CN114179832B - Lane changing method for automatic driving vehicle - Google Patents
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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
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
- B60W60/0015—Planning or execution of driving tasks specially adapted for safety
- B60W60/0016—Planning or execution of driving tasks specially adapted for safety of the vehicle or its occupants
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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
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
- B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
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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
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/18—Propelling the vehicle
- B60W30/18009—Propelling the vehicle related to particular drive situations
- B60W30/18163—Lane change; Overtaking manoeuvres
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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
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/403—Image sensing, e.g. optical camera
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Abstract
The disclosure provides a lane changing method for an automatic driving vehicle, relates to the technical field of artificial intelligence, and particularly relates to the technical field of automatic driving. The implementation scheme is as follows: acquiring a current lane and a current position of an autonomous vehicle; based on the current position, predicting lane change time of the autonomous vehicle from the current lane to the target lane and a lane change conflict area of the autonomous vehicle; obtaining a set of travel track points of a target vehicle around the autonomous vehicle corresponding to a plurality of time points within the lane change time based on the current position, each track point in the set of travel track points indicating a predicted position of the target vehicle at a corresponding time point in the plurality of time points; determining the target vehicle as an obstacle vehicle in response to the predicted position indicated by any one of the set of travel locus points being located in the lane-change conflict area; and obtaining a decision result based on the set of travel track points of the obstacle vehicle, the decision result indicating whether the autonomous vehicle changes lanes.
Description
Technical Field
The present disclosure relates to the field of data processing technology, and in particular, to the field of autopilot technology, and more particularly, to a lane changing method, apparatus, electronic device, computer readable storage medium, and computer program product for an autopilot vehicle.
Background
In the field of autopilot, more and more autopilot vehicles are being developed and put into use and become a powerful competitor in the field of traffic logistics and the like. As the convenience of automatically driving vehicles is accepted by more and more people, the safety problem of automatic driving is also attracting attention.
The automatic driving lane change technology is one of key technologies for realizing automatic driving. And a lane change decision process in the automatic driving lane change technology carries out lane change decision according to the automatic driving vehicle and the surrounding environment thereof so as to decide whether the automatic driving vehicle changes lanes or not.
The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, the problems mentioned in this section should not be considered as having been recognized in any prior art unless otherwise indicated.
Disclosure of Invention
The present disclosure provides a lane-changing method, apparatus, electronic device, computer-readable storage medium, and computer program product for an autonomous vehicle.
According to an aspect of the present disclosure, there is provided a lane-changing method for an automatically driven vehicle, including: acquiring a current lane and a current position of an autonomous vehicle; predicting a lane change time of the autonomous vehicle from the current lane to a target lane and a lane change conflict area of the autonomous vehicle based on the current location; obtaining a set of travel track points of a target vehicle around the autonomous vehicle corresponding to a plurality of time points within the lane change time, each track point in the set of travel track points indicating a predicted position of the target vehicle at a corresponding time point in the plurality of time points; determining the target vehicle as an obstacle vehicle in response to a predicted position indicated by any one of the set of travel locus points being located in the lane-change conflict area; and obtaining a decision result based on the set of travel track points of the obstacle vehicle, the decision result indicating whether the autonomous vehicle changes lanes.
According to another aspect of the present disclosure, there is provided a lane-changing apparatus for an automatically driven vehicle, comprising: a first acquisition unit configured to acquire a current lane and a current position of an autonomous vehicle; a first prediction unit configured to predict a lane change time of the autonomous vehicle from the current lane to a target lane and a lane change collision area of the autonomous vehicle based on the current position; a second acquisition unit configured to acquire a set of travel track points of a target vehicle around the autonomous vehicle corresponding to a plurality of time points within the lane change time, each track point in the set of travel track points indicating a predicted position of the target vehicle at a corresponding time point in the plurality of time points; a first determination unit configured to determine the target vehicle as an obstacle vehicle in response to a predicted position indicated by any one of the set of travel locus points being located in the lane-change conflict area; and a decision unit configured to obtain a decision result indicating whether the autonomous vehicle changes lanes based on the set of travel locus points of the obstacle vehicle.
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 lane-changing method for an autonomous vehicle described in embodiments of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the lane-changing method for an autonomous vehicle described in the embodiments of the present disclosure.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements the lane-changing method for an autonomous vehicle described in embodiments of the present disclosure.
According to another aspect of the present disclosure, there is provided an autonomous vehicle 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 lane-changing method for an autonomous vehicle described in embodiments of the present disclosure.
According to one or more embodiments of the present disclosure, by obtaining a lane change conflict area based on a lane change time of a host vehicle, vehicles (such as vehicles located on a target lane and a lane adjacent to the target lane (different from the lane where the host vehicle is located)) which are expected to drive into the lane change conflict area are used as obstacle vehicles in the process of predicting obstacle vehicles according to the lane change conflict area, and the movement track of the vehicles is included in the judgment of the final lane change decision, so that the accuracy of the decision is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
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The accompanying drawings illustrate exemplary embodiments and, together with the description, serve to explain exemplary implementations of the embodiments. The illustrated embodiments are for exemplary purposes only and do not limit the scope of the claims. Throughout the drawings, identical reference numerals designate similar, but not necessarily identical, elements.
FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, in accordance with an embodiment of the present disclosure;
FIG. 2 illustrates a flow chart of a lane-change method for an autonomous vehicle according to an embodiment of the present disclosure;
FIG. 3 illustrates a flowchart of a process for predicting a lane change time of an autonomous vehicle from a current lane to a target lane in a lane change method for an autonomous vehicle according to an embodiment of the present disclosure;
FIG. 4 illustrates a scene graph implementing a lane-change method for an autonomous vehicle according to an embodiment of the disclosure;
FIG. 5 illustrates a flowchart of a process for obtaining a decision result based on a set of travel trajectory points of an obstacle vehicle in a lane-change method for an autonomous vehicle according to an embodiment of the disclosure;
FIG. 6 illustrates a flow chart of a process in which decision results may be obtained based on a plurality of differences corresponding to a plurality of points in time in a lane-change method for an autonomous vehicle according to an embodiment of the present disclosure;
FIG. 7 illustrates a flow chart of a process by which risk analysis results may be obtained based on statistical characteristics of a plurality of differences in a lane-change method for an autonomous vehicle according to an embodiment of the present disclosure;
FIG. 8 illustrates a block diagram of a lane-changing apparatus for an autonomous vehicle according to an embodiment of the present disclosure; and
fig. 9 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one 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 of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the present disclosure, the use of the terms "first," "second," and the like to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of the elements, unless otherwise indicated, and such terms are merely used to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, they may also refer to different instances based on the description of the context.
The terminology used in the description of the various illustrated examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, the elements may be one or more if the number of the elements is not specifically limited. Furthermore, the term "and/or" as used in this disclosure encompasses any and all possible combinations of the listed items.
Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented, in accordance with an embodiment of the present disclosure. Referring to fig. 1, the system 100 includes a motor vehicle 110, a server 120, and one or more communication networks 130 coupling the motor vehicle 110 to the server 120.
In an embodiment of the present disclosure, motor vehicle 110 may include a computing device in accordance with an embodiment of the present disclosure and/or be configured to perform a method in accordance with an embodiment of the present disclosure.
The server 120 may run one or more services or software applications that enable execution of a lane-changing method for an autonomous vehicle. In some embodiments, server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof that are executable by one or more processors. A user of motor vehicle 110 may in turn utilize one or more client applications to interact with server 120 to utilize the services provided by these components. It should be appreciated that a variety of different system configurations are possible, which may differ from system 100. Accordingly, FIG. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.
The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture that involves virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain virtual storage devices of the server). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.
The computing units in server 120 may run one or more operating systems including any of the operating systems described above as well as any commercially available server operating systems. Server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.
In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from motor vehicle 110. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of motor vehicle 110.
Network 130 may be any type of network known to those skilled in the art that may support data communications using any of a number of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a satellite communications network, a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (including, for example, bluetooth, wiFi), and/or any combination of these with other networks.
The system 100 may also include one or more databases 150. In some embodiments, these databases may be used to store data and other information. For example, one or more of databases 150 may be used to store information such as audio files and video files. The data store 150 may reside in various locations. For example, the data store used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The data store 150 may be of different types. In some embodiments, the data store used by server 120 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve the databases and data from the databases in response to the commands.
In some embodiments, one or more of databases 150 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key value stores, object stores, or conventional stores supported by the file system.
Motor vehicle 110 may include a sensor 111 for sensing the surrounding environment. The sensors 111 may include one or more of the following: visual cameras, infrared cameras, ultrasonic sensors, millimeter wave radar, and laser radar (LiDAR). Different sensors may provide different detection accuracy and range. The camera may be mounted in front of, behind or other locations on the vehicle. The vision cameras can capture the conditions inside and outside the vehicle in real time and present them to the driver and/or passengers. In addition, by analyzing the captured images of the visual camera, information such as traffic light indication, intersection situation, other vehicle running state, etc. can be acquired. The infrared camera can capture objects under night vision. The ultrasonic sensor can be arranged around the vehicle and is used for measuring the distance between an object outside the vehicle and the vehicle by utilizing the characteristics of strong ultrasonic directivity and the like. The millimeter wave radar may be installed in front of, behind, or other locations of the vehicle for measuring the distance of an object outside the vehicle from the vehicle using the characteristics of electromagnetic waves. Lidar may be mounted in front of, behind, or other locations on the vehicle for detecting object edges, shape information for object identification and tracking. The radar apparatus may also measure a change in the speed of the vehicle and the moving object due to the doppler effect.
Motor vehicle 110 may also include a communication device 112. The communication device 112 may include a satellite positioning module capable of receiving satellite positioning signals (e.g., beidou, GPS, GLONASS, and GALILEO) from satellites 141 and generating coordinates based on these signals. The communication device 112 may also include a module for communicating with the mobile communication base station 142, and the mobile communication network may implement any suitable communication technology, such as the current or evolving wireless communication technology (e.g., 5G technology) such as GSM/GPRS, CDMA, LTE. The communication device 112 may also have a Vehicle-to-Everything (V2X) module configured to enable, for example, vehicle-to-Vehicle (V2V) communication with other vehicles 143 and Vehicle-to-Infrastructure (V2I) communication with Infrastructure 144. In addition, the communication device 112 may also have a module configured to communicate with a user terminal 145 (including but not limited to a smart phone, tablet computer, or wearable device such as a watch), for example, by using a wireless local area network or bluetooth of the IEEE802.11 standard. With the communication device 112, the motor vehicle 110 can also access the server 120 via the network 130.
Motor vehicle 110 may also include a control device 113. The control device 113 may include a processor, such as a Central Processing Unit (CPU) or a Graphics Processing Unit (GPU), or other special purpose processor, etc., in communication with various types of computer readable storage devices or mediums. The control device 113 may include an autopilot system for automatically controlling various actuators in the vehicle. The autopilot system is configured to control a powertrain, steering system, braking system, etc. of a motor vehicle 110 (not shown) via a plurality of actuators in response to inputs from a plurality of sensors 111 or other input devices to control acceleration, steering, and braking, respectively, without human intervention or limited human intervention. Part of the processing functions of the control device 113 may be implemented by cloud computing. For example, some of the processing may be performed using an onboard processor while other processing may be performed using cloud computing resources. The control device 113 may be configured to perform a method according to the present disclosure. Furthermore, the control means 113 may be implemented as one example of a computing device on the motor vehicle side (client) according to the present disclosure.
The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.
Referring to fig. 2, a lane-changing method 200 for an autonomous vehicle according to some embodiments of the present disclosure includes:
step S210: acquiring a current lane and a current position of an autonomous vehicle;
step S220: predicting a lane change time of the autonomous vehicle from the current lane to a target lane and a lane change conflict area of the autonomous vehicle based on the current location;
step S230: obtaining a set of travel track points of a target vehicle around the autonomous vehicle corresponding to a plurality of time points within the lane change time based on the current position; and
step S240: determining the target vehicle as an obstacle vehicle in response to a predicted position indicated by any one of the set of travel locus points being located in the lane-change conflict area; and
step S250: based on the set of travel track points of the obstacle vehicle, a decision result is obtained, the decision result indicating whether the autonomous vehicle changes lanes.
According to the lane change method for the automatic driving vehicle, in the lane change process of the automatic driving vehicle, the lane change conflict area is obtained based on the lane change time of the vehicle of the automatic driving vehicle, so that vehicles (such as vehicles positioned on a target lane and a lane adjacent to the target lane (different from the lane where the vehicle is positioned) which are expected to enter the lane change conflict area in the process of predicting the obstacle vehicle according to the lane change conflict area) are taken as obstacle vehicles, the movement track of the obstacle vehicles is included in the judgment of the final lane change decision, and the accuracy of the decision is improved.
In the related art, in the lane change decision process, a predicted trajectory based on an autonomous vehicle, a predicted trajectory of a vehicle located in a current lane where the autonomous vehicle is located, and a predicted trajectory of a vehicle located in a target lane where the autonomous vehicle is to change the lane are taken as judgment bases for the lane change decision. For example, in a predicted time interval in which the autonomous vehicle performs lane changing, by determining whether there is a risk of collision between the vehicle located in the current lane and the vehicle located in the target lane and the autonomous vehicle based on the predicted trajectory of the autonomous vehicle, the predicted trajectory of the vehicle located in the current lane, and the predicted trajectory of the vehicle located in the target lane, a lane changing decision is made according to the collision risk. The method only brings the predicted track of the vehicle on the current lane and the predicted track of the vehicle on the target lane into the judging process of lane change decision, and does not consider the vehicle on the adjacent lane on the side of the target lane, which is far away from the current lane, so that the collision risk between the vehicle on the adjacent lane on the side of the target lane, which is far away from the current lane, and the vehicle on the adjacent lane on the side of the target lane is faced in the process of lane change based on the decision result. Meanwhile, if more predicted trajectories of vehicles are taken into consideration for decision, the process of judging collision risk based on the predicted trajectories is performed by taking the predicted trajectories of each of the autonomous vehicle and more vehicles into consideration, so that the calculation amount is large, and the acquisition efficiency of the decision result is affected.
In the embodiment of the disclosure, the lane change conflict area is firstly predicted according to the lane change time of the autonomous vehicle, so that all vehicles around the autonomous vehicle (such as vehicles located on a target lane and a lane adjacent to the target lane (different from the lane where the vehicle is located)) can be predicted according to the lane change conflict area, thereby realizing screening of the vehicles around the autonomous vehicle, and in the process of obtaining a decision result based on the predicted track of the screened obstacle vehicle, the obstacle vehicle is obtained by screening all the vehicles around the autonomous vehicle, and the obtaining process of the decision result considers all the vehicles around the autonomous vehicle, so that inaccurate decision result caused by collision risk between the vehicles located on the adjacent lane, for example, on the side of the target lane, far from the current lane is avoided, and the accuracy of the decision result is improved. Meanwhile, the number of the obstacle vehicles is greatly reduced because the obstacle vehicles are obtained through screening, the calculated amount in the process of obtaining the decision result based on the predicted track of the obstacle vehicles and the predicted track of the autonomous vehicles can be reduced, and the obtaining efficiency of the decision result is improved.
In some embodiments, a lane-changing method for an autonomous vehicle is performed by a processor, where an autonomous vehicle refers to a vehicle that is decided by the processor to achieve autonomous driving.
In some embodiments, the step of obtaining the current lane and the current position of the autonomous vehicle is performed in response to a lane change instruction.
In some embodiments, the lane change instruction is sent by the driver. In other embodiments, the lane change instruction is sent by the lane change intent decision module to make a decision based on environmental data of the autonomous vehicle. For example, the lane change intention decision module decides to send a lane change instruction indicating that the autonomous vehicle is changing to the left-turn lane based on the path planning of the autonomous vehicle and the identification of the current lane of the autonomous vehicle indicating that the current lane is a straight lane.
In some embodiments, as shown in fig. 3, predicting a lane change time of the autonomous vehicle from the current lane to a target lane includes:
step S310: obtaining a relative distance of the current position relative to the target lane in a first direction perpendicular to the extending direction and a lane width of the target lane; and
step S320: the lane change time is obtained based on the relative distance and the lane width of the target lane.
The lane change time is obtained based on the vertical distance between the autonomous vehicle and the target lane and the lane width, so that the calculation process of the lane change time is simplified, the calculated amount is reduced, the data processing time is saved, and the decision speed is improved.
Referring to fig. 4, a lane-changing method for an autonomous vehicle according to some embodiments of the present disclosure is illustratively described.
As shown in fig. 4, an autonomous vehicle 410 located in a current lane 401 is expected to make a lane change to a target lane 402, where the target lane 402 has a first vehicle 420 thereon and an adjacent lane 403 adjacent to the target lane 402 that is distinct from the current lane 401 has a second vehicle 430 thereon. Wherein the current locations of the autonomous vehicle 410, the first vehicle 420, and the second vehicle 430 are represented by solid line boxes, and the predicted locations of the autonomous vehicle 410, the first vehicle 420, and the second vehicle 430 are represented by dashed line boxes.
In predicting the lane change time of the autonomous vehicle 410 from the current lane 401 to the target lane 402, the lane change time T is obtained using the formula (1), in which
Where d is the relative distance of the current position of the autonomous vehicle 401 from the target lane 402 in a direction perpendicular to the direction of extension of the target lane 402; w is the lane width of the target lane 402; and Δt is the time to change lanes across the width w of one lane.
In some embodiments, a relative distance of the current position of the autonomous vehicle to the target lane in a first direction perpendicular to the direction of extension of the target lane and a lane width of the target lane are obtained based on an imaging device on the autonomous vehicle.
In some embodiments, a relative distance of the current position of the autonomous vehicle with respect to the target lane in a first direction perpendicular to the direction of extension of the target lane and a lane width of the target lane are obtained based on the high-precision map.
In some embodiments, after obtaining the lane change time, a lane change conflict area of the autonomous vehicle is further obtained. With continued reference to fig. 4, a lane change conflict area 404 is obtained.
In some embodiments, a method of obtaining a lane change conflict area includes obtaining a segment of a preset length on a target lane, wherein a starting position of the segment is flush with a current position of an autonomous vehicle.
In some embodiments, the method of obtaining the lane-change conflict area includes obtaining the lane-change conflict area based on a current vehicle speed and a lane-change time of the autonomous vehicle. The lane change conflict area at least covers a section of the target lane in the extending direction, a first end of the section is flush with the current position, and a distance between a second end of the section and the first end is not smaller than a predicted running distance of the autonomous vehicle, wherein the predicted running distance is obtained by predicting that the autonomous vehicle moves at a uniform speed on the current lane for the lane change time based on the current speed of the autonomous vehicle.
The lane change conflict area is obtained based on the assumption of uniform motion of the autonomous vehicle, so that on one hand, the method for obtaining the lane change conflict area is simple, on the other hand, the method for obtaining the lane change conflict area is related to the current speed of the autonomous vehicle, and the accuracy of the lane change conflict area is improved.
In some embodiments, the lane-change conflict area extends in a first direction perpendicular to the direction of extension from a side of the autonomous vehicle proximate to the target lane to at least a side of the target lane distal from the autonomous vehicle.
In some embodiments, obtaining a set of travel track points for a target vehicle surrounding the autonomous vehicle corresponding to a plurality of time points within the lane change time includes: acquiring a predicted track of a target vehicle around the autonomous vehicle within a preset time period, wherein the predicted track comprises a plurality of track points; and obtaining a running track point set corresponding to the lane changing time in the track points based on the track points, the preset time period and the lane changing time.
In some embodiments, a predicted trajectory of the target vehicle over a preset period of time is obtained based on the prediction module. The prediction module may, for example, employ a neural network to obtain a predicted trajectory based on the speed and position of the target vehicle. In some embodiments, for each of a plurality of target vehicles located around an autonomous vehicle, a prediction module is employed to predict a predicted trajectory thereof. In other embodiments, the prediction module further predicts a predicted trajectory of the autonomous vehicle.
In some embodiments, the plurality of track points included in the predicted track are represented by coordinates in a world space coordinate system, e.g., track points corresponding to time point t are represented as (x) t ，y t ). In some embodiments, the lane change conflict area is represented as a set of points (x, y) located in the world coordinate system, where x1<x<x2,y1<y<y2, x1, x2, y1 and y2 are lane change conflict areas located at boundaries of world space.
In some embodiments, by combining x t Compare with x1 and x2 and compare y t And comparing with y1 and y2, judging whether the predicted position indicated by the track point in the predicted track running track point set of the target vehicle is positioned in the lane change conflict area.
In some embodiments, in response to x1<x t <x2 andy1<y t <y2, determining a track point (x t ，y t ) Whether the indicated predicted position is located in a lane change conflict area.
In one example, as shown in fig. 4, the first vehicle 420 and the second vehicle 430 are each a target vehicle of the autonomous vehicle 410, and after the predicted trajectories (indicated by a dashed box) of the first vehicle 420 and the second vehicle 430 are determined by using the lane change conflict area 404, the first vehicle 420 and the second vehicle 430 are each an obstacle vehicle of the autonomous vehicle 410.
In some embodiments, as shown in fig. 5, obtaining a decision result based on the set of travel trajectory points of the obstacle vehicle includes:
step S510: obtaining a lane-change track point set corresponding to the plurality of time points when the autonomous vehicle changes lanes within the lane-change time, each track point in the lane-change track point set indicating a predicted position of the autonomous vehicle at a corresponding time point in the plurality of time points;
step S520: for each of the plurality of time points, obtaining a first distance in the extending direction of a second track point corresponding to the time point in the running track point set relative to a preset position, and a second distance in the extending direction of the first track point corresponding to the time point in the lane-changing track point set relative to the preset position, and subtracting the first distance from the second distance to obtain a difference; and
step S530: and obtaining the decision result based on a plurality of differences corresponding to the plurality of time points.
And obtaining a prediction result of whether the host vehicle collides with the obstacle vehicle or not through the difference value between a plurality of track points in the extending direction along the target lane in the lane change prediction track of the host vehicle and the running prediction track of the obstacle vehicle, so as to obtain a decision result, wherein the decision result is related to the plurality of track points of the host vehicle and the obstacle vehicle, so that the error is reduced, and the accuracy of the decision result is improved.
In one example, each track point in the lane-change track point set and each track point in the travel track point set are represented with an abscissa (l, s) in a Frenet coordinate system based on the target lane centerline.
In the Frenet coordinate system, the vertical axis is the center line of the target lane, and the horizontal axis is perpendicular to the center line of the target lane. During autonomous vehicle travel, the origin of coordinates of the Frenet coordinate system changes as the autonomous vehicle position changes.
In an embodiment according to the present disclosure, each track point in the set of travel track points of the obstacle vehicle and each track point in the set of lane-change track points of the autonomous vehicle are represented as an abscissa (l, s), the abscissa l indicating a distance of the track point from a center line of the target lane, and the ordinate indicating a distance of the center line track point along the target lane from an origin of the Frenet coordinate system.
In an embodiment according to the present disclosure, a travel track point set of an obstacle vehicle and a lane change track point set of an autonomous vehicle are obtained at the same plurality of time points such that a plurality of track points in the travel track point set and a plurality of track points in the lane change track point set are in one-to-one correspondence, each track point in the travel track point set and the lane change track point set is represented by a Frenet coordinate system, an ordinate of each track point in the travel track point set is a first distance, and an abscissa of each track point in the lane change track point set is a second distance. The difference of the second distance minus the first distance in step S520 can be calculated by using the ordinate of the corresponding track points in the running track point set and the lane change track point set.
Referring to fig. 4, a set of lane-change trajectory points of an autonomous vehicle 410 are obtained corresponding to a plurality of points in time (e.g., point in time t 0 、t 1 And t 2 ) Multiple track points, e.g. track point (l) 0 ，s 0 )、(l 1 ，s 1 ) And (l) 2 ，s 2 ) And obtaining a set of travel track points of the first vehicle 420 (also an obstacle vehicle) corresponding to a plurality of time points (e.g., time point t 0 、t 1 And t 2 ) Multiple track points, e.g. track point (l' 0 ，s′ 0 )、(l′ 1 ，s′ 1 ) And (l' 2 ，s′ 2 ). In step S520, for each of the plurality of time points, a difference is obtained, for example, for time point t 0 Obtaining a difference delta s 0 ＝s′ 0 -s 0 The method comprises the steps of carrying out a first treatment on the surface of the Also for time point t 1 Obtaining deltas 1 ＝s′ 1 -s 1 The method comprises the steps of carrying out a first treatment on the surface of the For time point t 2 Obtaining deltas 2 ＝s′ 2 -s 2 ；。
In some embodiments, obtaining the decision result based on a plurality of differences corresponding to the plurality of time points comprises:
responsive to a first difference of the plurality of differences being greater than zero, the decision result is obtained, the decision result indicating that the autonomous vehicle is unchanged.
When the first difference value in the plurality of difference values is larger than zero, the possibility of collision is indicated, a decision result without changing the road is made, and the driving safety is ensured.
In some embodiments, referring to fig. 6, obtaining the decision result based on a plurality of differences corresponding to the plurality of time points comprises:
Step S610: obtaining a risk analysis result based on the statistical characteristics of the plurality of differences, the risk analysis result indicating whether a collision risk exists between the obstacle vehicle and the autonomous vehicle; and
step S620: and obtaining the decision result based on the risk analysis result.
And the decision result is obtained based on the statistical characteristics of a plurality of differences, so that the accuracy of the decision result is improved, the calculated amount is reduced, and the decision efficiency is improved. Meanwhile, in the embodiment of the disclosure, the risk analysis result is obtained by obtaining the decision result based on the statistical characteristics of a plurality of differences, so that the problem of inaccurate estimation caused by obtaining the lane change conflict area based on the uniform motion assumption can be solved, and the accuracy of the obtained decision result is improved.
In some embodiments, the statistical characteristics of the plurality of differences include a mean, standard deviation, variance, or the like of the plurality of differences.
In some embodiments, a risk of collision between the obstacle vehicle and the autonomous vehicle is determined based on the mean being greater than a preset value.
In some embodiments, as shown in fig. 7, obtaining the risk analysis result based on the statistical characteristics of the plurality of differences comprises:
Step S710: obtaining a standard deviation sigma and a mean mu of the plurality of differences;
step S720: in response to determining that the sum of μ and 3σ is greater than zero, obtaining the risk analysis result that indicates a risk of collision between the obstacle vehicle and the autonomous vehicle.
And judging a 3-sigma rule by obtaining standard deviation and average values of a plurality of differences, and determining that collision risk exists between the obstacle vehicle and the autonomous vehicle when the sum of mu and 3 sigma (namely mu+3 sigma) is larger than 0, so that the accuracy of a risk analysis result is further improved.
In the judgment process of the 3-sigma rule, if the data obeys the normal distribution, the outlier is defined as a value, which deviates from the average value by more than three times of the standard deviation, in a set of result values. That is, under the assumption of normal distribution, the probability of occurrence of a value other than three times the standard deviation from the average value is small, and thus can be regarded as an abnormal value. And obtaining a risk analysis result according to a 3-sigma rule, so that the result of the risk analysis result is reliable, and the accuracy of the decision result is further improved.
In some embodiments, the decision result is obtained in response to the risk analysis result indicating that there is a risk of collision between the obstacle vehicle and the autonomous vehicle, the decision result indicating that the autonomous vehicle is not lane-changing.
In some embodiments, in response to the decision result indicating that the autonomous vehicle is unchanged, updating the current location to obtain an updated location; wherein the obtaining the current lane and the current position of the autonomous vehicle further comprises: and determining the updated position as the current position.
In the case of no lane change, the detection according to the present scheme is continued during the running of the autonomous vehicle based on the update of the current position of the autonomous vehicle until the decision result indicates that the autonomous vehicle can change lanes, and lane change is performed.
In some embodiments, a lane change method for a self-driving vehicle according to the present disclosure loops through steps S210-S250 for a change in vehicle position until the decision result indicates that the autonomous vehicle may change lanes.
In some embodiments, the autonomous vehicle obtains a lane change buffer by constructing a virtual obstacle, and obtains a decision result indicating lane change of the autonomous vehicle and immediately performs lane change in response to the risk analysis result indicating that a collision risk between the obstacle vehicle and the autonomous vehicle does not exist.
According to another aspect of the present disclosure, there is also provided a lane-changing apparatus for an autonomous vehicle, referring to fig. 8, an apparatus 800 includes: a first acquisition unit 810 configured to acquire a current lane and a current position of the autonomous vehicle; a first prediction unit 820 configured to predict a lane change time of the autonomous vehicle from the current lane to a target lane and a lane change collision area of the autonomous vehicle based on the current position; a second obtaining unit 830 configured to obtain, based on the current position, a set of travel track points of a target vehicle around the autonomous vehicle corresponding to a plurality of time points within the lane change time, each track point in the set of travel track points indicating a predicted position of the target vehicle at a corresponding time point of the plurality of time points; a first determining unit 840 configured to determine the target vehicle as an obstacle vehicle in response to a predicted position indicated by any one of the set of travel locus points being located in the lane-change conflict area; and a decision unit 850 configured to obtain a decision result indicating whether the autonomous vehicle changes lanes based on the set of travel locus points of the obstacle vehicle.
In some embodiments, the first prediction unit 820 includes: a first acquisition subunit configured to acquire a relative distance of the current position with respect to the target lane in a first direction perpendicular to the extending direction and a lane width of the target lane; and a first calculation unit configured to obtain the lane change time based on the relative distance and a lane width of the target lane.
In some embodiments, the lane-change conflict area covers at least a segment of the target lane in an extension direction, a first end of the segment being flush with the current position, a distance between a second end of the segment and the first end being not less than a predicted travel distance of the autonomous vehicle, the predicted travel distance being obtained by predicting the autonomous vehicle to move at a uniform speed over the current lane for the lane-change time based on a current speed of the autonomous vehicle.
In some embodiments, the decision unit 850 includes: a second acquisition subunit configured to obtain a set of lane-change trajectory points of the autonomous vehicle corresponding to the plurality of points in time when lane-change is performed within the lane-change time, each trajectory point of the set of lane-change trajectory points indicating a predicted position of the autonomous vehicle at a corresponding point in time of the plurality of points in time; a second calculation unit configured to obtain, for each of the plurality of time points, a first distance in the extending direction of a second trajectory point corresponding to the time point in the travel trajectory point set with respect to a preset position, and a second distance in the extending direction of the first trajectory point corresponding to the time point in the lane-change trajectory point set with respect to the preset position, and subtract the first distance with the second distance to obtain a difference value; and a decision subunit configured to obtain the decision result based on a plurality of differences corresponding to the plurality of time points.
In some embodiments, the decision subunit comprises: a first response unit configured to obtain the decision result in response to a first difference of the plurality of differences being greater than zero, the decision result indicating that the autonomous vehicle is unchanged.
In some embodiments, the decision subunit comprises: a risk analysis unit configured to obtain a risk analysis result indicating whether a collision risk exists between the obstacle vehicle and the autonomous vehicle, based on the statistical features of the plurality of differences; and the decision result acquisition unit is configured to acquire the decision result based on the risk analysis result.
In some embodiments, the risk analysis unit comprises: a third acquisition subunit configured to obtain a standard deviation σ and a mean μ of the plurality of differences; and a second response unit configured to obtain the risk analysis result indicating that there is a risk of collision between the obstacle vehicle and the autonomous vehicle in response to determining that the sum of μ and 3σ is greater than 0.
In some embodiments, further comprising: an updating unit configured to update the current location to obtain an updated location in response to the decision result indicating that the autonomous vehicle is unchanged; wherein the first acquisition unit further includes: and a determining unit configured to determine the updated position as a current position.
According to another aspect of the present disclosure, there is also provided an electronic apparatus 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 a method according to embodiments of the present disclosure.
According to another aspect of the present disclosure, there is also provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method according to an embodiment of the present disclosure.
According to another aspect of the present disclosure, there is also provided a computer program product comprising a computer program, wherein the computer program, when executed by a processor, implements a method according to embodiments of the present disclosure.
According to another aspect of the present disclosure, there is also provided an autonomous vehicle 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 a method according to embodiments of the present disclosure.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related user personal information all conform to the regulations of related laws and regulations, and the public sequence is not violated.
Referring to fig. 9, a block diagram of an electronic device 900 that may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 9, the apparatus 900 includes a computing unit 901 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 902 or a computer program loaded from a storage unit 909 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other by a bus 904. An input/output (I/O) interface 905 is also connected to the bus 904.
Various components in device 900 are connected to I/O interface 905, including: an input unit 906, an output unit 907, a storage unit 908, and a communication unit 909. The input unit 906 may be any type of device capable of inputting information to the device 900, and the input unit 906 may receive input digital or character information and generate and electronic deviceKey signal inputs related to user settings and/or function control of (a) and may include, but are not limited to, a mouse, keyboard, touch screen, track pad, track ball, joystick, microphone, and/or remote control. The output unit 907 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, video/audio output terminals, vibrators, and/or printers. Storage unit 908 may include, but is not limited to, magnetic disks, optical disks. Communication unit 909 allows device 900 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and/or chipsets, such as bluetooth TM Devices, 802.11 devices, wiFi devices, wiMax devices, cellular communication devices, and/or the like.
The computing unit 901 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 901 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 901 performs the various methods and processes described above, such as method 200. For example, in some embodiments, the method 200 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 900 via the ROM 902 and/or the communication unit 909. When the computer program is loaded into RAM 903 and executed by computing unit 901, one or more steps of method 200 described above may be performed. Alternatively, in other embodiments, computing unit 901 may be configured to perform method 200 by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. 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. The 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 pointing device (e.g., a mouse or 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 may 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 input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background 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 background, 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 a client and a server. The client and server are typically 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 incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the foregoing methods, systems, and apparatus are merely exemplary embodiments or examples, and that the scope of the present invention is not limited by these embodiments or examples but only by the claims following the grant and their equivalents. Various elements of the embodiments or examples may be omitted or replaced with equivalent elements thereof. Furthermore, the steps may be performed in a different order than described in the present disclosure. Further, various elements of the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after the disclosure.
Claims (17)
1. A lane-changing method for an autonomous vehicle, comprising:
Acquiring a current lane and a current position of an autonomous vehicle;
predicting a lane change time of the autonomous vehicle from the current lane to a target lane and a lane change conflict area of the autonomous vehicle based on the current location, wherein,
the lane-change conflict area is configured to predict a vehicle that is located on the target lane and on the other lane of the two lanes adjacent to the target lane other than the current lane,
the lane change collision area covers at least a section of the target lane in an extending direction, a first end of the section is flush with the current position, and a distance between a second end of the section and the first end is not smaller than a predicted travel distance of the autonomous vehicle, the predicted travel distance being obtained by predicting the autonomous vehicle to move at a constant speed on the current lane for the lane change time based on a current speed of the autonomous vehicle;
obtaining a set of travel track points around the autonomous vehicle on the target lane and on the other lane of the two lanes adjacent to the target lane other than the current lane, the set of travel track points corresponding to a plurality of points in time during the lane change, each track point in the set of travel track points indicating a predicted position of the target vehicle at a corresponding point in time of the plurality of points in time;
Determining the target vehicle as an obstacle vehicle in response to a predicted position indicated by any one of the set of travel locus points being located in the lane-change conflict area; and
based on the set of travel track points of the obstacle vehicle, a decision result is obtained, the decision result indicating whether the autonomous vehicle changes lanes.
2. The method of claim 1, wherein the predicting a lane change time of the autonomous vehicle from the current lane to a target lane comprises:
obtaining a relative distance of the current position relative to the target lane in a first direction perpendicular to the extending direction and a lane width of the target lane; and
the lane change time is obtained based on the relative distance and the lane width of the target lane.
3. The method of claim 1 or 2, wherein the obtaining a decision result based on the set of travel trajectory points of the obstacle vehicle comprises:
obtaining a lane-change track point set corresponding to the plurality of time points when the autonomous vehicle changes lanes within the lane-change time, each track point in the lane-change track point set indicating a predicted position of the autonomous vehicle at a corresponding time point in the plurality of time points;
For each of the plurality of time points, obtaining a first distance in the extending direction of a second track point corresponding to the time point in the running track point set relative to a preset position, and a second distance in the extending direction of the first track point corresponding to the time point in the lane-changing track point set relative to the preset position, and subtracting the first distance from the second distance to obtain a difference; and
and obtaining the decision result based on a plurality of differences corresponding to the plurality of time points.
4. The method of claim 3, wherein the obtaining the decision result based on the plurality of differences corresponding to the plurality of time points comprises:
responsive to a first difference of the plurality of differences being greater than zero, the decision result is obtained, the decision result indicating that the autonomous vehicle is unchanged.
5. The method of claim 3, wherein the obtaining the decision result based on the plurality of differences corresponding to the plurality of time points comprises:
obtaining a risk analysis result based on the statistical characteristics of the plurality of differences, the risk analysis result indicating whether a collision risk exists between the obstacle vehicle and the autonomous vehicle; and
And obtaining the decision result based on the risk analysis result.
6. The method of claim 5, wherein the obtaining risk analysis results based on the statistical characteristics of the plurality of differences comprises:
obtaining a standard deviation sigma and a mean mu of the plurality of differences; and
in response to determining that the sum of μ and 3σ is greater than zero, obtaining the risk analysis result that indicates a risk of collision between the obstacle vehicle and the autonomous vehicle.
7. The method of claim 1 or 2, further comprising:
updating the current location to obtain an updated location in response to the decision result indicating that the autonomous vehicle is not changing lanes; wherein the obtaining the current lane and the current position of the autonomous vehicle further comprises:
and determining the updated position as the current position.
8. A lane-changing apparatus for an autonomous vehicle, comprising:
a first acquisition unit configured to acquire a current lane and a current position of an autonomous vehicle;
a first prediction unit configured to predict a lane change time of the autonomous vehicle from the current lane to a target lane and a lane change collision area of the autonomous vehicle based on the current position, wherein,
The lane-change conflict area is configured to predict a vehicle that is located on the target lane and on the other lane of the two lanes adjacent to the target lane other than the current lane,
the lane change collision area covers at least a section of the target lane in an extending direction, a first end of the section is flush with the current position, and a distance between a second end of the section and the first end is not smaller than a predicted travel distance of the autonomous vehicle, the predicted travel distance being obtained by predicting the autonomous vehicle to move at a constant speed on the current lane for the lane change time based on a current speed of the autonomous vehicle;
a second acquisition unit configured to obtain, based on the current position, a set of travel locus points of which a target vehicle located on the target lane around the autonomous vehicle and on the other lane other than the current lane among two lanes adjacent to the target lane corresponds to a plurality of time points within the lane change time, each locus point in the set of travel locus points indicating a predicted position of the target vehicle at a corresponding time point in the plurality of time points;
A first determination unit configured to determine the target vehicle as an obstacle vehicle in response to a predicted position indicated by any one of the set of travel locus points being located in the lane-change conflict area; and
a decision unit configured to obtain a decision result indicating whether the autonomous vehicle changes lanes based on the set of travel locus points of the obstacle vehicle.
9. The apparatus of claim 8, wherein the first prediction unit comprises:
a first acquisition subunit configured to acquire a relative distance of the current position with respect to the target lane in a first direction perpendicular to the extending direction and a lane width of the target lane; and
a first calculation unit configured to obtain the lane change time based on the relative distance and a lane width of the target lane.
10. The apparatus according to claim 8 or 9, wherein the decision unit comprises:
a second acquisition subunit configured to obtain a set of lane-change trajectory points of the autonomous vehicle corresponding to the plurality of points in time when lane-change is performed within the lane-change time, each trajectory point of the set of lane-change trajectory points indicating a predicted position of the autonomous vehicle at a corresponding point in time of the plurality of points in time;
A second calculation unit configured to obtain, for each of the plurality of time points, a first distance in the extending direction of a second trajectory point corresponding to the time point in the travel trajectory point set with respect to a preset position, and a second distance in the extending direction of the first trajectory point corresponding to the time point in the lane-change trajectory point set with respect to the preset position, and subtract the first distance with the second distance to obtain a difference value; and
and the decision subunit is configured to obtain the decision result based on a plurality of difference values corresponding to the plurality of time points.
11. The apparatus of claim 10, wherein the decision subunit comprises:
a first response unit configured to obtain the decision result in response to a first difference of the plurality of differences being greater than zero, the decision result indicating that the autonomous vehicle is unchanged.
12. The apparatus of claim 10, wherein the decision subunit comprises:
a risk analysis unit configured to obtain a risk analysis result indicating whether a collision risk exists between the obstacle vehicle and the autonomous vehicle, based on the statistical features of the plurality of differences; and
And the decision result acquisition unit is configured to acquire the decision result based on the risk analysis result.
13. The apparatus of claim 12, wherein the risk analysis unit comprises:
a third acquisition subunit configured to obtain a standard deviation σ and a mean μ of the plurality of differences; and
and a second response unit configured to obtain the risk analysis result indicating that there is a risk of collision between the obstacle vehicle and the autonomous vehicle in response to determining that the sum of μ and 3σ is greater than 0.
14. The apparatus of claim 8 or 9, further comprising:
an updating unit configured to update the current location to obtain an updated location in response to the decision result indicating that the autonomous vehicle is unchanged; wherein the first acquisition unit further includes:
and a determining unit configured to determine the updated position as a current position.
15. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the method comprises the steps of
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
16. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-7.
17. An autonomous vehicle comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the method comprises the steps of
The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
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| CN115179949B (en) * | 2022-09-13 | 2022-11-29 | 毫末智行科技有限公司 | Vehicle speed-changing control method, device, equipment and storage medium |
| CN116142179A (en) * | 2022-12-07 | 2023-05-23 | 深圳曦华科技有限公司 | Method for preventing scratch during steering of vehicle, related device, storage medium and program |
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