CN113312760B - Network online motor vehicle right-turn track planning method and device based on traffic simulation - Google Patents
Network online motor vehicle right-turn track planning method and device based on traffic simulation Download PDFInfo
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Abstract
The invention discloses a network online motor car right-turn track planning method and device based on traffic simulation. The method comprises the following steps: determining a non-motor vehicle set which possibly generates machine non-interaction in a non-motor vehicle traffic flow according to the network connection right-turn vehicles, determining data such as non-motor vehicle speed, acceleration and the like in the non-motor vehicle set, and grouping the non-motor vehicles by combining the data; performing microscopic traffic simulation on the current non-motor vehicle data; combining the simulation result with an evolution formula, so that the non-motor vehicle group carries out dynamic evolution prediction; and finally, carrying out right-turning track planning on the network online vehicle according to the non-motor vehicle group after dynamic evolution. The right-turning track planning method provided by the invention considers the influence of the non-motor vehicle group on the online motor vehicle, and predicts the dynamic evolution of the group through microscopic traffic simulation, so that the track planning is more comprehensive and scientific.
Description
Technical Field
The invention relates to the technical field of traffic simulation control, in particular to a network online motor car right-turning track planning method and device based on traffic simulation.
Background
In recent years, due to traffic safety and environmental problems caused by rapid development of traffic systems, intelligent network vehicles have attracted a great deal of attention and have been rapidly developed in the past decade, connectivity and automation degree of vehicles have been greatly improved, so that vehicles can communicate with infrastructure, partial or full-automatic driving is realized by using information of vehicle-mounted sensors, and cooperative work can be realized by communication between vehicles.
In an urban intersection, the conflict and interaction of a right-turning motor vehicle and a straight-running non-motor vehicle are one of the most frequent phenomena of machine non-interaction and conflict in the intersection, and the phenomenon of complex machine non-interaction is caused by the phenomenon of mutually robbing at the intersection with large flow. In the current research on intelligent network vehicle connection simulation, the research on the interaction of a right-turning motor vehicle and a non-motor vehicle is less, and the current main research is focused on the interaction between motor vehicles; meanwhile, in a specific simulation process, the vehicle usually carries out a next behavior decision according to surrounding motor vehicle data, and the utilization of non-motor vehicle data in the decision forming process is less, so that the right-turning motor vehicle is difficult to determine right-turning time and safely turns right.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a network online motor vehicle right-turn track planning method and device based on traffic simulation, which take speed, acceleration and coordinate data of non-motor vehicles in a local range as basic information, carry out preliminary group division according to a distance threshold, then substitute the non-motor vehicle and non-motor vehicle group data into a microscopic traffic simulation system to carry out IDM simulation, combine simulation data with an evolution formula, and carry out dynamic evolution prediction on the non-motor vehicle group, thereby judging possible crossing gaps, and carry out reasonable right-turn track planning on the intelligent network online right-turn vehicle according to the possible crossing gaps.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, an embodiment of the present invention provides a network online motor vehicle right-hand turning track planning method based on traffic simulation, where the planning method includes:
defining a research range according to the position information and the running information of the net online motor vehicle which is planned to turn right, acquiring all non-motor vehicles which possibly do not interact with a generator of the net online motor vehicle in the research range, generating a non-motor vehicle set, and extracting microscopic traffic data of all the non-motor vehicles in the non-motor vehicle set;
calculating a distance threshold by combining the acquired vehicle lengths of all the non-motor vehicles, and dividing the acquired non-motor vehicles into groups according to the distance threshold so that the minimum distance between the groups is larger than the distance threshold;
performing microscopic traffic simulation aiming at microscopic traffic data of the non-motor vehicle;
combining the microcosmic traffic simulation result with an evolution formula, carrying out dynamic evolution prediction on each non-motor vehicle group, and adjusting members of each non-motor vehicle group by combining the non-motor vehicle position change trend;
and carrying out right-hand track planning on the online motor vehicles based on the adjusted running information of each member of the non-motor vehicle group.
Optionally, the microscopic traffic data includes speed, acceleration, and position coordinates.
Optionally, the process of grouping the acquired non-motor vehicles according to the distance threshold value by calculating the distance threshold value by combining the acquired vehicle lengths of all the non-motor vehicles includes the following steps:
determining the average vehicle length of the non-motor vehicles in the research range as a distance threshold;
taking the running direction of the non-motor vehicles as the positive direction of the y axis, sequencing all the non-motor vehicles according to the sequence from big to small of the y axis coordinate value of the real-time position of each non-motor vehicle, searching the first non-motor vehicle which is not divided, and taking the vehicle as the tail vehicle of the group I;
calculating the distance between the non-motor vehicle which is not divided into the group and the group I tail vehicle, if the distance is smaller than the distance threshold value, taking the non-motor vehicle as the tail vehicle which is counted into the group I and is used as a new tail vehicle, and if the distance is larger than the distance threshold value, taking the non-motor vehicle as the tail vehicle of the group I+1;
and sequentially calculating the distance between the non-motor vehicles of other non-divided groups and the current latest group tail vehicle according to the sequence, if the group division index of the selected non-motor vehicles is smaller than the group division threshold, counting the selected non-motor vehicles into the current latest group I+m-1 and taking the selected non-motor vehicles as new tail vehicles, otherwise, creating a new group I+m for the selected non-motor vehicles, taking the selected non-motor vehicles as the tail vehicles of the new groups, and dividing all the non-motor vehicles in the research range into corresponding groups until m=1, …, M-1 and M are the total number of the groups obtained by division.
Optionally, the process of performing microscopic traffic simulation on the microscopic traffic data of the non-motor vehicle includes:
substituting the non-motor vehicle data into a microscopic traffic simulation system, performing IDM traffic simulation on each non-motor vehicle in a non-motor vehicle set, and obtaining the time t required by each non-motor vehicle to reach a potential conflict point ij ，t ij Is the time required for the jth non-motor vehicle in the ith group to reach the potential conflict point, i=1, 2, …, M, j=1, 2, …, N i M is the total number of groups obtained by dividing, N i Is the total number of non-motor vehicles in the i-th group;
calculating average speeds for each group of non-motor vehiclesAnd average acceleration->And average y-axis coordinate>
Substituting the non-motor vehicle group as a non-motor vehicle to perform IDM traffic simulation, and obtaining the required time t for the non-motor vehicle group to reach a potential conflict point i 。
Optionally, the process of dynamically evolving and predicting each non-motor vehicle group includes:
judging whether each non-motor vehicle in the group has a tendency to deviate from the current group:
wherein if itIf the value of (2) is 1, the tendency of detachment is indicated, and if the value is 0, the tendency of detachment is not indicated;
judging whether the non-motor vehicle with the tendency of being out of the group is out of the group or not:
wherein t is turn Is the time required for the net online motor car to finish right turn, if f 2 (t i ,t ij ) If the value of (2) is 1, determining a detached group, and if the value of (2) is 0, determining that no detached group exists; if a group contains only one non-motor vehicle, then the groupAnd f 2 (t i ,t ij ) The value is 1;
judging whether the vehicles after detachment are combined with other groups or not:
wherein, if t i ＜t ij Then go through f 3 (t i-1 ,t ij ) Judging if f 3 (t i-1 ,t ij ) If f is 1, it is stated that the corresponding non-motor vehicle can be grouped in the i-1 th group 3 (t i-1 ,t ij ) If the value of (1) is-1, the vehicle cannot be integrated into other groups and needs to be singly grouped; if t i ≥t ij Then go through f 4 (t i-1 ,t ij ) Judging whether the current value of the current value is equal to or higher than the threshold value,if f 4 (t i-1 ,t ij ) A value of 2 indicates that the vehicle may be incorporated into the (i+1) th group, if f 4 (t i-1 ,t ij ) If the value of (2) is-2, the vehicle cannot be integrated into other groups and needs to be singly grouped;
generating an evolution formula F:
performing F operation on the non-motor vehicles in the collection: if the value of F is 1, predicting that the corresponding non-motor vehicle is integrated into the previous group; if the value is-1, the corresponding non-motor vehicle is illustrated to leave the current group, and a new group before the current group is additionally created; if the value is 2, the non-motor vehicles corresponding to the prediction are merged into the latter group; if the value is-2, the corresponding non-motor vehicles leave the group, and a new group after the current group is additionally created; if the value is 0, the corresponding non-motor vehicle is still kept in the current group.
Optionally, the process of performing right-hand track planning on the online motor vehicle based on the adjusted driving information of each member of the non-motor vehicle group:
calculating traversable gaps when the net online motor car turns right between adjacent groups according to the evolved groups:
gap i ＝t (i+1)-he -t i-las
wherein, gap i Represents the traversable gap, t, between the ith group and the (i+1) th group (i+1)-head Representing the time required for the first non-motor vehicle in the i+1 group to reach the potential conflict point, t i-las Representing the time required for the last non-motor vehicle in the ith group to reach a potential conflict point, wherein i=1, 2, …, M is the total number of the groups obtained by dividing;
comparing the predicted traversable gaps among the groups with the right turn time length of the online motor vehicle, and selecting one traversable gap which is longer than the right turn time length as the right turn gap of the online motor vehicle;
calculating the time of the network connection motor car reaching the parking line, and planning the speed according to the time of the network connection motor car reaching the parking line;
adopting a three-time Bezier curve, and carrying out right-turn track planning according to a non-motor vehicle group corresponding to the right-turn gap: and respectively selecting the midpoint of the parking line, the midpoint of the last non-motor vehicle before the right turn gap and the midpoint of the right turn entering lane line as three control points, and completing the track planning.
In a second aspect, an embodiment of the present invention provides a network online motor vehicle right-hand track planning device based on traffic simulation, where the planning device includes:
the non-motor vehicle set generating module is used for defining a research range according to the position information and the running information of the net-connected motor vehicle which is planned to turn right, acquiring all non-motor vehicles which possibly do not interact with the net-connected motor vehicle generator in the research range, generating a non-motor vehicle set, and extracting microscopic traffic data of all the non-motor vehicles in the non-motor vehicle set;
the group dividing module is used for calculating a distance threshold value by combining the acquired vehicle lengths of all the non-motor vehicles, and dividing the acquired non-motor vehicles into groups according to the distance threshold value so that the minimum distance between the groups is larger than the distance threshold value;
the micro traffic simulation module is used for carrying out micro traffic simulation on micro traffic data of the non-motor vehicle;
the dynamic evolution prediction module is used for combining the microcosmic traffic simulation result with an evolution formula, carrying out dynamic evolution prediction on each non-motor vehicle group, and adjusting members of each non-motor vehicle group by combining the non-motor vehicle position change trend;
and the right-hand track planning module is used for carrying out right-hand track planning on the online motor vehicles based on the adjusted driving information of each member of the non-motor vehicle group.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
one or more processors;
storage means for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the on-line railcar right-hand-turn trajectory planning method as previously described.
In a fourth aspect, an embodiment of the present invention proposes a computer readable storage medium having a computer program stored thereon, wherein the program when executed by a processor implements a network-connected motor train right-hand-turn track planning method as described above.
The beneficial effects of the invention are as follows:
according to the intelligent network right-turning vehicle intelligent network right-turning track planning method, speed, acceleration and coordinate data of the non-motor vehicles in a local range are used as basic information, preliminary group division is conducted according to a distance threshold, preliminary group division is conducted according to the distance threshold, then the non-motor vehicles and the non-motor vehicles are substituted into a microscopic traffic simulation system to conduct IDM simulation, the simulation data are combined with an evolution formula, dynamic evolution prediction is conducted on the non-motor vehicles, therefore possible crossing gaps are judged, and reasonable right-turning track planning is conducted on intelligent network right-turning vehicles according to the possible crossing gaps. The method comprehensively considers the influence of the non-motor vehicle group on the network on-line motor vehicle right-turn track planning, and has a good prediction on the dynamic evolution of the non-motor vehicle group through IDM microscopic traffic simulation.
Drawings
Fig. 1 is a flow chart of a network connection motor car right turn track planning method based on traffic simulation according to an embodiment of the invention.
FIG. 2 is a schematic illustration of a preliminary partitioned and dynamically evolving predicted non-motor vehicle group in an example of an embodiment of the invention.
Fig. 3 is a diagram illustrating a net online railcar right-hand-track planning in accordance with an embodiment of the present invention.
Detailed Description
The invention will now be described in further detail with reference to the accompanying drawings.
It should be noted that the terms like "upper", "lower", "left", "right", "front", "rear", and the like are also used for descriptive purposes only and are not intended to limit the scope of the invention in which the invention may be practiced, but rather the relative relationship of the terms may be altered or modified without materially altering the teachings of the invention.
Example 1
Fig. 1 is a flow chart of a network connection motor car right turn track planning method based on traffic simulation according to the first embodiment of the invention. The embodiment is applicable to the situation that the network-connected motor train is subjected to right-hand-turn track planning through equipment such as a server, the method can be executed by a network-connected motor train right-hand-turn track planning device based on traffic simulation, and the device can be realized in a software and/or hardware mode and can be integrated in electronic equipment, such as integrated server equipment.
Referring to fig. 1, the planning method includes the steps of:
step one, a research range is defined according to position information and running information of the net online motor vehicle which is planned to turn right, all non-motor vehicles which possibly do not interact with a generator of the net online motor vehicle in the research range are obtained, a non-motor vehicle set is generated, and microscopic traffic data of all the non-motor vehicles in the non-motor vehicle set are extracted.
For example, the microscopic simulation data of the motor vehicle to be turned right and the microscopic simulation data of all non-motor vehicles possibly not interacted with the right-turning motor vehicle generator in the traffic flow of the non-motor vehicles can be directly acquired through the microscopic traffic simulation device. Optionally, the microscopic traffic data includes speed, acceleration, and location coordinates.
And according to the position information and the running information of the motor vehicle to be subjected to right turning, the process for acquiring microscopic simulation data of all the non-motor vehicles possibly not interacted with the right turning motor vehicle generator in the traffic flow of the non-motor vehicles comprises the following steps:
determining a judging range of potential interaction in the traffic flow of the non-motor vehicle according to microscopic simulation data of the right-turning motor vehicle; and determining a non-motor vehicle set which is possibly non-interactive with the right-turning motor vehicle generator according to the judging range, and acquiring microscopic simulation data including the running speed and the position coordinates of the non-motor vehicles in the non-motor vehicle set.
For motor vehicle right turn trajectory planning, this step in fact performs a preliminary screening of non-motor vehicles to determine which non-motor vehicles are likely to collide with the motor vehicle at the road right turn. For a non-motor vehicle which obviously reaches a right turn port of a road before or after the motor vehicle turns right, the non-motor vehicle does not belong to an acquisition object.
And step two, calculating a distance threshold by combining the acquired vehicle lengths of all the non-motor vehicles, and dividing the acquired non-motor vehicles into groups according to the distance threshold so that the minimum distance between the groups is larger than the distance threshold.
Specifically, the distance threshold is calculated by combining the acquired lengths of all the non-motor vehicles, and the process of grouping the acquired non-motor vehicles according to the distance threshold comprises the following steps:
s21, determining the average vehicle length of the non-motor vehicles in the research range as a distance threshold.
S22, taking the running direction of the non-motor vehicles as the positive direction of the y axis, sequencing all the non-motor vehicles according to the sequence from the big to the small of the y axis coordinate value of the real-time position of each non-motor vehicle, searching the first non-motor vehicle which is not divided, and taking the vehicle as the tail vehicle of the group I.
S23, calculating the distance between the non-motor vehicle which is not divided into the groups and the tail vehicle of the group I, taking the non-motor vehicle as the tail vehicle which is counted into the group I and is used as a new tail vehicle if the distance is smaller than a distance threshold, and taking the non-motor vehicle as the tail vehicle of the group I+1 if the distance is larger than the distance threshold.
S24, sequentially calculating the distance between the non-motor vehicles of other non-divided groups and the current latest group tail vehicle according to the sequence, if the group division index of the selected non-motor vehicles is smaller than the group division threshold, counting the selected non-motor vehicles into the current latest group I+m-1 and taking the selected non-motor vehicles as new tail vehicles, otherwise, creating a new group I+m for the selected non-motor vehicles, taking the non-motor vehicles as the tail vehicles of the new group, and dividing all the non-motor vehicles in the research range into corresponding groups, wherein m=1, …, M-1 and M are the total number of the groups obtained by division.
Fig. 2 (a) is a schematic diagram of a non-motor vehicle group after preliminary division in an example of an embodiment of the invention. The left side is a motor vehicle lane, the right side is a non-motor vehicle lane, the non-motor vehicle lane is divided into four sub-lanes, 10 non-motor vehicles run on the non-motor vehicle lane, and all the 10 non-motor vehicles are possibly in non-interaction with the current right-turning motor vehicle generator. It should be understood that the non-motor vehicle lanes are divided into four or other number of sub-lanes herein only for the convenience of understanding the technical solution of the present invention, and are not limited to the use of the identification method of the present invention as similar fixed sub-lanes. According to the invention, the fact that the non-motor vehicle lanes are not further divided in the actual road environment is fully considered, and only the position information on the Y axis (namely the running direction of the non-motor vehicle) is introduced in the technical scheme, and the transverse position information is not introduced. Table 1 is microscopic traffic data obtained for all non-vehicles in the collection of non-vehicles.
Table 1 microscopic traffic data for all non-motor vehicles in the non-motor vehicle collection
| Non-motor vehicle numbering | Lane numbering | Vehicle length | Speed of speed | Acceleration of | y-axis coordinates |
| 0 | 2 | 2.0 | 1.25 | 0.37 | 51.06 |
| 1 | 3 | 1.9 | 2.13 | -0.65 | 50.21 |
| 2 | 1 | 2.0 | 1.45 | -0.02 | 49.37 |
| 3 | 0 | 2.1 | 2.29 | 0.14 | 48.45 |
| 4 | 1 | 1.9 | 3.67 | 2.11 | 39.77 |
| 5 | 2 | 2.0 | 3.78 | 0.34 | 38.19 |
| 6 | 0 | 2.1 | 4.29 | -1.34 | 37.24 |
| 7 | 0 | 2.0 | 5.54 | -0.94 | 30.11 |
| 8 | 2 | 2.2 | 3.91 | 0.87 | 21.15 |
| 9 | 3 | 2.1 | 4.02 | 0.69 | 21.93 |
Firstly, calculating average length of vehicle to obtain distance thresholdThen, all the non-motor vehicles in the non-motor vehicle set are grouped according to the distance threshold value. Step one, taking a non-motor vehicle 0 as a tail vehicle of a group 1; since the distance of non-motor vehicle 1 from non-motor vehicle 0 is less than the distance threshold, non-motor vehicle 1 is incorporated into group 1 and serves as a tail car for group 1; similarly, since the distances between the non-motor vehicle 2 and the non-motor vehicle 1 and the distances between the non-motor vehicle 3 and the non-motor vehicle 2 are smaller than the distance threshold value, the non-motor vehicle 2 and the non-motor vehicle 3 are sequentially classifiedGroup 1, the last added non-motor vehicle 3 is the tail car of group 1. Secondly, calculating the distance between the non-motor vehicle 4 and the non-motor vehicle 3, creating a new group 2 because the distance between the non-motor vehicle 4 and the non-motor vehicle 3 is larger than a distance threshold value, taking the non-motor vehicle 4 as a tail vehicle of the group 2, classifying the non-motor vehicle 5 and the non-motor vehicle 6 into the group 2 in sequence according to the group dividing method, and finally taking the added non-motor vehicle 6 as the tail vehicle of the group 2. Third, a group 3 and a group 4 are sequentially created according to the group division method, wherein the group 3 comprises a non-motor vehicle 7, and the group 4 comprises a non-motor vehicle 8 and a non-motor vehicle 9. The obtained preliminary group division results are shown in table 2.
TABLE 2 preliminary group partitioning results
| Group numbering | Group-contained non-motor vehicle numbering |
| 1 | 0,1,2,3 |
| 2 | 4,5,6, |
| 3 | 7 |
| 4 | 8,9 |
Illustratively, the following method may also be employed to obtain the group:
first, a distance between each non-motor vehicle and a previous non-motor vehicle in a non-motor vehicle set is calculated and obtained as a group division index of the non-motor vehicle. Secondly, taking the first vehicle as the head vehicle of the group 1, screening out non-motor vehicles with group division indexes larger than a distance threshold value as the head vehicles of other groups, wherein the number of the group corresponds to the position of the screened head vehicle. Finally, other non-motor vehicles are classified into groups according to positions, and home position ordering is maintained.
The non-motor vehicles 0 to 9 are also described as examples. First, the distance between the non-motor vehicles 0 to 9 and the corresponding preceding non-motor vehicle is calculated as a group division index of the non-motor vehicle. Secondly, non-motor vehicles, namely, a non-motor vehicle 4, a non-motor vehicle 7 and a non-motor vehicle 8, with the non-motor vehicle 0, with the group division index larger than the distance threshold are screened out to generate four groups 1-4, wherein the non-motor vehicle 0 is used as the head vehicle of the group 1, the non-motor vehicle 4 is used as the head vehicle of the group 2, the non-motor vehicle 7 is used as the head vehicle of the group 3, and the non-motor vehicle 8 is used as the head vehicle of the group 4. Finally, other non-motor vehicles are classified into groups according to positions, and the home position ordering is maintained, namely, the non-motor vehicles 1 to 3 are classified into the group 1, the non-motor vehicles 5 to 6 are classified into the group 2, the non-motor vehicles 9 are classified into the group 4, and the non-motor vehicles in each group are all maintained. The group division results of table 2 can also be obtained.
And thirdly, carrying out microscopic traffic simulation on microscopic traffic data of the non-motor vehicle.
Specifically, the process of performing microscopic traffic simulation for microscopic traffic data of a non-motor vehicle includes:
s31, substituting the non-motor vehicle data into a microscopic traffic simulation system, performing IDM traffic simulation on each non-motor vehicle in a non-motor vehicle set, and obtaining the time t required by each non-motor vehicle to reach a potential conflict point ij ，t ij Is the time required for the jth non-motor vehicle in the ith group to reach the potential conflict point, i=1, 2, …, M, j=1, 2, …, N i M is the total number of groups obtained by dividing, N i Is the total number of non-motor vehicles in the i-th group. Table 3 shows the calculated time t required for each non-motor vehicle to reach the potential conflict point ij 。
TABLE 3 time required for non-motor vehicles to reach potential conflict points
| Non-motor vehicle numbering | Time required to reach the conflict point | Non-motor vehicle numbering | Time required to reach the conflict point |
| 0 | 2.34 | 5 | 5.38 |
| 1 | 1.77 | 6 | 11.58 |
| 2 | 3.69 | 7 | 20.79 |
| 3 | 2.65 | 8 | 20.1 |
| 4 | 6.76 | 9 | 21.8 |
S32, calculating the average speed of each non-motor vehicle groupAnd average acceleration->And average y-axis coordinate>
S33, substituting the non-motor vehicle group as a non-motor vehicle to perform IDM traffic simulation, and obtaining the required time t for the non-motor vehicle group to reach the potential conflict point i . Table 4 shows the calculated average speeds for the non-motor vehicle groupAverage acceleration->Average y-axis coordinate>And the time t required for the non-motor vehicle group to reach the potential conflict point i 。
TABLE 4 microscopic traffic simulation results for non-motor vehicle groups
And fourthly, combining the microcosmic traffic simulation result with an evolution formula, carrying out dynamic evolution prediction on each non-motor vehicle group, and adjusting members of each non-motor vehicle group by combining the change trend of the non-motor vehicle position.
In order to improve the right turn safety of the online motor vehicles, more non-motor vehicles are usually screened before the online motor vehicles turn right to enter a non-motor vehicle set, the speeds of the non-motor vehicles are different, the position relationship among the non-motor vehicles can be possibly changed when the non-motor vehicles reach potential conflict points (e.g. right turn road junctions), and if the right turn track planning is still performed according to the original group, deviation can possibly occur. Therefore, it is necessary to dynamically predict the evolution of the non-motor vehicle groups, and adjust the members of each non-motor vehicle group in combination with the variation trend of the non-motor vehicle position, so as to ensure that the online motor vehicle can safely turn right.
Specifically, the process of dynamic evolution prediction for each non-motor vehicle group includes:
s41, judging whether each non-motor vehicle in the group has a tendency to deviate from the current group:
wherein if itA value of 1 indicates a tendency to disengage, and a value of 0 indicates no tendency to disengage.
S42, judging whether the non-motor vehicle with the tendency of being out of the group is out of the group or not:
wherein t is turn Is the time required for the net online motor car to finish right turn, if f 2 (t i ,t ij ) If the value of (2) is 1, determining a detached group, and if the value of (2) is 0, determining that no detached group exists; if a group contains only one non-motor vehicle, then the groupAnd f 2 (t i ,t ij ) The value is 1.
S43, judging whether the separated vehicles are combined with other groups or not:
wherein, if t i ＜t ij Then go through f 3 (t i-1 ,t ij ) Judging if f 3 (t i-1 ,t ij ) If f is 1, it is stated that the corresponding non-motor vehicle can be grouped in the i-1 th group 3 (t i-1 ,t ij ) If the value of (1) is-1, the vehicle cannot be integrated into other groups and needs to be singly grouped; if t i ≥t ij Then go through f 4 (t i-1 ,t ij ) Judging if f 4 (t i-1 ,t ij ) A value of 2 indicates that the vehicle may be incorporated into the (i+1) th group, if f 4 (t i-1 ,t ij ) A value of-2 indicates that the vehicle cannot be incorporated into other groups and needs to be individually grouped.
S44, generating an evolution formula F:
s45, performing F operation on the non-motor vehicles in the collection: if the value of F is 1, predicting that the corresponding non-motor vehicle is integrated into the previous group; if the value is-1, the corresponding non-motor vehicle is illustrated to leave the current group, and a new group before the current group is additionally created; if the value is 2, the non-motor vehicles corresponding to the prediction are merged into the latter group; if the value is-2, the corresponding non-motor vehicles leave the group, and a new group after the current group is additionally created; if the value is 0, the corresponding non-motor vehicle is still kept in the current group. It should be appreciated that the classification and value of each sub-term included in the evolution formula F is not limited to the previous example, as long as the type of F product obtained can be obtainedThe expression requirements of 5 group evolution classification results can be met. For example, f 4 (t i+1 ,t ij ) Can also be selected from 3 and-2, if f 4 (t i-1 ,t ij ) A value of 3 indicates that the vehicle may be incorporated into the (i+1) th group, if f 4 (t i-1 ,t ij ) A value of-2 indicates that the vehicle cannot be incorporated into other groups and needs to be individually grouped.
Let t be turn After calculation, the non-motor vehicle group evolution judgment result is shown in table 5:
TABLE 5 non-motor group evolution decisions
| Non-motor vehicle numbering | Evolution judgment | Non-motor vehicle numbering | Evolution judgment |
| 0 | Is kept unchanged | 5 | Is kept unchanged |
| 1 | Is kept unchanged | 6 | Is kept unchanged |
| 2 | Is kept unchanged | 7 | Merging the latter group |
| 3 | Is kept unchanged | 8 | Is kept unchanged |
| 4 | Is kept unchanged | 9 | Is kept unchanged |
The evolving group results are shown in table 6.
TABLE 6 evolved group results
| Group numbering | Group-contained non-motor vehicle numbering |
| 1 | 0,1,2,3 |
| 2 | 4,5,6 |
| 3 | 7,8,9 |
FIG. 2 (b) is a schematic diagram of a non-motor vehicle group after dynamic evolution prediction in an example of an embodiment of the invention.
And fifthly, carrying out right turning track planning on the online motor vehicles based on the adjusted running information of each member of the non-motor vehicle group.
Specifically, based on the adjusted running information of each member of the non-motor vehicle group, the process of right turning track planning is performed on the online motor vehicles:
s51, calculating traversable gaps between adjacent groups when the net online motor car turns right according to the evolved groups:
gap i ＝t (i+1)-head -t i-last
wherein, gap i Represents the traversable gap, t, between the ith group and the (i+1) th group (i+1)-head Representing the time required for the first non-motor vehicle in the i+1 group to reach the potential conflict point, t i-las Representing the time required for the last non-motor vehicle in the ith group to reach the potential conflict point, i=1, 2, …, M is the total number of groups obtained by division.
S52, comparing the predicted traversable gaps among the groups with the right turn duration of the online motor vehicle, and selecting one traversable gap which is longer than the right turn duration as the right turn gap of the online motor vehicle.
And S53, calculating the time of the network-connected motor train reaching the stop line, and planning the speed according to the time of the network-connected motor train reaching the stop line.
According to the dynamically evolved group, two traversable gaps are available, wherein the first traversable gap is 3.07s long, and the second traversable gap is 9.21s long. Assuming that 6.5s are required for the net online cart to complete the right turn, it can be determined that the net online cart can make the right turn in the 2 nd gap. The net online motor vehicle needs to carry out reasonable speed planning according to the time that the non-motor vehicle reaches a potential conflict point before crossing the gap, and reaches the front of a stop line before the 2 nd gap.
S54, adopting a three-time Bezier curve, and carrying out right-turn track planning according to the non-motor vehicle group corresponding to the right-turn gap: and respectively selecting the midpoint of the parking line, the midpoint of the last non-motor vehicle before the right turn gap and the midpoint of the right turn entering lane line as three control points, and completing the track planning. Fig. 3 is a schematic diagram of a net online motor vehicle right-turn track planning in an example of the embodiment of the invention, wherein an arc-shaped dotted line in the diagram is the planned right-turn track of the net online motor vehicle.
Example two
The embodiment of the invention provides a net online motor car right-turn track planning device, which comprises:
the non-motor vehicle set generating module is used for defining a research range according to the position information and the running information of the net-connected motor vehicle which is planned to turn right, acquiring all non-motor vehicles which possibly do not interact with the net-connected motor vehicle generator in the research range, generating a non-motor vehicle set, and extracting microscopic traffic data of all the non-motor vehicles in the non-motor vehicle set.
The group dividing module is used for calculating the distance threshold value by combining the acquired vehicle lengths of all the non-motor vehicles, and dividing the acquired non-motor vehicles into groups according to the distance threshold value, so that the minimum distance between the groups is larger than the distance threshold value.
And the microscopic traffic simulation module is used for performing microscopic traffic simulation on microscopic traffic data of the non-motor vehicle.
The dynamic evolution prediction module is used for combining the microcosmic traffic simulation result with an evolution formula, carrying out dynamic evolution prediction on each non-motor vehicle group, and adjusting members of each non-motor vehicle group by combining the non-motor vehicle position change trend.
And the right-hand track planning module is used for carrying out right-hand track planning on the online motor vehicles based on the adjusted driving information of each member of the non-motor vehicle group.
According to the right turning track planning device of the second embodiment of the invention, the speed, the acceleration and the coordinate data of the non-motor vehicles in the local range are taken as basic information, the preliminary group division is carried out according to the distance threshold value, then the non-motor vehicle and the non-motor vehicle group data are substituted into the microscopic traffic simulation system to carry out IDM simulation, the simulation data are combined with an evolution formula, the dynamic evolution prediction is carried out on the non-motor vehicle group, so that a possible crossing gap is judged, reasonable right turning track planning is carried out on the net online motor vehicle right turning vehicle according to the possible crossing gap, the group division is carried out on the non-motor vehicle traffic flow, and the aim of safe right turning of the net online motor vehicle is fulfilled. The track planning device provided by the embodiment of the invention can execute the network on-line motor train right-turn track planning method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example III
The embodiment of the application provides electronic equipment, which comprises a processor, a memory, an input device and an output device; in an electronic device, the number of processors may be one or more; the processor, memory, input devices, and output devices in the electronic device may be connected by a bus or other means.
The memory is used as a computer readable storage medium for storing a software program, a computer executable program and modules, such as program instructions/modules corresponding to the detection method in the embodiment of the present invention. The processor executes various functional applications and data processing of the electronic equipment by running software programs, instructions and modules stored in the memory, so that the network on-line motor train right-turn track planning method provided by the embodiment of the invention is realized.
The memory may mainly include a memory program area and a memory data area, wherein the memory program area may store an operating system, at least one application program required for a function; the storage data area may store data created according to the use of the terminal, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, the memory may further include memory remotely located with respect to the processor, the remote memory being connectable to the electronic device through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means may be used to receive entered numeric or character information and to generate key signal inputs related to user settings and function control of the electronic device, which may include a keyboard, mouse, etc. The output means may comprise a display device such as a display screen.
Example IV
Embodiments of the present application provide a computer readable storage medium having a computer program stored thereon, which when executed by a processor implements a net online railcar right hand transition trajectory planning method as described above.
Of course, the storage medium containing the computer executable instructions provided in the embodiments of the present invention is not limited to the method operations described above, but may also perform the related operations in the unified processing method based on the environmental context consistency provided in any embodiment of the present invention.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the invention without departing from the principles thereof are intended to be within the scope of the invention as set forth in the following claims.
Claims (7)
1. The network online motor vehicle right-turn track planning method based on traffic simulation is characterized by comprising the following steps of:
defining a research range according to the position information and the running information of the net online motor vehicle which is planned to turn right, acquiring all non-motor vehicles which possibly do not interact with a generator of the net online motor vehicle in the research range, generating a non-motor vehicle set, and extracting microscopic traffic data of all the non-motor vehicles in the non-motor vehicle set;
calculating a distance threshold by combining the acquired vehicle lengths of all the non-motor vehicles, and dividing the acquired non-motor vehicles into groups according to the distance threshold so that the minimum distance between the groups is larger than the distance threshold;
performing microscopic traffic simulation aiming at microscopic traffic data of the non-motor vehicle;
combining the microcosmic traffic simulation result with an evolution formula, carrying out dynamic evolution prediction on each non-motor vehicle group, and adjusting members of each non-motor vehicle group by combining the non-motor vehicle position change trend;
performing right track turning planning on the online motor vehicle based on the adjusted running information of each member of the non-motor vehicle group;
the process for carrying out microscopic traffic simulation on the microscopic traffic data of the non-motor vehicle comprises the following steps:
substituting the non-motor vehicle data into a microscopic traffic simulation system, performing IDM traffic simulation on each non-motor vehicle in a non-motor vehicle set, and obtaining the time t required by each non-motor vehicle to reach a potential conflict point ij ，t ij Is the time required for the jth non-motor vehicle in the ith group to reach the potential conflict point, i=1, 2, …, M, j=1, 2, …, N i M is the total number of groups obtained by dividing, N i Is the total number of non-motor vehicles in the i-th group;
calculating average speeds for each group of non-motor vehiclesAnd average acceleration->And average y-axis coordinate>
Substituting the non-motor vehicle group as a non-motor vehicle to perform IDM traffic simulation, and obtaining the required time t for the non-motor vehicle group to reach a potential conflict point i ；
The process for carrying out dynamic evolution prediction on each non-motor vehicle group comprises the following steps:
judging whether each non-motor vehicle in the group has a tendency to deviate from the current group:
wherein if itIf the value of (2) is 1, the tendency of detachment is indicated, and if the value is 0, the tendency of detachment is not indicated;
judging whether the non-motor vehicle with the tendency of being out of the group is out of the group or not:
wherein t is turn Is the time required for the net online motor car to finish right turn, if f 2 (t i ,t ij ) If the value of (2) is 1, determining a detached group, and if the value of (2) is 0, determining that no detached group exists; if a group contains only one non-motor vehicle, then the groupAnd f 2 (t i ,t ij ) The value is 1;
judging whether the vehicles after detachment are combined with other groups or not:
wherein, if t i <t ij Then go through f 3 (t i-1 ,t ij ) Judging if f 3 (t i-1 ,t ij ) If f is 1, it is stated that the corresponding non-motor vehicle can be grouped in the i-1 th group 3 (t i-1 ,t ij ) If the value of (1) is-1, the vehicle cannot be integrated into other groups and needs to be singly grouped; if t i ≥t ij Then go through f 4 (t i-1 ,t ij ) Judging if f 4 (t i-1 ,t ij ) A value of 2 indicates that the vehicle may be incorporated into the (i+1) th group, if f 4 (t i-1 ,t ij ) If the value of (2) is-2, the vehicle cannot be integrated into other groups and needs to be singly grouped;
generating an evolution formula F:
performing F operation on the non-motor vehicles in the collection: if the value of F is 1, predicting that the corresponding non-motor vehicle is integrated into the previous group; if the value is-1, the corresponding non-motor vehicle is illustrated to leave the current group, and a new group before the current group is additionally created; if the value is 2, the non-motor vehicles corresponding to the prediction are merged into the latter group; if the value is-2, the corresponding non-motor vehicles leave the group, and a new group after the current group is additionally created; if the value is 0, the corresponding non-motor vehicle is still kept in the current group.
2. The network on-line motor vehicle right turn trajectory planning method based on traffic simulation of claim 1, wherein the microscopic traffic data includes speed, acceleration and position coordinates.
3. The network on-line motor vehicle right turn track planning method based on traffic simulation according to claim 1, wherein the process of calculating the distance threshold value by combining the acquired vehicle lengths of all the non-motor vehicles and grouping the acquired non-motor vehicles according to the distance threshold value comprises the following steps:
determining the average vehicle length of the non-motor vehicles in the research range as a distance threshold;
taking the running direction of the non-motor vehicles as the positive direction of the y axis, sequencing all the non-motor vehicles according to the sequence from big to small of the y axis coordinate value of the real-time position of each non-motor vehicle, searching the first non-motor vehicle which is not divided, and taking the vehicle as the tail vehicle of the group I;
calculating the distance between the non-motor vehicle which is not divided into the group and the group I tail vehicle, if the distance is smaller than the distance threshold value, taking the non-motor vehicle as the tail vehicle which is counted into the group I and is used as a new tail vehicle, and if the distance is larger than the distance threshold value, taking the non-motor vehicle as the tail vehicle of the group I+1;
and sequentially calculating the distance between the non-motor vehicles of other non-divided groups and the current latest group tail vehicle according to the sequence, if the group division index of the selected non-motor vehicles is smaller than the group division threshold, counting the selected non-motor vehicles into the current latest group I+m-1 and taking the selected non-motor vehicles as new tail vehicles, otherwise, creating a new group I+m for the selected non-motor vehicles, taking the selected non-motor vehicles as the tail vehicles of the new groups, and dividing all the non-motor vehicles in the research range into corresponding groups until m=1, …, M-1 and M are the total number of the groups obtained by division.
4. The method for planning the right turn track of the online motor vehicle based on the traffic simulation according to claim 1, wherein the process of planning the right turn track of the online motor vehicle is based on the running information of each member of the adjusted non-motor vehicle group:
calculating traversable gaps when the net online motor car turns right between adjacent groups according to the evolved groups:
gap i ＝t (i+1)-head -t i-last
wherein, gap i Represents the traversable gap, t, between the ith group and the (i+1) th group (i+1)-head Representing the time required for the first non-motor vehicle in the i+1 group to reach the potential conflict point, t i-last Representing the time required for the last non-motor vehicle in the ith group to reach a potential conflict point, wherein i=1, 2, …, M is the total number of the groups obtained by dividing;
comparing the predicted traversable gaps among the groups with the right turn time length of the online motor vehicle, and selecting one traversable gap which is longer than the right turn time length as the right turn gap of the online motor vehicle;
calculating the time of the network connection motor car reaching the parking line, and planning the speed according to the time of the network connection motor car reaching the parking line;
adopting a three-time Bezier curve, and carrying out right-turn track planning according to a non-motor vehicle group corresponding to the right-turn gap: and respectively selecting the midpoint of the parking line, the midpoint of the last non-motor vehicle before the right turn gap and the midpoint of the right turn entering lane line as three control points, and completing the track planning.
5. A network on-line motor vehicle right turn track planning device based on the method of any one of claims 1-4, characterized in that the planning device comprises:
the non-motor vehicle set generating module is used for defining a research range according to the position information and the running information of the net-connected motor vehicle which is planned to turn right, acquiring all non-motor vehicles which possibly do not interact with the net-connected motor vehicle generator in the research range, generating a non-motor vehicle set, and extracting microscopic traffic data of all the non-motor vehicles in the non-motor vehicle set;
the group dividing module is used for calculating a distance threshold value by combining the acquired vehicle lengths of all the non-motor vehicles, and dividing the acquired non-motor vehicles into groups according to the distance threshold value so that the minimum distance between the groups is larger than the distance threshold value;
the micro traffic simulation module is used for carrying out micro traffic simulation on micro traffic data of the non-motor vehicle;
the dynamic evolution prediction module is used for combining the microcosmic traffic simulation result with an evolution formula, carrying out dynamic evolution prediction on each non-motor vehicle group, and adjusting members of each non-motor vehicle group by combining the non-motor vehicle position change trend;
and the right-hand track planning module is used for carrying out right-hand track planning on the online motor vehicles based on the adjusted driving information of each member of the non-motor vehicle group.
6. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the network on-line motor vehicle right turn track planning method of any of claims 1-4.
7. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a network on-line motor vehicle right hand-turn trajectory planning method based on traffic simulation as claimed in any one of claims 1-4.
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