CN115303275A - Vehicle lane change planning method and device, computer equipment and storage medium - Google Patents
Vehicle lane change planning method and device, computer equipment and storage medium Download PDFInfo
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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
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
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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
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
- B60W40/105—Speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
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- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
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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
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/05—Type of road, e.g. motorways, local streets, paved or unpaved roads
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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
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/50—Barriers
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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
- B60W2554/00—Input parameters relating to objects
- B60W2554/40—Dynamic objects, e.g. animals, windblown objects
- B60W2554/404—Characteristics
- B60W2554/4042—Longitudinal speed
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Abstract
The application relates to a method and a device for planning lane change of a vehicle, computer equipment and a storage medium. The method comprises the following steps: acquiring environment image information of a vehicle, and acquiring nearby lane information of the vehicle according to the environment image information; obtaining a first running speed of a vehicle right ahead of the vehicle and a second running speed of the vehicle obliquely ahead of the vehicle according to the nearby lane information, and obtaining a first steady-state speed and a second steady-state speed according to the first running speed and the second running speed, wherein the first steady-state speed is a predicted running speed of the vehicle on a current lane, and the second steady-state speed is a predicted running speed of the vehicle on a nearby lane; and judging whether the second steady-state speed is greater than the first steady-state speed, if so, performing lane change processing on the vehicle, and solving the problems of unreasonable lane change time, high time cost and the like of the vehicle.
Description
Technical Field
The invention relates to the technical field of automatic driving automobiles, in particular to a method and a device for planning lane change of a vehicle, computer equipment and a storage medium.
Background
During the driving of the vehicle, the driver usually changes lanes due to a traffic jam ahead or a change in his destination. With the development of intelligent driving technology, more and more vehicles are equipped with intelligent driving assistance systems to provide environmental information around the vehicles, so as to assist drivers to change lanes. However, the existing intelligent driving assistance system/method can only play a role when the driver actually changes lanes, and cannot inform the driver in advance when the lane should be changed and evaluate the time cost of the lane change. In particular, for an automatic driving vehicle, the problems of unreasonable lane changing time, high time cost and the like of the vehicle can be caused.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a computer device and a storage medium for planning lane change of a vehicle, so as to solve the problem of poor selection of the lane change timing during the driving process of the vehicle.
In one aspect, a vehicle lane change planning method is provided, and includes:
acquiring environment image information of a vehicle, and acquiring nearby lane information of the vehicle according to the environment image information;
obtaining a first running speed of a vehicle right in front of the vehicle and a second running speed of a vehicle in front of the vehicle according to the nearby lane information, and obtaining a first steady-state speed and a second steady-state speed according to the first running speed and the second running speed, wherein the first steady-state speed is a predicted running speed of the vehicle in a current lane, and the second steady-state speed is a predicted running speed of the vehicle in a nearby lane;
and judging whether the second steady-state speed is greater than the first steady-state speed, and if so, performing lane changing processing on the vehicle.
In one embodiment, the step of acquiring environment image information of a vehicle, and the step of acquiring nearby lane information of the vehicle according to the environment image information comprises the steps of:
acquiring a first environment image corresponding to a first moment, and acquiring first position information of the nearby vehicle and first lane type information of the nearby lane according to the first environment image;
acquiring a second environment image corresponding to a second moment, and acquiring second position information of the nearby vehicle and second lane type information of the nearby lane according to the second environment image;
the nearby lane information includes: the first location information, the first lane type information, the second location information, the second lane type information.
In one of the embodiments, the step of obtaining a first traveling speed of a vehicle directly in front of the vehicle and a second traveling speed of a vehicle diagonally in front of the vehicle from the nearby lane information includes:
obtaining forward running speeds of a plurality of vehicles in front according to the first position information and the second position information, and obtaining the first running speed according to the forward running speeds, wherein the vehicles in front belong to the same lane as the vehicles and are in front of the running direction of the vehicles;
obtaining oblique traveling speeds of a plurality of the oblique front vehicles according to the first position information, the second position information, the first lane type information and the second lane information, and obtaining the second traveling speed according to the oblique traveling speeds, wherein the oblique front vehicles and the vehicles belong to different lanes and are in front of the traveling direction of the vehicles.
In one embodiment, the step of obtaining a first steady state speed and a second steady state speed according to the first driving speed and the second driving speed comprises:
traversing a plurality of first running speeds corresponding to the sampling time, and obtaining a first intermediate steady-state speed according to a preset first weight value, wherein the mathematical expression of the first intermediate steady-state speed is as follows:
V1 m (t)＝w1*V1 d (t)+(1-w1)V1 m (t-1)
wherein, V1 m (t) represents said first intermediate steady-state speed, w1 represents said first weight value, and t represents said first intermediate steady-state speedThe corresponding time within the sampling time, V1 d Representing the first travel speed;
traversing a plurality of first intermediate steady-state speeds corresponding to the sampling time according to the first intermediate steady-state speed and a preset second weight value to obtain the first steady-state speed, wherein the mathematical expression of the first steady-state speed is as follows:
V1 a (t)＝w2*V1 m (t)+(1-w2)V1 a (t-1)
wherein, V1 a (t) represents said first steady state speed, w2 represents said second weight value;
traversing the plurality of second running speeds corresponding to the sampling time, and obtaining a second intermediate steady-state speed according to the first weight value, wherein the mathematical expression of the second intermediate steady-state speed is as follows:
V2 m (t)＝w1*V2 d (t)+(1-w1)*V2 m (t-1)
wherein, V2 m (t) represents the second intermediate steady-state speed, w1 represents the first weight value, t represents the corresponding time instant within the sampling time, V2 d Representing the second travel speed;
traversing a plurality of second intermediate steady-state speeds corresponding to the sampling time according to the second intermediate steady-state speeds and the second weight value to obtain the second steady-state speeds, wherein the second steady-state speeds are mathematically expressed as:
V2 a (t)＝w2*V2 m (t)+(1-w2)*V2 a (t-1)
wherein, V2 a (t) represents the second steady-state speed, and w2 represents the second weight value.
In one embodiment, the step after obtaining the nearby lane information of the vehicle further comprises:
obtaining obstacle information of the nearby lane according to the nearby lane information, wherein the obstacle information comprises: a size of an obstacle, a distance length between the obstacle and the vehicle;
according to the obstacle information, obtaining a first travelable width of the vehicle in the current lane and a second travelable width of the vehicle in the nearby lane;
determining whether the first driving width is greater than a width of the vehicle; if so, the vehicle normally runs; if not, judging whether the second running width is larger than the width of the vehicle, if so, performing lane change processing on the vehicle, and if not, performing deceleration processing on the vehicle.
In one embodiment, the step after obtaining the nearby lane information of the vehicle further comprises:
acquiring a travelable lane type corresponding to the vehicle;
acquiring the lane type of the current lane outside a preset sampling distance;
judging whether the type of the drivable lane is consistent with that of the current lane or not; if so, the vehicle normally runs; and if not, performing lane change processing on the vehicle.
In one embodiment, the step after obtaining the nearby lane information of the vehicle further comprises:
acquiring a driving destination of the vehicle, and acquiring a preset driving path of the vehicle according to the driving destination;
judging whether a corresponding driving lane of the vehicle outside a preset sampling distance is consistent with the current lane or not according to the preset driving path and the nearby lane information; if so, the vehicle normally runs; and if not, performing lane change processing on the vehicle.
In another aspect, a vehicle lane change planning apparatus is provided, including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring environment image information of a vehicle and acquiring nearby lane information of the vehicle according to the environment image information;
a second obtaining module, configured to obtain a first traveling speed of a vehicle directly ahead of the vehicle and a second traveling speed of a vehicle diagonally ahead of the vehicle according to the nearby lane information, and obtain a first steady-state speed and a second steady-state speed according to the first traveling speed and the second traveling speed, where the first steady-state speed is a predicted traveling speed of the vehicle in a current lane and the second steady-state speed is a predicted traveling speed of the vehicle in a nearby lane;
and the judging module is used for judging whether the second steady-state speed is greater than the first steady-state speed or not, and if so, performing lane changing processing on the vehicle.
In another aspect, a computer device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the following steps when executing the computer program:
acquiring environment image information of a vehicle, and acquiring nearby lane information of the vehicle according to the environment image information;
obtaining a first running speed of a vehicle right in front of the vehicle and a second running speed of a vehicle in front of the vehicle according to the nearby lane information, and obtaining a first steady-state speed and a second steady-state speed according to the first running speed and the second running speed, wherein the first steady-state speed is a predicted running speed of the vehicle in a current lane, and the second steady-state speed is a predicted running speed of the vehicle in a nearby lane;
and judging whether the second steady-state speed is greater than the first steady-state speed, if so, performing lane change processing on the vehicle.
In yet another aspect, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, performs the steps of:
acquiring environment image information of a vehicle, and acquiring nearby lane information of the vehicle according to the environment image information;
obtaining a first running speed of a vehicle right in front of the vehicle and a second running speed of the vehicle obliquely in front of the vehicle according to the nearby lane information, and obtaining a first steady-state speed and a second steady-state speed according to the first running speed and the second running speed, wherein the first steady-state speed is a predicted running speed of the vehicle in a current lane, and the second steady-state speed is a predicted running speed of the vehicle in a nearby lane;
and judging whether the second steady-state speed is greater than the first steady-state speed, if so, performing lane change processing on the vehicle.
According to the vehicle lane change planning method, the vehicle lane change planning device, the computer equipment and the storage medium, the nearby lane information of the vehicle is obtained according to the environment image information; obtaining a first running speed of a vehicle right ahead of the vehicle and a second running speed of a vehicle obliquely ahead of the vehicle according to the nearby lane information, and obtaining a first steady-state speed and a second steady-state speed according to the first running speed and the second running speed; and judging whether the second steady-state speed is greater than the first steady-state speed, if so, performing lane change processing on the vehicle, so as to solve the problems of unreasonable lane change time, high time cost and the like of the automatic driving vehicle.
Drawings
FIG. 1 is a diagram of an embodiment of a vehicle lane change planning method;
FIG. 2 is a schematic flow chart diagram of a vehicle lane change planning method in one embodiment;
FIG. 3 is a schematic diagram of a process for obtaining forward road information according to one embodiment;
FIG. 4 is a schematic flow chart illustrating a process for obtaining a first travel speed and a second travel speed according to one embodiment;
FIG. 5 is a schematic flow chart illustrating obtaining a first steady state speed and a second steady state speed according to one embodiment;
FIG. 6 is a flow diagram illustrating a process after obtaining nearby lane information in one embodiment;
FIG. 7 is a flowchart illustrating a process after obtaining nearby lane information in another embodiment;
FIG. 8 is a flowchart illustrating a process after obtaining nearby lane information in yet another embodiment;
FIG. 9 is a block diagram showing the construction of a lane-change planning apparatus for a vehicle according to an embodiment;
fig. 10 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The vehicle lane change planning method can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. For example, the vehicle lane change planning method provided by the application can be applied to a scene for planning lane change in the driving process of an automatic driving vehicle. During the driving of the vehicle, the driver usually changes lanes due to a traffic jam ahead or a change in his destination. With the development of intelligent driving technology, more and more vehicles are equipped with intelligent driving assistance systems to provide environmental information around the vehicles, so as to assist drivers to change lanes. However, the existing intelligent driving assistance system/method can only play a role when the driver actually changes lanes, and cannot inform the driver in advance when the lane should be changed and evaluate the time cost of the lane change. In particular, for an automatic driving vehicle, the problems of unreasonable lane changing time, high time cost and the like of the vehicle can be caused. Therefore, the method and the device obtain the nearby lane information of the vehicle according to the environment image information by obtaining the environment image information of the vehicle; obtaining a first running speed of a vehicle right ahead of the vehicle and a second running speed of a vehicle obliquely ahead of the vehicle according to the nearby lane information, and obtaining a first steady-state speed and a second steady-state speed according to the first running speed and the second running speed; and judging whether the second steady-state speed is greater than the first steady-state speed, if so, performing lane change processing on the vehicle, and solving the problems of unreasonable lane change time, high time cost and the like of the vehicle. In some implementations, the terminal 102 may collect environment image information of the autonomous vehicle, upload the environment image information to the server 104 for data analysis and calculation to obtain a lane change policy, and then the server 104 sends the lane change policy to the terminal 102. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, portable wearable devices, or sub-servers, and the server 104 may be implemented by an independent server or a server cluster formed by multiple servers, or a cloud computing platform.
In one embodiment, as shown in fig. 2, there is provided a vehicle lane change planning method, including the steps of:
s1: acquiring environment image information of a vehicle, and acquiring nearby lane information of the vehicle according to the environment image information;
s2: obtaining a first running speed of a vehicle right in front of the vehicle and a second running speed of a vehicle in front of the vehicle according to the nearby lane information, and obtaining a first steady-state speed and a second steady-state speed according to the first running speed and the second running speed, wherein the first steady-state speed is a predicted running speed of the vehicle in a current lane, and the second steady-state speed is a predicted running speed of the vehicle in a nearby lane;
s3: and judging whether the second steady-state speed is greater than the first steady-state speed, and if so, performing lane changing processing on the vehicle.
Through the steps, the problems that the lane changing time of the automatic driving vehicle is unreasonable, the time cost is high and the like can be solved.
In step S1, it is exemplarily explained that environment image information of the vehicle is acquired, and nearby lane information of the vehicle is acquired according to the environment image information, for example, other vehicle information, lane information, and obstacle information at any angle around the vehicle may be acquired by a vehicle-mounted camera as environment image information, for example, video information of the front, the rear, the left, and the right of the automatically driven vehicle (current vehicle) is acquired, and then the video is segmented and analyzed according to a preset period, in some implementations, the preset period may be 200 milliseconds or 500 milliseconds, where no specific numerical value is defined, an implementer may perform a numerical adjustment of the period according to a real-time requirement for image analysis, and it is explained that the nearby lane information may include not only a nearby lane of the current vehicle but also a lane to which the current vehicle belongs, and in some implementations, the environment image information of the current vehicle may be directly photographed and stored according to the preset period. After the environment image information is acquired, the front and rear information of the lane of the current vehicle and the front and rear information of the nearby lane can be acquired from the environment image information, wherein the front and rear information also comprises information such as whether other vehicles exist on the lane, the number of the vehicles, the types of the lanes, the shapes of the lanes and the like, the information is fused to acquire the nearby lane information, the nearby lane information is used as a data basis for subsequent lane change planning decision of the vehicles, and in other implementation processes, the nearby lane information of the lane where the vehicle runs can also be acquired through a map.
After the nearby lane information is acquired, in order to further evaluate the time cost of the vehicle lane change, in step S2, it is exemplarily explained that a first traveling speed of a vehicle immediately in front of the vehicle and a second traveling speed of a vehicle diagonally in front of the vehicle are obtained from the nearby lane information, and the first and second steady-state speeds are obtained from the first and second traveling speeds, for example, a traveling speed of a vehicle immediately in front of and belonging to the same lane as the current vehicle and traveling ahead of the current vehicle may be obtained as the first traveling speed, and a traveling speed of a vehicle diagonally in front of and belonging to a different lane from the current vehicle and traveling ahead of the current vehicle may be obtained as the second traveling speed; since the number of nearby lanes, the number of other vehicles, and the positions of the other vehicles in the nearby lanes are included in the nearby lane information, the relative positions between the other vehicles (a vehicle right ahead, a vehicle diagonally ahead) and the current vehicle may be acquired according to the nearby lane information, it should be noted that there may be more than one other vehicle, and thus the first travel speed and the second travel speed may actually be a set of travel speeds of a plurality of other vehicles. From the first travel speed and the second travel speed, an average travel speed or a maximum travel speed that the current vehicle can reach if it continues to travel in the current lane for a period of time, i.e., a first steady-state speed, can be predicted, while from the knowledge of the second travel speeds of other vehicles in nearby lanes, an average travel speed or a maximum travel speed that can be reached if the current vehicle makes a lane change during a period of time in nearby lanes, i.e., a second steady-state speed, can be predicted.
After the first steady-state speed and the second steady-state speed are obtained, the two speeds may be compared, and a lane change planning decision is obtained according to the comparison result, in step S3, it is exemplarily described that whether the second steady-state speed is greater than the first steady-state speed is judged, if so, the lane change processing is performed on the vehicle, for example, when the first steady-state speed is 50km/h and the second steady-state speed is 60km/h, it may be considered that if the vehicle continues to keep running on the current lane, the speed that can be reached is 50km/h, and the speed that can be reached when the vehicle is running on a nearby lane after lane change is 60km/h, when the overall path reaching the destination is not affected, the lane change processing may be performed on the vehicle, so that the vehicle obtains a faster running speed that can be reached, thereby saving the running time. However, if the first steady-state speed is 60km/h and the second steady-state speed is 50km/h, it is considered that a faster traveling speed can be achieved when the current vehicle continues to keep traveling in the current lane, where km/h represents a unit of speed: kilometers per hour. By the method, the running information of other vehicles on the current lane and the nearby lane of the automatic driving vehicle can be analyzed before actually changing the lane, so that the decision of lane changing planning is obtained in advance, the time cost of changing the lane of the vehicle is saved, and repeated lane changing is avoided.
In some embodiments, as shown in fig. 3, the step of acquiring environment image information of a vehicle, and the step of acquiring nearby lane information of the vehicle according to the environment image information includes:
s11: acquiring a first environment image corresponding to a first moment, and acquiring first position information of the nearby vehicle and first lane type information of the nearby lane according to the first environment image;
s12: acquiring a second environment image corresponding to a second moment, and acquiring second position information of the nearby vehicle and second lane type information of the nearby lane according to the second environment image;
s13: the nearby lane information includes: the first location information, the first lane type information, the second location information, the second lane type information.
As shown in fig. 3, in steps S11 to S13, it is exemplarily explained that the first environment image and the second environment image at the first time and the second time are respectively acquired, for example, the first environment image at the first time is acquired with the current time as the first time, wherein, the first environment image can be a photo shot by a camera in real time, and also can be an image of a certain frame in a real-time video, wherein the lane information comprises the lane information in front of and behind the lane where the current vehicle is positioned, the vehicle information and the obstacle information, according to the information, first position information (position coordinates) of other vehicles and first lane type information (such as a truck-only lane, a passenger car-only lane, an emergency lane, a straight lane and the like) of nearby lanes can be obtained, then adding 1 second or 2 seconds on the basis of the first time as a second time to obtain a second environment image corresponding to the second time, and obtaining updated second position information of the nearby vehicle and second lane type information of the nearby lane according to the second environment image, it should be noted that the first environment image and the second environment image only represent surrounding environment images that can be acquired by the current vehicle at different times, and because the current vehicle is in a driving state, the collected surrounding environment image can be changed correspondingly, the application does not limit the specific numerical values of the first time and the second time, an implementer can set according to the real-time requirement of the implementer on image collection, preferably, the first time and the second time are not only a single time point, but may be a collection of multiple points in time, for example, an implementer may employ more than two times as the first time or the second time.
As shown in fig. 4, in some embodiments, the step of obtaining a first traveling speed of a vehicle directly in front of the vehicle and a second traveling speed of a vehicle diagonally in front of the vehicle from the nearby lane information includes:
s21: obtaining forward running speeds of a plurality of vehicles in front according to the first position information and the second position information, and obtaining the first running speed according to the forward running speeds, wherein the vehicles in front belong to the same lane as the vehicles and are in front of the running direction of the vehicles;
s22: and obtaining oblique driving speeds of a plurality of oblique front vehicles according to the first position information, the second position information, the first lane type information and the second lane information, and obtaining the second driving speed according to the oblique driving speeds, wherein the oblique front vehicles and the vehicles belong to different lanes and are in front of the driving direction of the vehicles.
As shown in fig. 4, in steps S21 to S22, it is exemplarily explained that forward traveling speeds of a plurality of vehicles ahead are obtained according to first position information and second position information, a first traveling speed is obtained according to the plurality of forward traveling speeds, oblique traveling speeds of a plurality of oblique vehicles ahead are obtained according to the first position information, second position information, first lane type information and second lane information, and a second traveling speed is obtained according to the plurality of oblique traveling speeds, for example, the position information of the vehicle ahead of the current vehicle can be obtained according to the first position information corresponding to a first time and the second position information corresponding to a second time, and the forward traveling speeds of the vehicle ahead can be obtained by detecting the plurality of position information, where it is required to be explained that the first time and the second time are only descriptive words representing different times, and the number of specifically obtained times is not limited, so that two (two frames) or more forward traveling speeds can be obtained by performing fusion analysis, the first traveling speed of the vehicle ahead is required to be explained, and the vehicle ahead belongs to the same multi-frame direction, and the vehicle ahead is in front of the vehicle; for the second driving speed, the oblique driving speed of the oblique front vehicle can be obtained according to the first position information, the second position information, the first lane type and the second lane type, and the second driving speed can be obtained by performing multi-frame detection on a plurality of oblique driving speeds; in some implementations, the driving speeds of other vehicles, namely the first driving speed of the vehicle in front and the second driving speed of the vehicle in front of the vehicle in an inclined direction, can be calculated according to the sensing module on the current vehicle by using historical multi-frame information.
In order to obtain the first steady-state speed and the second steady-state speed, as shown in fig. 5, the step of obtaining the first steady-state speed and the second steady-state speed according to the first driving speed and the second driving speed includes:
s31: traversing a plurality of first running speeds corresponding to the sampling time, and obtaining a first intermediate steady-state speed according to a preset first weight value, wherein the mathematical expression of the first intermediate steady-state speed is as follows:
V1 m (t)＝w1*V1 d (t)+(1-w1)*V1 m (t-1)
wherein, V1 m (t) represents the first intermediate steady-state speed, w1 represents the first weight value, t represents the corresponding time within the sampling time, V1 d Represents the first travel speed;
s32: traversing a plurality of first intermediate steady-state speeds corresponding to the sampling time according to the first intermediate steady-state speed and a preset second weight value to obtain the first steady-state speed, wherein the mathematical expression of the first steady-state speed is as follows:
V1 a (t)＝w2*V1 m (t)+(1-w2)*V1 a (t-1)
wherein, V1 a (t) represents said first steady state speed, w2 represents said second weight value;
s33: traversing a plurality of second running speeds corresponding to the sampling time, and obtaining a second intermediate steady-state speed according to the first weight value, wherein the mathematical expression of the second intermediate steady-state speed is as follows:
V2 m (t)＝w1*V2 d (t)+(1-w1)*V2 m (t-1)
wherein, V2 m (t) represents the second steady-state velocity, w1 represents the first weight value, t represents the corresponding time within the sampling time, V2 d Representing the second travel speed;
s34: traversing a plurality of second intermediate steady-state speeds corresponding to the sampling time according to the second intermediate steady-state speeds and the second weight value to obtain the second steady-state speed, wherein the second steady-state speed is mathematically expressed as:
V2 a (t)＝w2*V2 m (t)+(1-w2)*V2 a (t-1)
wherein, V2 a (t) represents the second steady-state speed, and w2 represents the second weight value.
As shown in fig. 5, in step S31, it is exemplarily illustrated that a plurality of first traveling speeds corresponding to sampling times are traversed, and a first intermediate steady-state speed is obtained according to a preset first weight value, for example, the first intermediate steady-state speed is obtained from t =1 in a sequence of time t, in some implementations, the first weight w1 may be set to 0.1, and a value range of t (sampling time) may be 2 seconds, 10 seconds, and at an initial time V1 m (0) Is 0 and then iteratively updated starting from t =1 on the value of the first intermediate steady-state speed.
As shown in fig. 5, in step S32, it is exemplarily illustrated that, according to the first intermediate steady-state speed and a preset second weight value, a plurality of first intermediate steady-state speeds corresponding to the sampling time are traversed to obtain the first steady-state speed, for example, the first steady-state speed is obtained from t =1 in sequence of time t, in some implementations, the first weight w2 may be set to 0.01, and the value range of t (sampling time) may be 2 seconds, 10 seconds, and at the initial time V1 a (0) Is 0 and then iteratively updated starting from t =1 on the value of the first steady-state speed. In this way, a first steady-state speed may then be obtained.
As shown in fig. 5, in step S33, it is exemplarily explained that a plurality of second traveling speeds corresponding to sampling times are traversed, and a second intermediate steady-state speed is obtained according to a preset first weight value, for example, the second intermediate steady-state speed is obtained from t =1 in a sequence of time t, in some implementations, the first weight w1 may be set to 0.1, and a value range (sampling time) of t may be 2 seconds, 10 seconds, and at an initial time V1 m (0) Is 0 and then iteratively updated starting from t =1 on the value of the intermediate steady-state speed.
As shown in fig. 5, in step S34, it is exemplarily illustrated that, according to the second intermediate steady-state speeds and a preset second weight value, a plurality of second intermediate steady-state speeds corresponding to the sampling time are traversed to obtain the second steady-state speed, for example, the second steady-state speed is obtained from t =1 in sequence of time t, in some implementations, the first weight w2 may be set to 0.01, and the value range of t (sampling time) may be 2 seconds, 10 seconds, and at the initial time V2 a (0) Is 0 and then iteratively updated starting from t =1 and starting with the traversal of the value of the second steady-state speed. By the mode, the second steady-state speed can be obtained, the second steady-state speed can be compared with the first steady-state speed conveniently in the subsequent process, and the time cost of vehicle lane changing is reasonably considered and reduced.
In some implementations, for autonomous vehicles with larger body sizes, such as truck-type vehicles, it is also necessary to check whether the autonomous vehicle is allowed to pass through in lane space within a certain distance in front of the target lane, including the current lane and nearby lanes.
As shown in fig. 6, the step after obtaining the nearby lane information of the vehicle further includes:
s41: obtaining obstacle information of the nearby lane according to the nearby lane information, wherein the obstacle information comprises: a size of an obstacle, a distance length between the obstacle and the vehicle;
s42: according to the obstacle information, obtaining a first driving width of the vehicle in the current lane and a second driving width of the vehicle in the nearby lane;
s43: determining whether the first driving width is greater than a width of the vehicle; if so, the vehicle normally runs; if not, judging whether the second running width is larger than the width of the vehicle or not, if so, performing lane changing processing on the vehicle, and if not, performing deceleration processing on the vehicle.
As shown in fig. 6, in steps S41 to S43, it is exemplarily explained that the obstacle information of the nearby lane is obtained and it is determined whether the current vehicle can avoid the obstacle, for example, there may be a case of an accident, construction, slow vehicle, etc. (collectively referred to as an obstacle herein) in front of the current lane, in which the current vehicle needs to perform operations of decelerating, stopping, changing lane, etc., and the obstacle occupies a partial lane position, so it is necessary to identify the type of the obstacle, the size of the obstacle, and the distance length between the obstacle and the current vehicle, and it is also necessary to specify that the obstacle may exist in front of the current lane or in front of the nearby lane, so it is necessary to obtain a first travelable width of the current vehicle in the current lane and a second travelable width of the nearby lane, and then it is determined whether the first travelable width is greater than the width required for the vehicle to normally travel, and if so, the current vehicle normally travels the obstacle in the current lane; if not, judging whether the second running width is larger than the width required by the normal running of the vehicle, if so, performing lane change processing on the current vehicle, and if not, forcibly performing deceleration processing on the current vehicle until the current vehicle stops.
As shown in fig. 7, the step after obtaining the nearby lane information of the vehicle further includes:
s51: acquiring a travelable lane type corresponding to the vehicle;
s52: acquiring the lane type of the current lane outside a preset sampling distance;
s53: judging whether the type of the travelable lane is consistent with the type of the current lane; if so, the vehicle normally runs; and if not, performing lane change processing on the vehicle.
As shown in fig. 7, in steps S51 to S53, it is exemplarily described that whether to perform lane change processing on the current vehicle is determined for a lane type in front of the current vehicle, for example, a lane in front of the current vehicle may not belong to a lane type allowed to pass by the current vehicle, for example, for a truck type autonomous vehicle, the lane in front may not be a truck lane or a truck is prohibited from entering the lane in front during a current driving time period, at this time, it is necessary to identify a lane type outside a certain distance of the lane where the current vehicle is located, that is, a sampling distance may be set to 50 meters or 100 meters, after the lane type of the current lane outside the sampling distance is acquired, it is determined whether the lane type is consistent with a feasible lane type corresponding to the current vehicle, if the lane type is consistent, the current vehicle normally runs, and if the lane type is not consistent, it is necessary to identify other nearby lanes, and perform lane change processing on the current vehicle, for example, a lane change decision to the right lane is performed.
As shown in fig. 8, the step after obtaining the nearby lane information of the vehicle further includes:
s61: acquiring a driving destination of the vehicle, and acquiring a preset driving path of the vehicle according to the driving destination;
s62: judging whether a corresponding driving lane of the vehicle outside a preset sampling distance is consistent with the current lane or not according to the preset driving path and the information of the nearby lane; if so, the vehicle normally runs; and if not, performing lane change processing on the vehicle.
As shown in fig. 8, in steps S61 to S62, it is exemplarily described that, according to the preset driving path and the nearby lane information, it is determined whether the driving lane corresponding to the vehicle outside the preset sampling distance is consistent with the current lane, for example, after the destination or the destination is determined to be changed, the driving path may be selected in multiple ways, if the current vehicle needs to approach to the right lower ramp or enter a certain toll gate after driving for a certain distance length, lane change planning needs to be performed in advance to prevent that the lane change cannot be performed in time, at this time, it needs to determine whether the driving lane corresponding to the outside the sampling distance is consistent with the current lane according to the driving path and the nearby lane information, if so, the current vehicle normally runs, and if not, it needs to perform lane change processing on the current vehicle.
In some implementation processes, the vehicle can be interconnected with a server, a cloud platform and a cloud end to obtain the wheel congestion condition of the lanes in the area around the current vehicle, and the planning of changing lanes in advance is carried out in advance aiming at the congested road scene.
It should be understood that, although the steps in the flowcharts of fig. 2 to 8 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-8 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 9, there is provided a vehicle lane change planning apparatus including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring environment image information of a vehicle and acquiring nearby lane information of the vehicle according to the environment image information;
the second acquisition module is used for acquiring a first running speed of the vehicle, acquiring a second running speed of a vehicle nearby the vehicle according to the nearby lane information, and acquiring a first steady-state speed and a second steady-state speed according to the first running speed and the second running speed, wherein the first steady-state speed is a predicted running speed of the vehicle in a current lane, and the second steady-state speed is a predicted running speed of the vehicle in a nearby lane;
and the judging module is used for judging whether the second steady-state speed is greater than the first steady-state speed or not, and if so, performing lane changing processing on the vehicle.
In the first obtaining module, it is exemplarily illustrated that environment image information of a vehicle is obtained, nearby lane information of the vehicle is obtained according to the environment image information, for example, other vehicle information, lane information, and obstacle information at any angle around the vehicle may be obtained by using a vehicle-mounted camera as the environment image information, for example, video information of the front, the rear, the left, and the right of an autonomous vehicle (current vehicle) is obtained, and then the video is segmented and analyzed according to a preset period, in some implementations, the preset period may be 300 milliseconds or 600 milliseconds, where no specific numerical value is defined, an implementer may perform a numerical adjustment of the period according to a real-time requirement for image analysis, and it is to be noted that the nearby lane information may include not only a nearby lane of the current vehicle but also a lane to which the current vehicle belongs, and in some implementations, a photograph may be directly taken according to the preset period to store the environment image information of the current vehicle. After the environmental image information is acquired, the information of the front and the back of the lane of the current vehicle and the information of the front and the back of the nearby lane can be acquired from the environmental image information, wherein the information of the front and the back also comprises the information of whether other vehicles exist on the lane, the number of the vehicles, the types of the lanes, the shapes of the lanes and the like, the information of the nearby lane is acquired by fusing the information, the information is used as a data base for subsequent lane change planning decision of the vehicles, and in other implementation processes, the information of the nearby lane of the driving lane of the vehicle can be acquired through a map.
In the second acquisition module, it is exemplarily explained that the first traveling speed of the vehicle immediately in front of the vehicle and the second traveling speed of the vehicle diagonally in front of the vehicle are obtained from the nearby lane information, and the first and second steady-state speeds are obtained from the first and second traveling speeds, for example, the traveling speed of the vehicle immediately in front of the current vehicle and belonging to the same lane as the current vehicle may be acquired as the first traveling speed, and the traveling speed of the vehicle diagonally in front of the current vehicle and belonging to a different lane from the current vehicle may be acquired as the second traveling speed; since the number of nearby lanes, the number of other vehicles, and the positions of the other vehicles in the nearby lanes are included in the nearby lane information, the relative positions between the other vehicles (a vehicle right ahead, a vehicle diagonally ahead) and the current vehicle may be acquired according to the nearby lane information, it is noted that there may be more than one other vehicle, and thus the first traveling speed and the second traveling speed may actually be an aggregate of the traveling speeds of a plurality of other vehicles. From the first travel speed and the second travel speed, an average travel speed or a maximum travel speed that the current vehicle can reach if it continues to travel in the current lane for a period of time, i.e., a first steady-state speed, can be predicted, while from the knowledge of the second travel speeds of other vehicles in nearby lanes, an average travel speed or a maximum travel speed that can be reached if the current vehicle makes a lane change during a period of time in nearby lanes, i.e., a second steady-state speed, can be predicted.
In the determining module, it is exemplarily illustrated that whether the second steady-state speed is greater than the first steady-state speed is determined, if so, lane change processing is performed on the vehicle, for example, when the first steady-state speed is 40km/h and the second steady-state speed is 60km/h, it can be considered that if the vehicle keeps running on the current lane, the achievable speed is 40km/h and is less than the achievable speed of 60km/h when the vehicle runs on the nearby lane after lane change, the lane change processing can be performed on the vehicle when the overall route to the destination is not affected, so that the vehicle obtains a achievable faster running speed, thereby saving running time. However, if the first steady-state speed is 60km/h and the second steady-state speed is 50km/h, it is considered that a faster travel speed can be achieved when the current vehicle continues to keep traveling in the current lane, where km/h represents a unit of speed: kilometers per hour. By the method, the running information of other vehicles on the current lane and the nearby lane of the automatic driving vehicle can be analyzed before actually changing the lane, so that the decision of lane changing planning is obtained in advance, the time cost of changing the lane of the vehicle is saved, and repeated lane changing is avoided.
The device can be applied to a scene for planning lane change in the driving process of the automatic driving vehicle. The method comprises the steps that environment image information of a vehicle is obtained through a first obtaining module, and nearby lane information of the vehicle is obtained according to the environment image information; acquiring a first running speed of the vehicle through a second acquisition module, acquiring a second running speed of a vehicle nearby the vehicle according to nearby lane information, and acquiring a first steady-state speed and a second steady-state speed according to the first running speed and the second running speed; the judgment module judges whether the second steady-state speed is greater than the first steady-state speed, if so, lane changing processing is carried out on the vehicle, and the problems that the lane changing time of the vehicle is unreasonable, the time cost is high and the like can be solved.
For specific limitations of the vehicle lane change planning device, reference may be made to the above limitations of the vehicle lane change planning method, which are not described herein again. The modules in the vehicle lane-changing planning device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 10. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data of the lane change plan of the vehicle. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a vehicle lane change planning method.
Those skilled in the art will appreciate that the architecture shown in fig. 10 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring environment image information of a vehicle, and acquiring nearby lane information of the vehicle according to the environment image information;
obtaining a first running speed of a vehicle right in front of the vehicle and a second running speed of a vehicle in front of the vehicle according to the nearby lane information, and obtaining a first steady-state speed and a second steady-state speed according to the first running speed and the second running speed, wherein the first steady-state speed is a predicted running speed of the vehicle in a current lane, and the second steady-state speed is a predicted running speed of the vehicle in a nearby lane;
and judging whether the second steady-state speed is greater than the first steady-state speed, if so, performing lane change processing on the vehicle.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring environment image information of a vehicle, and acquiring nearby lane information of the vehicle according to the environment image information;
obtaining a first running speed of a vehicle right in front of the vehicle and a second running speed of a vehicle in front of the vehicle according to the nearby lane information, and obtaining a first steady-state speed and a second steady-state speed according to the first running speed and the second running speed, wherein the first steady-state speed is a predicted running speed of the vehicle in a current lane, and the second steady-state speed is a predicted running speed of the vehicle in a nearby lane;
and judging whether the second steady-state speed is greater than the first steady-state speed, and if so, performing lane changing processing on the vehicle.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. The non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically erasable programmable RO M (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is specific and detailed, but not to be understood as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for vehicle lane change planning, comprising:
acquiring environment image information of a vehicle, and acquiring nearby lane information of the vehicle according to the environment image information;
obtaining a first running speed of a vehicle right in front of the vehicle and a second running speed of the vehicle obliquely in front of the vehicle according to the nearby lane information, and obtaining a first steady-state speed and a second steady-state speed according to the first running speed and the second running speed, wherein the first steady-state speed is a predicted running speed of the vehicle in a current lane, and the second steady-state speed is a predicted running speed of the vehicle in a nearby lane;
and judging whether the second steady-state speed is greater than the first steady-state speed, and if so, performing lane changing processing on the vehicle.
2. The vehicle lane change planning method according to claim 1, wherein acquiring environment image information of a vehicle, and the step of acquiring nearby lane information of the vehicle based on the environment image information includes:
acquiring a first environment image corresponding to a first moment, and acquiring first position information of the nearby vehicle and first lane type information of the nearby lane according to the first environment image;
acquiring a second environment image corresponding to a second moment, and acquiring second position information of the nearby vehicle and second lane type information of the nearby lane according to the second environment image;
the nearby lane information includes: the first location information, the first lane type information, the second location information, the second lane type information.
3. The vehicle lane-change planning method according to claim 2, wherein the step of obtaining a first traveling speed of a vehicle directly in front of the vehicle and a second traveling speed of a vehicle diagonally in front of the vehicle from the nearby lane information includes:
obtaining forward running speeds of a plurality of vehicles in front according to the first position information and the second position information, and obtaining the first running speed according to the forward running speeds, wherein the vehicles in front belong to the same lane as the vehicles and are in front of the running direction of the vehicles;
and obtaining oblique driving speeds of a plurality of oblique front vehicles according to the first position information, the second position information, the first lane type information and the second lane information, and obtaining the second driving speed according to the oblique driving speeds, wherein the oblique front vehicles and the vehicles belong to different lanes and are in front of the driving direction of the vehicles.
4. The vehicle lane change planning method according to claim 1, wherein the step of obtaining a first steady-state speed and a second steady-state speed from the first traveling speed and the second traveling speed comprises:
traversing a plurality of first running speeds corresponding to the sampling time, and obtaining a first intermediate steady-state speed according to a preset first weight value, wherein the mathematical expression of the first intermediate steady-state speed is as follows:
V1 m (t)＝w1*V1 d (t)+(1-w1)*V1 m (t-1)
wherein, V1 m (t) represents the first intermediate steady-state speed, w1 represents the first weight value, t represents the corresponding time within the sampling time, V1 d Representing the first travel speed;
traversing a plurality of first intermediate steady-state speeds corresponding to the sampling time according to the first intermediate steady-state speed and a preset second weight value to obtain the first steady-state speed, wherein the mathematical expression of the first steady-state speed is as follows:
V1 a (t)＝w2*V1 m (t)+(1-w2)*V1 a (t-1)
wherein, V1 a (t) represents said first steady state speed, w2 represents said second weight value;
traversing the plurality of second running speeds corresponding to the sampling time, and obtaining a second intermediate steady-state speed according to the first weight value, wherein the mathematical expression of the second intermediate steady-state speed is as follows:
V2 m (t)＝w1*V2 d (t)+(1-w1)*V2 m (t-1)
wherein, V2 m (t) represents the second steady-state velocity, w1 represents the first weight value, t represents the corresponding time within the sampling time, V2 d Representing the second travel speed;
traversing a plurality of second intermediate steady-state speeds corresponding to the sampling time according to the second intermediate steady-state speeds and the second weight value to obtain the second steady-state speeds, wherein the second steady-state speeds are mathematically expressed as:
V2 a (t)＝w2*V2 m (t)+(1-w2)*V2 a (t-1)
wherein, V2 a (t) represents the second steady-state speed, and w2 represents the second weight value.
5. The vehicle lane-change planning method according to claim 1, wherein the step after obtaining the nearby lane information of the vehicle further comprises:
obtaining obstacle information of the nearby lane according to the nearby lane information, wherein the obstacle information comprises: a size of an obstacle, a distance length between the obstacle and the vehicle;
according to the obstacle information, obtaining a first travelable width of the vehicle in the current lane and a second travelable width of the vehicle in the nearby lane;
determining whether the first driving width is greater than a width of the vehicle; if so, the vehicle normally runs; if not, judging whether the second running width is larger than the width of the vehicle, if so, performing lane change processing on the vehicle, and if not, performing deceleration processing on the vehicle.
6. The vehicle lane change planning method according to claim 1, wherein the step after obtaining the nearby lane information of the vehicle further comprises:
acquiring a travelable lane type corresponding to the vehicle;
acquiring the lane type of the current lane outside a preset sampling distance;
judging whether the type of the drivable lane is consistent with that of the current lane or not; if so, the vehicle normally runs; and if not, performing lane change processing on the vehicle.
7. The vehicle lane change planning method according to claim 1, wherein the step after obtaining the nearby lane information of the vehicle further comprises:
acquiring a driving destination of the vehicle, and acquiring a preset driving path of the vehicle according to the driving destination;
judging whether a corresponding driving lane of the vehicle outside a preset sampling distance is consistent with the current lane or not according to the preset driving path and the information of the nearby lane; if so, the vehicle normally runs; and if not, performing lane change processing on the vehicle.
8. A vehicle lane change planning apparatus, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring environment image information of a vehicle and acquiring nearby lane information of the vehicle according to the environment image information;
the second obtaining module is used for obtaining a first running speed of the vehicle, obtaining a second running speed of a vehicle nearby the vehicle according to the nearby lane information, and obtaining a first steady-state speed and a second steady-state speed according to the first running speed and the second running speed, wherein the first steady-state speed is a predicted running speed of the vehicle in a current lane, and the second steady-state speed is a predicted running speed of the vehicle in a nearby lane;
and the judging module is used for judging whether the second steady-state speed is greater than the first steady-state speed or not, and if so, performing lane change processing on the vehicle.
9. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the computer program, carries out the steps of the method for vehicle lane change planning of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for vehicle lane change planning of any of claims 1 to 7.
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