CN118035676A - Method, device and equipment for determining lane where vehicle track point is located - Google Patents
Method, device and equipment for determining lane where vehicle track point is located Download PDFInfo
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Abstract
The embodiment of the specification discloses a method, a device and equipment for determining a lane where a vehicle track point is located, comprising the following steps: acquiring a preset number of vehicle track point sequences acquired for a target vehicle at a target road; acquiring an initial state probability matrix preset for a lane where a first track point in a vehicle track point sequence is located; according to the distances between each other track point in the vehicle track point sequence and the lane center line of each lane, calculating to obtain an observation state probability matrix corresponding to the other track points; calculating the target probability of the tail end track point in the vehicle track point sequence on the basis of the initial state probability matrix, the observation state probability matrix and the state transition probability matrix at the target road; and determining the lane corresponding to the maximum value of the target probability as the lane where the tail end track point is located. The scheme of the invention is beneficial to improving the fault tolerance and the accuracy of the vehicle track point when locating the lane.
Description
Technical Field
The present disclosure relates to the field of vehicle positioning, and in particular, to a method, an apparatus, and a device for determining a lane where a vehicle track point is located.
Background
The road side sensing device comprises a radar, a camera and the like, when the position of a vehicle on a road is identified, the position of the vehicle is identified by a certain probability to be wrong, so that the position of the same vehicle on the road is transversely offset, for example, the road side sensing device identifies that the historical track data of a certain vehicle are all on a lane 1, and the position information of the vehicle suddenly appears to be displayed on a lane 3, and the situation that the vehicle positioning system is wrong when the vehicle is positioned on the lane is caused due to the abnormal data.
In the prior art, when lane information of a vehicle track point is determined, a lane of the vehicle track point is positioned only according to the position information of the vehicle track point reported by a road side sensing device, the data quality reported by the road side sensing device is seriously depended, if the position information of the vehicle track point reported by the road side sensing device is wrong, the lane information of the positioned vehicle track point is wrong, and therefore the fault tolerance rate and the accuracy rate are lower when the lane of the vehicle track point is positioned.
Disclosure of Invention
In view of this, the embodiments of the present disclosure provide a method, an apparatus, and a device for determining a lane where a vehicle track point is located, which are used to improve the fault tolerance and the accuracy when locating the lane where the vehicle track point is located.
In order to solve the above technical problems, the embodiments of the present specification are implemented as follows:
the embodiment of the specification provides a method for determining a lane where a vehicle track point is located, which comprises the following steps:
acquiring a preset number of vehicle track point sequences acquired for a target vehicle at a target road;
Acquiring an initial state probability matrix preset for a lane where a first track point in the vehicle track point sequence is located; the initial state probability matrix is used for reflecting the probability of each lane of the first track point at the target road;
According to the distances between each other track point in the vehicle track point sequence and the lane center line of each lane, calculating to obtain an observation state probability matrix corresponding to each other track point; the observation state probability matrix is used for reflecting the probability that the other track points are positioned in each lane;
calculating the target probability of the tail end track point in the vehicle track point sequence on each lane based on the initial state probability matrix, the observation state probability matrix and the state transition probability matrix at the target road; the state transition probability matrix is used for reflecting the probability that the vehicle transits from each lane to any lane for running;
And determining the lane corresponding to the maximum value of the target probability as the lane where the tail end track point is located.
The embodiment of the specification provides a lane determining device where a vehicle track point is located, which comprises:
The first acquisition module is used for acquiring a preset number of vehicle track point sequences acquired for a target vehicle at a target road;
the second acquisition module is used for acquiring an initial state probability matrix preset for a lane where a first track point in the vehicle track point sequence is located; the initial state probability matrix is used for reflecting the probability of each lane of the first track point at the target road;
The first calculation module is used for calculating an observation state probability matrix corresponding to each other track point according to the distance between each other track point in the vehicle track point sequence and the lane center line of each lane; the observation state probability matrix is used for reflecting the probability that the other track points are positioned in each lane;
The second calculation module is used for calculating the target probability of the tail end track point in the vehicle track point sequence on each lane based on the initial state probability matrix, the observation state probability matrix and the state transition probability matrix at the target road; the state transition probability matrix is used for reflecting the probability that the vehicle transits from each lane to any lane for running;
And the first determining module is used for determining the lane corresponding to the maximum value of the target probability as the lane where the tail end track point is located.
An embodiment of the present specification provides a lane determining apparatus in which a vehicle trajectory point is located, including:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring a preset number of vehicle track point sequences acquired for a target vehicle at a target road;
Acquiring an initial state probability matrix preset for a lane where a first track point in the vehicle track point sequence is located; the initial state probability matrix is used for reflecting the probability of each lane of the first track point at the target road;
According to the distances between each other track point in the vehicle track point sequence and the lane center line of each lane, calculating to obtain an observation state probability matrix corresponding to each other track point; the observation state probability matrix is used for reflecting the probability that the other track points are positioned in each lane;
calculating the target probability of the tail end track point in the vehicle track point sequence on each lane based on the initial state probability matrix, the observation state probability matrix and the state transition probability matrix at the target road; the state transition probability matrix is used for reflecting the probability that the vehicle transits from each lane to any lane for running;
And determining the lane corresponding to the maximum value of the target probability as the lane where the tail end track point is located.
At least one embodiment provided in this specification enables the following benefits:
According to the scheme, an initial state probability matrix and an observation state probability matrix are determined according to position data of each vehicle track point in a vehicle track point sequence containing a preset number of vehicle track points, a complete hidden Markov model can be determined by combining the state transition probability matrix at a target road, probability that the tail end track point in the vehicle track point sequence is located in each lane is calculated through a Viterbi algorithm, and a lane with the highest probability is determined to be the lane in which the tail end track point is located. Therefore, based on the vehicle track point data before the tail end track point, the reasonable track change trend of the vehicle is fully considered, the lane information of the tail end track point which is more accurate is determined by using the hidden Markov model, the problem that the lane information of the positioned vehicle track point is wrong due to the fact that the position information of the vehicle track point reported by the road side sensing equipment is wrong can be prevented, and the fault tolerance and the accuracy in positioning the lane of the vehicle track point are improved.
Drawings
In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments described in the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a method for determining a lane where a vehicle track point is located according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a lane determining device corresponding to the vehicle track point of fig. 1 according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a lane determining apparatus corresponding to the vehicle track point of fig. 1 according to an embodiment of the present disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of one or more embodiments of the present specification more clear, the technical solutions of one or more embodiments of the present specification will be clearly and completely described below in connection with specific embodiments of the present specification and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present specification. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without undue burden, are intended to be within the scope of one or more embodiments herein.
The following describes in detail the technical solutions provided by the embodiments of the present specification with reference to the accompanying drawings.
Fig. 1 is a flow chart of a method for determining a lane where a vehicle track point is located according to an embodiment of the present disclosure. As shown in fig. 1, the process may include the steps of:
Step 102: a preset number of sequences of vehicle track points acquired for a target vehicle at a target road are acquired.
In the embodiment of the present disclosure, the target road may be a pre-selected section of road, and the target road may be a section of road on a city road or a section of road on a country road, which is not limited specifically. When the target vehicle runs on the target road, the road side sensing equipment or the vehicle-mounted sensing equipment can be utilized to collect the point data of the vehicle track points in the running process of the target vehicle.
In the embodiment of the present disclosure, the preset number may be set and adjusted according to actual needs, and may be 8 or 10, which is not limited specifically. The vehicle track point sequence comprises a preset number of vehicle track points which are collected for a target vehicle at a target road, and the vehicle track points can be sequentially arranged in the vehicle track point sequence according to the collected time and the order of the collected time from first to last. For example: if the preset number is 10, a vehicle track point of the target vehicle is acquired at the current moment, lane information of the vehicle at the current moment is wanted to be determined, and the continuous 9 vehicle track points acquired before the current moment and the vehicle track points acquired at the current moment can be formed into a vehicle track point sequence for subsequent analysis and calculation.
Step 104: acquiring an initial state probability matrix preset for a lane where a first track point in the vehicle track point sequence is located; the initial state probability matrix is used for reflecting the probability of each lane where the first track point is located at the target road.
In this embodiment of the present disclosure, the first track point in the vehicle track point sequence may be the vehicle track point with the earliest acquisition time in the vehicle track point sequence, and also corresponds to the vehicle track point with the forefront position in the vehicle track point sequence. The lane information where the first track point is located may be determined based on the vehicle position acquisition information at the first track point and the high-precision map data, and since the subsequent embodiments in the embodiments of the present disclosure will explain the lane process where the first track point is determined in detail, the details are omitted herein.
In the embodiment of the present disclosure, after lane information where the first track point is located is determined, a corresponding initial state probability matrix that can reflect the probability of each lane where the first track point is located at the target road may be determined. The lane information of the first track point is determined based on the vehicle position acquisition information of the first track point, but the vehicle position acquisition information of the first track point is not necessarily accurate, so that the probability of reflecting each other lane of the first track point in the determined initial state probability matrix is not 0. For example: if the target road has 3 lanes, namely lane 1, lane 2 and lane 3, and the lane where the first track point is determined to be lane 2, an empirical value, for example, 0.8, can be determined according to the historical track data of the historical vehicle at the target road, the probability of reflecting the first track point on the lane 2 at the target road in the initial state probability matrix is determined to be 0.8, and correspondingly, the probability of reflecting the first track point on the lane 1 or the lane 3 at the target road in the initial state probability matrix is also determined to be 0.1.
Step 106: according to the distances between each other track point in the vehicle track point sequence and the lane center line of each lane, calculating to obtain an observation state probability matrix corresponding to each other track point; the observation state probability matrix is used for reflecting the probability that the other track points are positioned in each lane.
In the embodiment of the present disclosure, the obtained sequence of the preset number of vehicle track points collected for the target vehicle at the target road may include vehicle position collection information of each vehicle track point, where the vehicle position collection information may be longitude and latitude information, or may be other information that may represent a vehicle position, which is not limited specifically. Therefore, according to the vehicle position acquisition information of a certain vehicle track point, the distance between the vehicle track point and the lane center line of each lane at the target road can be determined, and then the observation state probability matrix corresponding to the vehicle track point is calculated based on the distances. Since the following embodiments in the embodiments of the present disclosure will explain the process of calculating the observation state probability matrix in detail, they will not be described herein.
Step 108: calculating the target probability of the tail end track point in the vehicle track point sequence on each lane based on the initial state probability matrix, the observation state probability matrix and the state transition probability matrix at the target road; the state transition probability matrix is used for reflecting the probability that the vehicle transits from each lane to any lane for driving.
In this embodiment of the present disclosure, the state transition probability matrix may be bound to the target road, and each element value in the state transition probability matrix may be determined based on the historical track data of the historical vehicle at the target road, and since the process of calculating the state transition probability matrix will be explained in detail in the subsequent embodiment of this embodiment of the present disclosure, the description will be omitted herein.
In the embodiment of the present disclosure, after determining the initial state probability matrix, the observed state probability matrix, and the state transition probability matrix at the target road, a hidden markov model may be constructed from the three matrices, where the hidden markov model (Hidden Markov Model, HMM) is a statistical model that may be used to describe a markov process that contains implicit unknown parameters. And then the hidden Markov model can be used for calculating the hidden Markov model by utilizing a Viterbi algorithm (the Viterbi algorithm is a dynamic programming algorithm and can be used in the hidden Markov model to find the hidden state sequence most likely to generate the observation event sequence), so as to obtain the target probability of the tail end track point in the vehicle track point sequence.
Step 110: and determining the lane corresponding to the maximum value of the target probability as the lane where the tail end track point is located.
In the embodiment of the specification, after the target probabilities of the tail end track points in the track point sequence of the vehicle are determined, normalization processing can be performed on the target probabilities, so that the sum of the target probabilities is 1, and analysis of result data by later-stage analysts can be facilitated. For example: if the calculated target probabilities of the tail end track points on the lanes 1,2 and 3 are respectively: 0.05, 0.4 and 0.05, and after normalization treatment, the following steps are respectively: 0.1, 0.8 and 0.1, and determining the lane 2 corresponding to the maximum value (0.8) of the target probability as the lane where the tail end track point is located.
According to the method in FIG. 1, firstly, an initial state probability matrix and an observation state probability matrix are determined according to position data of each vehicle track point in a vehicle track point sequence containing a preset number of vehicle track points, then a complete hidden Markov model can be determined by combining the state transition probability matrix at a target road, then the probability that the tail end track point in the vehicle track point sequence is positioned in each lane is calculated through a Viterbi algorithm, and then the lane with the highest probability is determined to be the lane in which the tail end track point is positioned. Therefore, based on the vehicle track point data before the tail end track point, the reasonable track change trend of the vehicle is fully considered, the lane information of the tail end track point which is more accurate is determined by using the hidden Markov model, the problem that the lane information of the positioned vehicle track point is wrong due to the fact that the position information of the vehicle track point reported by the road side sensing equipment is wrong can be prevented, and the fault tolerance and the accuracy in positioning the lane of the vehicle track point are improved. .
Based on the method in fig. 1, the examples of the present specification also provide some specific embodiments of the method, as described below.
In the embodiment of the present disclosure, the number of rows and columns of the state transition probability matrix at the target road corresponds to the number of lanes of the target road, and each element value in the state transition probability matrix is also an empirical value determined based on historical track data of the historical vehicle at the target road.
Based on this, the method in fig. 1 may further comprise:
A first expression of a state transition probability matrix at the target road is determined based on the number of lanes at the target road.
The first expression is:
Wherein n is the number of lanes at the target road; a is a state transition probability matrix at the target road; a 11 denotes the probability that the vehicle remains traveling on lane 1; a 1n represents the probability of the vehicle moving from lane 1 to lane n; a n1 represents the probability of the vehicle moving from lane n to lane 1; a nn represents the probability that the vehicle remains traveling on lane n.
And determining the values of all elements in the first expression based on the historical track data of the historical vehicle at the target road to obtain the state transition probability matrix at the target road.
The determining, based on the historical track data of the historical vehicle at the target road, each element value in the first expression to obtain the state transition probability matrix at the target road may specifically include:
Determining a first probability value of the vehicle for lane keeping running based on historical track data of the historical vehicle at the target road, and obtaining an element value corresponding to a ii in the first expression; wherein i is any integer from 1 to n.
Determining a quotient of a first difference value between 1 and the first probability value and a second difference value between n and 1 as a second probability value of the vehicle for lane change driving, and obtaining an element value corresponding to a ij in the first expression; wherein j is any integer from 1 to n, and j is different from i.
In the embodiment of the present disclosure, the first probability value of the vehicle for lane keeping running may be determined by big data analysis based on the historical track data of the historical vehicle at the target road, so as to obtain the element value (i is any integer from 1 to n) corresponding to a ii in the first expression. For example: the target road is provided with three lanes, and through analysis of historical data, the probability that the track points of adjacent vehicles are positioned on the same lane in the running process of the vehicles at the target road is 0.8, and then 0.8 can be determined to be a first probability value of the vehicles for lane keeping running, so that the values of an element a 11, an element a 22 and an element a 33 in the state transition probability matrix at the target road are determined to be 0.8.
In the embodiment of the present disclosure, after determining the first probability value of the vehicle performing the lane keeping operation, the difference probability between 1 and the first probability value may be equally divided to other lanes to obtain the second probability value of the same vehicle performing the lane changing operation. Such as: still taking three lanes as an example, if it is determined that the first probability value of the vehicle driving on lane 1 is 0.7, the first difference value is 0.3, the second difference value is 2, and the second probability value is 0.15.
In the embodiment of the present disclosure, each vehicle track point may correspond to an observation state probability matrix, and the observation state probability matrix corresponding to the vehicle track point may be calculated according to a distance between the vehicle track point and a lane center line of each lane at the target road and a lane width of each lane.
Based on this, the method in fig. 1, step 106: according to the distance between each other track point in the vehicle track point sequence and the lane center line of each lane, the observation state probability matrix corresponding to the other track point is calculated, which specifically includes:
And determining the distance between the track point and the lane center line of each lane according to the vehicle position acquisition information of the track point for any track point in each other track point in the vehicle track point sequence.
And calculating the observation state probability matrix B= [ B 1 … bn ] corresponding to the track points by using a second expression based on the distance between the track points and the lane central lines of the lanes and the lane width of the lanes.
The second expression is:
Wherein b n is the probability that the track point is located on lane n; w is the lane width of the lane n; pi is the circumference ratio; sigma is a calculation constant; d n is the projection distance from the track point to the lane center line of the lane n; x is the projection distance from any point on the lane n to the lane center line of the lane n.
In this embodiment of the present disclosure, each other track point in the vehicle track point sequence may be another track point in the vehicle track point sequence except for the first track point, and for the vehicle position acquisition information of any one track point, a specific position of the track point on the target road may be determined, so that a distance between the track point and a lane center line of each lane on the target road may be determined, where the vehicle position acquisition information of the track point may be acquired by a road side sensing device or may be acquired by a vehicle-mounted sensing device, and this is not limited specifically; the vehicle position acquisition information may be latitude and longitude information, or may be other positioning information that may represent the vehicle position, which is not particularly limited.
In practical application, the high-precision map data of the target road and the vehicle position acquisition information of the vehicle track point can be imported by using the high-precision map software to obtain the distance between the vehicle track point and the lane center line of each lane at the target road, or the distance between the vehicle track point and the lane center line of each lane at the target road can be determined by other modes according to the vehicle position acquisition information of the vehicle track point, which is not particularly limited.
In the embodiment of the present disclosure, after determining the distance between a certain track point and the lane center line of each lane on the target road, the probability of the track point on each lane may be obtained by calculating through the second expression according to the distance results and the lane width of each lane, so as to obtain the observation state probability matrix corresponding to the track point. For example: three lanes, namely lane 1, lane 2 and lane 3, are arranged at the target road, and the second expression is used for calculating B 1 to be 0.1, B 2 to be 0.7 and B 3 to be 0.2 aiming at the track point A, so that the observation state probability matrix B= [ 0.1.0.7.2 ] corresponding to the track point A.
In the embodiment of the present disclosure, the initial state probability matrix is determined according to the lane in which the first track point in the vehicle track point sequence is located, so before determining the initial state probability matrix, the lane in which the first track point in the vehicle track point sequence is located is determined first.
Based on this, the method in fig. 1, step 104: the obtaining an initial state probability matrix preset for the lane where the first track point in the vehicle track point sequence is located specifically may include:
And acquiring vehicle position acquisition information at the first track point.
And acquiring a target lane in which the vehicle position acquisition information is positioned, which is determined based on the high-precision map data.
Determining a target initial state probability matrix with binding relation with the target lane from a plurality of initial state probability matrices in an initial state probability matrix set; wherein lanes at the target road having a binding relationship with the different initial state probability matrices are different; each of the initial state probability matrices is determined based on historical trajectory data of a historical vehicle at the target road.
In the embodiment of the present disclosure, the high-precision map data may include map data of a target road, so that a target lane where a first track point is located on the target road may be determined by acquiring information of a vehicle position at the first track point and the high-precision map data.
In the embodiment of the present disclosure, the number of lanes of the target road may be identical to the number of initial state probability matrices in the initial state probability matrix set, each lane may have a unique binding relationship with one initial state probability matrix, and the initial state probability matrices bound by different lanes are also different.
In the embodiment of the present disclosure, the number of elements in the initial state probability matrix may be consistent with the number of lanes on the target road, so that a representation form of the initial state probability matrix may be determined according to the number of lanes on the target road, and then the values of the elements in the initial state probability matrix may be determined based on the empirical values determined by the historical track data of the historical vehicle on the target road.
Based on this, the determining, from a plurality of initial state probability matrices in the initial state probability matrix set, a target initial state probability matrix having a binding relationship with the target lane may specifically include:
A third expression of the target initial state probability matrix in the set of initial state probability matrices is determined based on the number of lanes at the target road.
The third expression is:
C＝[p1 … pn] (3)
Wherein C is the target initial state probability matrix; p 1 denotes the probability that the first track point is located in lane 1 at the target road; p n denotes the probability that the first track point is located in lane n at the target road.
Determining a third probability value of the target vehicle on the target lane based on the historical track data of the historical vehicle on the target road, and obtaining an element value corresponding to p k in the third expression; wherein k is any integer from 1 to n, and lane k is the target lane.
Determining a quotient of a third difference value between 1 and the third probability value and the second difference value as a fourth probability value of the target vehicle in other lanes except the target lane, and obtaining an element value corresponding to p s in the third expression; wherein s is any integer from 1 to n, and s is different from k.
In this embodiment of the present disclosure, the third probability value that the target vehicle is located in the target lane may be an empirical probability value obtained by analyzing historical track data of the historical vehicle at the target road, and may represent a probability that the first track point is actually located in the target lane when the first track point is determined according to the vehicle position acquisition information at the first track point. For example: the method comprises the steps that three lanes are arranged on a target road, a target lane where a first track point is located is determined to be lane 2, based on historical track data of a historical vehicle on the target road, the probability value of the first track point located in the lane 2 is determined to be 0.9, the probability value of the first track point located in the lane 1 is calculated to be 0.05 ((1-0.9)/(3-1) =0.05), the probability value of the first track point located in the lane 3 is also determined to be 0.05, and therefore a target initial state probability matrix with a binding relation with the target lane is determined to be C= [ 0.05.0.9.05 ].
In the embodiment of the specification, after the initial state probability matrix, the observation state probability matrix corresponding to each track point and the state transition probability matrix at the target road are determined, the target probability of each track point at the tail end can be obtained by calculating the tail end track point from the first track point in the track point sequence of the vehicle through recursive calculation by using a viterbi algorithm.
Based on this, the method in fig. 1, step 108: based on the initial state probability matrix, the observation state probability matrix and the state transition probability matrix at the target road, calculating to obtain the target probability that the tail end track point in the vehicle track point sequence is located in each lane, wherein the method specifically comprises the following steps:
Determining a fourth expression of a probability matrix of an mth track point in the vehicle track point sequence being positioned in each lane based on the target initial state probability matrix, the observation state probability matrix and the state transition probability matrix at the target road; wherein m is any integer from 2 to n.
The fourth expression is:
Tm＝Tm-1*A*Bm (4)
Wherein T m is a probability matrix that the mth track point is located in the respective lanes; t m-1 is a probability matrix of the m-1 track points in each lane, and when m-1 is 1, T m-1 is the target initial state probability matrix; a is a state transition probability matrix at the target road; b m is the observation state probability matrix corresponding to the mth track point.
And recursively calculating to obtain a probability matrix of the nth track point in the vehicle track point sequence positioned on each lane by using the fourth expression.
And determining the target probability that the tail end track point in the vehicle track point sequence is positioned in each lane according to the probability matrix that the nth track point is positioned in each lane.
In the embodiment of the present disclosure, the probability matrix of each other track point in the track point sequence of the vehicle except for the first track point is calculated by the fourth expression, where the probability matrix of the first track point in each lane is the initial state probability matrix, that is: t 1 = C, where C is the target initial state probability matrix. The nth track point in the vehicle track point sequence corresponds to the tail end track point in the vehicle track point sequence, so that after T n is calculated, the target probability of the tail end track point in the vehicle track point sequence in each lane can be determined according to T n.
Based on the same thought, the embodiment of the specification also provides a device corresponding to the method. Fig. 2 is a schematic structural diagram of a lane determining device corresponding to the vehicle track point in fig. 1 according to an embodiment of the present disclosure. As shown in fig. 2, the apparatus may include:
The first acquisition module 202 is configured to acquire a preset number of vehicle track point sequences acquired for a target vehicle at a target road.
A second obtaining module 204, configured to obtain an initial state probability matrix preset for a lane where a first track point in the vehicle track point sequence is located; the initial state probability matrix is used for reflecting the probability of each lane where the first track point is located at the target road.
A first calculation module 206, configured to calculate an observation state probability matrix corresponding to each other track point in the vehicle track point sequence according to a distance between the other track point and a lane center line of the lane; the observation state probability matrix is used for reflecting the probability that the other track points are positioned in each lane.
A second calculation module 208, configured to calculate, based on the initial state probability matrix, the observed state probability matrix, and the state transition probability matrix at the target road, a target probability that a terminal track point in the vehicle track point sequence is located in each lane; the state transition probability matrix is used for reflecting the probability that the vehicle transits from each lane to any lane for driving.
The first determining module 210 is configured to determine the lane corresponding to the maximum value of the target probability as the lane where the end trajectory point is located.
The present description example also provides some specific embodiments of the device based on the device of fig. 2, which is described below.
Optionally, the apparatus in fig. 2 may further include:
And the second determining module is used for determining a first expression of the state transition probability matrix at the target road based on the number of lanes at the target road.
The first expression is:
Wherein n is the number of lanes at the target road; a is a state transition probability matrix at the target road; a 11 denotes the probability that the vehicle remains traveling on lane 1; a 1n represents the probability of the vehicle moving from lane 1 to lane n; a n1 represents the probability of the vehicle moving from lane n to lane 1; a nn represents the probability that the vehicle remains traveling on lane n.
And the third determining module is used for determining the values of all elements in the first expression based on the historical track data of the historical vehicle at the target road to obtain the state transition probability matrix at the target road.
Optionally, the third determining module may specifically include:
The first determining submodule is used for determining a first probability value of the vehicle for lane keeping running based on the historical track data of the historical vehicle at the target road, and obtaining an element value corresponding to a ii in the first expression; wherein i is any integer from 1 to n.
A second determining submodule, configured to determine a quotient of a first difference value between 1 and the first probability value and a second difference value between n and 1 as a second probability value for the vehicle to perform lane change driving, and obtain an element value corresponding to a ij in the first expression; wherein j is any integer from 1 to n, and j is different from i.
Optionally, in the apparatus of fig. 2, the first calculating module 206 may specifically include:
The determining submodule is used for determining the distance between the track point and the lane center line of each lane according to the vehicle position acquisition information of the track point aiming at any track point in each other track point in the vehicle track point sequence.
And the calculation sub-module is used for calculating the observation state probability matrix B= [ B 1 … bn ] corresponding to the track points by using a second expression based on the distance between the track points and the lane center line of each lane and the lane width of each lane.
The second expression is:
Wherein b n is the probability that the track point is located on lane n; w is the lane width of the lane n; pi is the circumference ratio; sigma is a calculation constant; d n is the projection distance from the track point to the lane center line of the lane n; x is the projection distance from any point on the lane n to the lane center line of the lane n.
Optionally, in the apparatus of fig. 2, the second obtaining module 204 may specifically include:
and the first acquisition sub-module is used for acquiring the vehicle position acquisition information at the first track point.
And the second acquisition sub-module is used for acquiring a target lane where the vehicle position acquisition information is located, which is determined based on the high-precision map data.
The initial state probability matrix determining submodule is used for determining a target initial state probability matrix with a binding relation with the target lane from a plurality of initial state probability matrices in the initial state probability matrix set; wherein lanes at the target road having a binding relationship with the different initial state probability matrices are different; each of the initial state probability matrices is determined based on historical trajectory data of a historical vehicle at the target road.
Optionally, the initial state probability matrix determining submodule may specifically include:
A first determining unit configured to determine a third expression of the target initial state probability matrix in the initial state probability matrix set based on the number of lanes at the target road.
The third expression is:
C＝[p1 … pn] (3)
Wherein C is the target initial state probability matrix; p 1 denotes the probability that the first track point is located in lane 1 at the target road; p n denotes the probability that the first track point is located in lane n at the target road.
The second determining unit is used for determining a third probability value of the target vehicle in the target lane based on the historical track data of the historical vehicle at the target road, and obtaining an element value corresponding to p k in the third expression; wherein k is any integer from 1 to n, and lane k is the target lane.
A third determining unit, configured to determine a quotient of a third difference value between 1 and the third probability value and the second difference value as a fourth probability value of the target vehicle being located in a lane other than the target lane, and obtain an element value corresponding to p s in the third expression; wherein s is any integer from 1 to n, and s is different from k.
Optionally, in the apparatus of fig. 2, the second calculating module 208 may specifically include:
A fourth expression determining submodule, configured to determine a fourth expression of a probability matrix of an mth track point in the vehicle track point sequence being located in each lane based on the target initial state probability matrix, the observation state probability matrix, and the state transition probability matrix at the target road; wherein m is any integer from 2 to n.
The fourth expression is:
Tm＝Tm-1*A*Bm (4)
Wherein T m is a probability matrix that the mth track point is located in the respective lanes; t m-1 is a probability matrix of the m-1 track points in each lane, and when m-1 is 1, T m-1 is the target initial state probability matrix; a is a state transition probability matrix at the target road; b m is the observation state probability matrix corresponding to the mth track point.
And the recursion calculation sub-module is used for recursively calculating to obtain a probability matrix of the nth track point in the vehicle track point sequence positioned in each lane by using the fourth expression.
And the target probability determination submodule is used for determining the target probability that the tail end track point in the vehicle track point sequence is positioned in each lane according to the probability matrix that the nth track point is positioned in each lane.
Fig. 3 is a schematic structural diagram of a lane determining apparatus corresponding to the vehicle track point of fig. 1 according to an embodiment of the present disclosure. As shown in fig. 3, the apparatus 300 may include:
At least one processor 310; and
A memory 330 communicatively coupled to the at least one processor; wherein,
The memory 330 stores instructions 320 executable by the at least one processor 310, the instructions being executable by the at least one processor 310 to enable the at least one processor 310 to:
A preset number of sequences of vehicle track points acquired for a target vehicle at a target road are acquired.
Acquiring an initial state probability matrix preset for a lane where a first track point in the vehicle track point sequence is located; the initial state probability matrix is used for reflecting the probability of each lane where the first track point is located at the target road.
According to the distances between each other track point in the vehicle track point sequence and the lane center line of each lane, calculating to obtain an observation state probability matrix corresponding to each other track point; the observation state probability matrix is used for reflecting the probability that the other track points are positioned in each lane.
Calculating the target probability of the tail end track point in the vehicle track point sequence on each lane based on the initial state probability matrix, the observation state probability matrix and the state transition probability matrix at the target road; the state transition probability matrix is used for reflecting the probability that the vehicle transits from each lane to any lane for driving.
And determining the lane corresponding to the maximum value of the target probability as the lane where the tail end track point is located.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the apparatus shown in fig. 3, the description is relatively simple, as it is substantially similar to the method embodiment, with reference to the partial description of the method embodiment.
In the 90 s of the 20 th century, improvements to one technology could clearly be distinguished as improvements in hardware (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software (improvements to the process flow). However, with the development of technology, many improvements of the current method flows can be regarded as direct improvements of hardware circuit structures. Designers almost always obtain corresponding hardware circuit structures by programming improved method flows into hardware circuits. Therefore, an improvement of a method flow cannot be said to be realized by a hardware entity module. For example, a programmable logic device (Programmable Logic Device, PLD) (e.g., field programmable gate array (Field Programmable GATE ARRAY, FPGA)) is an integrated circuit whose logic functions are determined by user programming of the device. A designer programs to "integrate" a digital system onto a PLD without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, such programming is mostly implemented with "logic compiler (logic compiler)" software, which is similar to the software compiler used in program development and writing, and the original code before being compiled is also written in a specific programming language, which is called hardware description language (Hardware Description Language, HDL), but HDL is not just one, but a plurality of kinds, such as ABEL(Advanced Boolean Expression Language)、AHDL(Altera Hardware Description Language)、Confluence、CUPL(Cornell UniversityProgramming Language)、HDCal、JHDL(Java Hardware Description Language)、Lava、Lola、MyHDL、PALASM、RHDL(Ruby Hardware Description Language), and VHDL (Very-High-SPEED INTEGRATED Circuit Hardware Description Language) and Verilog are currently most commonly used. It will also be apparent to those skilled in the art that a hardware circuit implementing the logic method flow can be readily obtained by merely slightly programming the method flow into an integrated circuit using several of the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application SPECIFIC INTEGRATED Circuits (ASICs), programmable logic controllers, and embedded microcontrollers, examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller may thus be regarded as a kind of hardware component, and means for performing various functions included therein may also be regarded as structures within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in one or more software and/or hardware elements when implemented in the present specification.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The foregoing is merely exemplary of the present disclosure and is not intended to limit the disclosure. Various modifications and alterations to this specification will become apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, or the like, which are within the spirit and principles of the present description, are intended to be included within the scope of the claims of the present description.
Claims (10)
1. A method for determining a lane in which a vehicle track point is located, the method comprising:
acquiring a preset number of vehicle track point sequences acquired for a target vehicle at a target road;
Acquiring an initial state probability matrix preset for a lane where a first track point in the vehicle track point sequence is located; the initial state probability matrix is used for reflecting the probability of each lane of the first track point at the target road;
According to the distances between each other track point in the vehicle track point sequence and the lane center line of each lane, calculating to obtain an observation state probability matrix corresponding to each other track point; the observation state probability matrix is used for reflecting the probability that the other track points are positioned in each lane;
calculating the target probability of the tail end track point in the vehicle track point sequence on each lane based on the initial state probability matrix, the observation state probability matrix and the state transition probability matrix at the target road; the state transition probability matrix is used for reflecting the probability that the vehicle transits from each lane to any lane for running;
And determining the lane corresponding to the maximum value of the target probability as the lane where the tail end track point is located.
2. The method of claim 1, wherein the calculating, based on the initial state probability matrix, the observed state probability matrix, and the state transition probability matrix at the target road, a target probability that an end track point in the sequence of vehicle track points is located in the respective lane is preceded by:
determining a first expression of a state transition probability matrix at the target road based on the number of lanes at the target road;
the first expression is:
Wherein n is the number of lanes at the target road; a is a state transition probability matrix at the target road; a 11 denotes the probability that the vehicle remains traveling on lane 1; a 1n represents the probability of the vehicle moving from lane 1 to lane n; a n1 represents the probability of the vehicle moving from lane n to lane 1; a nn denotes the probability that the vehicle remains traveling on lane n;
and determining the values of all elements in the first expression based on the historical track data of the historical vehicle at the target road to obtain the state transition probability matrix at the target road.
3. The method according to claim 2, wherein determining the element values in the first expression based on the historical track data of the historical vehicle at the target road, to obtain the state transition probability matrix at the target road, specifically comprises:
Determining a first probability value of the vehicle for lane keeping running based on historical track data of the historical vehicle at the target road, and obtaining an element value corresponding to a ii in the first expression; wherein i is any integer from 1 to n;
Determining a quotient of a first difference value between 1 and the first probability value and a second difference value between n and 1 as a second probability value of the vehicle for lane change driving, and obtaining an element value corresponding to a ij in the first expression; wherein j is any integer from 1 to n, and j is different from i.
4. The method according to claim 3, wherein the calculating, according to the distance between each other track point in the vehicle track point sequence and the lane center line of each lane, the observation state probability matrix corresponding to the other track point specifically includes:
Determining the distance between the track point and the lane center line of each lane according to the vehicle position acquisition information of the track point for any track point in each other track point in the vehicle track point sequence;
calculating the observation state probability matrix B= [ B 1…bn ] corresponding to the track points by using a second expression based on the distance between the track points and the lane central lines of the lanes and the lane width of the lanes;
The second expression is:
Wherein b n is the probability that the track point is located on lane n; w is the lane width of the lane n; pi is the circumference ratio; sigma is a calculation constant; d n is the projection distance from the track point to the lane center line of the lane n; x is the projection distance from any point on the lane n to the lane center line of the lane n.
5. The method according to claim 4, wherein the obtaining an initial state probability matrix preset for a lane in which a first track point in the vehicle track point sequence is located specifically includes:
acquiring vehicle position acquisition information at the first track point;
Acquiring a target lane where the vehicle position acquisition information is located, which is determined based on high-precision map data;
Determining a target initial state probability matrix with binding relation with the target lane from a plurality of initial state probability matrices in an initial state probability matrix set; wherein lanes at the target road having a binding relationship with the different initial state probability matrices are different; each of the initial state probability matrices is determined based on historical trajectory data of a historical vehicle at the target road.
6. The method according to claim 5, wherein the determining a target initial state probability matrix having a binding relationship with the target lane from a plurality of initial state probability matrices in the initial state probability matrix set specifically comprises:
determining a third expression of the target initial state probability matrix in the initial state probability matrix set based on the number of lanes at the target road;
The third expression is:
C＝[p1…pn] (3)
Wherein C is the target initial state probability matrix; p 1 denotes the probability that the first track point is located in lane 1 at the target road; p n denotes the probability that the first track point is located in lane n at the target road;
Determining a third probability value of the target vehicle on the target lane based on the historical track data of the historical vehicle on the target road, and obtaining an element value corresponding to p k in the third expression; wherein k is any integer from 1 to n, and lane k is the target lane;
Determining a quotient of a third difference value between 1 and the third probability value and the second difference value as a fourth probability value of the target vehicle in other lanes except the target lane, and obtaining an element value corresponding to p s in the third expression; wherein s is any integer from 1 to n, and s is different from k.
7. The method according to claim 6, wherein the calculating, based on the initial state probability matrix, the observed state probability matrix, and the state transition probability matrix at the target road, the target probability that the terminal track point in the sequence of vehicle track points is located in the respective lanes specifically includes:
Determining a fourth expression of a probability matrix of an mth track point in the vehicle track point sequence being positioned in each lane based on the target initial state probability matrix, the observation state probability matrix and the state transition probability matrix at the target road; wherein m is any integer from 2 to n;
the fourth expression is:
Tm＝Tm-1*A*Bm (4)
Wherein T m is a probability matrix that the mth track point is located in the respective lanes; t m-1 is a probability matrix of the m-1 track points in each lane, and when m-1 is 1, T m-1 is the target initial state probability matrix; a is a state transition probability matrix at the target road; b m is the observation state probability matrix corresponding to the mth track point;
Using the fourth expression to recursively calculate to obtain a probability matrix of the nth track point in the vehicle track point sequence located in each lane;
And determining the target probability that the tail end track point in the vehicle track point sequence is positioned in each lane according to the probability matrix that the nth track point is positioned in each lane.
8. A lane determining apparatus in which a vehicle trajectory point is located, the apparatus comprising:
The first acquisition module is used for acquiring a preset number of vehicle track point sequences acquired for a target vehicle at a target road;
the second acquisition module is used for acquiring an initial state probability matrix preset for a lane where a first track point in the vehicle track point sequence is located; the initial state probability matrix is used for reflecting the probability of each lane of the first track point at the target road;
The first calculation module is used for calculating an observation state probability matrix corresponding to each other track point according to the distance between each other track point in the vehicle track point sequence and the lane center line of each lane; the observation state probability matrix is used for reflecting the probability that the other track points are positioned in each lane;
The second calculation module is used for calculating the target probability of the tail end track point in the vehicle track point sequence on each lane based on the initial state probability matrix, the observation state probability matrix and the state transition probability matrix at the target road; the state transition probability matrix is used for reflecting the probability that the vehicle transits from each lane to any lane for running;
And the first determining module is used for determining the lane corresponding to the maximum value of the target probability as the lane where the tail end track point is located.
9. The apparatus of claim 8, wherein the apparatus further comprises:
a second determining module, configured to determine a first expression of a state transition probability matrix at the target road based on the number of lanes at the target road;
the first expression is:
Wherein n is the number of lanes at the target road; a is a state transition probability matrix at the target road; a 11 denotes the probability that the vehicle remains traveling on lane 1; a 1n represents the probability of the vehicle moving from lane 1 to lane n; a n1 represents the probability of the vehicle moving from lane n to lane 1; a nn denotes the probability that the vehicle remains traveling on lane n;
and the third determining module is used for determining the values of all elements in the first expression based on the historical track data of the historical vehicle at the target road to obtain the state transition probability matrix at the target road.
10. A lane determining apparatus in which a vehicle trajectory point is located, the apparatus comprising:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring a preset number of vehicle track point sequences acquired for a target vehicle at a target road;
Acquiring an initial state probability matrix preset for a lane where a first track point in the vehicle track point sequence is located; the initial state probability matrix is used for reflecting the probability of each lane of the first track point at the target road;
According to the distances between each other track point in the vehicle track point sequence and the lane center line of each lane, calculating to obtain an observation state probability matrix corresponding to each other track point; the observation state probability matrix is used for reflecting the probability that the other track points are positioned in each lane;
calculating the target probability of the tail end track point in the vehicle track point sequence on each lane based on the initial state probability matrix, the observation state probability matrix and the state transition probability matrix at the target road; the state transition probability matrix is used for reflecting the probability that the vehicle transits from each lane to any lane for running;
And determining the lane corresponding to the maximum value of the target probability as the lane where the tail end track point is located.
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