Road collapse event processing method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention discloses a road collapse event processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring road detection data of a target road; determining whether a road collapse event occurs on the target road based on the road detection data; if the road collapse event occurs, vehicle information of each running vehicle in j running vehicles corresponding to the target road is obtained, j pieces of vehicle information are obtained, and the j pieces of vehicle information comprise j vehicle positions and j vehicle speeds; j is a natural number; the server is in communication connection with the j running vehicles; determining n target vehicles from the j traveling vehicles based on the j vehicle positions; n is a natural number less than or equal to j; and based on the n vehicle positions and the n vehicle speeds corresponding to the n target vehicles in the j pieces of vehicle information, the vehicles in the n target vehicles are controlled to conduct corresponding braking operation, so that each vehicle in the n target vehicles is safely parked in front of the dangerous area corresponding to the road collapse event.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to a road collapse event processing method, device, electronic equipment and storage medium. Background Art

[0002] Emergency events on the road, such as road collapse, occur from time to time, especially on highways, where a large number of passing vehicles are involved, which may cause significant casualties or property losses.

[0003] Due to the unpredictability of road collapse events, the high speed on the highway and the lack of driving experience of drivers, it is difficult for drivers to detect such emergencies, or they are unable to make correct judgments and make correct decisions and handle them immediately after they detect them, which poses a great hidden danger to the personal safety of relevant personnel. For example, drivers may mistake cracks in the road surface that appear when the highway collapses as puddles, and when drivers react to the collapse, they may not have time to brake the vehicle.

[0004] Therefore, there is an urgent need for a road collapse event processing method, device, electronic equipment and storage medium to timely judge and process relevant vehicles when a collapse event occurs on a highway to ensure the personal safety of relevant personnel. Summary of the invention

[0005] In order to solve the above problems, the embodiments of the present invention provide a road collapse event processing method, device, electronic device and storage medium, which can timely judge and process related vehicles when a collapse event occurs on a highway to ensure the personal safety of related personnel.

[0006] In a first aspect, an embodiment of the present invention provides a method for processing a road collapse event, which is applied to a server, and the method includes:

[0007] Acquire road detection data of the target road;

[0008] Determining whether a road collapse event occurs on the target road based on the road detection data;

[0009] If the road collapse event occurs, obtain vehicle information of each of j vehicles traveling on the target road to obtain j vehicle information, wherein the j vehicle information includes j vehicle positions and j vehicle speeds; j is a natural number; and the server maintains a communication connection with the j vehicles.

[0010] Determining n target vehicles from the j moving vehicles based on the j vehicle positions; n is a natural number less than or equal to j;

[0011] Based on the n vehicle positions and n vehicle speeds corresponding to the n target vehicles in the j vehicle information, corresponding vehicles among the n target vehicles are controlled to perform corresponding braking operations, so that each of the n target vehicles can stop safely in front of the dangerous area corresponding to the road collapse event.

[0012] In a second aspect, an embodiment of the present invention provides a road collapse event processing device, which is applied to a server, and the device includes an acquisition unit and a processing unit;

[0013] The acquisition unit is used to acquire road detection data of the target road;

[0014] The processing unit is used to determine whether a road collapse event occurs on the target road based on the road detection data;

[0015] If the road collapse event occurs, obtain vehicle information of each of j vehicles traveling on the target road to obtain j vehicle information, wherein the j vehicle information includes j vehicle positions and j vehicle speeds; j is a natural number; and the server maintains a communication connection with the j vehicles.

[0016] Determining n target vehicles from the j moving vehicles based on the j vehicle positions; n is a natural number less than or equal to j;

[0017] Based on the n vehicle positions and n vehicle speeds corresponding to the n target vehicles in the j vehicle information, corresponding vehicles among the n target vehicles are controlled to perform corresponding braking operations, so that each of the n target vehicles can stop safely in front of the dangerous area corresponding to the road collapse event.

[0018] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device performs the method described in the first aspect.

[0019] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method as described in the first aspect.

[0020] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer is operable to cause the computer to execute the method described in the first aspect.

[0021] Implementing the embodiments of the present application has the following beneficial effects:

[0022] In the implementation mode of the present application, the road detection data of the target road is first obtained, and it is determined whether a road collapse event occurs on the target road based on the road detection data. If a road collapse event occurs, the vehicle information of each of the j vehicles corresponding to the target road is obtained to obtain j vehicle information, and the j vehicle information includes j vehicle positions and j vehicle speeds, where j is a natural number, and the server maintains a communication connection with the j vehicles. Then, n target vehicles are determined from the j vehicles based on the j vehicle positions, where n is a natural number less than or equal to j. Finally, the corresponding vehicles in the n target vehicles are controlled to perform corresponding braking operations based on the n vehicle positions and n vehicle speeds corresponding to the n target vehicles in the j vehicle information, so that each of the n target vehicles safely stops in front of the dangerous area corresponding to the road collapse event. Thus, it is determined whether a road collapse event occurs on the target road through the road detection data and a plurality of vehicle information is obtained when a road collapse event occurs, and the corresponding vehicles are braked based on the vehicle information, so that the relevant vehicles can be judged and processed in time when a collapse event occurs on the highway to ensure the personal safety of the relevant personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 It is a schematic diagram of the architecture of a road collapse event processing system provided in an embodiment of the present application;

[0025] Figure 2 is a flow chart of a method for handling a road collapse event provided by an embodiment of the present application;

[0026] Figure 3 It is a structural schematic diagram of a road collapse event processing device provided in an embodiment of the present application;

[0027] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] The terms "first", "second", "third" and "fourth" etc. in the specification and claims of the present application and the drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not limited to the listed steps or modules, but optionally includes steps or modules that are not listed, or optionally includes other steps or modules inherent to these processes, methods, products or devices.

[0030] Reference to "embodiments" herein means that a particular feature, result, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0031] The road collapse event processing method provided in the embodiment of the present application is applied to the server, see Figure 1 , Figure 1 Schematic diagram of the architecture of a road collapse event processing system provided in an embodiment of the present application. Figure 1 As shown, the road collapse event processing system includes a server and a vehicle, and the server and the vehicle maintain a communication connection. The vehicle can be a moving vehicle on the target road. The server and the vehicle can exchange data through various communication connection methods such as cellular communication networks, dedicated short-range communications, satellite communications, Bluetooth communications, etc., and there is no limitation here.

[0032] See also Figure 2 , Figure 2 1 is a flow chart of a method for handling a road collapse event provided by an embodiment of the present application. Figure 2 As shown, the road collapse event processing method provided in the embodiment of the present application includes but is not limited to the following steps:

[0033] Step S101: Acquire road detection data of a target road;

[0034] Step S102: determining whether a road collapse event occurs on the target road based on the road detection data;

[0035] Step S103: if a road collapse event occurs, obtain vehicle information of each of j vehicles traveling on the target road to obtain j pieces of vehicle information;

[0036] The j vehicle information includes j vehicle positions and j vehicle speeds; j is a natural number; the server maintains a communication connection with the j moving vehicles;

[0037] Step S104: determining n target vehicles from j moving vehicles based on j vehicle positions;

[0038] Wherein, n is a natural number less than or equal to j;

[0039] Step S105: Based on the n vehicle positions and n vehicle speeds corresponding to the n target vehicles in the j vehicle information, control the corresponding vehicles among the n target vehicles to perform corresponding braking operations, so that each of the n target vehicles stops safely in front of the dangerous area corresponding to the road collapse event.

[0040] In a possible embodiment, the target road may be a road with a high risk of road collapse or a road that will cause great losses after road collapse. The road detection data of the target road is obtained by a road detection device, which can be set at one or more preset detection stations of the target road. For example, detection stations are set at preset distances along the target road, and each detection station is equipped with a high-precision laser rangefinder, a geological radar, a microseismic sensor, a high-definition camera and other types of sensors, among which the high-precision laser rangefinder is used to measure the slight deformation and settlement of the road surface, the geological radar is used to detect the changes in the stratum structure under the road to detect whether there are underground cavities or loose soil, the microseismic sensor is used to detect the tiny vibration signals generated by the road and the surrounding geological bodies due to structural changes, and the high-definition camera is used to shoot the road surface from different angles and detect the development of visible damage such as cracks and potholes. In addition to the preset inspection stations, satellite remote sensing can also be used to assist monitoring. Satellites can be used to regularly perform high-resolution imaging of target roads, compare image data from different periods, and analyze changes in the overall appearance of the road, such as linear offsets of the road, changes in surrounding terrain, etc. Satellite data can cover a large area, which helps to identify potential problems in areas far away from the inspection stations, and complement and verify with ground sensor data. In addition, road monitoring data can be obtained through vehicle sensor data feedback. The on-board sensors of vehicles traveling on the target road collect data such as road flatness and bumpiness, and transmit them back to the server in real time. The distributed data collection of a large number of vehicles can provide more comprehensive road condition information, especially some temporary or local abnormal conditions.

[0041] In one possible embodiment, if a suspected road collapse signal is detected, the historical data and the surrounding area data are automatically compared for preliminary verification. If the abnormal signal persists and meets the collapse characteristics, the monitoring and early warning mechanism is activated, and an early warning message is sent to the monitoring center. Inspection drones or vehicles near the target road are dispatched to the scene of the road collapse for rapid verification. The high-definition aerial images of the drone and the on-site inspection of the on-site vehicles are used to finally determine whether a road collapse has occurred.

[0042] In a possible embodiment, the Internet of Vehicles communication technology is used to maintain a high-speed and stable communication connection between the server and the vehicles traveling on the target road, and each vehicle uploads vehicle information to the server at a preset time interval. The vehicle information includes the vehicle position and vehicle speed. The vehicle information may also include the vehicle's driving direction, vehicle type, and vehicle load information, etc. The vehicle position can be obtained through the Beidou and GPS systems, the vehicle speed can be measured and obtained by the on-board speed sensor, the vehicle type can be divided into sedans, trucks, buses, etc., and the vehicle load information can be obtained for vehicles of the truck type.

[0043] In a possible embodiment, the risk of each vehicle entering the collapse danger zone is quantitatively assessed by comprehensively considering factors such as the distance between the vehicle and the collapse zone, the vehicle speed, the vehicle type, and the road congestion. Based on the assessment results, n target vehicles are screened in order of risk from high to low, and braking control operations are performed preferentially on high-risk vehicles.

[0044] In a possible embodiment, the mass distribution of each target vehicle is determined based on the vehicle type and load information of each target vehicle, the friction coefficient between the tire of the target vehicle and the road surface is determined based on the road surface type and weather conditions, and the braking distance and required braking force of the target vehicle under the current driving state are calculated based on the mass distribution of the target vehicle, the friction coefficient, the performance parameters of the braking system, and other factors. Combined with the real-time distance between the target vehicle and the collapsed area, the road slope, the surrounding traffic conditions, and other factors, the braking strategy is dynamically adjusted. For example, for vehicles that are close to the collapsed area and have no other vehicles around, an emergency braking strategy is adopted to stop the vehicle in the shortest time and distance. For vehicles in congested traffic, a strategy combining slow braking and distance keeping is adopted to gradually slow down and stop the vehicle while avoiding collisions. For vehicles traveling on steep slopes, the braking force is appropriately increased and vehicle stability control is considered to prevent the vehicle from skidding or losing control.

[0045] In a possible embodiment, the security, integrity and non-tamperability of the braking instructions during transmission are ensured by an encrypted communication protocol based on blockchain technology. The server encrypts the generated braking instructions and sends them to the target vehicle. The vehicle decrypts and verifies the instructions after receiving them, and only performs the braking operation after passing the verification. In addition, during the braking operation, the target vehicle transmits the braking execution status information to the server in real time, including the braking pressure, wheel speed, vehicle deceleration, etc. The server remotely monitors and adjusts the braking process based on this feedback information. If it is determined that the vehicle braking is abnormal, such as insufficient braking force due to a brake system failure, the braking strategy of the surrounding vehicles is adjusted in time to avoid collision accidents.

[0046] Optionally, step S101, obtaining road detection data of a target road, may include the following steps:

[0047] Step S201: Obtaining road information of a target road, where the road information includes at least one of the following: road age, road location, road type, and road load condition;

[0048] Step S202: determining a road detection level of the target road based on the road information, where the road detection level is used to reflect the detection requirement of the target road;

[0049] Step S203: Acquire climate data corresponding to the target road;

[0050] Step S204: determining the detection frequency of the target road based on the road detection level and climate data;

[0051] Step S205: acquiring first detection data of the target road based on the detection frequency, the first detection data including the settlement distance and the road sound;

[0052] Step S206: determining a collapse risk score of the target road based on the first detection data;

[0053] Step S207: when the collapse risk score is higher than a preset risk score threshold, determining m detection vehicles that passed through the target road within a preset historical time period; m is a positive integer greater than 1;

[0054] Step S208: obtaining second detection data of the target road through m detection vehicles, where the second detection data is used to reflect the road surface condition of the target road;

[0055] Step S209: Integrate the first detection data and the second detection data to obtain road detection data.

[0056] In a possible embodiment, the road information of the target road is obtained and the road detection level of the target road is determined based on the road information, wherein the road information includes at least one of the following: road age, road location, road type, and road load. The road detection level is used to reflect the detection demand of the target road. Then, the climate data corresponding to the target road is obtained, and the detection frequency of the target road is determined based on the road detection level and the climate data. For example, for a road with a long age and located in a geologically complex area, such as a bridge or tunnel road with a soft soil foundation, an earthquake active zone, or a landslide-prone area, or a main road with a very large road load, the road detection level is determined to be a high detection level, while for a newly built ordinary road with stable geological conditions and a light load, the road detection level is determined to be a low detection level. The detection frequency of the target road is dynamically adjusted based on the road detection level and climate data. For a target road with a high detection level, if it is in a bad climate period, such as a rainstorm season, a cold and freezing period, or a typhoon high-incidence period, the detection frequency of the target road is increased, and in a relatively stable climate period, the detection frequency is appropriately reduced. For a target road with a low detection level, the detection frequency is increased under extreme climate conditions, and a lower detection frequency is maintained under normal climate. In addition, based on real-time meteorological warning information, such as sudden rainstorms, strong winds and other alerts, inspection arrangements for specific road sections are increased to ensure that potential risks of road collapse caused by climate factors are identified in a timely manner.

[0057] In a possible embodiment, first detection data of the target road is obtained based on the detection frequency, wherein the first detection data includes the settlement distance and the road sound, then, the collapse risk score of the target road is determined based on the first detection data, when the collapse risk score is higher than the preset risk score threshold, m detection vehicles passing through the target road in a preset historical time period are determined, m is a positive integer greater than 1, and second detection data of the target road is obtained through the m detection vehicles, wherein the second detection data is used to reflect the road surface condition of the target road, and finally the first detection data and the second detection data are integrated to obtain the road detection data. When obtaining the second detection data, it can be obtained through a variety of road surface condition monitoring equipment equipped on the detection vehicle, and the road surface monitoring equipment can be a camera, a bump sensor, a road surface humidity sensor, a road surface temperature sensor, and a tire pressure monitoring system, wherein the type, size, and severity of cracks in the road surface can be automatically identified through the high-definition image recognition technology of the camera, and the bump sensor combined with the tire pressure monitoring data can more accurately determine the bumpiness of the vehicle during driving, thereby determining the flatness and structural integrity of the road surface, and the road surface humidity and temperature sensors can determine the environmental information of the road surface. When integrating data, for the second detection data from different detection vehicles, duplicate data and abnormal data points are removed, and the second detection data is matched with the specific sections of the road according to the vehicle's driving trajectory and location information to build a complete road condition map to intuitively display the road conditions of different sections of the road.

[0058] In the embodiment of the present application, by dynamically adjusting the detection frequency, it is possible to ensure close monitoring of roads during high-risk periods and timely discover road problems caused by climate, while avoiding excessive detection during low-risk periods, saving manpower, material resources and time costs. The second detection data is obtained through the feedback of vehicle sensor data, making the detection vehicle a mobile road monitoring station, broadening the monitoring range, and being able to widely collect road surface information at different locations and time periods.

[0059] Optionally, step S102, determining whether a road collapse event occurs on the target road based on the road detection data, may include the following steps:

[0060] Step S301: determining i bump positions of the target road based on the road detection data; i is a natural number;

[0061] Step S302: determining a road height change trend, a road gap change trend, and a road sound feature based on the road detection data, wherein the road gap change trend includes a change trend in the number, length, and width of the road gaps;

[0062] Step S303: obtaining vehicle bump data of m detection vehicles;

[0063] Step S304: determining the bump coefficient of the target road based on the i bump positions and the vehicle bump data;

[0064] Step S305: If the road height change trend is consistent with the preset first change trend, or the road gap change trend is consistent with the preset second change trend, or the road sound feature is consistent with the preset sound feature, or the bump coefficient is higher than the preset bump coefficient threshold, it is determined that a road collapse event occurs on the target road;

[0065] Step S306: Otherwise, it is determined that no road collapse event occurs on the target road.

[0066] In a possible embodiment, i bump positions of the target road are determined based on the road detection data. The bump positions may be the positions of speed bumps on the target road, where i is a natural number. The road height change trend, road gap change trend, and road sound characteristics are determined based on the road detection data. The road gap change trend includes the change trend of the number, length, and width of road gaps. Then, the vehicle bump data of m detection vehicles are obtained. The bump data includes the amplitude, frequency, and position of the bumps, and the fluctuation of tire pressure. The bump coefficient of the target road is determined based on the i bump positions and the vehicle bump data. If the detection vehicle is determined to have bumps at other positions of the i bump positions through the vehicle bump data, it indicates that new cracks or road settlements have appeared on the target road, and the bump coefficient is correspondingly high. If the position where the detection vehicle is bumped on the target road determined through the vehicle bump data is consistent with the i bump positions, the bump coefficient is low. If the road height change trend is consistent with the preset first change trend, or the road gap change trend is consistent with the preset second change trend, or the road sound feature is consistent with the preset sound feature, or the bump coefficient is higher than the preset bump coefficient threshold, it is determined that the target road has a road collapse event, otherwise, it is determined that the target road has not a road collapse event, wherein the preset first change trend may be a lower road height, the preset second change trend may be an increase in the number of gaps, an increase in the gap length, and an increase in the gap width, and the preset sound feature may be determined based on the collapse sound of the target road. In addition, it is also possible to determine whether a road collapse event has occurred by determining whether multiple conditions are met at the same time.

[0067] Optionally, step S104, determining n target vehicles from j moving vehicles based on j vehicle positions, may include the following steps:

[0068] Step S401: determining the location of the road collapse event based on the road detection data;

[0069] Step S402: determining a first relative position relationship between each of the j moving vehicles and the occurrence position based on the occurrence position and the j vehicle positions, and obtaining j first relative position relationships;

[0070] Step S403: The moving vehicles in the j first relative position relationships that are consistent with the preset relative position relationship and in the j vehicle information whose vehicle speed is greater than the preset speed threshold are taken as n target vehicles.

[0071] In a possible embodiment, the preset relative position relationship and speed threshold are determined according to the traffic rules and road structure characteristics of the target road. For example, a vehicle that is within a certain range from the location where the collapse event occurs and is traveling in the direction of the collapse area and whose vehicle speed is greater than the preset speed threshold of the road speed limit is taken as the target vehicle. These preset parameters can be dynamically adjusted and optimized according to different types of roads, such as urban roads or highways, and actual traffic conditions. For other moving vehicles that are not target vehicles, an early warning message can be sent first to remind the driver to slow down and pay attention to changes in road conditions, and further instructions can be issued according to actual conditions. Different types of vehicles have different coping capabilities and risk levels when facing road collapse events. By determining the target vehicle among multiple moving vehicles, emergency resources can be reasonably allocated, high-risk vehicles can be given priority, and the efficiency and effectiveness of emergency disposal can be improved.

[0072] Optionally, step S105, controlling corresponding vehicles among the n target vehicles to perform corresponding braking operations based on the n vehicle positions and n vehicle speeds corresponding to the n target vehicles in the j vehicle information, may include the following steps:

[0073] Step S501: determining the minimum braking distance of each of the n target vehicles based on the current road condition of the target road and the n vehicle speeds, and obtaining n minimum braking distances;

[0074] Step S502: determining a second relative position relationship between each target vehicle and an adjacent vehicle among the n target vehicles based on the n vehicle positions, and obtaining n second relative position relationships;

[0075] Step S503: determining a first distance between each of the n target vehicles and the occurrence position based on the n first relative position relationships, to obtain n first distances;

[0076] Step S504: determining a second distance between each of the n target vehicles and the preceding vehicle based on the n second relative position relationships, to obtain n second distances;

[0077] Step S505: determining a candidate braking distance of each of the n target vehicles based on the n first distances and the n second distances to obtain n candidate braking distances;

[0078] Step S506: Controlling a corresponding target vehicle among the n target vehicles to brake based on the n candidate braking distances and the n minimum braking distances.

[0079] In a possible embodiment, in addition to determining the basic parameters such as the mass and speed of the target vehicle, the performance of the target vehicle's braking system is also determined, such as the friction coefficient between the brake disc and the brake pad, the transmission efficiency of the braking pressure, etc., and the friction change between the tire and the road surface is determined according to the current road conditions, such as dry, wet, icy, etc., so as to determine the minimum braking distance of the target vehicle. After determining the first distance and the second distance of each target vehicle, the candidate braking distance of each target vehicle is determined based on the n first distances and the n second distances. The candidate braking distance is the maximum braking distance that the target vehicle can choose under the current driving state. The candidate braking distance of the target vehicle is greater than its minimum braking distance to ensure that collisions between different target vehicles are avoided. When the candidate braking distance of the target vehicle is greater than the minimum braking distance, the target vehicle is braked based on the candidate braking distance to ensure smooth braking of the target vehicle and ensure the driving experience of the passengers.

[0080] Optionally, step S506, controlling a corresponding target vehicle among the n target vehicles to brake based on the n candidate braking distances and the n minimum braking distances, may include the following steps:

[0081] Step S601: determining a target braking distance of each of n target vehicles based on n preset distances, n vehicle positions, n candidate braking distances and n minimum braking distances, and obtaining n target braking distances, so that the distance between adjacent vehicles of the n target vehicles is greater than a corresponding preset distance among the n preset distances; and the n target braking distances are greater than or equal to a corresponding minimum braking distance among the n minimum braking distances;

[0082] Step S602: determining n braking instructions based on n target braking distances and n vehicle speeds, wherein the n braking instructions include n braking parameter information, and the n braking parameter information includes n braking pressure value sequences and n braking time sequences;

[0083] Step S603: n braking instructions are issued to n target vehicles, so that the n target vehicles brake based on the n braking instructions.

[0084] In a possible embodiment, the preset distance is the minimum distance that needs to be maintained between the target vehicle and the adjacent vehicle. For the target vehicle, if the candidate braking distance is greater than or equal to the minimum braking distance, or the candidate braking distance is less than or equal to the sum of the minimum braking distance and the preset distance, the target braking distance is the minimum braking distance. If the candidate braking distance is greater than the sum of the minimum braking distance and the preset distance, the candidate braking distance can be used as the target braking distance, or a distance value between the sum and the candidate braking distance can be used as the target braking distance.

[0085] In a possible embodiment, a personalized braking instruction is generated according to the vehicle type, braking system characteristics and candidate braking distance of each target vehicle. The braking instruction not only includes basic parameters such as the start time of braking, the magnitude and change law of braking pressure, but also optimizes and adjusts according to the characteristics of different vehicles. For example, for vehicles equipped with advanced electronic stability programs and anti-lock braking systems, the functions of these systems can be fully utilized in the braking instruction to achieve a smoother and more efficient braking process. For heavy trucks, due to their large inertia, the braking instruction focuses on slowly and steadily increasing the braking pressure to avoid the vehicle from losing control due to sudden braking. The braking instruction is transmitted to the electronic control unit of the target vehicle through the Internet of Vehicles communication technology, thereby controlling the vehicle's braking system to perform the braking operation.

[0086] In a possible embodiment, in order to ensure the safety and reliability of the braking command during transmission, encryption, authentication and verification technology are used. The braking command is encrypted on the server side to prevent the command from being tampered with or forged during transmission. The received command is decrypted and authenticated on the vehicle side. Only authenticated legal commands will be executed. During the command transmission process, data verification technology, such as cyclic redundancy check, is used to ensure the integrity of the command data. In addition, a feedback mechanism for command transmission is established. After receiving the braking command, the vehicle immediately sends feedback information to the server. The feedback information includes the command reception status and execution preparation status. The server monitors and adjusts the command transmission based on the feedback information to ensure that the braking command can accurately reach the target vehicle and be effectively executed. During the braking operation of the target vehicle, the vehicle's braking status data is collected in real time using on-board sensors such as wheel speed sensors, acceleration sensors, and vehicle attitude sensors. The wheel speed sensor is used to monitor the change in wheel speed and determine whether the vehicle is locked. The acceleration sensor is used to measure the vehicle's deceleration and evaluate the strength and effect of the braking. The vehicle attitude sensor is used to detect the change in the vehicle's attitude during the braking process, such as whether there is unstable conditions such as side slipping and tail swinging. These sensor data are then transmitted back to the server in real time through the Internet of Vehicles technology. The server analyzes and processes the data to evaluate the braking effect of each target vehicle. When evaluating the braking effect, multiple indicators are included, including whether the braking distance meets expectations, whether the braking process is smooth, such as whether the change in deceleration is uniform, whether the vehicle remains stable, such as whether there is side slipping, tail swinging, etc., and whether the safe distance with the vehicle in front is maintained. If the braking effect of the target vehicle is not ideal, such as the braking distance is too long or unstable conditions occur, the server will adjust the braking strategy in time, recalculate the target braking distance, and send a revised braking instruction to the target vehicle to ensure that the vehicle can stop safely and smoothly.

[0087] Optionally, before controlling the corresponding vehicle among the n target vehicles to brake based on the n candidate braking distances and the n minimum braking distances, the following steps are also included:

[0088] Step S701: taking a target vehicle whose braking distance among n candidate braking distances is smaller than the corresponding minimum braking distance among n minimum braking distances as a first vehicle, and obtaining a first vehicles; a is a natural number less than or equal to n;

[0089] Step S702: Acquire at least one adjacent lane driving data of each of a first vehicles to obtain a adjacent lane driving data sets; the adjacent lane driving data is used to reflect the driving conditions of vehicles in the adjacent lanes;

[0090] Step S703: determining a target driving lane for each of the a first vehicles based on the a adjacent lane driving data and the a minimum braking distances corresponding to the a first vehicles, to obtain a target driving lanes;

[0091] Step S704: determining a driving parameter of each of the a first vehicles based on the a target driving lanes and the a vehicle speeds corresponding to the a first vehicles, and obtaining a driving parameters, wherein the driving parameters include a steering wheel angle sequence and a steering time sequence;

[0092] Step S705: sending a driving parameters to a first vehicles, so that each of the a first vehicles travels based on a corresponding driving parameter among the a driving parameters.

[0093] In a possible embodiment, if the candidate braking distance of the target vehicle is less than its minimum braking distance, then if the target vehicle is braked forcibly, the expected effect cannot be achieved, and the target vehicle may enter the dangerous area corresponding to the road collapse event, or collide with other vehicles. In this case, the target vehicle is controlled to change the driving lane without affecting the driving of other vehicles, such as temporarily driving into the opposite lane when there is no vehicle in the opposite lane, or driving into the emergency lane, etc., to reduce the losses caused by the road collapse accident.

[0094] In a possible embodiment, the target driving lane of the first vehicle is determined based on a adjacent lane driving data and a minimum braking distances corresponding to a first vehicle. First, the traffic conditions of the adjacent lanes are comprehensively evaluated, including factors such as the congestion level of the lanes, the average speed of vehicles, and the distance between vehicles. Based on the minimum braking distance of the first vehicle, the collision risk that may be encountered if braking in the lane and the risk that may be encountered if switching to the adjacent lane are analyzed at the current speed. The collision risk can be determined based on the collision probability and the collision severity. For example, if the traffic in the adjacent lanes is smooth and the distance between vehicles is large, and there is congestion or potential braking risk in front of the lane, the first vehicle is selected to switch to a suitable adjacent lane. The driving parameters of the first vehicle are calculated based on the determined a target driving lanes and a vehicle speeds corresponding to the a first vehicles, including the steering wheel angle sequence and the steering time sequence, to ensure the stability and safety of the vehicle during the lane change process. For example, the appropriate initial steering wheel angle is calculated based on the vehicle's current speed and the angle between the target driving lane and the current lane. Then, the subsequent steering wheel angle change sequence is gradually determined based on the vehicle's steering response characteristics and the desired lane change trajectory. At the same time, a reasonable steering time sequence is calculated based on the vehicle's speed and road conditions to control the rhythm of the lane change process and avoid vehicle loss of control or affecting the driving of other vehicles due to steering too fast or too slow.

[0095] In a possible embodiment, after the first vehicle receives the driving parameter instruction, the on-board electronic control unit controls the steering system of the vehicle to perform the lane change operation according to the instruction. During the execution process, the vehicle's driving state is monitored in real time through the on-board sensor, including the steering wheel angle, the vehicle's lateral acceleration, the distance between the vehicle and the adjacent lane, and other information, and the information is fed back to the server. The server analyzes the feedback information to determine whether the vehicle is driving according to the expected driving parameters. If the actual driving state of the vehicle does not meet the expectations, such as the steering wheel angle deviation is too large, the vehicle's lateral acceleration is abnormal, etc., the server recalculates the driving parameters according to the actual situation of the vehicle, generates a new instruction and sends it to the vehicle, and adjusts the vehicle's driving process in time. During the vehicle's lane change operation, the on-board human-computer interaction system provides the driver with relevant information prompts, such as lane change intention prompts, steering operation prompts, etc., so that the driver can understand the vehicle's driving intention and improve the driver's sense of security and participation. If the driver believes that the vehicle's automatic lane change operation poses a safety hazard or does not meet actual driving needs, the driver can manually intervene in the vehicle's driving through the human-computer interaction system, suspend or cancel the automatic lane change operation. While ensuring the safety of the vehicle's automatic driving, it fully combines the driver's subjective judgment and driving experience to achieve human-computer collaborative driving and improve driving comfort and flexibility.

[0096] In a possible embodiment, the method for handling road collapse events of the present application in a highway scenario is implemented through detection equipment, display equipment, vehicle-mounted audio system, server, auxiliary braking system, auxiliary parking system and diagnostic equipment, wherein the detection equipment can be a high-definition camera, a thermal imaging camera, a driving recorder, etc., which is used to detect the road condition and the condition of the vehicle in front, the display device is used to display abnormal conditions, the vehicle-mounted audio system is used to broadcast abnormal conditions, guide the driver, etc., the server is used to issue control instructions and interact with other devices or systems for instructions, the auxiliary braking system is used to assist the vehicle in braking after an abnormal condition is detected, the auxiliary parking system is used to assist the vehicle in parking after an abnormal condition is detected, and the diagnostic equipment diagnoses the vehicle speed and abnormal conditions.

[0097] Specifically, the road condition is detected in real time during the driving process of the vehicle, and the vehicle is diagnosed. When the vehicle speed reaches the preset speed, the detection equipment is turned on. Based on the detection equipment, the road condition in front of the vehicle is detected in real time through one or more methods of visual sensors, laser radars, millimeter wave radars, vehicle dynamic data, satellite positioning and map data, communication and network technologies. Exemplarily, the road condition is detected by visual sensors, and multiple high-definition cameras can be installed on the front, top or side of the vehicle to capture images of the road ahead in real time. Then, the captured images are analyzed using computer vision and image processing algorithms to identify features such as sunken areas of road subsidence, the shape and length of cracks, and road deformation caused by loose roadbed. Exemplarily, the detection results are obtained by comparing satellite positioning with map data, and the precise position of the vehicle is obtained with the help of a satellite positioning system. The real-time position is compared with the pre-stored high-precision map data. If it is found that there is a large difference between the actual road surface and the height, flatness and other information in the map, it is suggested that there may be abnormal road surface problems. Exemplarily, vehicle-to-vehicle communication or vehicle-to-infrastructure communication is performed based on communication and network technologies, and road condition information is shared between vehicles, and road condition information is obtained from infrastructure beside the road.

[0098] When an abnormal situation is detected, the abnormal situation is judged. The abnormal situation can be road subsidence, crack expansion, loose roadbed, traffic interruption, etc. When one or more of the above abnormal situations are detected, it is judged that a highway collapse event may occur. Further, when an abnormal situation on the ground is detected, a more accurate comprehensive judgment is made on the abnormal situation in combination with geological survey data, wherein the geological survey data can be road geological data obtained by intelligent monitoring equipment such as drones, video inspections, and slope warning systems. Further, the abnormal situation can be a dangerous accident signal of the front vehicle, which can be sent by the front vehicle, or it can be a judgment signal after the vehicle detects that the front vehicle is in danger. The detection of the front vehicle can be carried out by equipment such as thermal imaging cameras. Generally speaking, equipment such as thermal imaging cameras can detect the thermal radiation of human bodies or objects at a distance of tens of meters or even farther, and measure their surface temperature. For example, some thermal imaging sensors can detect pedestrians, animals and other vehicles, and the maximum distance can reach four times that of traditional headlights. For example, when a large number of vehicles gather in front, or the front vehicle suddenly accelerates, decelerates, or has excessive vertical displacement, it is judged that a highway collapse event may occur.

[0099] When the abnormal situation is judged to be a highway collapse, the system will assist in deceleration and parking, and broadcast the abnormal prompt to the driver. When the abnormal situation is judged to be a highway collapse, first, the server will issue instructions to gradually increase the braking force of the vehicle's auxiliary braking system to reduce the speed in a stable and safe manner. During the deceleration process, the warning lights in the car will flash quickly to remind the driver to pay attention. In addition, the vehicle's double flash warning lights will be turned on to remind the drivers of other vehicles. During the entire deceleration process, the distance to the front and rear vehicles is continuously monitored, and the intensity and rhythm of deceleration are intelligently adjusted according to the driving conditions of the surrounding vehicles to avoid collision accidents to the greatest extent. When the speed is reduced to a safe range, the auxiliary parking system will automatically find a suitable location to park by the side of the road to ensure the safety of the driver and the vehicle.

[0100] Optionally, the abnormal situation is displayed to the driver through a display device, for example, "the highway ahead has collapsed" is displayed on the display device, and the abnormal prompt is broadcast to the driver through the vehicle audio system: "Emergency alert! The highway ahead has collapsed, please prepare to stop immediately, stay calm, and operate with caution."

[0101] Optionally, for a vehicle that is traveling on a collapsed road surface, the vehicle may be accelerated to move the vehicle away from the collapsed road surface as quickly as possible.

[0102] Furthermore, the location data of the collapse site is obtained and uploaded to the server, detour plans are made for other vehicles that may pass by, and early warnings are sent in time so that relevant rescue personnel can take further action.

[0103] In summary, in the implementation of the present application, the road detection data of the target road is first obtained, and based on the road detection data, it is determined whether a road collapse event occurs on the target road. If a road collapse event occurs, the vehicle information of each of the j vehicles corresponding to the target road is obtained to obtain j vehicle information, and the j vehicle information includes j vehicle positions and j vehicle speeds, where j is a natural number, and the server maintains a communication connection with the j vehicles. Then, based on the j vehicle positions, n target vehicles are determined from the j vehicles, where n is a natural number less than or equal to j. Finally, based on the n vehicle positions and n vehicle speeds corresponding to the n target vehicles in the j vehicle information, the corresponding vehicles in the n target vehicles are controlled to perform corresponding braking operations, so that each of the n target vehicles safely stops in front of the dangerous area corresponding to the road collapse event. Thus, it is determined whether a road collapse event occurs on the target road through the road detection data and a plurality of vehicle information is obtained when a road collapse event occurs, and the corresponding vehicles are braked based on the vehicle information, so that the relevant vehicles can be judged and processed in time when a collapse event occurs on the highway to ensure the personal safety of the relevant personnel.

[0104] The method of the embodiment of the present invention is described in detail above, and the device of the embodiment of the present invention is provided below.

[0105] See also Figure 3 , Figure 3 800 is a schematic diagram of a road collapse event processing device provided in an embodiment of the present application. The road collapse event processing device 800 is applied to a server, and the road collapse event processing device 800 includes an acquisition unit 801 and a processing unit 802;

[0106] An acquisition unit 801 is used to acquire road detection data of a target road;

[0107] A processing unit 802 is used to determine whether a road collapse event occurs on a target road based on the road detection data;

[0108] If a road collapse event occurs, the vehicle information of each of the j vehicles running on the target road is obtained, and j vehicle information is obtained, where the j vehicle information includes j vehicle positions and j vehicle speeds; j is a natural number; the server maintains a communication connection with the j vehicles;

[0109] n target vehicles are determined from j moving vehicles based on j vehicle positions; n is a natural number less than or equal to j;

[0110] Based on the n vehicle positions and n vehicle speeds corresponding to the n target vehicles in the j vehicle information, the corresponding vehicles among the n target vehicles are controlled to perform corresponding braking operations, so that each of the n target vehicles can stop safely in front of the dangerous area corresponding to the road collapse event.

[0111] In some possible embodiments, in terms of acquiring the road detection data of the target road, the acquiring unit 801 is specifically used to:

[0112] Acquire road information of a target road, where the road information includes at least one of the following: road age, road location, road type, and road load condition;

[0113] Determine a road detection level of the target road based on the road information, where the road detection level is used to reflect the detection requirement of the target road;

[0114] Obtain climate data corresponding to the target road;

[0115] Determine the inspection frequency of the target road based on the road inspection level and climate data;

[0116] Acquire first detection data of the target road based on the detection frequency, the first detection data including a settlement distance and a road sound;

[0117] Determining a collapse risk score of the target road based on the first detection data;

[0118] When the collapse risk score is higher than the preset risk score threshold, m detection vehicles passing through the target road within a preset historical time period are determined; m is a positive integer greater than 1;

[0119] Acquire second detection data of the target road through m detection vehicles, where the second detection data is used to reflect the road surface condition of the target road;

[0120] The first detection data and the second detection data are integrated to obtain road detection data.

[0121] In some possible embodiments, in determining whether a road collapse event occurs on a target road based on the road detection data, the processing unit 802 is specifically configured to:

[0122] Determine i bump positions of the target road based on the road detection data; i is a natural number;

[0123] Determine the road height change trend, road gap change trend, and road sound characteristics based on the road detection data, where the road gap change trend includes the change trend of the number, length, and width of the road gaps;

[0124] Obtain vehicle bump data of m detected vehicles;

[0125] Determine a bump coefficient of the target road based on the i bump positions and the vehicle bump data;

[0126] If the road height change trend is consistent with the preset first change trend, or the road gap change trend is consistent with the preset second change trend, or the road sound feature is consistent with the preset sound feature, or the bump coefficient is higher than the preset bump coefficient threshold, it is determined that a road collapse event occurs on the target road;

[0127] Otherwise, it is determined that no road collapse event occurs on the target road.

[0128] In some possible embodiments, in determining n target vehicles from j moving vehicles based on j vehicle positions, the processing unit 802 is specifically configured to:

[0129] Determine the location of the road collapse event based on the road detection data;

[0130] Determine a first relative position relationship between each of the j traveling vehicles and the occurrence position based on the occurrence position and the j vehicle positions, and obtain j first relative position relationships;

[0131] The moving vehicles whose j first relative position relationships are consistent with the preset relative position relationship and whose vehicle speeds in the j vehicle information are greater than the preset speed threshold are taken as n target vehicles.

[0132] In some possible embodiments, in terms of controlling corresponding vehicles among the n target vehicles to perform corresponding braking operations based on the n vehicle positions and n vehicle speeds corresponding to the n target vehicles in the j vehicle information, the processing unit 802 is specifically configured to:

[0133] Determine a minimum braking distance of each of the n target vehicles based on the current road condition of the target road and the n vehicle speeds, and obtain n minimum braking distances;

[0134] Determine a second relative position relationship between each target vehicle and an adjacent vehicle in the n target vehicles based on the n vehicle positions, and obtain n second relative position relationships;

[0135] Determine a first distance between each of the n target vehicles and the occurrence position based on the n first relative position relationships to obtain n first distances;

[0136] Determine a second distance between each of the n target vehicles and the preceding vehicle based on the n second relative position relationships, and obtain n second distances;

[0137] Determine a candidate braking distance for each of the n target vehicles based on the n first distances and the n second distances to obtain n candidate braking distances;

[0138] A corresponding target vehicle among the n target vehicles is controlled to brake based on the n candidate braking distances and the n minimum braking distances.

[0139] In some possible embodiments, in terms of controlling the corresponding target vehicle among the n target vehicles to brake based on the n candidate braking distances and the n minimum braking distances, the processing unit 802 is specifically configured to:

[0140] Determine a target braking distance for each of the n target vehicles based on the n preset distances, the n vehicle positions, the n candidate braking distances and the n minimum braking distances, and obtain n target braking distances, so that the distance between adjacent vehicles of the n target vehicles is greater than a corresponding preset distance among the n preset distances; and the n target braking distances are greater than or equal to a corresponding minimum braking distance among the n minimum braking distances;

[0141] Determining n braking instructions based on n target braking distances and n vehicle speeds, the n braking instructions including n braking parameter information, the n braking parameter information including n braking pressure value sequences and n braking time sequences;

[0142] N braking instructions are sent to n target vehicles, so that the n target vehicles brake based on the n braking instructions.

[0143] In some possible embodiments, before controlling a corresponding vehicle among the n target vehicles to brake based on the n candidate braking distances and the n minimum braking distances, the processing unit 802 is further configured to:

[0144] A target vehicle whose braking distance among the n candidate braking distances is smaller than the corresponding minimum braking distance among the n minimum braking distances is taken as a first vehicle, and a first vehicle is obtained; a is a natural number less than or equal to n;

[0145] Acquire at least one adjacent lane driving data of each of the a first vehicles to obtain a adjacent lane driving data sets; the adjacent lane driving data is used to reflect the driving conditions of vehicles in the adjacent lanes;

[0146] Determine a target driving lane for each of the a first vehicles based on a adjacent lane driving data and a minimum braking distances corresponding to the a first vehicles, to obtain a target driving lanes;

[0147] Determine a driving parameter of each of the a first vehicles based on the a target driving lanes and a vehicle speeds corresponding to the a first vehicles, and obtain a driving parameters, wherein the driving parameters include a steering wheel angle sequence and a steering time sequence;

[0148] The a driving parameters are sent to the a first vehicles, so that each of the a first vehicles drives based on a corresponding driving parameter among the a driving parameters.

[0149] See also Figure 4 , Figure 4 Schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 4 As shown, the electronic device 900 includes a transceiver 901, a processor 902 and a memory 903, which are connected via a bus 904. The memory 903 is used to store computer programs and data, and can transmit the data stored in the memory 903 to the processor 902. The electronic device can be the above-mentioned road collapse event processing device, and the processor 902 can be the above-mentioned acquisition unit 801 and processing unit 802.

[0150] The processor 902 is used to read the computer program in the memory 903 and perform the following operations:

[0151] Acquire road detection data of the target road;

[0152] Determine whether a road collapse event occurs on the target road based on the road detection data;

[0153] If a road collapse event occurs, the vehicle information of each of the j vehicles running on the target road is obtained, and j vehicle information is obtained, where the j vehicle information includes j vehicle positions and j vehicle speeds; j is a natural number; the server maintains a communication connection with the j vehicles;

[0154] n target vehicles are determined from j moving vehicles based on j vehicle positions; n is a natural number less than or equal to j;

[0155] Based on the n vehicle positions and n vehicle speeds corresponding to the n target vehicles in the j vehicle information, the corresponding vehicles among the n target vehicles are controlled to perform corresponding braking operations, so that each of the n target vehicles can stop safely in front of the dangerous area corresponding to the road collapse event.

[0156] In some possible embodiments, in terms of acquiring the road detection data of the target road, the processor 902 is specifically configured to perform the following operations:

[0157] Acquire road information of a target road, where the road information includes at least one of the following: road age, road location, road type, and road load condition;

[0158] Determine a road detection level of the target road based on the road information, where the road detection level is used to reflect the detection requirement of the target road;

[0159] Obtain climate data corresponding to the target road;

[0160] Determine the inspection frequency of the target road based on the road inspection level and climate data;

[0161] Acquire first detection data of the target road based on the detection frequency, the first detection data including a settlement distance and a road sound;

[0162] Determining a collapse risk score of the target road based on the first detection data;

[0163] When the collapse risk score is higher than the preset risk score threshold, m detection vehicles passing through the target road within a preset historical time period are determined; m is a positive integer greater than 1;

[0164] Acquire second detection data of the target road through m detection vehicles, where the second detection data is used to reflect the road surface condition of the target road;

[0165] The first detection data and the second detection data are integrated to obtain road detection data.

[0166] In some possible embodiments, in determining whether a road collapse event occurs on a target road based on the road detection data, the processor 902 is specifically configured to perform the following operations:

[0167] Determine i bump positions of the target road based on the road detection data; i is a natural number;

[0168] Determine the road height change trend, road gap change trend, and road sound characteristics based on the road detection data, where the road gap change trend includes the change trend of the number, length, and width of the road gaps;

[0169] Obtain vehicle bump data of m detected vehicles;

[0170] Determine a bump coefficient of the target road based on the i bump positions and the vehicle bump data;

[0171] If the road height change trend is consistent with the preset first change trend, or the road gap change trend is consistent with the preset second change trend, or the road sound feature is consistent with the preset sound feature, or the bump coefficient is higher than the preset bump coefficient threshold, it is determined that a road collapse event occurs on the target road;

[0172] Otherwise, it is determined that no road collapse event occurs on the target road.

[0173] In some possible embodiments, in determining n target vehicles from j moving vehicles based on j vehicle positions, the processor 902 is specifically configured to perform the following operations:

[0174] Determine the location of the road collapse event based on the road detection data;

[0175] Determine a first relative position relationship between each of the j traveling vehicles and the occurrence position based on the occurrence position and the j vehicle positions, and obtain j first relative position relationships;

[0176] The moving vehicles whose j first relative position relationships are consistent with the preset relative position relationship and whose vehicle speeds in the j vehicle information are greater than the preset speed threshold are taken as n target vehicles.

[0177] In some possible embodiments, in terms of controlling corresponding vehicles among the n target vehicles to perform corresponding braking operations based on the n vehicle positions and n vehicle speeds corresponding to the n target vehicles in the j vehicle information, the processor 902 is specifically configured to perform the following operations:

[0178] Determine a minimum braking distance of each of the n target vehicles based on the current road condition of the target road and the n vehicle speeds, and obtain n minimum braking distances;

[0179] Determine a second relative position relationship between each target vehicle and an adjacent vehicle in the n target vehicles based on the n vehicle positions, and obtain n second relative position relationships;

[0180] Determine a first distance between each of the n target vehicles and the occurrence position based on the n first relative position relationships to obtain n first distances;

[0181] Determine a second distance between each of the n target vehicles and the preceding vehicle based on the n second relative position relationships, and obtain n second distances;

[0182] Determine a candidate braking distance for each of the n target vehicles based on the n first distances and the n second distances to obtain n candidate braking distances;

[0183] A corresponding target vehicle among the n target vehicles is controlled to brake based on the n candidate braking distances and the n minimum braking distances.

[0184] In some possible embodiments, in terms of controlling the corresponding target vehicle among the n target vehicles to brake based on the n candidate braking distances and the n minimum braking distances, the processor 902 is specifically configured to perform the following operations:

[0185] Determine a target braking distance for each of the n target vehicles based on the n preset distances, the n vehicle positions, the n candidate braking distances and the n minimum braking distances, and obtain n target braking distances, so that the distance between adjacent vehicles of the n target vehicles is greater than a corresponding preset distance among the n preset distances; and the n target braking distances are greater than or equal to a corresponding minimum braking distance among the n minimum braking distances;

[0186] Determining n braking instructions based on n target braking distances and n vehicle speeds, the n braking instructions including n braking parameter information, the n braking parameter information including n braking pressure value sequences and n braking time sequences;

[0187] N braking instructions are sent to n target vehicles, so that the n target vehicles brake based on the n braking instructions.

[0188] In some possible embodiments, before controlling a corresponding vehicle among the n target vehicles to brake based on the n candidate braking distances and the n minimum braking distances, the processor 902 is further configured to perform the following operations:

[0189] A target vehicle whose braking distance among the n candidate braking distances is smaller than the corresponding minimum braking distance among the n minimum braking distances is taken as a first vehicle, and a first vehicle is obtained; a is a natural number less than or equal to n;

[0190] Acquire at least one adjacent lane driving data of each of the a first vehicles to obtain a adjacent lane driving data sets; the adjacent lane driving data is used to reflect the driving conditions of vehicles in the adjacent lanes;

[0191] Determine a target driving lane for each of the a first vehicles based on a adjacent lane driving data and a minimum braking distances corresponding to the a first vehicles, to obtain a target driving lanes;

[0192] Determine a driving parameter of each of the a first vehicles based on the a target driving lanes and a vehicle speeds corresponding to the a first vehicles, and obtain a driving parameters, wherein the driving parameters include a steering wheel angle sequence and a steering time sequence;

[0193] The a driving parameters are sent to the a first vehicles, so that each of the a first vehicles drives based on a corresponding driving parameter among the a driving parameters.

[0194] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement part or all of the steps of any method for handling a road collapse event as recorded in the above method embodiments.

[0195] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any road collapse event handling method recorded in the above method embodiments.

[0196] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0197] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0198] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the modules, which is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or module can be electrical or other forms.

[0199] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0200] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or software program modules.

[0201] If the integrated module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0202] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for handling road collapse events, characterized in that: Applied to a server, the method comprises: Acquire road detection data of the target road; Determining whether a road collapse event occurs on the target road based on the road detection data; If the road collapse event occurs, obtain vehicle information of each of j vehicles traveling on the target road to obtain j vehicle information, wherein the j vehicle information includes j vehicle positions and j vehicle speeds; j is a natural number; and the server maintains a communication connection with the j vehicles. Determining n target vehicles from the j moving vehicles based on the j vehicle positions; n is a natural number less than or equal to j; Based on the n vehicle positions and n vehicle speeds corresponding to the n target vehicles in the j vehicle information, corresponding vehicles among the n target vehicles are controlled to perform corresponding braking operations, so that each of the n target vehicles can stop safely in front of the dangerous area corresponding to the road collapse event.

2. The method according to claim 1, characterized in that The step of acquiring the road detection data of the target road comprises: Acquire road information of the target road, wherein the road information includes at least one of the following: road age, road location, road type, and road load condition; Determining a road detection level of the target road based on the road information, wherein the road detection level is used to reflect a detection requirement degree of the target road; Acquiring climate data corresponding to the target road; determining a detection frequency of the target road based on the road detection level and the climate data; Acquire first detection data of the target road based on the detection frequency, the first detection data including a settlement distance and a road sound; Determining a collapse risk score of the target road based on the first detection data; When the collapse risk score is higher than a preset risk score threshold, m detection vehicles passing through the target road within a preset historical time period are determined; m is a positive integer greater than 1; Acquiring second detection data of the target road through the m detection vehicles, where the second detection data is used to reflect the road surface condition of the target road; The first detection data and the second detection data are integrated to obtain the road detection data.

3. The method according to claim 2, characterized in that The determining whether a road collapse event occurs on the target road based on the road detection data comprises: Determine i bump positions of the target road based on the road detection data; i is a natural number; Determine a road height change trend, a road gap change trend, and a road sound feature based on the road detection data, wherein the road gap change trend includes a change trend in the number, length, and width of the road gaps; Acquiring vehicle bump data of the m detection vehicles; Determining a bump coefficient of the target road based on the i bump positions and the vehicle bump data; If the road height change trend is consistent with the preset first change trend, or the road gap change trend is consistent with the preset second change trend, or the road sound feature is consistent with the preset sound feature, or the bump coefficient is higher than the preset bump coefficient threshold, it is determined that the road collapse event occurs on the target road; Otherwise, it is determined that the road collapse event does not occur on the target road.

4. The method according to claim 3, characterized in that The determining n target vehicles from the j moving vehicles based on the j vehicle positions comprises: Determining the occurrence location of the road collapse event based on the road detection data; Determine a first relative position relationship between each of the j traveling vehicles and the occurrence location based on the occurrence location and the j vehicle locations, and obtain j first relative position relationships; The moving vehicles that are consistent with the preset relative position relationship in the j first relative position relationships and whose vehicle speeds in the j vehicle information are greater than a preset speed threshold are taken as the n target vehicles.

5. The method according to any one of claims 1 to 4, characterized in that: The controlling the corresponding vehicle among the n target vehicles to perform corresponding braking operations based on the n vehicle positions and n vehicle speeds corresponding to the n target vehicles in the j vehicle information includes: Determining a minimum braking distance of each of the n target vehicles based on the current road condition of the target road and the n vehicle speeds, to obtain n minimum braking distances; Determine a second relative position relationship between each of the n target vehicles and an adjacent vehicle based on the n vehicle positions to obtain n second relative position relationships; Determine a first distance between each of the n target vehicles and the occurrence position based on the n first relative position relationships to obtain n first distances; Determine a second distance between each of the n target vehicles and the preceding vehicle based on the n second relative position relationships to obtain n second distances; Determine a candidate braking distance for each of the n target vehicles based on the n first distances and the n second distances to obtain n candidate braking distances; A corresponding target vehicle among the n target vehicles is controlled to brake based on the n candidate braking distances and the n minimum braking distances.

6. The method according to claim 5, characterized in that The controlling the corresponding target vehicle among the n target vehicles to brake based on the n candidate braking distances and the n minimum braking distances includes: Determine a target braking distance for each of the n target vehicles based on the n preset distances, the n vehicle positions, the n candidate braking distances and the n minimum braking distances, and obtain n target braking distances, so that the distance between adjacent vehicles of the n target vehicles is greater than a corresponding preset distance among the n preset distances; and the n target braking distances are greater than or equal to a corresponding minimum braking distance among the n minimum braking distances; Determining n braking instructions based on the n target braking distances and the n vehicle speeds, the n braking instructions including n braking parameter information, the n braking parameter information including n braking pressure value sequences and n braking time sequences; The n braking instructions are issued to the n target vehicles, so that the n target vehicles brake based on the n braking instructions.

7. The method according to claim 6, characterized in that Before controlling the corresponding vehicle among the n target vehicles to brake based on the n candidate braking distances and the n minimum braking distances, the method further includes: Taking a target vehicle whose braking distance among the n candidate braking distances is smaller than the corresponding minimum braking distance among the n minimum braking distances as a first vehicle, and obtaining a first vehicles; a is a natural number less than or equal to n; Acquire at least one adjacent lane driving data of each of the a first vehicles to obtain a adjacent lane driving data sets; the adjacent lane driving data is used to reflect the driving conditions of vehicles in the adjacent lanes; Determine a target driving lane for each of the a first vehicles based on the a adjacent lane driving data and a minimum braking distances corresponding to the a first vehicles to obtain a target driving lanes; Determine a driving parameter of each of the a first vehicles based on the a target driving lanes and a vehicle speeds corresponding to the a first vehicles to obtain a driving parameters, wherein the driving parameters include a steering wheel angle sequence and a steering time sequence; The a driving parameters are sent to the a first vehicles, so that each of the a first vehicles travels based on a corresponding driving parameter among the a driving parameters.

8. A road collapse event processing device, characterized in that: Applied to a server, the device comprises an acquisition unit and a processing unit; The acquisition unit is used to acquire road detection data of the target road; The processing unit is used to determine whether a road collapse event occurs on the target road based on the road detection data; If the road collapse event occurs, obtain vehicle information of each of j vehicles traveling on the target road to obtain j vehicle information, wherein the j vehicle information includes j vehicle positions and j vehicle speeds; j is a natural number; and the server maintains a communication connection with the j vehicles. Determining n target vehicles from the j moving vehicles based on the j vehicle positions; n is a natural number less than or equal to j; Based on the n vehicle positions and n vehicle speeds corresponding to the n target vehicles in the j vehicle information, corresponding vehicles among the n target vehicles are controlled to perform corresponding braking operations, so that each of the n target vehicles can stop safely in front of the dangerous area corresponding to the road collapse event.

9. An electronic device, characterized in that: include: A processor and a memory, the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 7.

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