CN115447617A - Vehicle control method, device, equipment and medium - Google Patents
Vehicle control method, device, equipment and medium Download PDFInfo
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Abstract
The invention discloses a vehicle control method, a vehicle control device, vehicle control equipment and a vehicle control medium. The method comprises the following steps: determining a target vehicle of the current vehicle according to historical operation data of the current vehicle and historical operation data of at least one adjacent vehicle; determining the predicted state quantity of the current vehicle at least one moment in a future preset time period according to the state quantity of the current vehicle at the current moment and the control quantity of the current vehicle at the current moment; constructing an objective function based on the minimum difference value between the motion path of the current vehicle in the future preset time period and the preset path; and determining a target control quantity of the current vehicle at least one moment under the constraint of the collision constraint condition between the current vehicle and the target vehicle according to the target function and each predicted state quantity. By executing the scheme, the method and the device can flexibly control the individualized running of the automatic driving vehicle, so that the control result is more in line with the individualized requirement of a driver, and the cooperative control of the automatic driving vehicle is realized.
Description
Technical Field
The invention relates to the technical field of intelligent traffic safety, in particular to a vehicle control method, device, equipment and medium.
Background
With the popularization of automatic driving systems for automobiles, when the automatic driving systems provide services for human beings, the individualized driving requirements of the human beings need to be considered. In actual road traffic, the vehicle operating characteristics of different drivers are different. In a following scene, a personality-aggressive driver tends to keep a closer following distance, while a personality-conservative passenger tends to keep a larger following distance; in the lane changing scene, the lane changing of the individual aggressive drivers is rapid, and the lane changing process of the vehicle can be smoothly finished by the individual conservative drivers as far as possible.
The traditional automatic driving decision and control mode in a fixed mode cannot adapt to the driving requirements of different drivers, so that the automatic driving behavior is not consistent with the expectation of a user, and the automatic driving decision and control mode is difficult to adapt to the various individual requirements of different drivers/passengers on vehicle driving, thereby causing safety accidents.
Disclosure of Invention
The invention provides a vehicle control method, a vehicle control device, vehicle control equipment and a vehicle control medium, which can flexibly control the individualized running of an automatic driving vehicle, enable a control result to better meet the individualized requirement of a driver and realize the cooperative control of the automatic driving vehicle.
According to an aspect of the present invention, there is provided a vehicle control method including:
determining a target vehicle of the current vehicle according to historical operation data of the current vehicle and historical operation data of at least one adjacent vehicle;
determining the predicted state quantity of the current vehicle at least one moment in a future preset time period according to the state quantity of the current vehicle at the current moment and the control quantity of the current vehicle at the current moment; the state quantity comprises at least one of speed, acceleration, sensing information and wireless communication information;
constructing an objective function based on the minimum difference value between the motion path of the current vehicle in a future preset time period and a preset path;
and determining a target control quantity of the current vehicle at the at least one moment under the constraint of the collision constraint condition between the current vehicle and the target vehicle according to the target function and each predicted state quantity, so that the current vehicle controls the current vehicle at the corresponding moment according to the target control quantity.
According to another aspect of the present invention, there is provided a vehicle control apparatus including:
the target vehicle determining module is used for determining a target vehicle of the current vehicle according to historical operation data of the current vehicle and historical operation data of at least one adjacent vehicle;
the predicted state quantity determining module is used for determining the predicted state quantity of the current vehicle at least one moment in a future preset time period according to the state quantity of the current vehicle at the current moment and the control quantity of the current vehicle at the current moment; the state quantity comprises at least one of speed, acceleration, sensing information and wireless communication information; the control amount includes a steering wheel angle;
the target function constructing module is used for constructing a target function based on the minimum difference value between the motion path of the current vehicle in a future preset time period and a preset path;
and the target control quantity determining module is used for determining the target control quantity of the current vehicle at the at least one moment under the constraint of the collision constraint condition between the current vehicle and the target vehicle according to the target function and each predicted state quantity so as to enable the current vehicle to control the current vehicle at the corresponding moment according to the target control quantity.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform a vehicle control method according to any of the embodiments of the invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement a vehicle control method according to any one of the embodiments of the present invention when executed.
According to the technical scheme of the embodiment of the invention, a target vehicle of a current vehicle is determined according to historical operation data of the current vehicle and historical operation data of at least one adjacent vehicle; determining the predicted state quantity of the current vehicle at least one moment in a future preset time period according to the state quantity of the current vehicle at the current moment and the control quantity of the current vehicle at the current moment; the state quantity comprises at least one of speed, acceleration, sensing information and wireless communication information; constructing an objective function based on the minimum difference value between the motion path of the current vehicle in the future preset time period and the preset path; and determining a target control quantity of the current vehicle at least one moment under the constraint of the collision constraint condition between the current vehicle and the target vehicle according to the target function and each predicted state quantity, so that the current vehicle controls the current vehicle at the corresponding moment according to the target control quantity. By executing the scheme provided by the embodiment of the invention, the individualized running of the automatic driving vehicle can be flexibly controlled, the control result is more in line with the individualized requirement of a driver, and the cooperative control of the automatic driving vehicle is realized.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a vehicle control method provided by an embodiment of the present invention;
FIG. 2 is a flow chart of another vehicle control method provided by an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a vehicle control device provided in an embodiment of the invention;
fig. 4 is a schematic configuration diagram of an electronic device that implements a vehicle control method of an embodiment of the invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a flowchart of a vehicle control method provided in an embodiment of the present invention, where the embodiment is applicable to a case where cooperative control is performed on an autonomous vehicle, and the method may be executed by a vehicle control device, which may be implemented in the form of hardware and/or software, and the vehicle control device may be configured in an electronic device for vehicle control. As shown in fig. 1, the method includes:
and S110, determining a target vehicle of the current vehicle according to the historical operation data of the current vehicle and the historical operation data of at least one adjacent vehicle.
The historical operation data can be speed, the historical operation data can also be lane offset, the historical operation data can also be yaw velocity, the historical operation data can also be yaw acceleration, and the historical operation data can be set according to actual needs. The target vehicle may be an adjacent vehicle that has the closest distance to the current vehicle and has the greatest influence on the current vehicle. The scheme can determine the driving behavior correlation degree between the current vehicle and the adjacent vehicle according to the historical operation data of the current vehicle and the historical operation data of the adjacent vehicle for each adjacent vehicle; and taking the driving behavior correlation degree smaller than the preset threshold value as a candidate driving behavior correlation degree, performing ascending sequencing or descending sequencing on the candidate driving behavior correlation degrees, and taking the adjacent vehicle corresponding to the candidate driving behavior correlation degree with the minimum value as a target vehicle.
And S120, determining the predicted state quantity of the current vehicle at least one moment in a future preset time period according to the state quantity of the current vehicle at the current moment and the control quantity of the current vehicle at the current moment.
The state quantity includes at least one of speed, acceleration, sensing information, and wireless communication information.
For example, the future preset time period may be 10min after the current time, or may be 15min after the current time, and the future preset time period may be set according to actual needs. The control amount may be set according to actual needs, and may be a steering wheel angle, for example. The state quantity can be set according to actual needs, for example, the state quantity can be at least one of current vehicle state information, environmental vehicle state information and traffic environment state information. The state quantity may be a speed, the state quantity may be an acceleration, the state quantity may be sensing information, and the state quantity may be wireless communication information. The scheme can adopt a model prediction control method based on a current vehicle dynamics/kinematics model to determine the predicted state quantity of the current vehicle at least one moment in a future preset time period according to the state quantity of the current vehicle at the current moment and the control quantity of the current vehicle at the current moment at fixed time intervals (for example, 10 s).
And S130, constructing an objective function based on the minimum difference value between the motion path of the current vehicle in the future preset time period and the preset path.
The preset path may be a planned path from a starting point to a target point of a current vehicle planned in advance. The movement path is a path determined by the control amount of the current vehicle at least one moment in a future preset time period determined by the cooperative mechanism with the adjacent vehicle. The target function can be determined according to the difference value between the motion path of the current vehicle in the future preset time period and the preset path, the difference value between the predicted end point position of the current vehicle in the future preset time period and the end point position of the preset path, the cooperative lane changing rule factor of the current vehicle and the target vehicle at the corresponding moment in the future preset time period, and the weight of the cooperative lane changing rule factor.
And S140, determining a target control quantity of the current vehicle at the at least one moment under the constraint of the collision constraint condition between the current vehicle and the target vehicle according to the target function and each predicted state quantity, so that the current vehicle at the corresponding moment is controlled according to the target control quantity.
The collision constraint condition between the current vehicle and the target vehicle may be a constraint condition that an area occupied by each vehicle is regarded as a square area and that no collision between the current vehicle and an adjacent vehicle is ensured. The target control amount may be such that a difference between a preset path and a movement path of the current vehicle is minimum. The scheme can determine a target control quantity of the current vehicle at least one moment under the constraint of the collision constraint condition between the current vehicle and the target vehicle according to the target function, the vehicle dynamics constraint condition and each predicted state quantity, so that the current vehicle controls the current vehicle at the corresponding moment according to the target control quantity, for example, a steering wheel angle is determined according to the target control quantity, and then the moving direction of the vehicle is controlled according to the steering wheel angle.
According to the technical scheme of the embodiment of the invention, the target vehicle of the current vehicle is determined according to the historical operating data of the current vehicle and the historical operating data of at least one adjacent vehicle; determining the predicted state quantity of the current vehicle at least one moment in a future preset time period according to the state quantity of the current vehicle at the current moment and the control quantity of the current vehicle at the current moment; the state quantity comprises at least one of speed, acceleration, sensing information and wireless communication information; constructing an objective function based on the minimum difference value between the motion path of the current vehicle in the future preset time period and the preset path; and determining a target control quantity of the current vehicle at least one moment under the constraint of the collision constraint condition between the current vehicle and the target vehicle according to the target function and each predicted state quantity, so that the current vehicle controls the current vehicle at the corresponding moment according to the target control quantity. By executing the scheme provided by the embodiment of the invention, the individualized running of the automatic driving vehicle can be flexibly controlled, the control result is more in line with the individualized requirement of a driver, and the cooperative control of the automatic driving vehicle is realized.
Fig. 2 is a flowchart of a vehicle control method according to an embodiment of the present invention, and the embodiment is optimized based on the above embodiment. As shown in fig. 2, a vehicle control method in an embodiment of the invention may include:
and S210, determining the driving behavior correlation degree between the current vehicle and each adjacent vehicle according to the historical operation data of the current vehicle and the historical operation data of the adjacent vehicles.
The driving behavior correlation degree can represent the similarity degree of the individual driving behavior characteristics among different drivers. The present solution may determine the degree of correlation of the driving behavior between the current vehicle and each of the neighboring vehicles based on the historical operation data of the current vehicle and the historical operation data of each of the neighboring vehicles.
In this embodiment, optionally, the determining the driving behavior correlation between the current vehicle and the adjacent vehicle according to the historical operation data of the current vehicle and the historical operation data of the adjacent vehicle includes: determining a first lane offset, a first yaw rate and a first yaw acceleration of the current vehicle in at least one motion stage within a preset historical time period according to historical operation data of the current vehicle; determining a second lane offset, a second yaw rate and a second yaw acceleration of the adjacent vehicle in each motion phase within the preset historical time period according to historical operation data of the adjacent vehicle; for each motion stage, determining a lane offset correlation coefficient between the current vehicle and the adjacent vehicle according to the first lane offset and the second lane offset; determining a yaw-rate correlation coefficient between the current vehicle and the adjacent vehicle according to the first yaw rate and the second yaw rate; determining a yaw angular acceleration correlation coefficient between the current vehicle and the adjacent vehicle according to the first yaw angular acceleration and the second yaw angular acceleration; determining a driving behavior correlation degree between the current vehicle and the adjacent vehicle according to each of the lane offset correlation coefficients, each of the yaw rate correlation coefficients, and each of the yaw acceleration correlation coefficients.
For example, the preset historical time period may be set according to actual needs, for example, the time period within 30 days before the current time. The motion phase may be set according to actual needs, for example, the motion phase may be a lane change starting phase, the motion phase may also be a lane change middle phase, and the motion phase may also be a lane change completion phase. The first lane offset may be a lane offset of the current vehicle for at least one motion phase within a preset historical time period. The first yaw rate may be a yaw rate of the current vehicle for at least one motion phase within a preset historical period of time. The first yaw acceleration may be a yaw acceleration of the current vehicle in at least one motion phase within a preset historical period of time. The second lane offset may be a lane offset of the adjacent vehicle at each motion phase within a preset historical time period. The second yaw rate may be a yaw rate of the adjacent vehicle at each movement stage within a preset history period. The second yaw acceleration may be a yaw acceleration of the adjacent vehicle at each motion phase within a preset history period.
The lane offset correlation coefficient of a certain motion stage is determined by the first lane offset and the second lane offset of the motion stage. The yaw-rate correlation coefficient for a certain motion phase is determined from the first yaw-rate and the second yaw-rate for that motion phase. The yaw acceleration correlation coefficient of a certain motion phase is determined by the first yaw acceleration and the second yaw acceleration of the motion phase.
In the probability statistics, the correlation coefficient may represent the variation trend of two random variables and the degree of correlation of the features, which is defined as follows:
wherein,
representing random variables
And
the covariance of (a) of (b),
and
respectively represent
And
standard deviation of (2).
The driving behavior compatibility in the embodiment is taken under an expressway scene as an example, lane change is the most main driving condition under the expressway scene, and the personality of the driver's lane change behavior is mainly embodied in the speed, the lane offset, the yaw rate and the yaw acceleration of the vehicle in three stages of b1 starting lane change stage, b2 stage in lane change and b3 finishing lane change stage.
Because of the correlation coefficient
Has a value range of [ -1,1]Get it
As a determination index. Specifically, the data model of the compatibility of the driving behaviors of the two drivers is shown as follows:
wherein V represents a data model of driver driving behavior compatibility,
the lane offset correlation coefficient of the current vehicle and the adjacent vehicle at the stage of starting lane changing b1 is shown,
a lane offset correlation coefficient representing the b2 stage of the current vehicle and the adjacent vehicle in lane changing,
the lane offset correlation coefficient represents the lane change completion b3 stage of the current vehicle and the adjacent vehicle,
represents the yaw-rate correlation coefficient of the current vehicle and the adjacent vehicle at the beginning of the lane change b1,
represents the yaw-rate correlation coefficient of the current vehicle and the adjacent vehicle at the b2 stage in the lane change,
represents the yaw-rate correlation coefficient of the current vehicle and the adjacent vehicle at the stage b3 of lane change completion,
represents the yaw acceleration correlation coefficient of the current vehicle and the adjacent vehicle at the stage of starting lane change b1,
represents the yaw acceleration correlation coefficient of the current vehicle and the adjacent vehicle in the b2 stage of the lane change,
to representAnd the current vehicle and the adjacent vehicle have the related coefficient of the yaw angular acceleration in the stage b3 of finishing lane changing.
Get matrix
As the driving behavior correlation, the Frobenius norm of (a) is shown as follows:
according to the conclusion of probability statistics
The similarity of the two groups of data is considered to be larger, so the similarity is obtained through calculation when
The driving behaviors of the two vehicles are considered to be high in similarity degree, a cooperative mechanism is not needed to participate, and the adjacent vehicles meeting the condition can be excluded when the two vehicles are driven in a team. When in use
And in the method, the driving behaviors of the two vehicles are considered to be low in similarity degree, the two vehicles need to be cooperatively controlled, and the target vehicle which has the largest influence on the current vehicle is further determined from the adjacent vehicles with low similarity degree.
Thus, the driving behavior correlation degree between the present vehicle and the adjacent vehicle is determined by the correlation coefficient for each lane offset, the correlation coefficient for each yaw rate, and the correlation coefficient for each yaw acceleration. The method and the device can determine the adjacent vehicle with low similarity to the current driving behavior of the vehicle, and provide a reliable data source for the subsequent steps.
And S220, taking the driving behavior correlation degree smaller than a preset threshold value as a candidate driving behavior correlation degree, sequencing the candidate driving behavior correlation degrees in an ascending order, and taking the adjacent vehicle corresponding to the candidate driving behavior correlation degree sequenced at the head as a target vehicle.
The preset threshold may be set according to actual needs, and may be 5.1, for example. Because there may be a plurality of adjacent vehicles of the current vehicle, the present solution may use the driving behavior correlation smaller than the preset threshold as the candidate driving behavior correlation, and screen out the corresponding adjacent vehicle with the smallest value from the candidate driving behavior correlation as the target vehicle. For example, the relevance of each candidate driving behavior is sorted in an ascending order, and the adjacent vehicle corresponding to the relevance of the candidate driving behavior sorted at the head is taken as the target vehicle.
And S230, determining the predicted state quantity of the current vehicle at least one moment in a future preset time period according to the state quantity of the current vehicle at the current moment and the control quantity of the current vehicle at the current moment.
In this embodiment, optionally, the determining the predicted state quantity of the current vehicle at least one time in the future preset time period according to the state quantity of the current vehicle at the current time and the control quantity of the current vehicle at the current time includes: and processing the state quantity of the current vehicle at the current moment and the control quantity of the current vehicle at the current moment based on a model predictive control algorithm to obtain the predicted state quantity of the current vehicle at least one moment in a future preset time period.
Wherein, the control quantity of the vehicle at a certain moment corresponds to the state quantity one by one. The method can process the state quantity of the current vehicle at the current moment and the control quantity of the current vehicle at the current moment by using the following vehicle state equation based on a model predictive control algorithm to obtain the predicted state quantity of the current vehicle at least one moment in a future preset time period:
. Wherein,
indicating the state quantity of the current vehicle at the next time,
indicating the current vehicleThe amount of state at the present moment in time,
indicates the control amount of the present vehicle at the present time,
representing a model predictive control algorithm.
Therefore, the predicted state quantity of the current vehicle at least one moment in a future preset time period is obtained by processing the state quantity of the current vehicle at the current moment and the control quantity of the current vehicle at the current moment based on the model prediction control algorithm. The state quantity of the current vehicle at least one moment in a future preset time period can be determined by utilizing the model predictive control algorithm, and a reliable data source is provided for the subsequent steps.
And S240, constructing an objective function based on the minimum difference value between the motion path of the current vehicle in the future preset time period and the preset path.
In one possible embodiment, optionally, constructing the objective function based on the minimum difference between the motion path of the current vehicle in the future preset time period and the preset path includes: and determining the target function according to the difference between the motion path of the current vehicle in the future preset time period and the preset path, the difference between the predicted end point position of the current vehicle in the future preset time period and the end point position of the preset path, and the cooperative lane change rule factor of the current vehicle and the target vehicle at the corresponding moment in the future preset time period.
The difference between the motion path of the current vehicle in the future preset time period and the preset path may be the sum of the difference between the motion control amount of the current vehicle at least one time in the future preset time period and the predicted control amount of the corresponding time in the preset path. The difference value between the predicted end position of the present vehicle in the preset time period in the future and the preset route end position may be a difference value between the motion control amount of the predicted end position of the present vehicle in the preset time period in the future and the preset control amount of the preset route end position. The coordinated lane-change rule factor may be determined synthetically based on various possible lane-change patterns between the current vehicle and the target vehicle. For example, the scheme can determine at least one lane change mode between the current vehicle and the target vehicle; determining a reference function of each lane changing mode based on a Boolean operation method; determining a cooperation rule between the current vehicle and the target vehicle based on the reference function; and determining a cooperative lane change rule factor of the current vehicle and the target vehicle according to the cooperative rule.
And determining the target function according to the difference value between the motion path of the current vehicle in the future preset time period and the preset path, the difference value between the predicted end point position of the current vehicle in the future preset time period and the end point position of the preset path, and the cooperative lane change rule factor of the current vehicle and the target vehicle at the corresponding time in the future preset time period. The determination of at least one predicted control quantity of the current vehicle at least one moment in time within a future preset time period can be realized, and a reliable data source is provided for the subsequent steps.
In another possible embodiment, optionally, the determining of the co-lane change rule factor includes: determining at least one lane change pattern between the current vehicle and the target vehicle; determining a reference function of each lane changing mode based on a Boolean operation method; determining a collaborative rule between the current vehicle and the target vehicle based on the reference function; and determining a cooperative lane change rule factor of the current vehicle and the target vehicle according to the cooperative rule.
In the scheme, the environmental vehicle is also taken as an automatic driving vehicle as an example, the automatic driving vehicle can be communicated with the current vehicle and can cooperate with the current vehicle to a certain degree, and under the condition, the control problem of the current vehicle and the environmental vehicle can be processed in a cooperative control mode. The design of the controller is carried out by using a framework of cooperative control, so that the control of a single vehicle can be processed, and the cooperative control of a plurality of vehicles can also be processed. In distributed control, each controlled object is based on decision control, only state information is exchanged among different objects, and the requirement on computing power is low, so that the scheme adopts a distributed control method.
The multi-vehicle distributed cooperative control system based on the hybrid automaton mainly solves the problems in two aspects: distributed control of autonomous vehicles, and cooperative control of different autonomous vehicles. Specifically, the autonomous driving vehicle realizes autonomous control of the autonomous driving vehicle through a distributed controller of the autonomous driving vehicle so as to ensure the driving safety of the autonomous driving vehicle, and meanwhile, realizes cooperative operation among different autonomous driving vehicles based on a cooperative control algorithm. And determining a vehicle cooperation strategy according to the current vehicle state, the environmental vehicle state and the traffic environment, so that the driving safety is improved. Single vehicle control in a multi-vehicle cooperative system has significant intermingling, i.e., having continuous dynamics of different vehicles within a discrete, independent cooperative strategy. The distributed local controllers calculate respective control output quantity by using the acquired information (the current vehicle state, the environmental vehicle state, the traffic environment state and the like) and by adopting a model prediction control method based on a current vehicle dynamics/kinematics model, so that automatic driving of the vehicle is realized; and the local controller acquires nearby collaboratable vehicles by using a vehicle-vehicle and vehicle-road communication mode, and integrates the transverse and longitudinal dynamics/kinematics of a single automatically-driven vehicle and the transverse and longitudinal dynamics/kinematics characteristics of a workshop into a multi-vehicle collaborative control system, namely, the local controllers of the collaboratable vehicles are combined in a decision system through a communication technology so as to realize collaborative operation required by multiple vehicles within a certain range.
When the two vehicles approach, the two vehicles need to be cooperatively controlled in order to prevent collision. In order to realize the consistency of the moving directions, the constraint of a coordination rule needs to be added, so that the vehicle runs according to a certain rule.
The scheme can adopt a Boolean operation method to determine the cooperative rule of the two vehicles with poor driving behavior compatibility. First, the following auxiliary functions a to d are introduced:
a. 
b. 
c. 
d. 
when the cooperation rule of the two vehicles with poor driving behavior compatibility is determined by utilizing Boolean operation, the vehicle position and the road position can adopt a plane coordinate system fixed on a road surface as a coordinate system, the road advancing direction is the positive direction of a transverse axis of the coordinate system, and the left side of the road advancing direction is the positive direction of a longitudinal axis of the coordinate system.
Indicating the difference between the two adjacent vehicles in the transverse direction or the longitudinal direction.
And the product of the difference value of the coordinates of two adjacent vehicles at the previous moment and the current moment in the transverse direction or the longitudinal direction is represented.
Indicating the azimuthal relationship between the two coordinate points. For example when
It means that the coordinate point at which the current vehicle is located is behind its target coordinate point at the current time.
It indicates whether the relationship between the adjacent two vehicles in the transverse or longitudinal direction changes at the adjacent time. For example when the previous moment
At the current moment
Then, then
Indicating that the vehicle is moving from the previous time to the present time
From the current vehicle
Either the front or the left or the back is moved to the right.
The safe vehicle distance is represented and can be set according to actual requirements.
Wherein,
indicating the position of the vehicle over the time series,
the abscissa indicating the current vehicle at the current time,
the abscissa indicates a target coordinate point, which may be the position of the target vehicle j at the present time.
A vertical coordinate representing the present vehicle at a previous time,
representing the ordinate of the target vehicle at a previous instant.
Represents the ordinate of the target vehicle at the present time,
indicating the ordinate of the current vehicle at the current time.
Assuming that the current vehicle i is a vehicle with a biased aggressive behavior and the target vehicle j is a vehicle with a biased conservative behavior, the defined coordination rule is as follows:
wherein,
wherein,
it is indicated that there is a lane change of the vehicle at present,
indicating that the current vehicle is in a fast lane,
indicating that the current vehicle is not in a fast lane,
indicating that the target vehicle is in a fast lane,
indicating that the target vehicle is not in a fast lane,
indicating that the current vehicle is behind the target vehicle,
indicating that the current vehicle is in front of the target vehicle,
it is indicated that the target vehicle has a lane change,
indicating that the target vehicle has not changed lanes, and l represents the ordinate of the vertical axis of the lane line.
Thereby, by determining at least one lane change pattern between the current vehicle and the target vehicle; determining a reference function of each lane changing mode based on a Boolean operation method; determining a cooperation rule between the current vehicle and the target vehicle based on the reference function; and determining a cooperative lane change rule factor of the current vehicle and the target vehicle according to the cooperative rule. The lane changing behavior between the current vehicle and the adjacent vehicle can be expressed, and a reliable data source is provided for the realization of a cooperation mechanism between the vehicles.
In yet another possible embodiment, optionally, determining the objective function according to a difference between a motion path of the current vehicle in a preset time period in the future and a preset path, a difference between a predicted end position of the current vehicle in the preset time period in the future and an end position of the preset path, and a coordinated lane change regulation factor of the current vehicle and the target vehicle at a corresponding time in the preset time period in the future includes: determining the objective function based on the following formula:
wherein,
represents a set of respective control amounts of the current vehicle in a future preset time period, J represents an objective function of roll optimization of the current vehicle at the current time, N represents an end time of the future preset time period,
indicating the predicted state quantity of the present vehicle at the tth time of the preset time period in the future,
indicating that the current vehicle is at presentThe target control amount at the t-th time of the preset time period,
indicating the predicted state quantity of the target vehicle at the next time,
indicating the control amount of the target vehicle at the previous time,
representing a difference between a movement path of the current vehicle within a future preset time period and a preset path,
indicating the difference between the predicted end position of the current vehicle within a future preset time period and the preset path end position,
indicating the predicted state quantity of the present vehicle at the nth time in the future preset time period,
indicating the predicted state quantity of the target vehicle at the nth time in the future preset time period,
a coordinated lane-change regulation factor representing a corresponding moment of the current vehicle and the target vehicle within a future preset time period,
representing the weight of the coordinated lane change regulation factor of the current vehicle at the tth moment of the future preset time period,
representing a set of adjacent vehicles.
Wherein,
、
the method can be set according to actual needs, and for example, the method can be a gradient descent algorithm or an ant colony algorithm.
The influence value can be set according to actual needs, and the value is determined by enabling the motion path to be closest to the preset path.
And S250, determining a target control quantity of the current vehicle at the at least one moment under the constraint of the collision constraint condition between the current vehicle and the target vehicle according to the target function and each predicted state quantity, so that the current vehicle controls the current vehicle at the corresponding moment according to the target control quantity.
Is exemplarily provided with
The vehicles form a system, the vehicles are assumed to move in the same plane, and the area occupied by each vehicle is a square area defined in the current vehicle
The vehicles in a certain nearby range are adjacent vehicles, and the expression is shown as the following formula:
wherein,
indicating the coordinate position of the current vehicle at the current time,
indicating the coordinate position of the adjacent vehicle at the present time,
representing a preset distance.
The collision relationship between the current vehicle and the target vehicle is defined and analyzed, and since the area occupied by the vehicle is defined as a square, the collision relationship between the current vehicle and the target vehicle is as follows:
Wherein,
indicates the length of the vehicle at the present time,
indicates the length of the subject vehicle,
an abscissa indicating the position of the target vehicle,
a vertical coordinate representing the position of the target vehicle,
an abscissa indicating the position of the current vehicle,
and a vertical coordinate representing the position of the current vehicle.
Based on the above collision relations, the collisions can be classified into the following four categories:
1) 
2) 
3) 
4) 
wherein,
、
、
、
respectively represents parameters of four directions of east, west, south and north, M represents a safety factor,
。
the scheme can determine a plurality of predicted control quantities at least one moment in a future preset time period according to an objective function, and then determine a unique target control quantity of the current vehicle at least one moment under the constraint of the collision constraint condition between the current vehicle and the target vehicle by combining the vehicle dynamics constraint condition and each predicted state quantity so that the current vehicle controls the current vehicle at the corresponding moment according to the target control quantity. For example, the steering wheel angle is determined in accordance with the target control amount, and then the moving direction of the vehicle is controlled in accordance with the steering wheel angle.
According to the technical scheme provided by the embodiment of the invention, for each adjacent vehicle, the driving behavior correlation degree between the current vehicle and the adjacent vehicle is determined according to the historical operation data of the current vehicle and the historical operation data of the adjacent vehicle; taking the driving behavior correlation degree smaller than a preset threshold value as a candidate driving behavior correlation degree, sequencing the candidate driving behavior correlation degrees in an ascending order, and taking an adjacent vehicle corresponding to the candidate driving behavior correlation degree sequenced at the head as a target vehicle; determining the predicted state quantity of the current vehicle at least one moment in a future preset time period according to the state quantity of the current vehicle at the current moment and the control quantity of the current vehicle at the current moment; constructing an objective function based on the minimum difference value between the motion path of the current vehicle in the future preset time period and the preset path; and determining a target control quantity of the current vehicle at least one moment under the constraint of the collision constraint condition between the current vehicle and the target vehicle according to the target function and each predicted state quantity, so that the current vehicle controls the current vehicle at the corresponding moment according to the target control quantity. By executing the scheme provided by the embodiment of the invention, the individualized running of the automatic driving vehicle can be flexibly controlled, the control result is more in line with the individualized requirement of a driver, and the cooperative control of the automatic driving vehicle is realized.
Fig. 3 is a schematic structural diagram of a vehicle control device according to an embodiment of the present invention. As shown in fig. 3, the apparatus includes:
a target vehicle determination module 310 for determining a target vehicle of a current vehicle according to historical operation data of the current vehicle and historical operation data of at least one neighboring vehicle;
the predicted state quantity determining module 320 is used for determining the predicted state quantity of the current vehicle at least one moment in a future preset time period according to the state quantity of the current vehicle at the current moment and the control quantity of the current vehicle at the current moment; the state quantity comprises at least one of speed, acceleration, sensing information and wireless communication information; the control amount includes a steering wheel angle;
an objective function constructing module 330, configured to construct an objective function based on a minimum difference between a motion path of the current vehicle in a future preset time period and a preset path;
and a target control amount determining module 340, configured to determine, according to the objective function and each of the predicted state amounts, a target control amount of the current vehicle at the at least one time under constraint of a collision constraint between the current vehicle and the target vehicle, so that the current vehicle controls the current vehicle at the corresponding time according to the target control amount.
Optionally, the target vehicle determining module 310 includes a driving behavior correlation determining unit, configured to determine, for each neighboring vehicle, a driving behavior correlation between the current vehicle and the neighboring vehicle according to historical operation data of the current vehicle and historical operation data of the neighboring vehicle; and the target vehicle determining unit is used for taking the driving behavior correlation degree smaller than the preset threshold value as the candidate driving behavior correlation degree, sequencing the candidate driving behavior correlation degrees in an ascending order, and taking the adjacent vehicle corresponding to the candidate driving behavior correlation degree sequenced at the head as the target vehicle.
Optionally, the driving behavior correlation determining unit is specifically configured to determine, according to historical operating data of the current vehicle, a first lane offset, a first yaw rate, and a first yaw acceleration of the current vehicle in at least one motion phase within a preset historical time period; determining a second lane offset, a second yaw rate and a second yaw acceleration of the adjacent vehicle in each motion phase within the preset historical time period according to historical operation data of the adjacent vehicle; for each motion stage, determining a lane offset correlation coefficient between the current vehicle and the adjacent vehicle according to the first lane offset and the second lane offset; determining a yaw-rate correlation coefficient between the current vehicle and the adjacent vehicle according to the first yaw rate and the second yaw rate; determining a yaw angular acceleration correlation coefficient between the current vehicle and the adjacent vehicle according to the first yaw angular acceleration and the second yaw angular acceleration; determining a driving behavior correlation degree between the current vehicle and the adjacent vehicle according to each of the lane offset correlation coefficients, each of the yaw rate correlation coefficients, and each of the yaw acceleration correlation coefficients.
Optionally, the predicted state quantity determining module 320 is specifically configured to process the state quantity of the current vehicle at the current time and the control quantity of the current vehicle at the current time based on a model predictive control algorithm, so as to obtain the predicted state quantity of the current vehicle at least one time within a future preset time period.
Optionally, the objective function constructing module 330 is specifically configured to determine the objective function according to a difference between a motion path of the current vehicle in a future preset time period and a preset path, a difference between a predicted end position of the current vehicle in the future preset time period and an end position of the preset path, and a coordinated lane change rule factor of the current vehicle and the target vehicle at a corresponding time in the future preset time period.
Optionally, the determining process of the collaborative lane changing rule factor includes: determining at least one lane change pattern between the current vehicle and the target vehicle; determining a reference function of each lane changing mode based on a Boolean operation method; determining a collaborative rule between the current vehicle and the target vehicle based on the reference function; and determining a cooperative lane change rule factor of the current vehicle and the target vehicle according to the cooperative rule.
Optionally, determining the objective function according to a difference between a motion path of the current vehicle in a future preset time period and a preset path, a difference between a predicted end position of the current vehicle in the future preset time period and an end position of the preset path, and a coordinated lane change rule factor of the current vehicle and the target vehicle at a corresponding time in the future preset time period includes: determining the objective function based on the following formula:
wherein,
represents the set of control quantities of the current vehicle in the future preset time period, J represents the objective function of the rolling optimization of the current vehicle at the current moment, N represents the future preset time periodThe end time of the time period is set,
indicating the predicted state quantity of the present vehicle at the tth time of the preset time period in the future,
a target control amount indicating a target control amount of the present vehicle at a time t of a preset time period in the future,
indicating the predicted state quantity of the target vehicle at the next time,
indicating the control amount of the target vehicle at the previous time,
representing a difference between a movement path of the current vehicle within a future preset time period and a preset path,
indicating the difference between the predicted end position of the current vehicle within a future preset time period and the preset path end position,
indicating the predicted state quantity of the present vehicle at the nth time in the future preset time period,
indicating the predicted state quantity of the target vehicle at the nth time in the future preset time period,
a coordinated lane-change regulation factor representing a corresponding moment of the current vehicle and the target vehicle within a future preset time period,
represents the weight of the coordinated lane-change regulation factor of the current vehicle at the tth time of the preset time period in the future,
representing a set of adjacent vehicles.
The vehicle control device provided by the embodiment of the invention can execute the vehicle control method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
FIG. 4 shows a schematic block diagram of an electronic device 40 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), 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 inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 40 includes at least one processor 41, and a memory communicatively connected to the at least one processor 41, such as a Read Only Memory (ROM) 42, a Random Access Memory (RAM) 43, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 41 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 42 or the computer program loaded from a storage unit 48 into the Random Access Memory (RAM) 43. In the RAM 43, various programs and data necessary for the operation of the electronic apparatus 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other via a bus 44. An input/output (I/O) interface 45 is also connected to bus 44.
A number of components in the electronic device 40 are connected to the I/O interface 45, including: an input unit 46 such as a keyboard, a mouse, etc.; an output unit 47 such as various types of displays, speakers, and the like; a storage unit 48 such as a magnetic disk, an optical disk, or the like; and a communication unit 49 such as a network card, modem, wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
In some embodiments, the vehicle control method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 40 via the ROM 42 and/or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the vehicle control method described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to perform the vehicle control method by any other suitable means (e.g., by way of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), 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 that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage 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. Alternatively, the computer readable storage medium may be a machine readable signal medium. 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 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 an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired result of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A vehicle control method characterized by comprising:
determining a target vehicle of the current vehicle according to historical operation data of the current vehicle and historical operation data of at least one adjacent vehicle;
determining the predicted state quantity of the current vehicle at least one moment in a future preset time period according to the state quantity of the current vehicle at the current moment and the control quantity of the current vehicle at the current moment; the state quantity comprises at least one of speed, acceleration, sensing information and wireless communication information;
constructing an objective function based on the minimum difference value between the motion path of the current vehicle in a future preset time period and a preset path;
and determining a target control quantity of the current vehicle at the at least one moment under the constraint of the collision constraint condition between the current vehicle and the target vehicle according to the target function and each predicted state quantity, so that the current vehicle controls the current vehicle at the corresponding moment according to the target control quantity.
2. The method of claim 1, wherein determining a target vehicle for a current vehicle based on historical operating data for the current vehicle and historical operating data for at least one neighboring vehicle comprises:
for each adjacent vehicle, determining a driving behavior correlation degree between the current vehicle and the adjacent vehicle according to historical operation data of the current vehicle and historical operation data of the adjacent vehicle;
and taking the driving behavior correlation degree smaller than a preset threshold value as a candidate driving behavior correlation degree, sequencing the candidate driving behavior correlation degrees in an ascending order, and taking the adjacent vehicle corresponding to the candidate driving behavior correlation degree sequenced at the head as a target vehicle.
3. The method of claim 2, wherein determining a driving behavior correlation between a current vehicle and the neighboring vehicle based on historical operating data of the current vehicle and historical operating data of the neighboring vehicle comprises:
determining a first lane offset, a first yaw rate and a first yaw acceleration of the current vehicle in at least one motion stage within a preset historical time period according to historical operation data of the current vehicle;
determining a second lane offset, a second yaw rate and a second yaw acceleration of the adjacent vehicle in each motion phase within the preset historical time period according to historical operation data of the adjacent vehicle;
for each motion stage, determining a lane offset correlation coefficient between the current vehicle and the adjacent vehicle according to the first lane offset and the second lane offset;
determining a yaw-rate correlation coefficient between the current vehicle and the adjacent vehicle according to the first yaw rate and the second yaw rate;
determining a yaw angular acceleration correlation coefficient between the current vehicle and the adjacent vehicle according to the first yaw angular acceleration and the second yaw angular acceleration;
and determining the driving behavior correlation degree between the current vehicle and the adjacent vehicle according to each lane offset correlation coefficient, each yaw angular velocity correlation coefficient and each yaw angular acceleration correlation coefficient.
4. The method according to claim 1, wherein determining the predicted state quantity of the current vehicle at least one time within a preset time period in the future according to the state quantity of the current vehicle at the current time and the control quantity of the current vehicle at the current time comprises:
and processing the state quantity of the current vehicle at the current moment and the control quantity of the current vehicle at the current moment based on a model predictive control algorithm to obtain the predicted state quantity of the current vehicle at least one moment in a future preset time period.
5. The method of claim 1, wherein constructing an objective function based on a minimum difference between a movement path of the current vehicle within a preset time period in the future and a preset path comprises:
and determining the target function according to the difference between the motion path of the current vehicle in the future preset time period and the preset path, the difference between the predicted end point position of the current vehicle in the future preset time period and the end point position of the preset path, and the cooperative lane change rule factor of the current vehicle and the target vehicle at the corresponding moment in the future preset time period.
6. The method of claim 5, wherein determining the co-lane-change rule factor comprises:
determining at least one lane change pattern between the current vehicle and the target vehicle;
determining a reference function of each lane changing mode based on a Boolean operation method;
determining a collaborative rule between the current vehicle and the target vehicle based on the reference function;
and determining a cooperative lane change rule factor of the current vehicle and the target vehicle according to the cooperative rule.
7. The method of claim 5, wherein determining the objective function according to a difference between a motion path of the current vehicle in a future preset time period and a preset path, a difference between a predicted end position of the current vehicle in the future preset time period and an end position of the preset path, and a coordinated lane change regulation factor of the current vehicle and the target vehicle at corresponding time in the future preset time period comprises: determining the objective function based on the following formula:
wherein,
represents a set of respective control amounts of the current vehicle in a future preset time period, J represents an objective function of roll optimization of the current vehicle at the current time, N represents an end time of the future preset time period,
indicating the predicted state quantity of the present vehicle at the tth time of the preset time period in the future,
a target control amount indicating a target control amount of the present vehicle at a time t of a preset time period in the future,
indicating the predicted state quantity of the target vehicle at the next time,
indicating the control amount of the target vehicle at the previous time,
representing a difference between a movement path of the current vehicle within a future preset time period and a preset path,
indicating the difference between the predicted end position of the current vehicle within a future preset time period and the preset path end position,
indicating the predicted state quantity of the present vehicle at the nth time in the future preset time period,
representing the predicted state quantity of the target vehicle at the nth time in the future preset time period,
a set of adjacent vehicles is represented as,
a coordinated lane-change regulation factor representing a corresponding moment of the current vehicle and the target vehicle within a future preset time period,
and representing the weight of the coordinated lane change regulation factor of the current vehicle at the tth moment of the future preset time period.
8. A vehicle control apparatus characterized by comprising:
the target vehicle determining module is used for determining a target vehicle of the current vehicle according to historical operation data of the current vehicle and historical operation data of at least one adjacent vehicle;
the predicted state quantity determining module is used for determining the predicted state quantity of the current vehicle at least one moment in a future preset time period according to the state quantity of the current vehicle at the current moment and the control quantity of the current vehicle at the current moment; the state quantity comprises at least one of speed, acceleration, sensing information and wireless communication information; the control amount includes a steering wheel angle;
the target function constructing module is used for constructing a target function based on the minimum difference value between the motion path of the current vehicle in a future preset time period and a preset path;
and the target control quantity determining module is used for determining the target control quantity of the current vehicle at the at least one moment under the constraint of the collision constraint condition between the current vehicle and the target vehicle according to the target function and each predicted state quantity so as to enable the current vehicle to control the current vehicle at the corresponding moment according to the target control quantity.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the vehicle control method of any one of claims 1-7.
10. A computer-readable storage medium storing computer instructions for causing a processor to implement the vehicle control method of any one of claims 1-7 when executed.
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| CN114945885A (en) * | 2020-01-19 | 2022-08-26 | 三菱电机株式会社 | Adaptive control of autonomous or semi-autonomous vehicles |
| CN114332817A (en) * | 2021-12-28 | 2022-04-12 | 北京世纪高通科技有限公司 | Method, device, equipment and storage medium for determining vehicles in same-driving |
| CN114348026A (en) * | 2022-01-30 | 2022-04-15 | 中国第一汽车股份有限公司 | Vehicle control method, device, equipment and storage medium |
| CN114523962A (en) * | 2022-03-16 | 2022-05-24 | 中国第一汽车股份有限公司 | Vehicle control method, device, equipment and medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116499487A (en) * | 2023-06-28 | 2023-07-28 | 新石器慧通（北京）科技有限公司 | Vehicle path planning method, device, equipment and medium |
| CN116499487B (en) * | 2023-06-28 | 2023-09-05 | 新石器慧通（北京）科技有限公司 | Vehicle path planning method, device, equipment and medium |
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