CN112967516B - Global dynamic path planning method for matching of key parameters of quick parking lot end with whole vehicle - Google Patents
Global dynamic path planning method for matching of key parameters of quick parking lot end with whole vehicle Download PDFInfo
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Abstract
The invention belongs to the field of automatic driving of vehicles, and discloses a global dynamic path planning method for matching key parameters of a quick parking lot end with a whole vehicle, which comprises the following five steps: parameters and parking space states are input into a field end cloud platform; identifying the vehicle model through the license plate and matching a target parking space and a corresponding global path for each vehicle; sensing the latest positioning information of the vehicle through radar equipment; planning an optimal global path and a target parking space corresponding to the latest positioning point by using a field end cloud platform according to the monitored latest parking space state and the latest positioning point position of the vehicle; and (4) selecting an optimal parking route according to the planned path, and repeating the steps until the parking task is completed. According to the invention, through information interaction between the field end cloud platform and the vehicle-mounted communication equipment, vehicle type parameters and motion information in the parking field are obtained in real time, and a globally optimal parking track is planned for each vehicle with a parking task in real time through the field end cloud platform, so that vehicles of different types can always complete the parking task as soon as possible.
Description
Technical Field
The invention relates to a global dynamic path planning method for matching key parameters of a quick parking lot end with a whole vehicle, and belongs to the technical field of automatic driving of vehicles.
Background
In recent years, with the gradual increase of the vehicle intelligence level, the automatic driving technology is primarily realized in many scenes, such as adaptive cruise ACC, automatic emergency braking AEB, automatic parking and the like. The ACC and the AEB still have a plurality of working conditions which are difficult to handle under the complex urban traffic roads, so that the actual experience brought to drivers is not ideal enough, and even some collision risks can be brought under the limit working conditions. The automatic parking technology in the parking lot is due to the fact that under a closed scene, few pedestrians and other traffic participants exist. In addition, the speed of the vehicles in the parking lot is low and is basically kept below 10km/h, and the cloud platform in the parking lot issues instructions to each vehicle for cooperative control, so that great convenience is brought to an automatic parking system in the parking lot, and the automatic parking technology in the parking lot becomes more promising and urgent.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a global dynamic path planning method for matching key parameters of a quick parking lot end with a whole vehicle, and solves the problem that vehicles of different types cannot finish parking tasks as soon as possible.
The invention adopts the following technical scheme for solving the technical problems:
the invention relates to a global dynamic path planning method for matching key parameters of a quick parking lot end with a whole vehicle, which comprises the following steps:
the method comprises the following steps: recording parking lot end key parameters and the current parking stall state into a lot end cloud platform in real time;
step two: when each vehicle passes through the entrance of the parking lot, the field end cloud platform can identify the vehicle model through the license plate, and matches each vehicle with a target parking space and a corresponding global path by using a reinforcement learning method in combination with the current parking space state of the parking lot;
step three: when a vehicle enters a parking lot, the position of the vehicle and the latest locating point information of the vehicle are sensed through a vehicle-mounted millimeter wave radar and a camera in combination with a map in the parking lot;
step four: the field end cloud platform re-utilizes a reinforcement learning method to plan an optimal global path and a target parking space corresponding to a latest positioning point in real time according to the latest parking space state monitored in the parking field and the latest positioning point position of the vehicle;
step five: and sending a tracking control command to the bottom controller and the parking lot holder according to the planned path so as to enter the next moment, and repeating the third step to the fifth step until the parking task is completed.
Further, in the first step, when the parking lot end key parameters and the current parking space state are recorded into the lot end cloud platform in real time, the method specifically includes the following steps:
1) the key parameters of the parking lot end comprise parking space subareas A, B, C, D, E and the like, wherein different subareas can stop different types of vehicles, for example, a district A can stop a large vehicle with the wheelbase exceeding 2m, a district B can stop a little smaller vehicle, and concretely, as follows,
A＝{a 1 ,a 2 ,…,a p }B＝{b 1 ,b 2 ,…,b q }
wherein a is i ，b i For the specific parking space condition, if the current parking space is empty, the current parking space is 1, and if the current parking space is already parked, the current parking space is 0;
2) key parameter bag of parking lot endIncluding pre-marked locating points in the field, representing road characteristic information in the field, using each locating point as a state S, marking all locating points to obtain n states, S ═ S 1 ,s 2 ,…,s n }；
3) The information of the parking space at the key positioning point is specifically expressed as follows, when a vehicle is required to stop at a certain parking space, the vehicle can stop after passing through a certain positioning point,
wherein s is m Is a key anchor point, a i ,a i+1 ,b j ,b j+1 ,c j ,c j+1 … represents a parking space that can be directly reached via the positioning point, such as a parking space that wants to enter a 1 -a 5 The five parking spaces must pass through s 3 Or s 6 Can be reached, thereby representing for example
4) Defining actions and transfer relations between positioning points and positioning points, wherein 4 basic actions A1-A4 can be performed when a specific vehicle arrives at each positioning point, wherein A1 represents forward driving, A2 represents left turning, A3 represents right turning, and A4 represents static, the corresponding state transfer relation can be determined by the internal structure parameters of the parking lot, and if the action A1 is taken at S6, the vehicle arrives at S5; take action A3 and go to S4; taking other actions and reaching the self state S6, the transition relations corresponding to all the states are characterized to obtain a state transition matrix T.
Further, in the second step, each vehicle is matched with a target parking space and a corresponding global path by using a reinforcement learning method, and the specific steps are as follows:
1) after the vehicle type is identified, the corresponding partition can be arranged according to the vehicle type, all vacant positions in the partition can become final target parking spaces at the moment, positioning points belonging to the vacant parking spaces are extracted, and the positioning points become final positioning points of the global path, and can be expressed as follows:
wherein S f In order to obtain the set of final anchor points,
the final anchor point which the global path may pass through is obtained;
2) a reinforcement learning process that establishes a global path, wherein a set of states: s ═ S 1 ,s 2 ,…,s n Action set Action ═ a 1 ,A 2 ,A 3 ,A 4 Both the metrics T and the state transition matrix T have been established in 2.2, 2.4, and the corresponding reward function can be expressed as follows:
wherein s is t+1 Is in a state S t Take action A t The next state reached, derived from the state transition matrix: s is t+1 ＝T(s t ,A t ) The return represents that the return is particularly large when the next state reaches the final anchor point; if not, paying a certain distance cost;
3) establishing a Q learning iterative equation:
wherein alpha is the learning rate and gamma is the forgetting factor;
4) random initialization state S t Continuously learning through an iterative equation, when the state s t+1 ∈S f Entering the next cycle, and obtaining the current positioning point s after the cycle is converged for multiple times 0 Final positioning point S to target parking space f The corresponding global path:
s 0 a 0 s 1 a 1 … s f
5) to the final positioning point S f And then, selecting the empty parking space closest to the positioning point as the target parking space.
Further, when the reinforcement learning method is reused in the fourth step to plan the optimal global path and the target parking space in real time, the specific steps are as follows:
1) according to the latest parking space state monitored in the parking lot, the final positioning point set in 3.1) needs to be updated to obtain a new positioning point corresponding to the current time t
2) Repeating the reinforcement learning process of 3.2) -3.3), carrying out iterative training again, and carrying out the current state
And then entering the next cycle, and after the cycle is converged for multiple times, obtaining a global path from the latest positioning point where the vehicle is located to the updated final positioning point:
compared with the prior art, the invention has the beneficial effects that:
vehicle type parameters and motion information in the parking lot are obtained in real time through information interaction between the field end cloud platform and the vehicle-mounted communication equipment, and a globally optimal parking track is planned for each vehicle with a parking task in real time through the field end cloud platform, so that vehicles of different types can finish the parking task efficiently and quickly.
Drawings
Figure 1 is the parking lot key parameter of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following detailed description of the present invention is made with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
As shown in fig. 1, the method for global dynamic path planning by matching of key parameters of a fast parking lot end with a whole vehicle, provided by the invention, comprises the following steps:
the method comprises the following steps: recording parking lot end key parameters and the current parking stall state into a lot end cloud platform in real time;
step two: when each vehicle passes through the entrance of the parking lot, the field end cloud platform can identify the vehicle model through the license plate, and matches each vehicle with a target parking space and a corresponding global path by using a reinforcement learning method in combination with the current parking space state of the parking lot;
step three: when a vehicle enters a parking lot, the position of the vehicle and the latest locating point information of the vehicle are sensed through a vehicle-mounted millimeter wave radar and a camera in combination with a map in the parking lot;
step four: according to the latest parking space state monitored in the parking lot and the latest positioning point position of the vehicle, the field end cloud platform re-utilizes a reinforcement learning method to plan an optimal global path and a target parking space corresponding to the latest positioning point in real time;
step five: and sending a tracking control command to the bottom controller and the parking lot holder according to the planned path so as to enter the next moment, and repeating the third step to the fifth step until the parking task is completed.
Further, in the first step, when the parking lot end key parameters and the current parking space state are recorded into the lot end cloud platform in real time, the method specifically includes the following steps:
1) the key parameters of the parking lot end comprise parking space subareas A, B, C, D, E and the like, wherein different subareas can stop different types of vehicles, for example, a district A can stop a large vehicle with the wheelbase exceeding 2m, a district B can stop a little smaller vehicle, and concretely, as follows,
A＝{a 1 ,a 2 ,…,a p }B＝{b 1 ,b 2 ,…,b q }
wherein a is i ，b i For the specific parking space condition, if the current parking space is empty, the current parking space is1, 0 if the vehicle has stopped;
2) the key parameters of the parking lot end comprise positioning points marked in advance in the parking lot and representing road characteristic information in the parking lot, each positioning point is used as a state S, and n states can be obtained after all the positioning points are marked, wherein S is equal to { S ═ S } 1 ,s 2 ,…,s n }；
3) The parking space information at the key positioning point is that the vehicle can stop at a certain parking space only after passing through a certain positioning point,
wherein s is m Is a key anchor point, a i ,a i+1 ,b j ,b j+1 ,c j ,c j+1 … represents a parking space that can be directly reached via the positioning point, if a is desired 1 -a 5 The five parking spaces must pass through s 3 Or s 6 Can be reached, and thus is expressed as follows,
4) defining actions and transfer relations between positioning points and positioning points, wherein 4 basic actions A1-A4 can be performed when a specific vehicle arrives at each positioning point, wherein A1 represents forward driving, A2 represents leftward turning, A3 represents rightward turning, A4 represents static, the corresponding state transfer relation can be determined by structural parameters in a parking lot, and S5 is reached if an action A1 is taken at S6; take action A3 and go to S4; other actions are taken to reach the self state S6, and the transition relationships corresponding to all the states are characterized to obtain a state transition matrix T.
Further, in the second step, each vehicle is matched with a target parking space and a corresponding global path by using a reinforcement learning method, and the specific steps are as follows:
1) after the vehicle type is identified, the corresponding partition can be arranged according to the vehicle type, all the vacant positions in the partition can become final target parking spaces at this time, the positioning points belonging to the vacant parking spaces are extracted, and the positioning points become final positioning points of the global path, which can be expressed as follows:
wherein S f In order to obtain the set of final anchor points,
final anchor points which can be passed by the global path;
2) a reinforcement learning process that establishes a global path, wherein a set of states: s ═ S 1 ,s 2 ,…,s n }, Action set Action ═ a 1 ,A 2 ,A 3 ,A 4 Both the metrics T and the state transition matrix T have been established in 2.2, 2.4, and the corresponding reward function can be expressed as follows:
wherein s is t+1 Is in a state S t Take action A t The next state reached, derived from the state transition matrix: s is t+1 ＝T(s t ,A t ) The return represents that the return is particularly large when the next state reaches the final anchor point; if not, paying a certain distance cost;
3) establishing a Q learning iterative equation:
wherein alpha is the learning rate, and gamma is the forgetting factor;
4) random initialization state S t Continuously learning through an iterative equation, when the state s t+1 ∈S f Then entering the next cycle, and obtaining the current time after the cycle is converged for multiple timesFront anchor point s 0 Final positioning point S to target parking space f The corresponding global path:
s 0 a 0 s 1 a 1 … s f
5) to the final positioning point S f And then, selecting the empty parking space closest to the positioning point as the target parking space.
Further, when the reinforcement learning method is reused in the fourth step to plan the optimal global path and the target parking space in real time, the specific steps are as follows:
1) according to the latest parking space state monitored in the parking lot, the final positioning point set in 3.1) needs to be updated to obtain a new positioning point corresponding to the current time t
2) Repeating the reinforcement learning process of 3.2) -3.3), and performing iterative training again in the current state
And then entering the next cycle, and after the cycle is converged for multiple times, obtaining a global path from the latest positioning point where the vehicle is located to the updated final positioning point:
the patent mainly researches key technologies in automatic parking, and mainly relates to a technology for performing dynamic path planning on various whole vehicles when key parameters of a parking lot end are considered, so that a globally optimal parking track is planned in real time for each vehicle with a parking task, and vehicles of different models can always finish the parking task efficiently and quickly.
Claims (2)
1. The global dynamic path planning method for matching the key parameters of the fast parking lot end with the whole vehicle is characterized by comprising the following steps of:
the method comprises the following steps: recording parking lot end key parameters and the current parking stall state into a lot end cloud platform in real time;
step two: when each vehicle passes through the entrance of the parking lot, the field end cloud platform can identify the vehicle model through the license plate, and matches each vehicle with a target parking space and a corresponding global path by using a reinforcement learning method in combination with the current parking space state of the parking lot;
step three: when a vehicle enters a parking lot, the position of the vehicle and the latest positioning point information of the vehicle are sensed through a vehicle-mounted millimeter wave radar and a camera in combination with a map in the parking lot;
step four: the field end cloud platform re-utilizes a reinforcement learning method to plan an optimal global path and a target parking space corresponding to a latest positioning point in real time according to the latest parking space state monitored in the parking field and the latest positioning point position of the vehicle;
step five: according to the planned path, sending a tracking control instruction to the bottom controller and the parking lot holder so as to enter the next moment, and repeating the third step to the fifth step until the parking task is completed;
when the parking lot end key parameters and the current parking space state are recorded into the lot end cloud platform in real time, the method specifically comprises the following contents:
2.1) the key parameters of the parking lot end comprise parking space subareas A, B, C, D and E, wherein different parking spaces stop different types of vehicles, for example, a large vehicle with the wheelbase exceeding 2m stops in the area A, a slightly smaller vehicle stops in the area B, and concretely,
A＝{a 1 ,a 2 ,…,a p }B＝{b 1 ,b 2 ,…,b q }
wherein a is i ，b i For the specific parking space condition, if the current parking space is empty, the current parking space is 1, and if the current parking space is already parked, the current parking space is 0;
2.2) key parameters of the parking lot end comprise anchor points marked in advance in the parking lot, representing road characteristic information in the parking lot, taking each anchor point as a state s, and marking all the anchor points to obtain n states:
S＝{s 1 ,s 2 ,…,s n }；
2.3) the information of the vehicle position under the key positioning point, the vehicle can stop after passing through a certain positioning point when the vehicle is stopped in a certain position, which is specifically expressed as follows,
wherein s is m Is a key anchor point, a i ,a i+1 ,b j ,b j+1 ,c j ,c j+1 … indicates a parking space directly accessible via the location point, for example, to enter a 1 -a 5 The five parking spaces must pass through s 3 Or s 6 Can be reached, and thus is represented as follows,
2.4) defining the action and transfer relation between the positioning points, specifically, the vehicle arrives at each positioning point to perform 4 basic actions A1-A4, wherein A1 represents forward driving, A2 represents leftward turning, A3 represents rightward turning, A4 represents static, the corresponding state transfer relation can be determined by the structural parameters in the parking lot, and if the action A1 is taken at S6, the vehicle arrives at S5; take action A3 to S4; taking other actions and reaching the self state S6, and representing the transfer relations corresponding to all the states to obtain a state transfer matrix T;
a target parking space and a corresponding global path are matched for each vehicle by using a reinforcement learning method, and the method comprises the following specific steps:
3.1) after the vehicle type is identified, arranging a corresponding partition according to the vehicle type, wherein all vacant positions in the partition can become final target parking spaces, extracting positioning points belonging to the vacant parking spaces, and the positioning points become final positioning points of the global path, and can be represented as follows:
wherein S f To be the finalA set of anchor points is set up,
final anchor points which can be passed by the global path;
3.2) a reinforcement learning process to establish a global path, wherein the state set: s ═ S 1 ,s 2 ,…,s n }, Action set Action ═ a 1 ,A 2 ,A 3 ,A 4 The, and state transition matrix T are already established in 2.2), 2.4), the corresponding return function is expressed as follows:
wherein s is t+1 Is in a state S t Take action A t The next state reached, derived from the state transition matrix:
s t+1 ＝T(s t ,A t ) The return represents that the return is particularly large when the next state reaches the final anchor point; if not, paying a certain distance cost;
3.3) establishing a Q learning iterative equation:
Q(s t ,a t )＝Q(s t ,a t )+α[r t +γmax ai Q(s t+1 ,a i )-Q(s t ,a t )]
wherein alpha is the learning rate and gamma is the forgetting factor;
3.4) random initialization State S t Continuously learning through an iterative equation, when the state s t+1 ∈S f Entering the next cycle, and obtaining the current positioning point s after the cycle is converged for multiple times 0 Final positioning point S to target parking space f The corresponding global path:
s 0 →a 0 →s 1 →a 1 →…→s f
3.5) reach the final location S f And then, selecting the empty parking space closest to the positioning point as the target parking space.
2. The method for planning the global dynamic path for matching the key parameters of the fast parking lot end with the whole vehicle according to claim 1, wherein when the optimal global path and the target parking space are planned in real time by the reinforcement learning method in the fourth step, the specific steps are as follows:
4.1) according to the latest parking space state monitored in the parking lot, updating the final positioning point set in 3.1) to obtain a new positioning point corresponding to the current time t
4.2) repeating the reinforcement learning process of 3.2) -3.3), and carrying out iterative training again, when the state is the same
And then entering the next cycle, and after the cycle is converged for multiple times, obtaining a global path from the latest positioning point where the vehicle is located to the updated final positioning point:
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| CN114913708B (en) * | 2022-07-18 | 2022-10-28 | 深圳市华睿智兴信息科技有限公司 | Parking path guiding system and method for intelligent parking lot |
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| CN104021375B (en) * | 2014-05-29 | 2017-11-07 | 银江股份有限公司 | A kind of model recognizing method based on machine learning |
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Effective date of registration: 20230320 Address after: 241002 20th Floor, Building 7, Science and Technology Industrial Park, Wuhu High and New Technology Industrial Development Zone, Yijiang District, Wuhu City, Anhui Province Patentee after: Wuhu Ruite Microelectronics Co.,Ltd. Address before: 241002 No.100, Huajin South Road, Yijiang District, Wuhu City, Anhui Province Patentee before: Wuhu bolatu Information Technology Co.,Ltd. |