Vehicle driving scheduling method and vehicle driving scheduling system

Abstract

The embodiment of the invention provides a vehicle driving scheduling method and system, and the method comprises the steps: detecting a group of vehicle information of each reference vehicle in a reference vehicle set through a vehicle detection system which is located in front of a bridge getting-on end of a target bridge, the group of vehicle information comprises vehicle weight, driving speed and vehicle passing time, and the vehicle passing time is the time when the vehicle passes through a specified position before the getting-on end of the target bridge; according to the group of vehicle information of each reference vehicle, predicting bridge floor load distribution of the target bridge, and under the condition that it is determined that vehicles are allowed to enter the target bridge according to the bridge floor load distribution, carrying out vehicle driving scheduling based on the bridge floor load distribution so as to make the bridge floor load of the target bridge uniform; according to the embodiment of the invention, the problems that the installation difficulty of a bridge sensor is large, and the collected weight load data is inaccurate are solved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of vehicle identification, and more particularly, to a vehicle driving scheduling method and a vehicle driving scheduling system. Background Art

[0002] As an important part of modern infrastructure, bridges carry people's travel and cargo transportation. However, with the increase in the service life of bridges and the improvement of load requirements, the potential safety hazards and risks have increased significantly. The damage or failure of bridges may not only cause huge losses of life and property, but also have a serious impact on society, economy and environment.

[0003] Bridge safety accidents are usually caused by the load borne by the bridge exceeding the load limit that the bridge can bear, resulting in the rollover or collapse of the bridge. To this end, bridge sensors can be installed on the bridge deck to detect the deck load of the bridge, and then vehicle driving scheduling can be carried out based on the detected deck load to reduce safety risks such as bridge collapse.

[0004] However, due to the difficult installation of bridge sensors, which will damage the original bridge deck structure, and it is difficult to collect accurate weight load data. It can be seen that the vehicle driving scheduling method in the related technology has problems such as difficult installation of bridge sensors and inaccurate weight load data collected. Summary of the Invention

[0005] The embodiments of the present application provide a vehicle driving scheduling method and a vehicle driving scheduling system to at least solve the problems in the vehicle driving scheduling method in the related technology, such as difficult installation of bridge sensors and inaccurate weight load data collected.

[0006] According to an embodiment of the present application, a vehicle driving scheduling method is provided, including: a set of vehicle information of each reference vehicle in a reference vehicle set detected by a vehicle detection system, where the vehicle detection system is located before the on-ramp of a target bridge, and the set of vehicle information includes vehicle weight, driving speed, and passing time, and the passing time is the time when the vehicle passes a specified position before the on-ramp of the target bridge; predicting the deck load distribution of the target bridge according to the set of vehicle information of each reference vehicle, where the deck load distribution is used to represent the distribution of the deck load of the target bridge; and when it is determined that the target bridge allows vehicles to enter based on the deck load distribution, performing vehicle driving scheduling based on the deck load distribution to make the deck load of the target bridge uniform.

[0007] According to an embodiment of the present application, a vehicle driving scheduling system is provided, including: a weighing sensor, located before the on-ramp of the target bridge, for weighing the passing vehicles and transmitting the vehicle weight data obtained by weighing to a data processor; the data processor is configured to receive the vehicle weight data transmitted by the weighing sensor, and based on the received vehicle weight data, obtain the vehicle weight of each reference vehicle in the reference vehicle set; determine the deck load distribution parameter of the target bridge according to the vehicle weight of each reference vehicle and the vehicle position of each reference vehicle, wherein the deck load distribution parameter is used to represent the distribution of the deck load of the target bridge; in the case where it is determined according to the deck load distribution parameter that the target bridge allows vehicles to enter, perform vehicle driving scheduling based on the deck load distribution parameter to make the deck load of the target bridge uniform.

[0008] According to another embodiment of the present application, a computer-readable storage medium is further provided, in which a computer program is stored, wherein the computer program is configured to execute the steps in any one of the above method embodiments when running.

[0009] According to another embodiment of the present application, an electronic device is further provided, including a memory and a processor, a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0010] Through the embodiment provided by the present application, the vehicle detection system is installed before the on-ramp of the target bridge; without affecting the bridge (road surface), the weight data, driving speed, and passing time of each reference vehicle can be accurately collected; then, based on the vehicle information of each reference vehicle, the deck load of the bridge during the vehicle driving on the bridge can be accurately predicted, and vehicle scheduling is performed based on this to make the deck load of the target bridge uniform, thereby solving the problems in the related vehicle driving scheduling method that the installation of bridge sensors is difficult and the collected weight load data is inaccurate. Description of the Drawings

[0011] Figure 1 is a schematic flow chart of an optional vehicle driving scheduling method provided by an embodiment of the present application;

[0012] Figure 2 is a schematic flow chart of another optional vehicle driving scheduling method provided by an embodiment of the present application;

[0013] Figure 3 is a schematic flow chart of another optional vehicle driving scheduling method provided by an embodiment of the present application;

[0014] Figure 4It is a schematic flow chart of another optional vehicle driving scheduling method provided by an embodiment of the present application;

[0015] Figure 5 It is a schematic flow chart of another optional vehicle driving scheduling method provided by an embodiment of the present application;

[0016] Figure 6 It is a schematic structural diagram of an optional vehicle model feature recognition system provided by an embodiment of the present application;

[0017] Figure 7 It is a structural block diagram of an optional vehicle driving scheduling system according to an embodiment of the present application;

[0018] Figure 8 It is a structural block diagram of a computer system of an optional electronic device according to an embodiment of the present application. Detailed implementation manners

[0019] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0020] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0022] As an important part of modern infrastructure, bridges carry the driving of vehicles and the transportation of goods. However, with the increase in the service life of bridges and the improvement of load requirements, their potential safety hazards and risks have also increased significantly.

[0023] Bridges may be damaged or even collapsed due to the passage of overloaded trucks. The passage of overloaded trucks imposes huge pressure on the bridge structure. When the load pressure on the bridge exceeds the load limit they can bear, it will cause the bridge to overturn or collapse. In addition, excessive vibration and shock may also cause fatigue damage to the bridge, increasing the risk of its collapse.

[0024] It can be seen from this that the vehicle driving scheduling methods in the related technologies mainly have two defects:

[0025] First, it is difficult to install bridge sensors, especially road surface weight sensors. Installing them on the existing bridge deck will damage the structure of the original bridge deck. There are difficulties in construction such as closing the bridge, and it is also difficult in terms of comprehensive implementation factors such as power supply and networking. Because it is difficult to install weight sensors on old bridges, the construction period is long, resulting in difficulties in installing weight sensors on old bridges and making it difficult to collect weight load data.

[0026] Second, bridge overturning and collapse are the main manifestations of bridge safety accidents. The essence is that the load distribution on the bridge body is uneven when overloaded vehicles pass, especially in the case of a fleet of overloaded trucks, which is particularly serious, exceeding the load distribution threshold on one side of the bridge and causing the side of the bridge to overturn and collapse.

[0027] Through the embodiments of the present application, a vehicle detection system is deployed on a conventional road surface at a certain distance in front of the bridge to collect vehicle information (including vehicle weight, driving speed, and vehicle passing time); using the sensor data in front of the bridge, the bridge deck load data during the vehicle's driving on the bridge deck is predicted; and through further processing of the pressure data of the bridge lanes, refined lane scheduling data is obtained, so as to guide the vehicle to drive in the lane and realize vehicle-road communication.

[0028] The embodiments of the present application provide a vehicle driving scheduling method and a vehicle driving scheduling system, which can solve the problems of difficult installation of bridge sensors and inaccurate weight load data collected. The following describes an exemplary application of the electronic device provided by the embodiments of the present application. Optionally, the above vehicle driving scheduling method may be executed alone by a processing device (i.e., a data processor, such as a terminal or a server), or jointly executed by a weighing sensor and a processing device, or executed by other devices other than the weighing sensor and the processing device.

[0029] As an optional implementation manner, take the processing device executing the vehicle driving scheduling method in this embodiment as an example. As Figure 1 shown, the process of the above vehicle driving scheduling method may include the following steps.

[0030] In step S102, a set of vehicle information of each reference vehicle in the set of reference vehicles detected by the vehicle detection system, where the vehicle detection system is located before the on-ramp of the target bridge, and the set of vehicle information includes vehicle weight, driving speed, and passing time, and the passing time is the time when the vehicle passes a specified position before the on-ramp of the target bridge.

[0031] In order to ensure the collection of load data on the bridge road surface without affecting the bridge structure (road surface), in the embodiments of the present application, the vehicle detection system is arranged before the on-ramp of the target bridge to collect vehicle information (including vehicle weight, driving speed, and passing time) for each vehicle in the reference vehicles preparing to pass the bridge.

[0032] For example, the vehicle detection system includes a weighing sensor and a data processor. The weighing sensor can be implemented as a vehicle weight measurement device such as a dynamic vehicle scale or a pressure sensor. The specified position before the vehicle passes the on-ramp of the target bridge can be the position of the weighing sensor or other pre-set positions (such as vehicle detection points, gantry positions, etc.), which can be specifically determined according to the actual application scenario.

[0033] For example, the specified position is the position of the weighing sensor, and the weighing sensor is located 200 meters before the on-ramp of the bridge.

[0034] In some embodiments, a picture acquisition device associated with the weighing sensor is also pre-deployed to collect information such as license plate information, vehicle distance information, and number of vehicle axles of each vehicle, so as to facilitate subsequent vehicle scheduling.

[0035] It should be noted that the position and number of vehicle axles of the current target vehicle (reference vehicle) can also be judged according to the pressure distribution position where the weighing sensor bears pressure.

[0036] Through the embodiments provided by the present application, the weighing sensor, data processor, etc. can be pre-deployed at the front end of the bridge to avoid affecting the bridge.

[0037] In step S104, according to a set of vehicle information of each reference vehicle, the bridge deck load distribution of the target bridge is predicted, where the bridge deck load distribution is used to represent the distribution of the bridge deck load of the target bridge.

[0038] Since the vehicle information of each reference vehicle has been pre-acquired, according to the vehicle weight of each reference vehicle and then according to the vehicle position of each reference vehicle on the target bridge (determined based on the driving speed and passing time), the bridge deck load distribution of the target bridge can be determined.

[0039] It can be understood that the load distribution of each vehicle on the bridge deck is determined according to the axle weight of the vehicle and the position of the vehicle, and the vehicle position of each vehicle can be determined according to the driving speed and driving time of the vehicle.

[0040] For example, according to the time difference between the current time and the passing time of each reference vehicle, and the driving speed of each reference vehicle, the vehicle position of each reference vehicle is determined, where the passing time of each reference vehicle is the time when each reference vehicle passes the weighing sensor detected by the weighing sensor.

[0041] In some embodiments, the bridge deck load (bridge load) refers to the general term of various possible loads of the bridge structure, including dead load, live load and other loads. It includes railway train live load or highway vehicle load, and the impact force, centrifugal force, lateral sway force (railway train), braking force or traction force caused by them, crowd load, and earth pressure increased by train vehicles, etc.

[0042] Through the embodiments provided by the present application, it is possible to judge the position of each reference vehicle according to the vehicle driving time and vehicle driving speed, and determine the bridge load distribution of the target bridge based on the weight and position of each reference vehicle, so as to facilitate the entry judgment or precise scheduling of subsequent vehicles to be parked based on the current bridge load distribution.

[0043] In step S106, when it is determined that the target bridge allows vehicles to enter according to the bridge deck load distribution, vehicle driving scheduling is performed based on the bridge deck load distribution to make the bridge deck load of the target bridge uniform.

[0044] According to the current bridge deck load distribution parameters, it is determined whether the target bridge can continue to allow vehicles to enter. When the target bridge allows vehicles to enter, vehicle driving scheduling is performed based on the bridge deck load distribution parameters. For example, a guiding prompt message is sent to the vehicle to be parked, prompting the vehicle to be parked to enter the target bridge lane (which can be the bridge lane with the smallest load in the current lane).

[0045] When the bridge deck load distribution parameter is greater than or equal to the predicted lane load of the preset load threshold, it is determined that the vehicle to be parked is not allowed to enter the target bridge.

[0046] Here, a guiding promotion message can be sent to the vehicle to be parked through V2X (Vehicle-to-Everything, vehicle networking technology) to guide the vehicle to enter the target bridge lane or park temporarily at a specific speed.

[0047] Through the embodiments provided by the present application, it is possible to schedule the vehicles to be parked according to the bridge deck load distribution, so as to ensure that the vehicles get on the bridge in a relatively uniform distribution manner, preventing rollover or excessive pressure on one side of the bridge deck during a certain period of time.

[0048] Through the above steps of the embodiments of the present application, a set of vehicle information of each reference vehicle in the reference vehicle set detected by the vehicle detection system is obtained. The vehicle detection system is located before the on-ramp of the target bridge. The set of vehicle information includes vehicle weight, driving speed, and passing time. The passing time is the time when the vehicle passes through a specified position before the on-ramp of the target bridge. According to the set of vehicle information of each reference vehicle, the deck load distribution of the target bridge is predicted, where the deck load distribution is used to represent the distribution of the deck load of the target bridge. When it is determined that the target bridge allows vehicles to enter based on the deck load distribution, vehicle driving scheduling is performed based on the deck load distribution to make the deck load of the target bridge uniform. It is possible to accurately obtain the load condition of the bridge road surface and perform vehicle driving scheduling without affecting the bridge, thereby solving the problems in the related art of vehicle driving scheduling methods, such as the difficulty in installing bridge sensors and the inaccuracy of the collected weight load data.

[0049] In an exemplary embodiment, the target bridge includes multiple bridge lanes, and the reference vehicle set includes a set of lane vehicles located in each of the multiple bridge lanes.

[0050] Predicting the deck load distribution of the target bridge according to the set of vehicle information of each reference vehicle includes:

[0051] S11. Determine the lane load of each bridge lane according to the set of vehicle information of each lane vehicle located in each bridge lane, where the deck load distribution includes the lane load of each bridge lane.

[0052] Since a bridge usually includes multiple bridge lanes (two-way multi-lanes), to ensure the overall load balance of the bridge, it is necessary to determine the lane loads of each bridge lane in the target bridge.

[0053] Here, the lane load of each bridge can be determined respectively according to the vehicle weight and vehicle position in each bridge lane.

[0054] In an exemplary embodiment, reference Figure 2 , determining the lane load of each bridge lane according to the set of vehicle information of each lane vehicle located in each bridge lane includes:

[0055] S21. Take each bridge lane as the current bridge lane and perform the following load determination operation to obtain the lane load of each bridge lane, where the set of lane vehicles located in the current bridge lane is the current vehicle sequence:

[0056] S211. Determine the vehicle position of each reference vehicle according to the time difference between the current time and the passing time of each current vehicle in the current vehicle sequence, and the driving speed of each reference vehicle.

[0057] S212. Determine the vehicle average load corresponding to each current vehicle according to the vehicle weight of each current vehicle and the vehicle position of each current vehicle. Here, the vehicle average load corresponding to each current vehicle is the average load of the road surface within the vehicle spacing range corresponding to each current vehicle, and the vehicle spacing range corresponding to each current vehicle is the range from the vehicle position of each current vehicle to the vehicle position of the next current vehicle of each current vehicle.

[0058] S213. Determine the sum of the vehicle average loads corresponding to each current vehicle as the lane load of the current bridge lane.

[0059] Since the lane load of the bridge lane includes the road surface between the road surface where the vehicle is already carrying and the vehicle spacing range, it is necessary to determine the vehicle average load corresponding to each current vehicle, that is, the average load of the road surface within the vehicle spacing range corresponding to each current vehicle.

[0060] For example, set the time window length to T, the passing time of the i-th vehicle is T i , the driving speed when the vehicle passes is V i (km / h), the current speed is T c , the average axle load is W i (the ratio of vehicle weight to the number of vehicle axles), the total number of lanes is m. At this time, taking a specified position (for example, a weighing sensor) as the starting point, the vehicle coordinates of the i-th vehicle can refer to formula (1):

[0061] Y i =(T c -T i )*V i (1)

[0062] where Y i represents the coordinate of the i-th vehicle on the target bridge lane, T c represents the current time, T i represents the time when the i-th vehicle passes the specified position, and V i represents the driving speed when the vehicle passes.

[0063] Here, when the (i + 1)-th vehicle in the same lane passes, the average load of the road surface within the vehicle spacing range of the i-th vehicle (vehicle average load) can refer to formula (2):

[0064]

[0065] Among them, C i represents the average vehicle load corresponding to the i-th vehicle, and W i represents the average axle load of the i-th vehicle, and Y i represents the coordinates of the i-th vehicle, and Y i+1 represents the coordinates of the (i + 1)-th vehicle.

[0066] Within the T time period (time window), the sum of the average vehicle loads corresponding to each current vehicle is determined as the lane load of the current bridge lane. Reference can be made to formula (3):

[0067]

[0068] Among them, C k represents the lane load of the k-th bridge lane among 1 - m bridge lanes, represents the total sum of the average vehicle loads corresponding to the i-th vehicle to the j-th vehicle passing through the bridge lane within the T time period.

[0069] Through the embodiments provided by this application, the lane loads of each bridge lane in the target bridge can be determined respectively, so as to facilitate subsequent driving vehicle scheduling.

[0070] In an exemplary embodiment, determining the average vehicle load corresponding to each current vehicle according to the vehicle weight of each current vehicle and the vehicle position of each current vehicle includes:

[0071] S31. The value obtained by dividing the vehicle weight of each current vehicle by the position difference between the vehicle position of each current vehicle and the vehicle position of the next current vehicle of each current vehicle is determined as the average vehicle load corresponding to each current vehicle.

[0072] Here, the traveled time of the target vehicle can be determined according to the time when the target vehicle passes through the specified position and the current time; according to the traveling speed of the target vehicle passing through the specified position and the traveled time, the current position of the target vehicle (i.e., the distance between the target vehicle and the specified position) can be determined.

[0073] Determine the difference (distance difference or coordinate difference) between the vehicle position of the current vehicle and the vehicle position of the next current vehicle of the current vehicle, and use the ratio of the average axle load of the current vehicle (the ratio of the vehicle weight to the number of vehicle axles) to the above difference as the average vehicle load corresponding to the current vehicle. Specifically, reference can be made to the above formulas (1)-(2), and this application will not elaborate here.

[0074] Through the embodiments provided by this application, the average load of the road surface within the vehicle spacing range can be determined according to the distance difference between vehicles.

[0075] In an exemplary embodiment, with reference to Figure 3 after predicting the deck load distribution of the target bridge according to a set of vehicle information of each reference vehicle, the above method further includes:

[0076] S41, when a vehicle to be driven into the target bridge is detected by a vehicle detection system, predicting the driving trajectory of the vehicle to be driven into each bridge lane after it enters, and obtaining a vehicle driving trajectory corresponding to each bridge lane;

[0077] S42, predicting the probability of the target bridge tipping over based on the vehicle driving trajectory corresponding to each bridge lane, and obtaining a predicted probability corresponding to each bridge lane;

[0078] S43, when the predicted probability corresponding to each bridge lane is greater than or equal to a preset probability threshold, determining that the vehicle to be driven into is not allowed to enter the target bridge.

[0079] After determining the load conditions of each bridge lane of the target bridge deck according to the deck load distribution parameters, when a vehicle to be driven into the target bridge is detected by a vehicle detection system, in order to avoid most of the vehicles driving into concentrating on the same bridge lane and causing uneven bridge loads, it is necessary to predict the probability of the bridge tipping over after the vehicle to be driven into enters. When there is a predicted probability greater than or equal to the preset probability threshold among the predicted probabilities corresponding to each bridge lane, it is determined that the vehicle to be driven into is not allowed to enter the target bridge.

[0080] In some embodiments, through a picture (image) acquisition device associated with a weighing sensor, the current lane position of the vehicle to be driven into is determined (for example, converting the relative position of the vehicle and a preset reference object into actual position information), and the bridge lane connected to the current vehicle is used as the predicted bridge lane to be driven into.

[0081] It can be understood that the predicted bridge lane to be driven into is related to the current lane of the target vehicle (vehicle to be driven into) and the current road driving rules.

[0082] It should be noted that the position of each vehicle to be driven into on the weighing sensor can also be determined through the load conditions of the weighing sensor, and the position of the vehicle to be driven into on the bridge lane in the target bridge can be correspondingly determined (there is a preset position relationship between the weighing sensor and the bridge lane, for example, the weighing sensor is pre-deployed on the road surface of each bridge lane) to predict the driving trajectory of the vehicle to be driven into each bridge lane.

[0083] In an exemplary embodiment, with reference to Figure 4, after predicting the deck load distribution of the target bridge according to a set of vehicle information of each reference vehicle, the above method further includes:

[0084] S51, when a vehicle to be driven into the target bridge is detected by the vehicle detection system, predicting the lane load of each bridge lane after the vehicle to be driven into each bridge lane, so as to obtain the predicted lane load of each bridge lane;

[0085] S52, when the predicted lane load of each bridge lane is greater than or equal to a preset load threshold, it is determined that the vehicle to be driven into is not allowed to drive into the target bridge.

[0086] After determining the load conditions of each bridge lane of the target deck according to the deck load distribution parameters, when a vehicle to be driven into the target bridge is detected by the vehicle detection system, in order to ensure the fast passage of the vehicle to be driven into and avoid damage to the bridge structure caused by excessive lane loads in some bridge lanes, it is necessary to predict the lane load of each bridge lane after the vehicle to be driven into each bridge lane, so as to obtain the predicted lane load of each bridge lane. When there is a predicted lane load greater than or equal to the preset load threshold among the predicted lane loads of each bridge lane, it is determined that the vehicle to be driven into is not allowed to drive into the target bridge (until the predicted lane load is less than the preset load threshold).

[0087] It should be noted that the preset load thresholds of each bridge lane may be different. For example, in order to avoid bridge rollover, the preset load thresholds of the bridge lane at the edge on both sides of the bridge are less than the preset load thresholds of the bridge lanes in the center of the bridge. The preset load threshold can be set according to the actual application situation, and this application does not limit it here.

[0088] Through the embodiments provided by this application, when a vehicle to be driven into the target bridge is detected by the vehicle detection system, it is necessary to predict the driving conditions and accident probabilities (including bridge rollover, exceeding the load threshold, etc.) of each bridge lane, so as to ensure the safety of the bridge while ensuring the fast passage of the vehicle to be driven into.

[0089] In an exemplary embodiment, refer to Figure 5 , when it is determined that the target bridge allows vehicles to drive in according to the deck load distribution, vehicle driving scheduling is performed based on the deck load distribution, including:

[0090] S61, when it is determined that the target bridge allows vehicles to drive in according to the deck load distribution, select a target bridge lane from multiple bridge lanes, where the target bridge lane is the bridge lane with the smallest lane load among the multiple bridge lanes;

[0091] S62, control the lane prompt component to send vehicle guidance prompt information to the vehicle to be driven into. Among them, the lane prompt component is a component used for lane scheduling prompts, and the vehicle guidance prompt information is used to prompt the vehicle to be driven into to drive into the target bridge lane.

[0092] When it is determined that the target bridge allows vehicles to drive in according to the bridge deck load distribution, in order to balance the bridge deck load of the target bridge, it is necessary to select the bridge lane with the smallest lane load from multiple bridge lanes as the target bridge lane for the vehicle to be driven into, which can be represented by the following formula (4):

[0093] N = where(C N = min [C k ) (4)

[0094] Among them, N is used to represent the lane identifier of the target bridge lane (for example, lane number, etc.), C N represents the lane load of the bridge lane with the lane identifier N, min[] represents taking the minimum value, and C k represents the lane load of the bridge lane with the lane identifier k (one of the 1 - m bridge lanes).

[0095] Here, the lane prompt component can be implemented as a display screen located at the edge or above the lane, a pre - bound application (abbreviated as APP), V2X (Vehicle - to - Everything, a support technology for intelligent vehicles and intelligent transportation), etc. V2X includes various application communication scenarios such as vehicle - to - vehicle (V2V), vehicle - to - infrastructure (V2I), vehicle - to - pedestrian (V2P), vehicle - to - network (V2N), etc.

[0096] Through the embodiments provided in this application, after the target bridge lane is selected, vehicle guidance prompt information is sent to the vehicle to be driven into to instruct the vehicle to be driven into to drive into the target bridge lane.

[0097] In an exemplary embodiment, before controlling the lane prompt component to send vehicle guidance prompt information to the vehicle to be driven into, the above - mentioned method further includes:

[0098] S71, determine the load interval to which the lane load of the target bridge lane belongs in a preset group of load intervals, and obtain the target load interval;

[0099] S72. Determine the driving speed that matches the target load interval as the expected driving speed of the vehicle to enter, where the vehicle guiding prompt information is also used to prompt the expected driving speed.

[0100] In order to balance the lane load of the target bridge lane, it is necessary to adjust the driving speeds of the vehicles in the current target bridge lane to maintain the range of the vehicle spacing between lanes in the target bridge lane, thereby reducing the average vehicle load of each vehicle while maintaining a safe vehicle distance and improving the vehicle passing efficiency.

[0101] In some embodiments, after selecting the target bridge lane, according to the vehicle weight of the vehicle in front and the range of the vehicle spacing between the two vehicles, the lane prompt component sends a vehicle slow-down prompt to the vehicle to enter, so as to indicate the driving speed that matches the target load interval as the expected driving speed of the vehicle to enter.

[0102] For example, when the average vehicle load of the i-th vehicle is greater than or equal to the preset first average load threshold, the second preset speed is used as the expected driving speed of the (i + 1)-th vehicle, which can be expressed by the following formula (5):

[0103] C i ≥3.5t / m (5)

[0104] Wherein, C i represents the average vehicle load of the i-th vehicle, 3.5 represents the first average load threshold (a preset value, which can also be other values). When C i ≥3.5t / m, it prompts the (i + 1)-th vehicle to slow down to pass at V2. V2 represents the second preset speed (in units of t / m, where t represents tons and m represents meters).

[0105] When the average vehicle load of the i-th vehicle is less than the preset first average load threshold and greater than or equal to the preset second average load threshold, the first preset speed is used as the expected driving speed of the (i + 1)-th vehicle, which can be expressed by the following formula (6):

[0106] 3.5>C i ≥2t / m (6)

[0107] Wherein, 3.5 represents the first average load threshold, C i represents the average vehicle load of the i-th vehicle, 2 represents the second average load threshold (a preset value, which can also be other values). When 3.5>C i ≥2t / m, it prompts the (i + 1)-th vehicle to slow down to pass at V1. V1 represents the first preset speed.

[0108] When the average vehicle load of the i-th vehicle is less than the preset second average load threshold, the normal driving speed of the vehicle (i.e., the original speed) is taken as the expected driving speed of the (i + 1)-th vehicle, which can be expressed by the following formula (7):

[0109] C i <2t / s (7)

[0110] Where C i represents the average vehicle load of the i-th vehicle, 2 represents the second average load threshold (a preset value, which can also be other values). When C i <2t / s, it prompts the (i + 1)-th vehicle to pass normally. Here, passing normally means prompting the vehicle to pass at a normal speed (any speed within the bridge speed limit).

[0111] Among them, the first preset speed and the second preset speed can be determined by the following formulas (8)-(9):

[0112]

[0113]

[0114] Among them, 3.5 / 2 / 1.5 are all preset constant values, which can be changed according to the actual application situation, and 3.6 is used for unit conversion of vehicle speed (converting m / s to km / h).

[0115] In an exemplary embodiment, the vehicle detection system includes a weighing detection device and a license plate capture device.

[0116] Refer to Figure 6 Figure 6 which is a schematic structural diagram of an optional vehicle model feature recognition system provided by an embodiment of the present application; it includes a weighing sensor and a data interaction device (V2X, display screen, APP are exemplarily shown). Here, the weighing sensor is deployed before the upper bridge section of the bridge or at the initial section of the bridge; the data interaction device is used for lane pointing guidance.

[0117] Among them, both the weighing sensor and the data interaction device belong to the components in the vehicle detection system.

[0118] The above vehicle detection system includes three parts, namely, a vehicle axle load acquisition system, a bridge load prediction system, and a heavy vehicle vehicle-road interaction system.

[0119] The vehicle axle load acquisition system is deployed 200M away from the bridge upper bridge end. Using hardware devices such as weighing detection devices and license plate capture devices, it collects various information such as the license plate information of each truck, the total vehicle weight, the lane axle load weight, and the vehicle distance information; and synchronously uploads the data to the bridge load prediction system. ​

[0120] The bridge load prediction system will utilize the data collected by the vehicle axle load acquisition system, combine it with the bridge foundation data, predict the model data of vehicles driving synchronously onto the bridge, and determine whether the bridge lanes are allowed to be entered and whether regulation is required. Among them, the regulation mode will relieve the damage to the road caused by heavy vehicles to the greatest extent, achieve uniform vehicle loads, and reduce risks.

[0121] The heavy vehicle vehicle-road interaction system completes information release according to the entry judgment and regulation judgment, and realizes lane-level driving guidance on the bridge.

[0122] Through the embodiments provided in this application, it is possible to predict the pressure distribution on the bridge deck when vehicles arrive after a period of time based on the vehicle weight distribution passing through the weighing area within a certain period of time. And set rules to ensure that vehicles enter the bridge in a relatively uniform distribution manner by controlling the vehicle weight distribution within a certain period of time. Prevent rollover or excessive pressure on one side of the bridge deck during a certain period.

[0123] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be carried out in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0124] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the embodiments of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0125] According to another aspect of the embodiments of this application, a vehicle driving scheduling system is also provided for implementing the above vehicle driving scheduling method, refer to Figure 7 , the vehicle driving scheduling system may include:

[0126] A weighing sensor 702, located before the on-ramp of the target bridge, is used to weigh the passing vehicles and transmit the weighed vehicle weight data to the data processor;

[0127] A data processor 704 is configured to receive vehicle weight data transmitted by a load cell, and based on the received vehicle weight data, obtain the vehicle weight of each reference vehicle in a reference vehicle set; determine the deck load distribution parameter of a target bridge according to the vehicle weight of each reference vehicle and the vehicle position of each reference vehicle, wherein the deck load distribution parameter is used to represent the distribution of the deck load of the target bridge; when it is determined that vehicles are allowed to enter the target bridge according to the deck load distribution parameter, perform vehicle driving scheduling based on the deck load distribution parameter to make the deck load of the target bridge uniform.

[0128] Through the above vehicle driving scheduling system, a set of vehicle information of each reference vehicle in the reference vehicle set detected by a vehicle detection system is obtained, wherein the vehicle detection system is located before the on-ramp of the target bridge, and the set of vehicle information includes vehicle weight, driving speed, and passing time, and the passing time is the time when the vehicle passes a specified position before the on-ramp of the target bridge; predict the deck load distribution of the target bridge according to the set of vehicle information of each reference vehicle, wherein the deck load distribution is used to represent the distribution of the deck load of the target bridge; when it is determined that vehicles are allowed to enter the target bridge according to the deck load distribution, perform vehicle driving scheduling based on the deck load distribution to make the deck load of the target bridge uniform; thereby solving the technical problems in the related art that the installation of bridge sensors is difficult and the collected weight load data is inaccurate in the vehicle driving scheduling method.

[0129] In an exemplary embodiment, the target bridge includes multiple bridge lanes, and the reference vehicle set includes a set of lane vehicles located in each of the multiple bridge lanes; the above data processor is configured to determine the lane load of each bridge lane according to the set of vehicle information of each lane vehicle located in each bridge lane, wherein the deck load distribution includes the lane load of each bridge lane.

[0130] In an exemplary embodiment, the above data processor is configured to perform the following load determination operations for each bridge lane as the current bridge lane to obtain the lane load of each bridge lane. A set of lane vehicles located within the current bridge lane is the current vehicle sequence: Determine the vehicle position of each reference vehicle according to the time difference between the current time and the passing time of each current vehicle in the current vehicle sequence and the driving speed of each reference vehicle; Determine the vehicle average load corresponding to each current vehicle according to the vehicle weight of each current vehicle and the vehicle position of each current vehicle, where the vehicle average load corresponding to each current vehicle is the average load of the road surface within the vehicle spacing range corresponding to each current vehicle, and the vehicle spacing range corresponding to each current vehicle is the range from the vehicle position of each current vehicle to the vehicle position of the next current vehicle of each current vehicle; Determine the sum of the vehicle average loads corresponding to each current vehicle as the lane load of the current bridge lane.

[0131] In an exemplary embodiment, the above data processor is configured to determine the value obtained by dividing the vehicle weight of each current vehicle by the position difference between the vehicle position of each current vehicle and the vehicle position of the next current vehicle of each current vehicle as the vehicle average load corresponding to each current vehicle.

[0132] In an exemplary embodiment, after predicting the deck load distribution of the target bridge according to a set of vehicle information of each reference vehicle, the above data processor is further configured to, when a vehicle to be entered detected by the vehicle detection system is about to enter the target bridge, predict the driving trajectory of the vehicle to be entered after entering each bridge lane to obtain the vehicle driving trajectory corresponding to each bridge lane; Predict the probability of the target bridge tipping over based on the vehicle driving trajectory corresponding to each bridge lane to obtain the predicted probability corresponding to each bridge lane; When the predicted probability corresponding to each bridge lane is greater than or equal to the preset probability threshold, determine that the vehicle to be entered is not allowed to enter the target bridge.

[0133] In an exemplary embodiment, the above data processor is further configured to, when a vehicle to be entered detected by the vehicle detection system is about to enter the target bridge, predict the lane load of each bridge lane after the vehicle to be entered enters each bridge lane to obtain the predicted lane load of each bridge lane; When the predicted lane load of each bridge lane is greater than or equal to the preset load threshold, determine that the vehicle to be entered is not allowed to enter the target bridge.

[0134] In an exemplary embodiment, the above data processor is configured to select a target bridge lane from multiple bridge lanes when it is determined that the target bridge allows vehicles to enter according to the bridge deck load distribution, where the target bridge lane is the bridge lane with the minimum lane load among the multiple bridge lanes; control the lane prompting component to send a vehicle guiding prompt message to the vehicle to be entered, where the lane prompting component is a component for performing lane scheduling prompts, and the vehicle guiding prompt message is used to prompt the vehicle to be entered to enter the target bridge lane.

[0135] In an exemplary embodiment, before the control lane prompting component sends a vehicle guiding prompt message to the vehicle to be entered, the above data processor is further configured to determine the load interval to which the lane load of the target bridge lane belongs in a preset set of load intervals to obtain a target load interval; determine the driving speed matching the target load interval as the expected driving speed of the vehicle to be entered, where the vehicle guiding prompt message is further used to prompt the expected driving speed.

[0136] In an exemplary embodiment, the vehicle detection system includes a weighing detection device and a license plate capture device.

[0137] It should be noted that the above-mentioned respective units or components can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited thereto: the above modules are all located in the same processor; or, the above-mentioned respective modules are separately located in different processors in any combination form.

[0138] According to another aspect of the embodiments of the present application, there is also provided a computer-readable storage medium, in which a computer program is stored, where the computer program is set to execute the steps in any one of the above method embodiments when running.

[0139] In an exemplary embodiment, the above computer-readable storage medium may include, but is not limited to: various media such as a USB flash drive, a read-only memory (ROM for short), a random access memory (RAM for short), a mobile hard disk, a magnetic disk, or an optical disc that can store a computer program.

[0140] According to one aspect of the present application, there is provided a computer program product, which includes a computer program / instructions, and the computer program / instructions include program codes for executing the method shown in the flowchart. In such an embodiment, refer to Figure 8, the computer program can be downloaded and installed from a network through the communication section 809, and / or installed from the removable medium 811. When the computer program is executed by the central processing unit 801, various functions provided by the embodiments of the present application are executed. The serial numbers of the embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0141] Reference Figure 8 , Figure 8 is a structural block diagram of a computer system of an optional electronic device according to an embodiment of the present application.

[0142] Figure 8 Schematically shows a structural block diagram of a computer system of an electronic device for implementing the embodiments of the present application. As Figure 8 shown, the computer system 800 includes a central processing unit 801 (Central Processing Unit, CPU), which can execute various appropriate actions and processes according to a program stored in the read-only memory 802 (Read-Only Memory, ROM) or a program loaded from the storage section 808 into the random access memory 803 (Random Access Memory, RAM). In the random access memory 803, various programs and data required for system operation are also stored. The central processing unit 801, the read-only memory 802, and the random access memory 803 are connected to each other via a bus 804. The input / output interface 805 (Input / Output interface, i.e., I / O interface) is also connected to the bus 804.

[0143] The following components are connected to the input / output interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including, for example, a cathode ray tube (Cathode Ray Tube, CRT), a liquid crystal display (Liquid Crystal Display, LCD), etc. and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a local area network card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the input / output interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed so that a computer program read from it can be installed into the storage section 808 as needed.

[0144] In particular, according to an embodiment of the present application, the processes described in each method flow chart can be implemented as computer software programs. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the central processing unit 801, various functions defined in the system of the present application are executed.

[0145] It should be noted that Figure 8 The computer system 800 of the electronic device shown is only an example, and should not bring any restrictions to the functions and usage scope of the embodiments of the present application.

[0146] According to another aspect of the embodiments of the present application, an electronic device is further provided, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0147] In an exemplary embodiment, the above electronic device may further include a transmission device and an input / output device. Among them, the transmission device is connected to the above input / output resource pool, and the input / output device is connected to the above input / output resource pool.

[0148] For the specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary embodiments, and details are not described herein again.

[0149] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the embodiments of the present application can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. They can be implemented by program code executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order from here, or they can be separately made into individual integrated circuit modules, or multiple modules or steps among them can be made into a single integrated circuit module to implement. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0150] The above are only the preferred embodiments of the present application and are not used to limit the embodiments of the present application. For those skilled in the art, the embodiments of the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the principle of the embodiments of the present application should be included in the protection scope of the embodiments of the present application.

Claims

1. A vehicle driving scheduling method, characterized in that, Including: A set of vehicle information for each reference vehicle in a set of reference vehicles detected by a vehicle detection system, where the vehicle detection system is located before the on-ramp of the target bridge, and the set of vehicle information includes vehicle weight, driving speed, and passing time, and the passing time is the time when the vehicle passes a specified position before the on-ramp of the target bridge; Predicting the deck load distribution of the target bridge according to the set of vehicle information of each reference vehicle, where the deck load distribution is used to represent the distribution of the deck load of the target bridge; When it is determined that the target bridge allows vehicles to enter based on the deck load distribution, performing vehicle driving scheduling based on the deck load distribution to make the deck load of the target bridge uniform.

2. The method according to claim 1, characterized in that, The target bridge includes multiple bridge lanes, and the set of reference vehicles includes a set of lane vehicles located in each of the multiple bridge lanes; The predicting the deck load distribution of the target bridge according to the set of vehicle information of each reference vehicle includes: Determining the lane load of each bridge lane according to the set of vehicle information of each lane vehicle located in each bridge lane, where the deck load distribution includes the lane load of each bridge lane.

3. The method according to claim 2, wherein The determining the lane load of each bridge lane according to the set of vehicle information of each lane vehicle located in each bridge lane includes: Taking each bridge lane as the current bridge lane and performing the following load determination operation to obtain the lane load of each bridge lane, where a set of lane vehicles located in the current bridge lane is the current vehicle sequence: Determining the vehicle position of each reference vehicle according to the time difference between the current time and the passing time of each current vehicle in the current vehicle sequence and the driving speed of each reference vehicle; Determining the vehicle average load corresponding to each current vehicle according to the vehicle weight of each current vehicle and the vehicle position of each current vehicle, where the vehicle average load corresponding to each current vehicle is the average load of the road surface within the vehicle spacing range corresponding to each current vehicle, and the vehicle spacing range corresponding to each current vehicle is the range from the vehicle position of each current vehicle to the vehicle position of the next current vehicle of each current vehicle; Determining the sum of the vehicle average loads corresponding to each current vehicle as the lane load of the current bridge lane.

4. The method according to claim 3, wherein The determining the vehicle average load corresponding to each current vehicle according to the vehicle weight of each current vehicle and the vehicle position of each current vehicle includes: Determining the value obtained by dividing the vehicle weight of each current vehicle by the position difference between the vehicle position of each current vehicle and the vehicle position of the next current vehicle of each current vehicle as the vehicle average load corresponding to each current vehicle.

5. The method according to claim 2, wherein After predicting the deck load distribution of the target bridge according to the set of vehicle information of each reference vehicle, the method further includes: When a to-be-entered vehicle to enter the target bridge is detected by the vehicle detection system, predicting the driving trajectory of the to-be-entered vehicle after it enters each bridge lane, and obtaining vehicle driving trajectories corresponding to each bridge lane; Predicting the probability of the target bridge tipping over based on the vehicle driving trajectories corresponding to each bridge lane, and obtaining prediction probabilities corresponding to each bridge lane; When the prediction probabilities corresponding to each bridge lane are all greater than or equal to a preset probability threshold, determining that the to-be-entered vehicle is not allowed to enter the target bridge.

6. The method according to claim 2, wherein After predicting the deck load distribution of the target bridge according to the set of vehicle information of each reference vehicle, the method further includes: When a to-be-entered vehicle to enter the target bridge is detected by the vehicle detection system, predicting the lane load of each bridge lane after the to-be-entered vehicle enters each bridge lane, and obtaining predicted lane loads of each bridge lane; When the predicted lane loads of each bridge lane are all greater than or equal to a preset load threshold, determining that the to-be-entered vehicle is not allowed to enter the target bridge.

7. The method according to claim 2, wherein When it is determined that the target bridge allows vehicles to enter based on the deck load distribution, performing vehicle driving scheduling based on the deck load distribution, including: When it is determined that the target bridge allows vehicles to enter based on the deck load distribution, selecting a target bridge lane from the multiple bridge lanes, where the target bridge lane is the bridge lane with the smallest lane load among the multiple bridge lanes; Controlling a lane prompting component to send a vehicle guiding prompt message to the to-be-entered vehicle, where the lane prompting component is a component for performing lane scheduling prompts, and the vehicle guiding prompt message is used to prompt the to-be-entered vehicle to enter the target bridge lane.

8. The method according to claim 7, wherein Before the lane prompting component is controlled to send a vehicle guiding prompt message to the to-be-entered vehicle, the method further includes: Determining the load interval to which the lane load of the target bridge lane belongs in a preset set of load intervals, and obtaining a target load interval; Determining the expected driving speed of the to-be-entered vehicle as the driving speed matching the target load interval, where the vehicle guiding prompt message is also used to prompt the expected driving speed.

9. The method according to any one of claims 1 to 8, characterized in that, The vehicle detection system includes a weighing detection device and a license plate capture device.

10. A vehicle driving scheduling system, characterized in that, Including: A weighing sensor, located before the on-ramp of the target bridge, for weighing the passing vehicles and transmitting the vehicle weight data obtained by weighing to a data processor; The data processor is configured to receive the vehicle weight data transmitted by the weighing sensor and obtain the vehicle weight of each reference vehicle in the reference vehicle set based on the received vehicle weight data; Determine the deck load distribution parameters of the target bridge according to the vehicle weight of each reference vehicle and the vehicle position of each reference vehicle, wherein the deck load distribution parameters are used to represent the distribution of the deck load of the target bridge; when it is determined that vehicles are allowed to enter the target bridge according to the deck load distribution parameters, perform vehicle driving scheduling based on the deck load distribution parameters to make the deck load of the target bridge uniform.

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