LU101992B1

LU101992B1 - Intelligent bus fleet control method and system as well as computer readable storage medium

Info

Publication number: LU101992B1
Application number: LU101992A
Authority: LU
Inventors: Yu Xinjia; Cheng Tao
Original assignee: Univ Shenzhen Technology
Priority date: 2020-08-21
Filing date: 2020-08-21
Publication date: 2021-03-08

Abstract

The present application relates to the field of computer vision and provides an intelligent bus fleet control method and system as well as a computer readable storage medium. The method includes: acquiring route information of an intelligent bus fleet; determining a driving decision of the intelligent bus fleet according to the route information of the intelligent bus fleet, wherein the driving decision includes at least one of a gathering behavior strategy, a catch-up behavior strategy, a route change behavior strategy and a follow-up behavior strategy; and controlling the intelligent bus fleet according to the determined driving decision. According to technical solutions provided by the present application, different driving decisions may be made for a vehicle in the intelligent bus fleet according to different route information, thereby being beneficial for the vehicle to better run according to the driving decisions, guaranteeing the safety and cooperativity of the overall driving decision and well solving various problems existing in traditional buses, subways and single-rail straddle type light rails/single-rail suspension type light rails in existing cities.

Description

INTELLIGENT BUS FLEET CONTROL METHOD AND SYSTEM AS LU101962

WELL AS COMPUTER READABLE STORAGE MEDIUM TECHNICAL FIELD

[0001] The present application relates to the field of intelligent driving, and more particularly to an intelligent bus fleet control method and system as well as a computer readable storage medium.

BACKGROUND

[0002] With the rapid development of urbanization and the rapid expansion of urban scale, various “urban diseases” are becoming more and more serious, wherein public traffic is a typical example therein.

[0003] There are problems such as low carrying capacity, congestion, low speed per hour, intensive stations and high time consumption caused by long-distance travel in urban public traffic. It seems that the above-mentioned problems of traditional buses may be solved by developing subways and single-rail straddle type light rails/single-rail suspension type light rails. However, the subways have prominent problems on aspects such as high construction difficulty, long period, great investment, small input/output ratio, high operating cost, simplicity in road network structure adjustment, relatively poor adaptability to traffic change and greater influences to environmental safety. The single-rail straddle type light rails/single-rail suspension type light rails are restricted in terms of carrying capacity, line network coverage density, convenience for passengers, urban space and emergency disposal safety. Streetcars, light rails and the like have limitations and defects on aspects such as carrying capacity, running speed and efficiency, road right occupation, power supply and distribution line network construction and road network coverage.

[0004] Therefore, there is an urgent need for a solution to solve the problems existing in the above-mentioned urban public traffic.

SUMMARY

[0005] Embodiments of the present application provide an intelligent bus fleet control method and system and a computer readable storage medium, so as to solve various problems in the existing urban public traffic. The technical solution is described as follows:

[0006] In an aspect, provided is an intelligent bus fleet control method, including: 1

[0007] acquiring route information of an intelligent bus fleet; LU101992

[0008] determining a driving decision of the intelligent bus fleet according to the route information of the intelligent bus fleet, wherein the driving decision includes at least one of a gathering behavior strategy, a catch-up behavior strategy, a route change behavior strategy and a follow-up behavior strategy; and

[0009] controlling the intelligent bus fleet according to the determined driving decision.

[0010] In an aspect, provided is an intelligent bus fleet control system, including:

[0011] an information acquisition module, which is configured to acquire route information of an intelligent bus fleet;

[0012] a strategy determination module, which is configured to determine a driving decision of the intelligent bus fleet according to the route information of the intelligent bus fleet, wherein the driving decision includes at least one of a gathering behavior strategy, a catch-up behavior strategy, a route change behavior strategy and a follow-up behavior strategy; and

[0013] a fleet control module, which is configured to control the intelligent bus fleet according to the determined driving decision.

[0014] In an aspect, provided is an intelligent bus, including a memory, a processor and a computer program stored in the memory and capable of operating on the processor, wherein the computer program is loaded and executed by one or more of the processor so as to realize operations performed in the intelligent bus fleet control method.

[0015] In an aspect, provided is a computer readable storage medium on which a computer program is stored, and the computer program is loaded and executed by the processor, so as to realize operations performed in the intelligent bus fleet control method.

[0016] It may be known from the above that-mentioned technical solution provided by the present application, the route information of the intelligent bus fleet may be acquired by the vehicle in the intelligent bus fleet, then the driving decision of the intelligent bus fleet is determined according to the route information, and the intelligent bus fleet may be controlled according to the determined driving decision after the driving decision is determined. In such a way, on one hand, different driving decisions may be made for the vehicle in the intelligent bus fleet according to different route information, thereby being beneficial for the vehicle to better run according to the driving decisions and guaranteeing the safety and cooperativity of the overall driving decision. On the other hand, the intelligent bus fleet with certain safety and cooperativity has advantages such as carrying flexibility, low cost, economy and practicability, high line network density, extension in all directions, good reachability and way right occupation 2 flexibility. Therefore, various problems existing in traditional buses, subways and single-rail straddle type light rails/single-rail suspension type light rails in existing cities are solved. HU101992

BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The following will briefly introduce accompanying drawings required for describing the embodiments, in order to describe the technical solutions in the embodiments of the present application more clearly. Obviously, the accompanying drawings in the following description show merely some embodiments of the present application. Those ordinarily skilled in the art may derive other accompanying drawings from these accompanying drawings without paying any creative efforts.

[0018] FIG. 1 is a flow diagram of an intelligent bus fleet control method according to an embodiment of the present application;

[0019] FIG. 2 is a schematic diagram showing a structure of an intelligent bus fleet control system according to an embodiment of the present application; and

[0020] FIG. 3 is a schematic diagram showing a functional structure of an intelligent bus according to an embodiment of the present application.

DESCRIPTION OF THE EMBODIMENTS

[0021] In order to make objectives, technical solutions and advantages of the present application clearer, implementations of the present application will be further described in detail below in conjunction with accompanying drawings.

[0022] Reference is made to FIG. 1, which is an intelligent bus fleet control method according to an embodiment of the present application. The method mainly includes following steps S101 to S103 described in detail as follows.

[0023] In step S101, route information of an intelligent bus fleet is acquired.

[0024] In the embodiment of the present application, the intelligent bus fleet refers to a fleet composed of intelligent buses. In the embodiment of the present application, a vehicle, namely each of the intelligent buses, may acquire the route information, such as vehicle position information, surrounding environment information and vehicle speed information, of the intelligent bus fleet in real time by virtue of a sensor such as a GPS, radar and image shooting and may further acquire route information of other vehicles, and the information may be interchanged among all the vehicles.

3

[0025] In step S102, a driving decision of the intelligent bus fleet is determined according (© 101992 the route information of the intelligent bus fleet, wherein the driving decision includes at least one of a gathering behavior strategy, a catch-up behavior strategy, a route change behavior strategy and a follow-up behavior strategy; and

[0026] In step S103, the intelligent bus fleet is controlled according to the determined driving decision.

[0027] In the embodiment of the present application, there may be a command vehicle in the intelligent bus fleet, other vehicles in the intelligent bus fleet may transmit running states thereof to the command vehicle in real time, and thus, the command vehicle controls the running condition, such as a starting point and an ending point, of the overall fleet, and all the vehicles may be controlled by sending instructions. The vehicle in the present application may be the command vehicle or each of other vehicles in the intelligent bus fleet, the limitations thereof will be omitted in the embodiment of the present application.

[0028] After the vehicle acquires the route information (including route information of the vehicle and route information of other vehicles in the fleet) of the intelligent bus fleet, the driving decision of the intelligent bus fleet may be determined according to the route information of the intelligent bus fleet, wherein the driving decision may include at least one of a gathering behavior strategy, a catch-up behavior strategy, a route change behavior strategy and a follow-up behavior strategy. All behaviors are described below.

[0029] A gathering behavior means that the intelligent bus fleet is naturally gathered in groups in order to guarantee the overall driving efficiency and avoid a risk of an individual vehicle during advancing, and the vehicles are gathered according to the three rules as follows:

[0030] 1. spacing rule: avoiding excessive gather with adjacent vehicles;

[0031] 2. aligning rule: keeping consistent with front adjacent vehicles in an average direction:

[0032] 3. gathering rule: moving towards centers of the adjacent vehicles as much as possible.

[0033] A catch-up behavior means that a vehicle will tail after other vehicles to rapidly move in this direction when finding that driving environments of other vehicles in the intelligent bus fleet are better.

[0034] A route change behavior means that a vehicle runs at a constant speed along a lane under a general condition and may run towards a position of a better road environment by accelerating/decelerating and changing a lane when finding a better road environment.

[0035] A follow-up behavior means a behavior that a vehicle randomly selects a running state, 4 i.e., the vehicle randomly selects a state in the field of view and then moves to the direction, and LU101992 the follow-up behavior is a default behavior of the route change behavior.

[0036] As an embodiment of the present application, that the driving decision of the intelligent bus fleet is determined according to the route information of the intelligent bus fleet may be that a target value of a current position of a vehicle in the intelligent bus fleet is determined according to a preset maximum/minimum function and the route information of the intelligent bus fleet, and a driving decision of the vehicle in the intelligent bus fleet is determined according to the target value of the current position of the vehicle, wherein the maximum/minimum function is a function determined according to at least one of a surrounding vehicle running speed maximization target, a safe running distance maximization target and a surrounding vehicle number minimization target. Specifically, it is supposed that there are n vehicles in the intelligent bus fleet, the state of the intelligent bus fleet may be expressed as X = (fn, %2, ees An ), wherein Yi i=1, 2, 3, ...,n) is a to-be-optimized variable, namely state information of the i vehicle in the intelligent bus fleet, i represents coordinate position and speed information of a vehicle in a two-dimensional space and is expressed with a vector mi = [xil, xi2, xi3, xi4], wherein xil represents a longitude of the vehicle during running, xi2 represents a latitude of the vehicle during running, xi3 represents a speed of the vehicle during running, and xi4 represents a direction angle of the vehicle during running. An objective function of the current position of the vehicle is L=G(X) , wherein L is the target value.

[0037] A driving environment optimization problem is a multi-target optimization problem. At least one of the following three targets may be used as the objective function. An example in which the total objective function is obtained by combining the following three targets is shown in the present application.

[0038] 1. Surrounding vehicle running speed maximization target: 1 m max L =G,(X)=—> V(x;)

[0039] moi

[0040] wherein L represents a function of an average running speed of vehicles sensed around, m represents the number of the vehicles sensed around, and Vix) represents a speed value of a vehicle on a position % within a sensing range.

[0041] 2. Safe driving distance maximization target: max L = G,(X)=|x, —x

[0042] 2 = GX) =x, = «|

[0043] wherein L, is a function of a safe distance, and "7 is a position of a front vehicle.

Of course, during actual application, an objective function of safe driving distance maximization may be obtained by overall consideration on a safe driving distance from theo 100% vehicle to a juxtaposed vehicle or a rear vehicle, the limitations thereof will be omitted in the embodiment of the present application.

[0044] 3. Surrounding vehicle number minimization target:

[0045] max L, = G,(X)=M(x,)

[0046] wherein Ls is a function of the number of the vehicles sensed around, and M(x) represents the number of vehicles within the sensing range of the vehicle on the position i

[0047] The multi-target problem is converted into a single-target problem, and desired values of all the targets are given as $ 1 ‚5 > and $ ; .

[0048] The desired values of the targets are objective function values of all the objective functions in an ideal state. For example, a vehicle exclusively uses a road resource and runs in ways of keeping the optimal safe driving distance from other vehicles and keeping the highest speed limit, wherein Vina represents a road speed limit, then, 8 1 = Vim , 8 > =

3.2x "max x1/3.6, and ® 3 = 0, wherein 3.2 in & : is a safe distance coefficient given by experience or other values. Since the unit of Vmax js km/h, the unit is converted into m/s by multiplying by 1/3.6.

[0049] A difference of a true value of each target obtained on the current position and the corresponding desired value is calculated, the smaller a difference value is, the closer the true value is to the desired value, then seeking an optimal position is actually seeking a position with the minimum difference value, and therefore, the total objective function may be defined as: L=minY’[G,(0)-g]

[0050] i=l .

[0051] Certainly, with respect to a certain position, a target value of the position may be 3 Gx) -g!] directly calculated according to a formula =! . Meanwhile, it is possible that units of values of all the targets are different. For example, units of the number and running speed of the vehicles are different, then, de-dimensionalization may be performed firstly during calculation. In the embodiment of the present application, the smaller of a target value of a position is, the better the condition of the position is.

[0052] Meanwhile, the vehicle may not run over speed and has to keep a distance, greater than the safe distance, from the front vehicle, and thus, a constraint condition may be set as: 6

[0053] Vix) <"ma means that the vehicle may not run over speed, and G(X) 8 > mean$ 101992 that the vehicle keeps a distance, greater than or equal to the safe distance, from the front vehicle.

[0054] As an embodiment of the present application, that the driving decision of the vehicle in the intelligent bus fleet is determined according to the target value of the current position of the vehicle may be that whether there are other vehicles in the intelligent bus fleet within a sensing range of the vehicle is detected; central positions of the other vehicles are determined when it is detected that there are the other vehicles in the intelligent bus fleet within the sensing range; and the driving decision of the vehicle in the intelligent bus fleet is determined as the gathering behavior strategy for the central positions of other vehicles if target values of the central positions of other vehicles are smaller than the target value of the current position of the vehicle, and the gathering degrees of the central positions of other vehicles are smaller than a first preset gathering degree threshold. When the vehicle senses that there are vehicles in the intelligent bus fleet around, it is proven that the vehicle does not fall behind, then, the vehicle needs to get closer to centers of the vehicles in the intelligent bus fleet as much as possible, and furthermore, the vehicle may better move forwards together with the vehicles in the fleet. Therefore, for guaranteeing the overall cooperativity of the fleet, a priority value of the gathering behavior strategy may be maximized; and after the route information of the intelligent bus fleet is acquired, it is possible to determine whether the vehicle has met conditions of the gathering behavior strategy at present. A vehicle group has to follow two rules during advancing: firstly, the vehicle group moves towards the centers of the adjacent vehicles in the fleet as much as possible; and secondly, the vehicle group is prevented from being excessively gathered. The vehicle senses the number of the vehicles in the intelligent bus fleet within a current adjacent region and calculates central positions of the vehicles, and then, the newly acquired target values of the central positions are compared with the target value of the current position. If the target values of the central positions are smaller than the target value of the current position and the vehicles are not excessively gathered, the vehicle may move from the current position to the central positions to perform the gathering behavior, or else, other behavior strategies are performed.

[0055] A congestion degree is defined as: r= Mix) (0<r<1) NM (x)

[0056] i NM (x)

[0057] wherein ‘ represents a set of de-weighed vehicles sensed around all the vehicles in the fleet.

[0058] The current position of the vehicle is set as % | the number of vehicles in the 7 intelligent bus fleet within a visible region of the vehicle is set as "ys , and thus, formed is a sat; 41p1992 K:

[0059] K={*|*#-*<“im yi j= 1, 2, 3, n

[0060] sua represents a sensing distance of the vehicle and is defined as the maximum distance supported by communication among the vehicles.

[0061] If K is not a null set, it is proven that there are vehicles within a field of view, ie. "ys >1, then, states of the central positions Ye are sensed according to the following formula:

[0062] i My

[0063] a value of *e represents the states of the central positions “. À is used to represent a gathering degree factor, i.e., À is a first preset gathering degree threshold, if the gathering degrees on the central positions are A<r, (0<A<1) , and the target values of the central positions are smaller than the target value of the current position, i.e., L<h , it is proven that driving environments on the central positions are better and the vehicles are not excessively gathered, then, the vehicle runs towards the central positions Yo or else, other behaviors are performed. A formula is expressed as follows:

[0064] if (7 <a L <L) “then X; =x + Rand (st) x x,; .

[0065] wherein “ represents a position of the moved vehicle, st represents the moving step length of the vehicle, Rand (st) represents a random number within the range [O, st], and ei is a unit vector of Ye Yi,

[0066] As an embodiment of the present application, that the driving decision of the vehicle in the intelligent bus fleet is determined according to the target value of the current position of the vehicle may be that a target vehicle with the minimum target value is determined in other vehicles in the intelligent bus fleet included within the sensing range according to the maximum/minimum function if the target values of the central positions of other vehicles are greater than or equal to the target value of the current position of the vehicle, and/or the gathering degrees of the central positions of other vehicles are greater than or equal to the first preset gathering degree threshold; and the driving decision of the vehicle in the intelligent bus fleet is determined as the catch-up behavior strategy, and the target vehicle is determined as a catch-up object of the vehicle if a target value of a position of the target vehicle is smaller than the target value of the current position of the vehicle, and the gathering degree of the position of the target vehicle is smaller than a second preset gathering degree threshold. When the vehicle senses that there are vehicles in the intelligent bus fleet around, but the states of the central positions of the vehicles in the intelligent bus fleet are worse than the current state, it is possible 8 that the gathering behavior may not be performed, However, in order to ensure that the vehicle LU101992 does not fall behind as much as possible and may further follow the vehicles in the intelligent bus fleet, whether the vehicle meets conditions of the catch-up behavior strategy may be further detected, i.e., the catch-up behavior strategy may be set as a second priority strategy lower than the gathering behavior strategy. If the vehicle has not met the conditions of performing the gathering behavior strategy at present, it is possible to determine whether the vehicle meets the conditions of the catch-up behavior strategy. The current state of the vehicle is set as % | the state "" of a vehicle with the optimal state in the intelligent bus fleet within the adjacent region of the vehicle is sensed, if the target value of the ‘mx is smaller than the target value of the current position of the vehicle, i.e., Fax < Li , and the gathering degrees of the vehicles in the adjacent region of “mx js <A (O<A<1, wherein 4 represents the second preset gathering degree threshold and may be set as a value the same as or different from the first preset gathering degree threshold), it is proven that there is a better and uncrowded driving environment near Fina then the vehicle runs towards the position of Hoax or else, other behavior strategies are performed.

[0067] A formula is described as follows: (© <4 Lina <L )

[0068] ir(7 <A, Lum SF) | then“ 7% + Rand(st)x oi

[0069] wherein “/ represents a position of the moved vehicle, st represents the moving step length of the vehicle, Rand(st) represents a random number within the range [0, st], and Xi is a unit vector of “mx 7%, When whether the conditions of the catch-up behavior strategy are met is detected, due to the characteristic that the vehicle may only advance, a following vehicle may follow the front vehicle, and thus, it is possible to only detect whether the target values of the vehicles in the intelligent bus fleet within a certain range in the front of the vehicle are smaller than the target value of the current position of the vehicle. For example, a detection range may be a range in the right front of 45 DEG at the left and right of the vehicle and the like.

[0070] As an embodiment of the present application, that the driving decision of the vehicle in the intelligent bus fleet is determined according to the target value of the current position of the vehicle may be that a target position is determined within the sensing range if the target value of the position of the target vehicle is greater than or equal to the target value of the current position of the vehicle, and/or the gathering degree of the position of the target vehicle is 9 greater than or equal to the second preset gathering degree threshold; whether a target value of the target position is smaller than the target value of the current position of the vehicle jo 101992 detected; and the driving decision of the vehicle in the intelligent bus fleet is determined as the route change behavior strategy for the target position if the target value of the target position is smaller than the target value of the current position of the vehicle. If the vehicle does not sense other vehicles in the intelligent bus fleet within the sensing range, it is possible that the vehicle has fallen behind, then, the vehicle may be enabled to find a position with good conditions within the sensing range itself to perform the route change behavior. Meanwhile, the vehicle may periodically detect whether there are vehicles in the intelligent bus fleet around during advancing so as to be capable of catching up with the fleet. If there are other vehicles in the fleet within the sensing range of the vehicle, but neither determination conditions of the gathering behavior strategy nor determination conditions of the catch-up behavior strategy are met, it is proven that the vehicle is incapable of catching up with other vehicles in the fleet for the moment, then the position with good conditions may be found within the sensing range to perform the route change behavior. Meanwhile, whether the determination conditions of the gathering behavior strategy and the determination conditions of the catch-up behavior strategy are met is periodically detected during advancing, so that the vehicle may better catch up with other vehicles in the intelligent bus fleet. The route change behavior strategy may be set as a third priority strategy lower than the catch-up behavior strategy. When the vehicle senses that there are other vehicles in the intelligent bus fleet within a surrounding sensing range, it is possible to firstly determine whether the conditions of the gathering behavior strategy and the conditions of the catch-up behavior strategy are met. If the conditions of both the gathering behavior strategy and the catch-up behavior strategy are not met, it is possible to continuously determine whether the conditions of route change behavior strategy are met. Alternatively, when the vehicle detects that there are no other vehicles in the fleet within the surrounding sensing range, it is possible to directly determine whether the conditions of the route change behavior strategy are met. The route change behavior includes a behavior of running towards a specific place in a way of acceleration, deceleration, lane change and the like, the current state of the vehicle is set as ”* , à state at randomly selected within the field of view, if L,<h , the state ‘ is selected to make the vehicle further run; or else, the state “ is randomly reselected, and it is determined whether an advancing condition is met.

[0071] A formula is described as follows:

[0072] if (Li <Ly then 7% + Rand(st)x x;

[0073] wherein represents a position of the moved vehicle, st represents the moving stepy1p1992 length of the vehicle, Rand(st) represents a random number within the range [O, st], and ii is a unit vector of Yi.

[0074] As an embodiment of the present application, that the driving decision of the vehicle in the intelligent bus fleet is determined according to the target value of the current position of the vehicle may be that another target position is randomly re-determined within the sensing range, and that whether the target value of the target position is smaller than the target value of the current position of the vehicle is detected is re-performed if the target value of the target position is greater than or equal to the target value of the current position of the vehicle; and the driving decision of the vehicle in the intelligent bus fleet is determined as the follow-up behavior strategy if the target value of the target position randomly determined for continuous N times is not smaller than the target value of the current position of the vehicle. The follow-up behavior may be a default behavior of the route change behavior, and if the conditions of the route change behavior are still not met after N-time trial in a process of determining the route change behavior, the follow-up behavior may be performed, or the route change behavior may be kept. The follow-up behavior means that the vehicle randomly moves in the field of view, and the vehicle in * randomly moves for one step to reach a new state: x, = x + Rand (st) , wherein the step length st is a distance that the vehicle runs within a control period of one-time communication, st=V (i ) xt, and / in the formula is a control period for communication among the vehicles.

[0075] The vehicle may be controlled after the driving decision of the vehicle is determined. For example, the vehicle in the intelligent bus fleet may be an unmanned vehicle, namely each of the intelligent buses, then, the vehicle may be directly controlled to run according to a behavior corresponding to the determined driving decision. Alternatively, the vehicle in the fleet is not the unmanned vehicle, then for example, a vehicle instrument panel may output prompt information to further prompt a driver to drive according to the behavior corresponding to the determined driving decision.

[0076] Of course, due to running characteristics of vehicles, when a vehicle located in front detects that it needs to be gathered towards a rear vehicle, the vehicle may be controlled in a way of deceleration.

[0077] In the above-mentioned way, it is possible to better determine that which driving decision the vehicle has adopted at present may ensure that the vehicle runs more cooperatively and safely in the fleet, the moving integrity and consistency of the vehicle group may be 11 guaranteed, the space resources of roads may be utilized to the maximum extent, and the overall energy consumption of the intelligent bus fleet and traffic risks may be reduced. HU101992

[0078] Optionally, if the vehicle is the command vehicle in the intelligent bus fleet, respective state information transmitted by all the vehicles except the command vehicle in the intelligent bus fleet may be further received. Then, whether the running state of the intelligent bus fleet meets a set convergence condition is detected, wherein the convergence condition includes at least one of a speed convergence condition, a convergence condition for overall connectivity of the intelligent bus fleet and a surrounding vehicle interference convergence condition. When the running state of the intelligent bus fleet meets the convergence condition, first notification information is transmitted to each vehicle in the intelligent bus fleet so as to indicate each vehicle to keep the current running state. When the running state of the intelligent bus fleet does not meet the convergence condition, second notification information is transmitted to each vehicle in the intelligent bus fleet so as to indicate each vehicle to re-determine a driving strategy.

[0079] As an embodiment of the present application, the above-mentioned convergence condition may be:

[0080] 4 =I" Val,

[0081] 6, =D-32xvx(n-1.2)/3.6-(n-1.2)xL.

[0082] 9; “MC.

[0083] wherein D represents a distance from a vehicle at the head to a vehicle at the tail in the intelligent bus fleet, and L represents a length of a vehicle in the intelligent bus fleet. An optimal termination condition is an arbitrary weight combination of the three convergence conditions, namely ¢ =nd Une, Vo, wherein À embodies a principle of speed first, 0 embodies a continuity principle of the overall intelligent bus fleet, % embodies a noninterference principle of all the vehicles in the intelligent bus fleet, specific values of weights N, F and ® may be determined according to the attribute and business demand of the intelligent bus fleet. For example, for a business with relatively high requirements for the overall continuity of the intelligent bus fleet, the weight H may be set to be higher, etc. The command vehicle may detect whether the running state of the overall intelligent bus fleet meets the convergence condition according to a set period, for example, the detection is performed once every other one communication period. If the convergence condition is met, each vehicle may be notified to keep the current driving decision state, for example, each vehicle keeps the current running speed to run at the constant speed. If the convergence condition is not met, each vehicle may be 12 notified to re-determine the driving strategy. In this way, the overall intelligent bus fleet may be continuously kept to run cooperatively and consistently. HU101992

[0084] For a complete embodiment of the present application, which behavior the current route information of the vehicle in the intelligent bus fleet adapts to may be determined in an order of the gathering behavior strategy, the catch-up behavior strategy, the route change behavior strategy and the follow-up behavior strategy of which the priorities are ordered from high to low, and the vehicle may run according to the corresponding behavior after the driving decision is determined. During running, whether to keep the current running state or re-determine the driving decision may be determined according to a determination whether the current running state of the overall fleet meets the set convergence condition. Meanwhile, during advancing, the target value of the group may be circularly detected according to a certain period, i.e., for the command vehicle, the running state of each vehicle in the intelligent bus fleet may be acquired, and furthermore, whether the running state of the overall intelligent bus fleet meets the convergence condition is determined. For a non-command vehicle, the running state of the non-command vehicle may be transmitted to the command vehicle so as to be detected, and a detection result transmitted by the command vehicle is received.

[0085] It may be known from the above that-mentioned technical solution exampled in FIG. 1, the route information of the intelligent bus fleet may be acquired by the vehicle in the intelligent bus fleet, then, the driving decision of the intelligent bus fleet is determined according to the route information, and the intelligent bus fleet may be controlled according to the determined driving decision after the driving decision is determined. In such a way, on one hand, different driving decisions may be made for the vehicle in the intelligent bus fleet according to different route information, thereby being beneficial for the vehicle to better run according to the driving decisions and guaranteeing the safety and cooperativity of the overall driving decision. On the other hand, the intelligent bus fleet with certain safety and cooperativity has advantages such as carrying flexibility, low cost, economy and practicability, high line network density, extension in all directions, good reachability and way right occupation flexibility. Therefore, various problems existing in traditional buses, subways and single-rail straddle type light rails/single-rail suspension type light rails in existing cities are solved well.

[0086] Reference is made to FIG. 2, which is a schematic diagram showing a structure of an intelligent bus fleet control system according to an embodiment of the present application, The system may be integrated in an unmanned vehicle such as an intelligent bus and includes an information acquisition module 201, a strategy determination module 202 and a fleet control module 203, wherein 13

[0087] the information acquisition module 201 is configured to acquire route information of 101992 an intelligent bus fleet;

[0088] the strategy determination module 202 is configured to determine a driving decision of the intelligent bus fleet according to the route information of the intelligent bus fleet, wherein the driving decision includes at least one of a gathering behavior strategy, a catch-up behavior strategy, a route change behavior strategy and a follow-up behavior strategy; and

[0089] the fleet control module 203 is configured to control the intelligent bus fleet according to the determined driving decision.

[0090] In one possible implementation, the strategy determination module 202 may include a first determination unit and a second determination unit, wherein

[0091] the first determination unit is configured to determine a target value of a current position of a vehicle in the intelligent bus fleet according to a preset maximum/minimum function and the route information of the intelligent bus fleet, wherein the maximum/minimum function is a function determined according to at least one of a surrounding vehicle running speed maximization target, a safe driving distance maximization target and a surrounding vehicle number minimization target; and

[0092] the second determination unit is configured to determine a driving decision of the vehicle in the intelligent bus fleet according to the target value of the current position of the vehicle.

[0093] In one possible implementation, the strategy determination module 202 may include a detection unit, a third determination unit and a fourth determination unit, wherein

[0094] the detection unit is configured to detect whether there are other vehicles in the intelligent bus fleet within a sensing range of the vehicle;

[0095] the third determination unit is configured to determine central positions of other vehicles when detecting that there are other vehicles in the intelligent bus fleet within the sensing range; and

[0096] the fourth determination unit is configured to determine the driving decision of the vehicle in the intelligent bus fleet as the gathering behavior strategy for the central positions if target values of the central positions of other vehicles are smaller than the target value of the current position of the vehicle, and the gathering degrees of the central positions of other vehicles are smaller than a first preset gathering degree threshold.

[0097] In one possible implementation, the strategy determination module 202 may include a fifth determination unit, a sixth determination unit and a seventh determination unit, wherein 14

[0098] the fifth determination unit is configured to randomly determine a target position 91992 within the sensing range when detecting there are no other vehicles in the intelligent bus fleet within the sensing range of the vehicle;

[0099] the sixth determination unit is configured to detect whether a target value of the target position is smaller than the target value of the current position of the vehicle; and

[00100] the seventh determination unit is configured to determine the driving decision of the vehicle in the intelligent bus fleet as the route change behavior strategy for the target position if the target value of the target position is smaller than the target value of the current position of the vehicle.

[00101] In one possible implementation, the strategy determination module 202 may include an eighth determination unit and a ninth determination unit, wherein

[00102] the eighth determination unit is configured to determine a target vehicle with the minimum target value in other vehicles in the intelligent bus fleet included within the sensing range according to the maximum/minimum function if the target values of the central positions of other vehicles are greater than or equal to the target value of the current position of the vehicle, and/or the gathering degrees of the central positions of other vehicles are greater than or equal to the first preset gathering degree threshold; and

[00103] the ninth determination unit is configured to determine the driving decision of the vehicle in the intelligent bus fleet as the catch-up behavior strategy, and determine the target vehicle as a catch-up object of the vehicle if a target value of a position of the target vehicle is smaller than the target value of the current position of the vehicle, and the gathering degree of the position of the target vehicle is smaller than a second preset gathering degree threshold.

[00104] In one possible implementation, the strategy determination module 202 may include a tenth determination unit, a detection unit and an eleventh determination unit, wherein

[00105] the tenth determination unit is configured to randomly determine a target position within the sensing range 1f the target value of the position of the target vehicle is greater than or equal to the target value of the current position of the vehicle, and/or the gathering degree of the position of the target vehicle is greater than or equal to the second preset gathering degree threshold;

[00106] the detection unit is configured to detect whether a target value of the target position is smaller than the target value of the current position of the vehicle; and

[00107] the eleventh determination unit is configured to determine the driving decision of the vehicle in the intelligent bus fleet as the route change behavior strategy for the target position if the target value of the target position is smaller than the target value of the current position of the vehicle. HU101992

[00108] In one possible implementation, the strategy determination module 202 may include a twelfth determination unit and a thirteenth determination unit, wherein

[00109] the twelfth determination unit is configured to randomly re-determine another target position within the sensing range, and re-perform the step of detecting whether the target value of the target position is smaller than the target value of the current position of the vehicle is detected if the target value of the target position is greater than or equal to the target value of the current position of the vehicle; and

[00110] the thirteenth determination unit is configured to determine the driving decision of the vehicle in the intelligent bus fleet as the follow-up behavior strategy if the target value of the target position randomly determined for continuous N times is not smaller than the target value of the current position of the vehicle.

[00111] It should be noted that the intelligent bus fleet control system provided by the above-mentioned embodiment is merely illustrated by the division of each of the above-mentioned functional modules when controlling the intelligent bus fleet. During actual application, it is possible to achieve the above-mentioned functional distribution by the different functional modules as required, i.e., an internal structure of the system is divided into different functional modules so as to achieve all or a part of the functions described as above. In addition, the intelligent bus fleet control system provided by the above-mentioned embodiment has the same concept with the embodiment of the intelligent bus fleet control method, and the specific implementation process and technical effect of the intelligent bus fleet control system may specifically refer to the embodiment of the method, the descriptions thereof will be omitted herein.

[00112] An embodiment of the present application further provides an intelligent bus as shown in FIG. 3 which is a schematic diagram showing a structure of the intelligent bus related to the embodiment of the present application. Specifically,

[00113] the intelligent bus may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more computer readable storage media, a power supply 303 and an input unit 304. It should be understood for those skilled in the art that an intelligent bus structure shown in FIG. 3 does not limit the intelligent bus, the intelligent bus may include more or less components than those in the figure, or be combined with some components, or be provided with different components.

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[00114] The processor 301 is a control center of the intelligent bus, is connected with all 101992 parts of the overall intelligent bus by virtue of various interfaces and circuits and is used for performing various functions of the intelligent bus and processing data by operating or executing software programs and/or modules stored in the memory 302 and calling data stored in the memory 302 so as to monitor the intelligent bus as a whole. Optionally, the processor 301 may include the one or more processing cores. Preferably, the processor 301 may be integrated with an application processor and a modulation and demodulation processor, wherein the application processor is mainly used for processing an operating system, a user interface, an application program and the like, and the modulation and demodulation processor is mainly used for processing wireless communication. It should be understood that the above-mentioned modulation and demodulation processor is not integrated into the processor 301.

[00115] The memory 302 may be configured to store the 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 region and a data storage region, wherein the program storage region may be used for storing an operating system, an application program required by at least one function (such as a sound playing function and an image playing function) and the like; and the data storage region may be used for storing data and the like created according to the use of the intelligent bus. In addition, the memory 302 may include a high-speed random access memory and may further include a nonvolatile memory such as at least one magnetic disk storage device, a flash memory device or other volatile solid-state memory devices. Accordingly, the memory 302 may further include a memory controller so as to provide access of the processor 301 to the memory 302.

[00116] The intelligent bus further includes the power supply 303 for supplying power to each component. Optionally, the power supply 303 may be logically connected with the processor 301 by a power management system, so that functions such as charging management, discharging management and power consumption management are achieved by the power management system. The power supply 303 may further include any component of one or more direct-current or alternating-current power supply, a recharging system, a power failure detection circuit, a power adapter or inverter, a power state indicator and the like.

[00117] The intelligent bus may further include the input unit 304 which may be configured to receive input figure or character information and generate keyboard, mouse, operating rod and optical or trackball signal input related to user setting and functional control.

[00118] Although it is not shown, the intelligent bus may further include a display unit and 17 the like, the descriptions thereof will be omitted herein. Specifically, in the embodiment, the processor 301 of the intelligent bus may load executable files corresponding to progresses of 019% one or more application programs to the memory 302 according to following instructions, and the application programs stored in the memory 302 are operated by the processor 301, so that various functions are achieved. The instructions are described as follows: route information of an intelligent bus fleet is acquired; a driving decision of the intelligent bus fleet is determined according to the route information of the intelligent bus fleet, wherein the driving decision includes at least one of a gathering behavior strategy, a catch-up behavior strategy, a route change behavior strategy and a follow-up behavior strategy; and the intelligent bus fleet is controlled according to the determined driving decision.

[00119] The specific embodiment of each of the above-mentioned operations may refer to the foregoing embodiment, the descriptions thereof will be omitted herein.

[00120] It may be known from the above that, the route information of the intelligent bus fleet may be acquired by the vehicle in the intelligent bus fleet, then, the driving decision of the intelligent bus fleet is determined according to the route information, and the intelligent bus fleet may be controlled according to the determined driving decision after the driving decision is determined. In such a way, on one hand, different driving decisions may be made for the vehicle in the intelligent bus fleet according to different route information, thereby being beneficial for the vehicle to better run according to the driving decisions and guaranteeing the safety and cooperativity of the overall driving decision. On the other hand, the intelligent bus fleet with certain safety and cooperativity has advantages such as carrying flexibility, low cost, economy and practicability, high line network density, extension in all directions, good reachability and way right occupation flexibility. Therefore, various problems existing in traditional buses, subways and single-rail straddle type light rails/single-rail suspension type light rails in existing cities are solved.

[00121] It should be understood for those ordinarily skilled that all or a part of steps of various methods in the above-mentioned embodiments may be implemented by instructions or hardware related to instruction control, and the instructions may be stored in a computer readable storage medium and loaded and executed by a processor.

[00122] Therefore, an embodiment of the present application provides a computer readable storage medium in which a plurality of instructions are stored, and the instructions may be loaded by the processor, so as to perform the steps of any one intelligent bus fleet control method provided by the embodiment of the present application. For example, the instructions may be used for performing the following steps: route information of an intelligent bus fleet is 18 acquired; a driving decision of the intelligent bus fleet is determined according to the route information of the intelligent bus fleet, wherein the driving decision includes at least one of a 101992 gathering behavior strategy, a catch-up behavior strategy, a route change behavior strategy and a follow-up behavior strategy; and the intelligent bus fleet is controlled according to the determined driving decision.

[00123] The specific implementation of each of the above-mentioned operations may refer to the foregoing embodiment, the descriptions thereof will be omitted herein.

[00124] The computer readable storage medium may include a read only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk and the like.

[00125] The instructions stored in the computer readable storage medium may be used for performing the steps of any one intelligent bus fleet control method provided by the embodiment of the present application. Therefore, beneficial effects which may be achieved by any one intelligent bus fleet control method provided by the embodiment of the present application may be achieved, specifically referring to the foregoing embodiments, the descriptions thereof will be omitted herein.

[00126] The intelligent bus fleet control method and system as well as the computer readable storage medium according to the embodiments of the present application have been introduced in detail as above. The principle and implementations of the present application are described by applying specific examples herein, and the descriptions of the above-mentioned embodiments are merely intended to help the understanding of the method and the core concept thereof in the present application. Meanwhile, those skilled in the art may make modifications on the specific implementations and the application range according to the concept of the present application. In summary, the content of the specification should not be regarded as a limitation to the present application.

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Claims

Claims LU101992 WHAT IS CLAIMED IS:

1. An intelligent bus fleet control method, comprising: acquiring route information of an intelligent bus fleet; determining a driving decision of the intelligent bus fleet according to the route information of the intelligent bus fleet, wherein the driving decision comprises at least one of a gathering behavior strategy, a catch-up behavior strategy, a route change behavior strategy and a follow-up behavior strategy; and controlling the intelligent bus fleet according to the determined driving decision.

2. The intelligent bus fleet control method according to claim 1, wherein the determining the driving decision of the intelligent bus fleet according to the route information of the intelligent bus fleet comprises: determining a target value of a current position of a vehicle in the intelligent bus fleet according to a preset maximum/minimum function and the route information of the intelligent bus fleet, wherein the maximum/minimum function is a function determined according to at least one of a surrounding vehicle running speed maximization target, a safe driving distance maximization target and a surrounding vehicle number minimization target; and determining a driving decision of the vehicle in the intelligent bus fleet according to the target value of the current position of the vehicle.

3. The intelligent bus fleet control method according to claim 2, wherein the determining the driving decision of the vehicle in the intelligent bus fleet according to the target value of the current position of the vehicle comprises: detecting whether there are other vehicles in the intelligent bus fleet within a sensing range of the vehicle; determining central positions of other vehicles when detecting that there are other vehicles in the intelligent bus fleet within the sensing range; and determining the driving decision of the vehicle in the intelligent bus fleet as the gathering behavior strategy for the central positions if target values of the central positions are smaller than the target value of the current position of the vehicle, and the gathering degrees of the central positions are smaller than a first preset gathering degree threshold.

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4. The intelligent bus fleet control method according to claim 3, wherein the determining Ht! 01992 driving decision of the vehicle in the intelligent bus fleet according to the target value of the current position of the vehicle comprises: randomly determining a target position within the sensing range when detecting there are no other vehicles in the intelligent bus fleet within the sensing range of the vehicle; detecting whether a target value of the target position is smaller than the target value of the current position of the vehicle; and determining the driving decision of the vehicle in the intelligent bus fleet as the route change behavior strategy for the target position if the target value of the target position is smaller than the target value of the current position of the vehicle.

5. The intelligent bus fleet control method according to claim 3, wherein the determining the driving decision of the vehicle in the intelligent bus fleet according to the target value of the current position of the vehicle comprises: determining a target vehicle with the minimum target value in other vehicles in the intelligent bus fleet comprised within the sensing range according to the maximum/minimum function if the target values of the central positions are greater than or equal to the target value of the current position of the vehicle, and/or the gathering degrees of the central positions are greater than or equal to the first preset gathering degree threshold; and determining the driving decision of the vehicle in the intelligent bus fleet as the catch-up behavior strategy, and determining the target vehicle as a catch-up object of the vehicle if a target value of a position of the target vehicle is smaller than the target value of the current position of the vehicle, and the gathering degree of the position of the target vehicle is smaller than a second preset gathering degree threshold.

6. The intelligent bus fleet control method according to claim 5, wherein the determining the driving decision of the vehicle in the intelligent bus fleet according to the target value of the current position of the vehicle comprises: randomly determining a target position within the sensing range if the target value of the position of the target vehicle is greater than or equal to the target value of the current position of the vehicle, and/or the gathering degree of the position of the target vehicle is greater than or equal to the second preset gathering degree threshold; detecting whether a target value of the target position is smaller than the target value of the current 2 position of the vehicle; and LU101992 determining the driving decision of the vehicle in the intelligent bus fleet as the route change behavior strategy for the target position if the target value of the target position is smaller than the target value of the current position of the vehicle.

7. The intelligent bus fleet control method according to claim 4 or 6, wherein the determining the driving decision of the vehicle in the intelligent bus fleet according to the target value of the current position of the vehicle comprises: randomly re-determining another target position within the sensing range, and re-performing the detecting whether the target value of the target position is smaller than the target value of the current position of the vehicle if the target value of the target position is greater than or equal to the target value of the current position of the vehicle; and determining the driving decision of the vehicle in the intelligent bus fleet as the follow-up behavior strategy if the target value of the target position randomly determined for continuous N times is not smaller than the target value of the current position of the vehicle.

8. An intelligent bus fleet control system, comprising: an information acquisition module, which is configured to acquire route information of an intelligent bus fleet; a strategy determination module, which is configured to determine a driving decision of the intelligent bus fleet according to the route information of the intelligent bus fleet, wherein the driving decision comprises at least one of a gathering behavior strategy, a catch-up behavior strategy, a route change behavior strategy and a follow-up behavior strategy; and a fleet control module, which is configured to control the intelligent bus fleet according to the determined driving decision.

9. An intelligent bus, comprising a memory, a processor and a computer program stored in the memory and capable of operating on the processor, wherein the steps of the method according to any one of claims 1 to 8 are implemented when the computer program is executed by the processor.

10. A computer readable storage medium on which a computer program is stored, wherein the steps of the method according to any one of claims 1 to 8 are implemented when the computer program is executed by the processor. 3