5, a path planning apparatus 500 of an autonomous vehicle according to an embodiment of the present disclosure includes, for example, a first obtaining module 510, a first planning module 520, and a control module 530.
The first obtaining module 510 may be configured to obtain obstacle data and road edge data in response to detecting that an obstacle is present within a predetermined range during driving of the autonomous vehicle. According to an embodiment of the present disclosure, the first obtaining module 510 may perform, for example, the operation S210 described above with reference to fig. 2, which is not described herein again.
The first planning module 520 may be used to plan a trapped road based on the obstacle data and the road edge data. According to the embodiment of the present disclosure, the first planning module 520 may perform, for example, the operation S220 described above with reference to fig. 2, which is not described herein again.
The control module 530 may be configured to control the autonomous vehicle to travel based on the escape route in response to the travel condition of the escape route satisfying a preset travel condition. According to the embodiment of the present disclosure, the control module 530 may, for example, perform operation S230 described above with reference to fig. 2, which is not described herein again.
According to an embodiment of the present disclosure, the apparatus 500 may further include: a second obtaining module and a second planning module. The second acquisition module is used for responding to the fact that an obstacle exists in a preset range in the driving process of the automatic driving vehicle and acquiring lane line data; and the second planning module is used for planning the candidate path based on the obstacle data and the lane line data.
According to an embodiment of the present disclosure, the apparatus 500 may further include: and the determining module is used for determining whether the driving condition of the escaping route meets the preset driving condition or not based on the evaluation data of the escaping route and the evaluation data of the candidate route.
According to an embodiment of the disclosure, the determining module includes: a first determination submodule and a second determination submodule. The first determining submodule is used for determining whether the driving condition of the escaping route is better than the driving condition of the candidate route or not based on the evaluation data of the escaping route and the evaluation data of the candidate route; and the second determining submodule is used for responding to the determination that the driving condition of the escaping route is better than the driving condition of the candidate route and determining that the driving condition of the escaping route meets the preset driving condition.
According to an embodiment of the disclosure, the first planning module comprises at least one of: a first planning submodule and a second planning submodule. The first planning submodule is used for planning a escaping path associated with the current lane by taking the barrier data and the road edge data as limits; and the second planning submodule is used for planning the escaping path based on the current position and the target position of the automatic driving vehicle by taking the barrier data and the road edge data as the limit.
According to the embodiment of the disclosure, the candidate path comprises at least one of a straight path, a left lane change path and a right lane change path; the second planning module includes at least one of: a third planning submodule, a fourth planning submodule, and a fifth planning submodule. The third planning submodule is used for planning a straight-going path by taking the barrier data and the lane line data of the current lane as limits; the fourth planning submodule is used for planning a left lane change path by taking the barrier data and the lane line data of the left lane as limits; and the fifth planning submodule is used for planning a right lane change path by taking the barrier data and the lane line data of the right lane as the limit.
According to the embodiment of the disclosure, the evaluation data of the escaping route comprises at least one of the following items: curvature of the escaping route, deviation degree of the escaping route, smoothness degree between the escaping route and a historical planning route, safety of the escaping route and trafficability of the escaping route; the evaluation data of the candidate path includes at least one of: curvature of the candidate path, deviation degree of the candidate path, smoothness degree between the candidate path and the historical planning path, safety of the candidate path and trafficability of the candidate path.
In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.
The present disclosure also provides an electronic device, a readable storage medium, a computer program product, and an autonomous vehicle according to embodiments of the present disclosure. Autonomous vehicles include, for example, electronic devices.
FIG. 6 is a block diagram of an electronic device for performing path planning for an autonomous vehicle used to implement an embodiment of the present disclosure.
FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. The electronic device 600 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the apparatus 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 performs the various methods and processes described above, such as a path planning method for an autonomous vehicle. For example, in some embodiments, the path planning method for an autonomous vehicle may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the method for path planning for an autonomous vehicle described above may be performed. Alternatively, in other embodiments, the calculation unit 601 may be configured by any other suitable means (e.g. by means of firmware) to perform a path planning method for an autonomous vehicle.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable autonomous vehicle path planner, such that the program codes, when executed by the processor or controller, cause the functions/acts specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (18)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111447650.7A CN114264312A (en) | 2021-11-30 | 2021-11-30 | Path planning method, device and autonomous vehicle for autonomous vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111447650.7A CN114264312A (en) | 2021-11-30 | 2021-11-30 | Path planning method, device and autonomous vehicle for autonomous vehicle |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114264312A true CN114264312A (en) | 2022-04-01 |
Family
ID=80825913
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111447650.7A Pending CN114264312A (en) | 2021-11-30 | 2021-11-30 | Path planning method, device and autonomous vehicle for autonomous vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114264312A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115063769A (en) * | 2022-07-28 | 2022-09-16 | 阿波罗智联(北京)科技有限公司 | Lane positioning method, device and equipment and automatic driving vehicle |
| CN115562280A (en) * | 2022-10-12 | 2023-01-03 | 九识智行(北京)科技有限公司 | Path planning method, device and storage medium for automatically driving vehicle to get rid of trouble |
| CN115658832A (en) * | 2022-10-28 | 2023-01-31 | 北京百度网讯科技有限公司 | Map update method, device, electronic device and storage medium |
| CN115675527A (en) * | 2022-11-14 | 2023-02-03 | 深兰人工智能(深圳)有限公司 | Automatic driving vehicle escaping method and system and automatic driving vehicle |
| CN115790635A (en) * | 2023-01-31 | 2023-03-14 | 西华大学 | Block chain-based unmanned transport vehicle driving route planning method and device |
| CN115923842A (en) * | 2022-12-23 | 2023-04-07 | 阿波罗智联(北京)科技有限公司 | Method, device, electronic device and storage medium for controlling an automatic driving vehicle |
| WO2025007669A1 (en) * | 2023-07-06 | 2025-01-09 | 深圳引望智能技术有限公司 | Control method and apparatus, and intelligent driving device |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170236422A1 (en) * | 2014-09-29 | 2017-08-17 | Hitachi Construction Machinery Co., Ltd. | Obstacle avoidance system |
| CN107300914A (en) * | 2016-04-15 | 2017-10-27 | 现代自动车株式会社 | For the apparatus and method for the driving path for producing autonomous land vehicle |
| US20190317508A1 (en) * | 2018-04-16 | 2019-10-17 | Baidu Usa Llc | Cost design for path selection in autonomous driving technology |
| CN110345957A (en) * | 2018-04-05 | 2019-10-18 | 福特全球技术公司 | Vehicle route identification |
| CN111397611A (en) * | 2020-03-05 | 2020-07-10 | 北京百度网讯科技有限公司 | Path planning method, device and electronic device |
| CN112455467A (en) * | 2021-01-11 | 2021-03-09 | 湖南汽车工程职业学院 | Early warning method for depression congestion of road surface by intelligent driving automobile |
| CN112455445A (en) * | 2020-12-04 | 2021-03-09 | 苏州挚途科技有限公司 | Automatic driving lane change decision method and device and vehicle |
| CN112572424A (en) * | 2019-09-11 | 2021-03-30 | 北京百度网讯科技有限公司 | Vehicle control method, device, equipment and medium based on obstacle recognition |
| CN112964267A (en) * | 2021-02-07 | 2021-06-15 | 舜宇光学（浙江）研究院有限公司 | Autonomous obstacle avoidance navigation method, system and equipment thereof |
-
2021
- 2021-11-30 CN CN202111447650.7A patent/CN114264312A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170236422A1 (en) * | 2014-09-29 | 2017-08-17 | Hitachi Construction Machinery Co., Ltd. | Obstacle avoidance system |
| CN107300914A (en) * | 2016-04-15 | 2017-10-27 | 现代自动车株式会社 | For the apparatus and method for the driving path for producing autonomous land vehicle |
| CN110345957A (en) * | 2018-04-05 | 2019-10-18 | 福特全球技术公司 | Vehicle route identification |
| US20190317508A1 (en) * | 2018-04-16 | 2019-10-17 | Baidu Usa Llc | Cost design for path selection in autonomous driving technology |
| CN112572424A (en) * | 2019-09-11 | 2021-03-30 | 北京百度网讯科技有限公司 | Vehicle control method, device, equipment and medium based on obstacle recognition |
| CN111397611A (en) * | 2020-03-05 | 2020-07-10 | 北京百度网讯科技有限公司 | Path planning method, device and electronic device |
| CN112455445A (en) * | 2020-12-04 | 2021-03-09 | 苏州挚途科技有限公司 | Automatic driving lane change decision method and device and vehicle |
| CN112455467A (en) * | 2021-01-11 | 2021-03-09 | 湖南汽车工程职业学院 | Early warning method for depression congestion of road surface by intelligent driving automobile |
| CN112964267A (en) * | 2021-02-07 | 2021-06-15 | 舜宇光学（浙江）研究院有限公司 | Autonomous obstacle avoidance navigation method, system and equipment thereof |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115063769A (en) * | 2022-07-28 | 2022-09-16 | 阿波罗智联(北京)科技有限公司 | Lane positioning method, device and equipment and automatic driving vehicle |
| CN115562280A (en) * | 2022-10-12 | 2023-01-03 | 九识智行(北京)科技有限公司 | Path planning method, device and storage medium for automatically driving vehicle to get rid of trouble |
| CN115658832A (en) * | 2022-10-28 | 2023-01-31 | 北京百度网讯科技有限公司 | Map update method, device, electronic device and storage medium |
| CN115658832B (en) * | 2022-10-28 | 2025-11-04 | 北京百度网讯科技有限公司 | Map updating methods, devices, electronic devices and storage media |
| CN115675527A (en) * | 2022-11-14 | 2023-02-03 | 深兰人工智能(深圳)有限公司 | Automatic driving vehicle escaping method and system and automatic driving vehicle |
| CN115923842A (en) * | 2022-12-23 | 2023-04-07 | 阿波罗智联(北京)科技有限公司 | Method, device, electronic device and storage medium for controlling an automatic driving vehicle |
| CN115790635A (en) * | 2023-01-31 | 2023-03-14 | 西华大学 | Block chain-based unmanned transport vehicle driving route planning method and device |
| WO2025007669A1 (en) * | 2023-07-06 | 2025-01-09 | 深圳引望智能技术有限公司 | Control method and apparatus, and intelligent driving device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114264312A (en) | Path planning method, device and autonomous vehicle for autonomous vehicle | |
| CN111775961B (en) | Automatic driving vehicle planning method and device, electronic equipment and storage medium | |
| CN113085850B (en) | Vehicle obstacle avoidance method and device, electronic equipment and storage medium | |
| RU2682151C1 (en) | Device for determination of environment, motion assistance equipment and method for determination of environment | |
| CN113682318B (en) | Vehicle running control method and device | |
| RU2682095C1 (en) | Device for determination of environment, motion assistance equipment and method for determination of environment | |
| CN112634632B (en) | Vehicle scheduling method and device, electronic equipment and storage medium | |
| CN114526752B (en) | Path planning method and device, electronic equipment and storage medium | |
| CN113119999B (en) | Method, device, equipment, medium and program product for determining automatic driving characteristics | |
| CN111680362A (en) | A kind of automatic driving simulation scene acquisition method, device, equipment and storage medium | |
| CN113635912A (en) | Vehicle control method, device, equipment, storage medium and automatic driving vehicle | |
| CN114670823B (en) | A driving trajectory correction method, device, equipment and automatic driving vehicle | |
| CN115447612A (en) | Narrow road meeting method, device, equipment and storage medium | |
| CN112622924B (en) | Driving planning method and device, and vehicle | |
| CN114179815B (en) | Method and device for determining vehicle driving track, vehicle, electronic equipment and medium | |
| CN114584949B (en) | Method and equipment for determining attribute value of obstacle through vehicle-road cooperation and automatic driving vehicle | |
| CN114299758A (en) | Vehicle control method and apparatus, device, medium, and product | |
| CN114030483A (en) | Vehicle control method, device, electronic apparatus, and medium | |
| CN114485711B (en) | Driving information generation method, automatic driving method, device and electronic device | |
| CN113799798A (en) | Method and device for determining driving track of vehicle, electronic equipment and memory | |
| CN115230721B (en) | Perception data processing method and device and automatic driving vehicle | |
| CN114132344B (en) | Decision method, device and equipment for automatic driving vehicle and storage medium | |
| CN115675532A (en) | Vehicle turning method and device, electronic equipment and storage medium | |
| CN114689061A (en) | Navigation route processing method and device of automatic driving equipment and electronic equipment | |
| CN115063769A (en) | Lane positioning method, device and equipment and automatic driving vehicle |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20220401 |
|
| WD01 | Invention patent application deemed withdrawn after publication |