CN112037502A - Smart bus fleet control method, system and computer readable storage medium - Google Patents
Smart bus fleet control method, system and computer readable storage medium Download PDFInfo
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Abstract
The application relates to the field of computer vision, and provides a smart bus fleet control method, a smart bus fleet control system and a computer-readable storage medium. The method comprises the following steps: acquiring route information of the smart bus fleet; determining a driving decision of the smart bus fleet according to the route information of the smart bus fleet, wherein the driving decision comprises at least one of an aggregation behavior strategy, a following behavior strategy, a route changing behavior strategy and a following behavior strategy; and controlling the smart bus fleet according to the determined driving decision. The technical scheme that this application provided, the vehicle in the smart bus motorcade can make different driving decisions according to the route information of difference, is favorable to the better travel of vehicle in driving decision, guarantees security, the cooperativeness of whole driving decision, can solve a great deal of problems that traditional bus, subway and cloud rail/empty rail exist in present city well.
Description
Technical Field
The present application relates to the field of smart driving, and in particular, to a smart bus fleet control method, system, and computer-readable storage medium.
Background
With the rapid development of urbanization and the rapid expansion of urban dimensions, various "urban diseases" have become more serious, and public transportation is a typical example of the urban diseases.
The problems of urban public transport include the problems of small carrying capacity, congestion, slow speed, dense stations, long-distance riding time and the like of the traditional bus. For the above problems of the conventional bus, it seems that the development of the subway and the cloud rail/empty rail can be solved, however, the subway is also problematic in the aspects of large construction difficulty, long period, large investment, small input/output ratio, high operation cost, difficult adjustment of road network structure, poor adaptability to traffic change, great influence on environmental safety and the like, and the cloud rail/empty rail is limited and insufficient in the aspects of traffic volume, wire network coverage density, convenience for passengers to take, urban space limitation, emergency handling safety and the like, and the tramcar, the light rail and the like are limited and insufficient in the aspects of traffic volume, operation speed and efficiency, right of way occupation, power supply and distribution network construction, road network coverage and the like.
Therefore, a solution is needed to solve the above-mentioned problems of urban public transportation.
Disclosure of Invention
The embodiment of the application provides a smart bus fleet control method, a smart bus fleet control system and a computer readable storage medium, which are used for solving various problems of existing urban public transport. The technical scheme is as follows:
in one aspect, a smart bus fleet control method is provided, and the method comprises the following steps:
acquiring route information of the smart bus fleet;
determining a driving decision of the smart bus fleet according to the route information of the smart bus fleet, wherein the driving decision comprises at least one of an aggregation behavior strategy, a following behavior strategy, a route changing behavior strategy and a follow-up behavior strategy;
and controlling the smart bus fleet according to the determined driving decision.
In one aspect, a smart bus fleet control system is provided, the system comprising:
the information acquisition module is used for acquiring the route information of the smart bus fleet;
the strategy determination module is used for determining a driving decision of the smart bus fleet according to the route information of the smart bus fleet, wherein the driving decision comprises at least one of an aggregation behavior strategy, an immediate following behavior strategy, a route changing behavior strategy and a follow-up behavior strategy;
and the motorcade control module is used for controlling the smart bus motorcade according to the determined driving decision.
In one aspect, a smart bus is provided that includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program code being loaded and executed by the one or more processors to implement the operations performed by the smart bus fleet control method.
In one aspect, a computer-readable storage medium storing a computer program is loaded and executed by a processor to implement the operations performed by the smart bus fleet control method.
According to the technical scheme provided by the application, the vehicles in the smart bus fleet can acquire the route information of the smart bus fleet, then the driving decisions of the smart bus fleet are determined according to the route information, and after the driving decisions are determined, the smart bus fleet can be controlled according to the determined driving decisions; on the other hand, the smart bus fleet with certain safety and cooperativity has the advantages of flexible carrying, low cost, economy, high wire network density, good four-way and eight-way accessibility, flexible road right occupation and the like, and well solves a plurality of problems of the traditional buses, subways and cloud rails/empty rails in the city at present.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a smart bus fleet control method provided by an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a smart bus fleet control system provided by an embodiment of the present application;
fig. 3 is a functional structure schematic diagram of an intelligent bus according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, a method for controlling a smart bus fleet according to an embodiment of the present application mainly includes the following steps S101 to S103, which are described in detail as follows:
step S101: and acquiring the route information of the smart bus fleet.
In the embodiments of the present application, the smart bus fleet refers to a fleet made up of smart buses. In the embodiment of the application, the vehicle, that is, the smart bus itself may obtain route information, such as vehicle position information, surrounding environment information, vehicle speed information, and the like, of the smart bus fleet in real time through a sensor, such as a GPS, a radar, a camera, and the like, and the information may be interacted among the vehicles according to the obtained route information of other vehicles.
Step S102: and determining a driving decision of the smart bus fleet according to the route information of the smart bus fleet, wherein the driving decision comprises at least one of an aggregation behavior strategy, a following behavior strategy, a route changing behavior strategy and a follow-up behavior strategy.
Step S103: and controlling the smart bus fleet according to the determined driving decision.
In the embodiment of the application, there may be one command vehicle in the smart bus fleet, and other vehicles in the smart bus fleet may transmit their own driving states to the command vehicle in real time, so as to command the vehicle to control the driving conditions of the whole fleet, for example, to control the starting point and the ending point, and may control each vehicle by sending an instruction. The vehicle in this application can be the command vehicle in the wishbone motorcade, also can be other vehicles in the wishbone motorcade, and this is not limited in this application embodiment.
After the vehicle acquires the route information of the smart bus fleet (including the route information of the vehicle and the route information of other vehicles in the fleet), the vehicle can determine the driving decision of the smart bus fleet according to the route information of the smart bus fleet, wherein the driving decision can include at least one of an aggregation behavior strategy, a following behavior strategy, a route changing behavior strategy and a following behavior strategy. Each operation will be described below.
The clustering behavior is that in the process of traveling, the smart bus fleet naturally clusters in order to ensure the overall driving efficiency and the individual risk avoidance of the vehicle, and the vehicle clustering complies with the following three rules:
1. the interval is regular, namely, the excessive aggregation with adjacent vehicles is avoided;
2. alignment rules, i.e. consistent with the average direction of the adjacent vehicle in front;
3. the gathering rule is to try to move toward the center of the adjacent vehicle.
The act of following up, i.e. when a vehicle finds that the driving environment of other vehicles in the smart bus fleet is better, it will trail them in moving quickly in this direction.
And (3) route changing behaviors, namely, vehicles run at a constant speed along a lane under a general condition, and when a better road environment is found, the vehicles can drive to the better road environment position through acceleration, deceleration and lane change.
The follow-up behavior, which is a behavior in which the vehicle randomly selects a driving state, is a behavior in which the vehicle randomly selects a state in the field of view and then moves in the direction, and is a default behavior of the route change behavior.
As an embodiment of the present application, determining a driving decision of the smart bus fleet according to route information of the smart bus fleet may be: determining a target value of the current position of a vehicle in the wishba fleet according to a preset most function and the information of the route of the wishba fleet, and determining a driving decision of the vehicle in the wishba fleet according to the target value of the current position of the vehicle, wherein the most function is at least one of a driving speed maximization target, a safe driving distance maximization target and a number minimization target of surrounding vehiclesA determined function. In particular, assuming that there are n vehicles in the smart bus fleet, the status of the smart bus fleet may be expressed as X ═ (X)1，x2，…，xn) Wherein x isi(i ═ 1, 2, 3, …, n) is the variable to be optimized, i.e. the status information of the ith vehicle in the wishba fleet, xiRepresenting coordinate position and velocity information of a vehicle in two-dimensional space, x being represented by a vectori＝[xi1，xi2，xi3，xi4]Wherein xi1 represents the longitude of the vehicle, xi2 represents the latitude of the vehicle, xi3 represents the speed of the vehicle, and xi4 represents the direction angle of the vehicle. The target function of the current position of the vehicle is L ═ g (x), where L is the target value.
The driving environment optimization problem belongs to a multi-objective optimization problem, and at least one of the following three objectives can be used as an objective function.
1. Surrounding vehicle travel speed maximization target:
wherein L is1As a function of the average traveling speed of the periphery-sensing vehicles, m being the number of the periphery-sensing vehicles, V (x)i) Representing a position x within a sensing rangeiThe speed value of the vehicle.
2. Safe driving distance maximization target:
max L2＝G2(X)＝||xf-x||
wherein L is2As a function of the safety distance, xfIs the front vehicle position. Of course, in practical applications, the safe driving distance between the vehicle and the side-by-side vehicle or the vehicle behind the vehicle may be comprehensively considered to obtain the objective function of maximizing the safe driving distance, which is not limited in the embodiment of the present application.
3. The surrounding vehicle number minimization target:
max L3＝G3(X)＝M(xi)
wherein L is3M (x) as a function of the number of perimeter-sensing vehiclesi) Indicating that the vehicle is at position xiThe number of vehicles within the sensing range.
Converting the multi-target problem into a single-target problem, and giving expected values of all targets
And
the target desired value is the target function value of each target function under the ideal state, such as the vehicle sharing road resources, keeping the optimal safe driving distance and the highest speed limit with other vehicles, wherein vmaxIndicating a speed limit on the road
Wherein the content of the first and second substances,
3.2 in (1) is a safety distance coefficient given empirically, but can be other values, since v ismaxThe unit is km/h, and the unit is converted into m/s by multiplying by 1/3.6;
the real value of each target obtained from the current position is subtracted from the corresponding expected value, and if the difference is smaller, the difference is closer to the expected value, then the position where the optimum position is sought is actually the position where the difference is sought to be the minimum, so that the total objective function can be defined as:
of course, for a certain position, when calculating the target value of the position, directly through the formula
And (6) calculating. Meanwhile, since the unit of the value obtained under each target may be different,for example, if the number of vehicles and the traveling speed are different in units, the calculation is performed by first performing a de-dimensionalization process. In the embodiment of the present application, the smaller the target value of a position is, the better the condition of the position is.
Meanwhile, since the vehicle cannot overspeed and the distance from the front vehicle must be greater than the safe distance, the constraint conditions can be set:
V(xi)≤vmaxi.e., indicating that the vehicle cannot overspeed,
i.e. indicating that the distance to the leading vehicle is greater than or equal to the safe distance.
As an embodiment of the present application, determining a driving decision of a vehicle in a smart bus fleet according to a target value of a current position of the vehicle may be: detecting whether other vehicles in the smart bus fleet are present within a sensing range of the vehicle; upon detecting the presence of other vehicles in the smart bus fleet within the sensing range, determining a center position of the other vehicles; and if the target value of the center positions of the other vehicles is smaller than the target value of the current position of the vehicle, and the aggregation of the center positions of the other vehicles is smaller than a first preset aggregation threshold, determining the driving decision of the vehicle in the wishbone fleet as an aggregation behavior strategy for the center positions of the other vehicles. When the vehicles sense the vehicles with the smart bus fleet around, the vehicles do not fall behind, and then the vehicles need to be close to the center of the smart bus fleet vehicles as much as possible, so that the vehicles can better move forward together with the vehicles of the smart bus fleet. Therefore, in order to ensure the overall cooperativity of the fleet, the priority value of the clustering behavior strategy can be the highest, and after the route information of the intelligent bus fleet is obtained, whether the vehicle meets the condition of the clustering behavior strategy at present can be judged firstly. The vehicle population needs to follow two rules during travel: one is to try to move toward the center of the vehicles in the adjacent fleet and the other is to avoid excessive clustering. The vehicle senses the number of vehicles of the smart bus fleet in the current neighborhood and calculates the center position of the vehicle and then compares the newly derived target value for the center position with the target value for the current position. If the target value of the central position is smaller than the target value of the current position and is not very aggregated, the current position can be moved to the central position, and the aggregation action is executed, otherwise, other action strategies are executed.
The crowdedness degree is defined as follows:
wherein the content of the first and second substances,
representing the de-weighted aggregate of all vehicle perimeter sensing vehicles of a fleet.
The current position of the vehicle is xiLet n be the number of Benzhiba fleet vehicles in its visible regionfForming a set K:
K＝{xj|xj-xi≤dvisual}i，j＝1，2，3，…，n
dvisualrepresents the sensed distance of the vehicles and is defined as the maximum distance supported for communication between the vehicles.
If K is not an empty set, indicating the presence of a vehicle in the field of view, i.e. nf≧ 1, then the center position x is sensed as followscThe state of (2):
xcthe value of (A) represents the center position xcThe state of (1). Let λ denote the concentration factor, i.e. λ is a first predetermined concentration threshold, if the concentration of the central position is λ<r，(0<λ<1) And the target value of the center position is smaller than the target value of the current position, i.e., Lc<LiIf the driving environment of the central position is better and not too concentrated, the vehicle moves to the central position xcAnd driving, otherwise, executing other actions. The formula is expressed as follows:
if(r<λ，Lc<Li)，then xj＝xi+Rand(st)×xci；
wherein x isjDenotes the position of the vehicle after moving, st denotes the step length of the vehicle, and rand (st) denotes [0, st]Random number in between, xciIs xc-xiThe unit vector of (2).
As an embodiment of the present application, determining the driving decision of the vehicle in the smart bus fleet according to the target value of the current position of the vehicle may further be: if the target value of the center positions of the other vehicles is larger than or equal to the target value of the current position of the vehicle and/or the aggregation degree of the center positions of the other vehicles is larger than or equal to a first preset aggregation degree threshold value, determining the target vehicle with the minimum target value from the other vehicles of the wishbone fleet included in the sensing range according to the most valued function; and if the target value of the position of the target vehicle is smaller than the target value of the current position of the vehicle and the concentration of the position of the target vehicle is smaller than a second preset concentration threshold, determining that the driving decision of the vehicle in the smart bus fleet is the following behavior strategy, and determining the target vehicle as the following object of the vehicle. The vehicle is sensing the vehicle that has this wishba motorcade in the periphery, nevertheless obtain this wishba motorcade vehicle central point's situation when poor than current situation, probably can't carry out the action of gathering, and in order to let the vehicle not fall behind as far as possible, can continue to walk with this wishba motorcade's vehicle, can further detect whether the vehicle satisfies the condition that follows up the action strategy, namely, can set up with the action strategy of following as the second priority strategy that is lower than the action strategy of gathering, if the vehicle is current not to accord with the condition of carrying out the action of gathering, then can continue to judge whether the vehicle satisfies the condition that follows up the action strategy. Let the current state of the vehicle be xiSensing the state x of the Benzhiba fleet vehicle with the optimal state in the neighborhoodmaxIf xmaxIs smaller than the target value of the current position of the vehicle, i.e. Lmax<LiAnd x ismaxOf vehicles in the neighborhood r<λ，(0<λ<1, where λ represents a second preset concentration threshold, which may be set to the same value as the first preset concentration threshold, or may also be set to a different value), indicating xmaxThe vehicle has a better driving environment and is not too crowded, and the vehicle moves towards xmaxOtherwise, other behavior strategies are executed.
The formula is described as follows: (r)<λ，Lmax<Li)
if(r<λ，Lmax<Li)，then xj＝xi+Rand(st)×xmi
Wherein x isjDenotes the position of the vehicle after moving, st denotes the step length of the vehicle, and rand (st) denotes [0, st]Random number in between, xmiIs xmax-xiThe unit vector of (2). When detecting whether the condition of following behavior strategy is satisfied, the following vehicle can only move forward, and the following vehicle can only be the vehicle in front of the following vehicle, so that the target value of the vehicle of the smart bus fleet in a certain range in front of the vehicle can be detected only, and whether the target value is smaller than the target value of the current position of the vehicle can be detected. For example, the detection range may be a range directly in front of the vehicle at 45 degrees left and right, or the like.
As an embodiment of the present application, determining the driving decision of the vehicle in the smart bus fleet according to the target value of the current position of the vehicle may further be: if the target value of the position of the target vehicle is larger than or equal to the target value of the current position of the vehicle and/or the aggregation degree of the position of the target vehicle is larger than or equal to a second preset aggregation degree threshold value, randomly determining a target position in the sensing range; detecting whether the target value of the target position is smaller than the target value of the current position of the vehicle; and if the target value of the target position is smaller than the target value of the current position of the vehicle, determining the driving decision of the vehicle in the smart bus fleet as a route change behavior strategy aiming at the target position. If the vehicle does not detect other vehicles of the intelligent bus fleet in the sensing range, the vehicle is proved to be possible to fall behind, the vehicle can find a position with good condition in the sensing range of the vehicle firstly, the route changing behavior is carried out, meanwhile, the vehicle can periodically detect whether the vehicles of the intelligent bus fleet exist around in the traveling process so as to keep up with the fleet, and if the other vehicles of the intelligent bus fleet exist in the vehicle sensing range, the judgment condition of the aggregation behavior strategy and the judgment condition of the aggregation behavior strategy are not met, the vehicle is proved to be temporarily unable to keep up with the aggregation behavior strategyOther vehicles of the intelligent bus fleet can firstly find the position with good condition in the sensing range to perform route changing behavior, and simultaneously periodically detect whether the judgment condition of the gathering behavior strategy is met or the judgment condition of the following behavior strategy is met in the traveling process, so that the intelligent bus fleet can better keep up with other vehicles of the intelligent bus fleet. The route change behavior policy may be set to a lower third priority policy than the immediately following behavior policy. When the vehicle senses that other vehicles of the smart bus fleet exist in the peripheral sensing range, whether the conditions of the aggregation behavior strategy and the following behavior strategy are met or not is judged firstly, and if the conditions of the aggregation behavior strategy and the following behavior strategy are not met, whether the conditions of the route change behavior strategy are met or not is judged continuously; alternatively, when the vehicle detects that there are no other vehicles of the own fleet within the perimeter sensing range, it may be directly determined whether the condition of the route change behavior policy is satisfied. The route changing behaviors comprise behaviors of accelerating, decelerating, changing lanes, driving to a specific place and the like, and the current state of the vehicle is set to be xiRandomly selecting a state x within its field of viewjIf L isj<LiThen select state xjContinuing to drive; otherwise, the state x is randomly selected againjAnd judging whether the forward condition is met.
The formula is expressed as follows:
if(Lj<Li)，then xj＝xi+Rand(st)×xji
wherein x isjDenotes the position of the vehicle after moving, st denotes the step length of the vehicle, and rand (st) denotes [0, st]Random number in between, xjiIs xj-xiThe unit vector of (2).
As an embodiment of the present application, determining the driving decision of the vehicle in the smart bus fleet according to the target value of the current position of the vehicle may further be: if the target value of the target position is greater than or equal to the target value of the current position of the vehicle, another target position is randomly re-determined in the sensing range, and the step of detecting whether the target value of the target position is less than the target value of the current position of the vehicle is executed again; if the target value of the target position determined at random for N times is not less than the target value of the current position of the vehicleAnd (4) marking values, and determining the driving decision of the vehicle in the smart bus fleet as a follow-up behavior strategy. The follow-up behavior may be a default behavior of the route change behavior, and in the course of determining the route change behavior, if the condition of the route change behavior is not satisfied after N times of heuristics, the follow-up behavior may be executed or the route change behavior may be maintained. Follow-up behavior refers to the vehicle moving freely in the field of view, xiThe vehicle is moved one step at will, and a new state is reached: x is the number ofj＝xi+ rand (st), where the step st is the distance traveled by the vehicle in one communication control period, and st is V (x)i) And x t, where t is the inter-vehicle communication control period.
After determining the driving decision of the vehicle, the vehicle may be controlled. For example, if the vehicle in the smart bus fleet is an unmanned vehicle, that is, a smart bus, the vehicle may be directly controlled to drive according to the behavior corresponding to the determined driving decision, or if the vehicle in the fleet is not an unmanned vehicle, for example, a prompt message may be output through a vehicle dashboard, so as to prompt the driver to drive according to the behavior corresponding to the determined driving decision.
Of course, due to the nature of vehicle travel, when a vehicle in front detects a need for rearward vehicle clustering, the manner in which the vehicle is controlled may be to decelerate.
Through the mode, the vehicle can be well determined to be currently adopted which driving decision to drive, the vehicle can drive in the motorcade more cooperatively and safely, the integrity and consistency of the movement of the vehicle group are ensured, the road space resources are favorably utilized to the maximum, the overall energy consumption of the motorcade is saved, and the traffic risk is reduced.
Optionally, if the vehicle is a command vehicle in the wishbone fleet, the respective status information sent by all vehicles in the wishbone fleet except the command vehicle can be received; then detecting whether the running state of the intelligent bus fleet meets a set convergence condition or not according to the state information, wherein the convergence condition comprises at least one of a speed convergence condition, an intelligent bus fleet overall connectivity convergence condition and a surrounding vehicle interference convergence condition; when the running state of the smart bus fleet meets the convergence condition, sending first notification information to each vehicle in the smart bus fleet to indicate each vehicle to maintain the current running state; and when the running state of the smart bus fleet does not meet the convergence condition, sending second notification information to each vehicle in the smart bus fleet to instruct each vehicle to determine the running strategy again.
As an embodiment of the present application, the convergence condition may be:
wherein D represents the distance between the head and the tail of the vehicle in the smart bus fleet, and L represents the length of the vehicle body in the smart bus fleet. The optimal termination condition is an arbitrary weight combination of the three convergence conditions, i.e., phi ═ eta phi-1∪μφ2∪ωφ3Wherein phi is1Embodying the principle of speed priority, phi2Embodies the principle of the whole smart bus fleet coherent3The method includes the steps that the mutual noninterference principle of all vehicles in the smart bus fleet is embodied, specific values of the weights eta, mu and omega can be determined according to attributes of the smart bus fleet and the side weight surface of service requirements, for example, the weight mu can be set to be higher for services with high requirements on the overall coherence of the smart bus fleet, and the like. The command vehicle may detect whether the driving state of the entire smart bus fleet satisfies the convergence condition according to a set period, for example, once every other communication period. If the convergence condition is met, each vehicle can be informed to maintain the current driving decision state, for example, the current driving speed is kept to be driven at a constant speed; if the convergence condition is not satisfied, each vehicle may be notified to re-determine the driving strategy. In this way, the entire smart bus fleet can be continuously driven in coordination and in unison.
According to a complete embodiment of the method, which behavior the current path information of the vehicle in the smart bus fleet is adapted to can be judged according to the sequence of an aggregation behavior strategy, a following behavior strategy, a route changing behavior strategy and a following behavior strategy from high priority to low priority, and after the driving decision is determined, the vehicle can drive according to the corresponding behavior. In the driving process, whether the current driving state is kept or the driving decision needs to be determined again can be determined according to whether the current driving state of the whole motorcade meets the set convergence condition or not. Meanwhile, in the advancing process, the group target value can be detected circularly according to a certain period, namely, for a command vehicle, the running state of each vehicle in the smart bus fleet can be obtained, and whether the running state of the whole smart bus fleet meets the convergence condition is further determined; for the non-command vehicle, the driving state of the non-command vehicle can be sent to the command vehicle for detection, and the detection result sent by the command vehicle can be received.
According to the technical scheme illustrated in the attached drawing 1, the vehicles in the smart bus fleet can acquire the route information of the smart bus fleet, then determine the driving decisions of the smart bus fleet according to the route information, and after the driving decisions are determined, the smart bus fleet can be controlled according to the determined driving decisions; on the other hand, the smart bus fleet with certain safety and cooperativity has the advantages of flexible carrying, low cost, economy, high wire network density, good four-way and eight-way accessibility, flexible road right occupation and the like, and well solves a plurality of problems of the traditional buses, subways and cloud rails/empty rails in the city at present.
Referring to fig. 2, it is a schematic structural diagram of a smart bus fleet control system provided in an embodiment of the present application, which may be integrated in an unmanned vehicle such as a smart bus, and the system includes an information acquisition module 201, a policy determination module 202, and a fleet control module 203, where:
the information acquisition module 201 is used for acquiring route information of the smart bus fleet;
the strategy determination module 202 is configured to determine a driving decision of the smart bus fleet according to route information of the smart bus fleet, where the driving decision includes at least one of an aggregation behavior strategy, an immediate following behavior strategy, a route changing behavior strategy, and a follow-up behavior strategy;
and the motorcade control module 203 is used for controlling the intelligent bus motorcade according to the determined driving decision.
In one possible implementation, the policy determination module 202 may include a first determination unit and a second determination unit, wherein:
the intelligent bus fleet control system comprises a first determining unit, a second determining unit and a control unit, wherein the first determining unit is used for determining a target value of the current position of a vehicle in the intelligent bus fleet according to a preset most function and route information of the intelligent bus fleet, and the most function is a function determined according to at least one of a driving speed maximization target of surrounding vehicles, a safe driving distance maximization target and a number minimization target of the surrounding vehicles;
and the second determination unit is used for determining the driving decision of the vehicle in the smart bus fleet according to the target value of the current position of the vehicle.
In one possible implementation, the policy determination module 202 may include a detection unit, a third determination unit, and a fourth determination unit, wherein:
the detection unit is used for detecting whether other vehicles in the smart bus fleet exist in the sensing range of the vehicles;
a third determination unit for determining the center position of other vehicles in the smart bus fleet when detecting the presence of other vehicles within the sensing range;
and the fourth determination unit is used for determining the driving decision of the vehicle in the smart bus fleet as an aggregation behavior strategy aiming at the central positions of other vehicles if the target value of the central positions of other vehicles is smaller than the target value of the current position of the vehicle and the aggregation of the central positions of other vehicles is smaller than the first preset aggregation threshold.
In one possible implementation, the policy determination module 202 may include a fifth determination unit, a sixth determination unit, and a seventh determination unit, wherein:
a fifth determining unit, configured to randomly determine a target position within a sensing range of the vehicle when detecting that no other vehicle in the smart bus fleet exists within the sensing range of the vehicle;
a sixth determination unit for detecting whether the target value of the target position is smaller than the target value of the current position of the vehicle;
and the seventh determining unit is used for determining the driving decision of the vehicle in the smart bus fleet as the route change behavior strategy aiming at the target position if the target value of the target position is smaller than the target value of the current position of the vehicle.
In one possible implementation, the policy determination module 202 may include an eighth determination unit and a ninth determination unit, wherein:
an eighth determining unit, configured to determine, according to a most significant function, a target vehicle with a smallest target value among the other vehicles of the wishbone fleet included in the sensing range, if the target value of the center positions of the other vehicles is greater than or equal to the target value of the current position of the vehicle, and/or the aggregation degree of the center positions of the other vehicles is greater than or equal to a first preset aggregation degree threshold;
and the ninth determining unit is used for determining that the driving decision of the vehicle in the smart bus fleet is the following behavior strategy and determining the target vehicle as the following object of the vehicle if the target value of the position of the target vehicle is smaller than the target value of the current position of the vehicle and the concentration of the position of the target vehicle is smaller than a second preset concentration threshold value.
In one possible implementation, the policy determination module 202 may include a tenth determination unit, a detection unit, and an eleventh determination unit, wherein:
a tenth determining unit, configured to randomly determine a target position within the sensing range if the target value of the position where the target vehicle is located is greater than or equal to the target value of the current position of the vehicle, and/or the aggregation level of the position where the target vehicle is located is greater than or equal to a second preset aggregation level threshold;
a detection unit for detecting whether a target value of the target position is smaller than a target value of a current position of the vehicle;
and the eleventh determination unit is used for determining the driving decision of the vehicle in the smart bus fleet as the route change behavior strategy aiming at the target position if the target value of the target position is smaller than the target value of the current position of the vehicle.
In one possible implementation, the policy determination module 202 may include a twelfth determination unit and a thirteenth determination unit, wherein:
a twelfth determining unit for, if the target value of the target position is greater than or equal to the target value of the current position of the vehicle, randomly re-determining another target position within the sensing range, and again performing the step of detecting whether the target value of the target position is less than the target value of the current position of the vehicle;
and the thirteenth determining unit is used for determining that the driving decision of the vehicle in the smart bus fleet is the follow-up behavior decision if the target value of the target position determined at random for N times is not less than the target value of the current position of the vehicle.
It should be noted that, when the smart bus fleet control system provided in the foregoing embodiment is used to control a smart bus fleet, the division of the functional modules is merely used as an example, and in practical applications, the functions may be distributed to different functional modules according to needs, that is, the internal structure of the system may be divided into different functional modules to complete all or part of the functions described above. In addition, the smart bus fleet control system and the smart bus fleet control method provided by the embodiment belong to the same concept, and specific implementation processes and technical effects are detailed in the method embodiment and are not repeated herein.
The embodiment of the present application further provides an intelligent bus, which is shown in fig. 3, and shows a schematic structural diagram of the intelligent bus according to the embodiment of the present application, specifically:
the smart bus may include components such as a processor 301 of one or more processing cores, memory 302 of one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will appreciate that the smart bus architecture shown in FIG. 3 is not intended to be limiting of smart buses and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components. Wherein:
the processor 301 is a control center of the smart bus, connects various parts of the entire smart bus using various interfaces and lines, and performs various functions of the smart bus and processes data by operating or executing software programs and/or modules stored in the memory 302 and calling data stored in the memory 302, thereby performing overall monitoring of the smart bus. Optionally, processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 301.
The memory 302 may be used to store software programs and modules, and the processor 301 executes various functional applications and data processing by operating the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to the use of the smart bus, and the like. Further, the memory 302 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 volatile solid state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.
The smart bus also includes a power supply 303 for supplying power to the various components, and optionally, the power supply 303 may be logically connected to the processor 301 through a power management system, so as to implement functions of managing charging, discharging, and power consumption through the power management system. The power supply 303 may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.
The smart bus may also include an input unit 304, where the input unit 304 may be used to receive entered numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
Although not shown, the smart bus may further include a display unit and the like, which will not be described in detail herein. Specifically, in this embodiment, the processor 301 in the smart bus loads the executable file corresponding to the process of one or more application programs into the memory 302 according to the following instructions, and the processor 301 runs the application programs stored in the memory 302, thereby implementing various functions as follows: acquiring route information of the smart bus fleet; determining a driving decision of the smart bus fleet according to the route information of the smart bus fleet, wherein the driving decision comprises at least one of an aggregation behavior strategy, a following behavior strategy, a route changing behavior strategy and a following behavior strategy; and controlling the smart bus fleet according to the determined driving decision.
For the above embodiments, reference may be made to the foregoing embodiments, and details are not described herein.
According to the method, on one hand, the vehicles in the wishba motorcade can make different driving decisions according to different path information, so that the vehicles can well drive in the driving decisions, and the safety and the cooperativity of the whole driving decisions are ensured; on the other hand, the smart bus fleet with certain safety and cooperativity has the advantages of flexible carrying, low cost, economy, high wire network density, good four-way and eight-way accessibility, flexible road right occupation and the like, and well solves a plurality of problems of the traditional buses, subways and cloud rails/empty rails in the city at present.
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.
To this end, the present application provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any one of the methods for smart bus fleet control provided in the present application. For example, the instructions may perform the steps of: acquiring route information of the smart bus fleet; determining a driving decision of the smart bus fleet according to the route information of the smart bus fleet, wherein the driving decision comprises at least one of an aggregation behavior strategy, a following behavior strategy, a route changing behavior strategy and a following behavior strategy; and controlling the smart bus fleet according to the determined driving decision.
The above detailed implementation of each operation can refer to the foregoing embodiments, and is not described herein again.
Wherein the computer-readable storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
Since the instructions stored in the computer-readable storage medium can execute the steps in any one of the methods for controlling a smart bus fleet provided in the embodiments of the present application, the beneficial effects that can be achieved by any one of the methods for controlling a smart bus fleet provided in the embodiments of the present application can be achieved, which are detailed in the foregoing embodiments and will not be described herein again.
The smart bus fleet control method, device and computer-readable storage medium provided by the embodiments of the present application are described in detail above, and specific examples are applied herein to illustrate the principles and implementations of the present application, and the description of the embodiments above is only used to help understand the method and its core ideas of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A smart bus fleet control method, the method comprising:
acquiring route information of the smart bus fleet;
determining a driving decision of the smart bus fleet according to the route information of the smart bus fleet, wherein the driving decision comprises at least one of an aggregation behavior strategy, a following behavior strategy, a route changing behavior strategy and a follow-up behavior strategy;
and controlling the smart bus fleet according to the determined driving decision.
2. The method of claim 1, wherein determining the driving decision of the smart bus fleet according to the routing information of the smart bus fleet comprises:
determining a target value of the current position of a vehicle in the smart bus fleet according to a preset most function and the information of the route of the smart bus fleet, wherein the most function is a function determined according to at least one of a driving speed maximization target of surrounding vehicles, a safe driving distance maximization target and a number minimization target of the surrounding vehicles;
and determining the driving decision of the vehicle in the smart bus fleet according to the target value of the current position of the vehicle.
3. The smart bus fleet control method according to claim 2, wherein said determining a driving decision of said vehicle in said smart bus fleet based on said target value of said current position of said vehicle comprises:
detecting whether there are other vehicles in the smart bus fleet within a sensing range of the vehicle;
upon detecting the presence of other vehicles in the fleet of smartbars within the sensing range, determining a central location of the other vehicles;
and if the target value of the central position is smaller than the target value of the current position of the vehicle and the aggregation degree of the central position is smaller than a first preset aggregation degree threshold value, determining that the driving decision of the vehicle in the smart bus fleet is an aggregation behavior strategy aiming at the central position.
4. The smart bus fleet control method according to claim 3, wherein said determining a driving decision of said vehicle in said smart bus fleet based on said target value of said current position of said vehicle comprises:
upon detecting the absence of other vehicles in the fleet of smart buses within a sensing range of the vehicle, randomly determining a target location within the sensing range;
detecting whether the target value of the target position is smaller than the target value of the current position of the vehicle;
and if the target value of the target position is smaller than the target value of the current position of the vehicle, determining the driving decision of the vehicle in the smart bus fleet as a route change behavior strategy aiming at the target position.
5. The smart bus fleet control method according to claim 3, wherein said determining a driving decision of said vehicle in said smart bus fleet based on said target value of said current position of said vehicle comprises:
if the target value of the central position is larger than or equal to the target value of the current position of the vehicle and/or the concentration degree of the central position is larger than or equal to the first preset concentration degree threshold value, determining a target vehicle with the minimum target value in other vehicles of the wishbone fleet included in the sensing range according to the most valued function;
and if the target value of the position of the target vehicle is smaller than the target value of the current position of the vehicle and the concentration of the position of the target vehicle is smaller than a second preset concentration threshold value, determining that the driving decision of the vehicle in the wishbone fleet is an immediately following behavior strategy, and determining the target vehicle as an immediately following object of the vehicle.
6. The method of claim 5, wherein determining the driving decision of the vehicle in the wishbone fleet according to the target value of the current position of the vehicle comprises:
if the target value of the position of the target vehicle is larger than or equal to the target value of the current position of the vehicle, and/or the concentration degree of the position of the target vehicle is larger than or equal to the second preset concentration degree threshold value, randomly determining a target position in the sensing range;
detecting whether the target value of the target position is smaller than the target value of the current position of the vehicle;
and if the target value of the target position is smaller than the target value of the current position of the vehicle, determining the driving decision of the vehicle in the smart bus fleet as a route change behavior strategy aiming at the target position.
7. The smart bus fleet control method according to claim 4 or 6, wherein said determining a driving decision of said vehicle in said smart bus fleet based on said target value of said vehicle's current position comprises:
if the target value of the target position is greater than or equal to the target value of the current position of the vehicle, another target position is randomly re-determined within the sensing range, and the step of detecting whether the target value of the target position is less than the target value of the current position of the vehicle is executed again;
and if the target value of the target position determined at random for N times is not less than the target value of the current position of the vehicle, determining the driving decision of the vehicle in the smart bus fleet as a follow-up behavior strategy.
8. A smart bus fleet control system, the system comprising:
the information acquisition module is used for acquiring the route information of the smart bus fleet;
the strategy determination module is used for determining a driving decision of the smart bus fleet according to the route information of the smart bus fleet, wherein the driving decision comprises at least one of an aggregation behavior strategy, an immediate following behavior strategy, a route changing behavior strategy and a follow-up behavior strategy;
and the motorcade control module is used for controlling the smart bus motorcade according to the determined driving decision.
9. An intelligent bus comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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