Methods, devices, electronic equipment and storage media for handling road collapse incidents

Abstract

This invention discloses a method, apparatus, electronic device, and storage medium for handling road collapse events. The method includes: acquiring road detection data of a target road; determining whether a road collapse event has occurred on the target road based on the road detection data; if a road collapse event has occurred, acquiring vehicle information of each of j vehicles traveling on the target road to obtain j vehicle information, the j vehicle information including j vehicle positions and j vehicle speeds; j being a natural number; maintaining a communication connection between a server and the j vehicles; determining n target vehicles from the j vehicles based on the j vehicle positions; n being a natural number less than or equal to j; and controlling the 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, so that each of the n target vehicles can safely stop in front of the danger zone corresponding to the road collapse event.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and more particularly to methods, apparatus, electronic devices, and storage media for handling road collapse incidents. Background Technology

[0002] Unexpected incidents on the road, such as road collapses, occur frequently, especially on highways, where a large number of vehicles pass through, which may cause significant casualties or property damage.

[0003] Due to the unpredictability of road collapses, the high speeds on highways, and drivers' lack of experience, drivers often fail to notice such emergencies or, even when they do, are unable to make the correct judgment and take appropriate action immediately. This poses a significant threat to the personal safety of those involved. For example, drivers might mistake road surface cracks appearing during a highway collapse for puddles, or they might not have enough time to brake when they realize a collapse has occurred.

[0004] Therefore, there is an urgent need for a method, device, electronic equipment, and storage medium for handling road collapse incidents, so as to promptly assess and handle relevant vehicles in the event of a highway collapse and ensure the personal safety of relevant personnel. Summary of the Invention

[0005] To address the aforementioned problems, embodiments of the present invention provide a method, apparatus, electronic device, and storage medium for handling road collapse incidents, which can promptly assess and handle related vehicles when a collapse incident occurs on a highway, thereby ensuring the personal safety of relevant personnel.

[0006] In a first aspect, embodiments of the present invention provide a method for handling road collapse events, applied to a server, the method comprising:

[0007] Obtain road detection data for the target road;

[0008] Based on the road detection data, determine whether a road collapse event has occurred on the target road;

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

[0010] Based on the positions of the j vehicles, n target vehicles are determined from the j moving vehicles; 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, the corresponding vehicles among the n target vehicles are controlled to perform corresponding braking operations so that each of the n target vehicles can safely stop in front of the danger zone corresponding to the road collapse event.

[0012] Secondly, embodiments of the present invention provide a road collapse event processing device applied to a server, the device including 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 has occurred on the target road based on the road detection data;

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

[0016] Based on the positions of the j vehicles, n target vehicles are determined from the j moving vehicles; 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, the corresponding vehicles among the n target vehicles are controlled to perform corresponding braking operations so that each of the n target vehicles can safely stop in front of the danger zone corresponding to the road collapse event.

[0018] Thirdly, embodiments of the present invention provide an electronic device, the electronic device including a processor and a memory, the processor being connected to the memory, the memory being used to store a computer program, and the processor being used to execute the computer program stored in the memory, so that the electronic device performs the method as described in the first aspect.

[0019] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that is executed by a processor to implement the method described in the first aspect.

[0020] Fifthly, embodiments of this application provide a computer program product, the computer program product including a non-transitory computer-readable storage medium storing a computer program, the computer being operable to perform the method as described in the first aspect.

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

[0022] In this embodiment, road detection data of the target road is first acquired, and based on the road detection data, it is determined whether a road collapse event has occurred on the target road. If a road collapse event occurs, vehicle information of each of the j vehicles traveling on the target road is acquired, resulting in j vehicle information entries. These j vehicle information entries include j vehicle positions and j vehicle speeds, where j is a natural number. 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 among the n target vehicles are controlled to perform appropriate braking operations, so that each of the n target vehicles safely stops before the danger zone corresponding to the road collapse event. Therefore, by determining whether a road collapse event has occurred on the target road using road detection data and acquiring multiple vehicle information entries when a road collapse event occurs, braking operations are performed on the corresponding vehicles based on the vehicle information. This allows for timely judgment and handling of relevant vehicles in the event of a collapse event on a highway, ensuring the personal safety of relevant personnel. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the background art, the drawings used in the embodiments of the present invention or the background art will be described below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the architecture of a road collapse incident handling system provided in an embodiment of this application;

[0025] Figure 2 This is a flowchart of a road collapse event handling method provided in an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of the structure of a road collapse incident handling device provided in an embodiment of this application;

[0027] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] The terms "first," "second," "third," and "fourth," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. 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 may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to these processes, methods, products, or devices.

[0030] In this document, the term "embodiment" means that a particular feature, result, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0031] The road collapse event handling method provided in this application is applied to a server. See [link / reference]. Figure 1 , Figure 1 This is a schematic diagram of the architecture of a road collapse event handling system provided in an embodiment of this application, as shown below. Figure 1 As shown, the road collapse incident handling system includes a server and a vehicle. The server and the vehicle maintain a communication connection. The vehicle can be a vehicle traveling on the target road. The server and the vehicle can exchange data through various communication connection methods such as cellular communication network, dedicated short-range communication, satellite communication, and Bluetooth communication. No restrictions are imposed here.

[0032] See Figure 2 , Figure 2 This is a flowchart of a road collapse event handling method provided in an embodiment of this application. Figure 2 As shown, the road collapse event handling method provided in this application embodiment includes, but is not limited to, the following steps:

[0033] Step S101: Obtain road detection data for the target road;

[0034] Step S102: Determine whether a road collapse event has occurred on the target road based on road detection data;

[0035] Step S103: If a road collapse event occurs, obtain the vehicle information of each of the j vehicles traveling on the target road, and obtain the information of j vehicles.

[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 vehicles.

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

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

[0039] Step S105: Based on the positions and speeds of the n target vehicles corresponding to 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 can safely stop in front of the danger zone corresponding to the road collapse event.

[0040] In one possible embodiment, the target road can be a road with a high risk of collapse or a road that would cause significant damage if it collapsed. Road detection data of the target road is acquired through a road detection device, which can be set up at one or more pre-set detection stations along the target road. For example, detection stations can be set up at pre-set intervals along the target road. Each detection station is equipped with various types of sensors, such as a high-precision laser rangefinder, ground-penetrating radar, microseismic sensors, and high-definition cameras. The high-precision laser rangefinder is used to measure minute deformations and settlements of the road surface, the ground-penetrating radar is used to detect changes in the geological structure beneath the road to detect underground cavities or soil loosening, the microseismic sensors are used to detect minute vibration signals caused by structural changes in the road and surrounding geological bodies, and the high-definition cameras are used to photograph the road surface from different angles and detect the development of visible damage such as cracks and potholes. In addition to pre-set monitoring stations, satellite remote sensing can be used to assist monitoring. High-resolution images of the target road are periodically captured by satellites, and by comparing image data from different periods, changes in the overall appearance of the road can be analyzed, such as deviations in road linearity and alterations in the surrounding terrain. Satellite data can cover a wide area, helping to identify potential problems in areas far from monitoring stations, and can be used to supplement and verify ground sensor data. Furthermore, road monitoring data can be obtained through vehicle sensor data transmission. Vehicle-mounted sensors on the target road collect data on road smoothness, bumpiness, and other information, and transmit this data back to the server in real time. The distributed data collection from a large number of vehicles provides more comprehensive road condition information, especially for temporary or localized anomalies.

[0041] In one possible embodiment, if a suspected road collapse signal is detected, the system first automatically compares historical data with data from the surrounding area for preliminary verification. If the abnormal signal persists and matches the characteristics of a collapse, the monitoring and early warning mechanism is activated, an early warning message is sent to the monitoring center, and patrol drones or vehicles near the target road are dispatched to the site of the road collapse incident for rapid verification. Through high-definition aerial images from drones and on-site inspections by vehicles, it is finally determined whether a road collapse incident has occurred.

[0042] In one possible embodiment, vehicle-to-everything (V2X) communication technology enables a high-speed and stable communication connection between the server and vehicles traveling on the target road, and allows each vehicle to upload vehicle information to the server at preset time intervals. The vehicle information includes vehicle location and speed, and may also include vehicle direction, vehicle type, and vehicle load information, etc. The vehicle location can be obtained through BeiDou and Global Positioning System, the vehicle speed can be measured by onboard speed sensors, the vehicle type can be divided into cars, trucks, buses, etc., and the vehicle load information can be obtained for trucks.

[0043] In one possible embodiment, the risk of each vehicle entering the collapse danger zone is quantitatively assessed by taking into account factors such as the distance between the vehicle and the collapse area, vehicle speed, vehicle type, and road congestion. Based on the assessment results, n target vehicles are selected in descending order of risk, and braking control operations are performed on high-risk vehicles first.

[0044] In one possible embodiment, the mass distribution of each target vehicle is determined based on information such as vehicle type and load. The friction coefficient between the tires and the road surface is determined based on road surface type and weather conditions. Based on the mass distribution, friction coefficient, and braking system performance parameters, the braking distance and required braking force of the target vehicle under the current driving conditions are calculated. The braking strategy is dynamically adjusted by considering factors such as the real-time distance between the target vehicle and the collapse area, road slope, and surrounding traffic conditions. For example, for vehicles close to the collapse area and without other vehicles nearby, 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 maintenance is adopted to gradually decelerate 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 skidding or loss of control.

[0045] In one possible embodiment, a blockchain-based encrypted communication protocol ensures the security, integrity, and immutability of braking commands during transmission. The server encrypts the generated braking commands and sends them to the target vehicle. Upon receiving the commands, the vehicle decrypts and verifies them; braking is only performed after successful verification. Furthermore, during braking, the target vehicle transmits real-time braking status information back to the server, including braking pressure, wheel speed, and vehicle deceleration. Based on this feedback, the server remotely monitors and adjusts the braking process. If an abnormality is detected, such as insufficient braking force due to a braking system malfunction, the server promptly adjusts the braking strategies of surrounding vehicles to prevent collisions.

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

[0047] Step S201: Obtain road information for the target road, including at least one of the following: road age, road location, road type, and road load.

[0048] Step S202: Determine the road detection level of the target road based on road information. The road detection level is used to reflect the degree of detection requirements of the target road.

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

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

[0051] Step S205: Obtain the first detection data of the target road based on the detection frequency. The first detection data includes settlement distance and road sound.

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

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

[0054] Step S208: Obtain second detection data of the target road using m detection vehicles. 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 one possible embodiment, road information of the target road is acquired, and the road detection level of the target road is determined based on the road information. The road information includes at least one of the following: road age, road location, road type, and road load. The road detection level reflects the degree of detection demand for the target road. Then, climate data corresponding to the target road is acquired, and the detection frequency of the target road is determined based on the road detection level and the climate data. For example, for older roads located in geologically complex areas, such as bridges or tunnels with soft soil foundations, seismically active zones, or landslide-prone areas, or for main roads with extremely high loads, the road detection level is determined to be high. For newly built ordinary roads with stable geological conditions and light loads, the road detection level is determined to be low. The detection frequency of the target road is dynamically adjusted based on the road detection level and climate data. For target roads with high detection levels, the detection frequency is increased during periods of severe weather, such as the rainy season, cold and freezing periods, or typhoon season, and appropriately decreased during periods of relatively stable weather. For target roads with low detection levels, the detection frequency is increased under extreme weather conditions and maintained at a lower frequency under normal weather conditions. In addition, based on real-time weather warnings, such as alerts for sudden rainstorms and strong winds, we will increase inspection arrangements for specific road sections to ensure that potential risks of road collapse due to weather factors are identified in a timely manner.

[0057] In one possible embodiment, first detection data of the target road is acquired based on the detection frequency. This first detection data includes settlement distance and road sound. Then, a collapse risk score for the target road is determined based on the first detection data. When the collapse risk score is higher than a preset risk score threshold, m detection vehicles that have passed through the target road within a preset historical time period are identified, where m is a positive integer greater than 1. Second detection data of the target road is acquired using these m detection vehicles. This second detection data reflects the road surface condition of the target road. Finally, the first and second detection data are integrated to obtain the road detection data. The second detection data can be acquired using various road surface condition monitoring devices equipped on the detection vehicles. These devices can include cameras, bump sensors, road surface humidity sensors, road surface temperature sensors, and tire pressure monitoring systems. High-definition image recognition technology from cameras can automatically identify the type, size, and severity of cracks in the road surface. Bump sensors combined with tire pressure monitoring data can more accurately determine the degree of bumps experienced during vehicle travel, thereby determining the road surface smoothness and structural integrity. Road surface humidity and temperature sensors can determine the environmental information of the road surface. When integrating data, duplicate and abnormal data points are removed from the second detection data from different detection vehicles. Based on the vehicle's driving trajectory and location information, the second detection data is matched with specific road segments to construct a complete road condition map, which intuitively displays the road conditions of different road segments.

[0058] In this embodiment, by dynamically adjusting the detection frequency, close monitoring of the road is ensured during high-risk periods to promptly detect road problems caused by weather, while avoiding excessive detection during low-risk periods, thus saving manpower, material resources, and time costs. By obtaining second detection data through vehicle sensor data feedback, the detection vehicle becomes a mobile road monitoring station, expanding the monitoring range and enabling the collection of road surface information at different locations and times.

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

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

[0061] Step S302: Determine the road height change trend, road gap change trend, and road sound characteristics based on road detection data. The road gap change trend includes the change trends of the number, length, and width of road gaps.

[0062] Step S303: Obtain vehicle bump data for m detected vehicles;

[0063] Step S304: Determine the bump coefficient of the target road based on the i bump locations and 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 characteristics are consistent with the preset sound characteristics, or the bump coefficient is higher than the preset bump coefficient threshold, then it is determined that a road collapse event has occurred on the target road.

[0065] Step S306: Otherwise, determine that no road collapse event has occurred on the target road.

[0066] In one possible embodiment, i bump locations on the target road are determined based on road detection data. These bump locations can be the locations of speed bumps on the target road, where i is a natural number. The road detection data is also used to determine trends in road height variation, road gap variation, and road sound characteristics. The road gap variation trend includes changes in the number, length, and width of road gaps. Then, vehicle bump data for m detection vehicles is acquired, including the amplitude, frequency, location of bumps, and tire pressure fluctuations. The bump coefficient of the target road is determined based on the i bump locations and the vehicle bump data. If the vehicle bump data indicates that the detection vehicle also experiences bumps at other locations on the target road besides the i bump locations, it indicates the presence of new cracks or road subsidence, resulting in a higher bump coefficient. Conversely, if the vehicle bump data indicates that the detection vehicle experiences bumps at the same locations as the i bump locations on the target road, the bump coefficient is lower. If the road height change trend matches a preset first trend, or the road gap change trend matches a preset second trend, or the road sound characteristics match preset sound characteristics, or the bump coefficient is higher than a preset bump coefficient threshold, then a road collapse event is determined to have occurred on the target road. Otherwise, a road collapse event is determined not to have occurred on the target road. The preset first trend could be a decrease in road height, the preset second trend could be an increase in the number of gaps, an increase in gap length, or an increase in gap width, and the preset sound characteristics can be determined based on the collapse sound of the target road. Furthermore, a road collapse event can also be determined by whether multiple conditions are met simultaneously.

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

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

[0069] Step S402: Based on the location of the incident and the positions of the j vehicles, determine the first relative positional relationship between each of the j vehicles and the location of the incident, and obtain the j first relative positional relationships;

[0070] Step S403: Select the vehicles that have the same relative position relationship as the preset relative position relationship among the j first relative position relationships and whose vehicle speed is greater than the preset speed threshold among the j vehicle information as n target vehicles.

[0071] In one possible embodiment, preset relative positional relationships and speed thresholds are determined based on the traffic rules and road structure characteristics of the target road. For example, vehicles within a certain range of the collapse location, traveling towards the collapse area, and exceeding a preset speed threshold exceeding the road speed limit are designated as target vehicles. 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 vehicles that are not target vehicles, warning messages can be sent first to remind drivers to slow down and pay attention to changes in road conditions. Further instructions can be issued based on the actual situation. Different types of vehicles have different capabilities and risk levels when facing road collapse events. By identifying target vehicles among multiple vehicles, emergency resources can be rationally allocated, high-risk vehicles can be prioritized, and the efficiency and effectiveness of emergency response can be improved.

[0072] Optionally, step S105, 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, may include the following steps:

[0073] Step S501: Based on the current road surface conditions of the target road and the speeds of n vehicles, determine the minimum braking distance for each of the n target vehicles to obtain n minimum braking distances;

[0074] Step S502: Based on the n vehicle positions, determine the second relative positional relationship between each of the n target vehicles and its adjacent vehicles, thus obtaining n second relative positional relationships;

[0075] Step S503: Based on the n first relative positional relationships, determine the first distance between each of the n target vehicles and the location of occurrence, thus obtaining n first distances;

[0076] Step S504: Based on the n second relative positional relationships, determine the second distance between each of the n target vehicles and the vehicle in front, thus obtaining n second distances;

[0077] Step S505: Based on n first distances and n second distances, determine the candidate braking distance for each of the n target vehicles, and obtain n candidate braking distances;

[0078] Step S506: Based on the n candidate braking distances and the n minimum braking distances, control the corresponding target vehicle among the n target vehicles to brake.

[0079] In one possible embodiment, in addition to determining basic parameters such as the target vehicle's mass and speed, the performance of the target vehicle's braking system is also determined, such as the friction coefficient between the brake disc and brake pads, and the efficiency of brake pressure transmission. Furthermore, the change in tire-road friction is determined based on current road conditions, such as dryness, wetness, or icing, thereby determining the target vehicle's minimum braking distance. After determining the first and second distances for each target vehicle, a candidate braking distance for each target vehicle is determined based on n first distances and n second distances. The candidate braking distance is the maximum braking distance that the target vehicle can choose under the current driving conditions. The candidate braking distance of a 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 a target vehicle is greater than the minimum braking distance, the target vehicle is braked based on the candidate braking distance to ensure smooth braking and guarantee the driving experience of the passengers.

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

[0081] Step S601: Based on n preset distances, n vehicle positions, n candidate braking distances, and n minimum braking distances, determine the target braking distance for each of the n target vehicles, thus obtaining n target braking distances, such that the distance between adjacent vehicles among the n target vehicles is greater than the corresponding preset distance among the n preset distances; and the n target braking distances are greater than or equal to the corresponding minimum braking distance among the n minimum braking distances.

[0082] Step S602: Determine n braking commands based on n target braking distances and n vehicle speeds. The n braking commands include n braking parameter information, which includes n braking pressure value sequences and n braking time sequences.

[0083] Step S603: Issue n braking commands to n target vehicles so that the n target vehicles brake based on the n braking commands.

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

[0085] In one possible embodiment, personalized braking commands are generated based on the vehicle type, braking system characteristics, and candidate braking distances of each target vehicle. These commands include not only basic parameters such as braking initiation time, braking pressure magnitude, and variation patterns, but also optimizations tailored to the specific characteristics of different vehicles. For example, for vehicles equipped with advanced electronic stability programs and anti-lock braking systems (ABS), the braking commands can fully utilize the functions of these systems to achieve a smoother and more efficient braking process. For heavy-duty trucks, due to their high inertia, the braking commands focus on a slow and steady increase in braking pressure to prevent loss of control due to sudden braking. The braking commands are transmitted to the target vehicle's electronic control unit via vehicle-to-everything (V2X) communication technology, thereby controlling the vehicle's braking system to execute the braking operation.

[0086] In one possible embodiment, to ensure the security and reliability of braking commands during transmission, encryption, authentication, and verification technologies are employed. The braking commands are encrypted on the server side to prevent tampering or forgery during transmission. On the vehicle side, the received commands are decrypted and authenticated; only legitimate commands that pass authentication are executed. During command transmission, data verification technologies, such as cyclic redundancy check, are used to ensure the integrity of the command data. Furthermore, a feedback mechanism for command transmission is established. Upon receiving a braking command, the vehicle immediately sends feedback information to the server, including the command reception status and execution readiness status. The server monitors and adjusts the command transmission based on the feedback information to ensure that the braking commands accurately reach the target vehicle and are effectively executed. During the braking operation of the target vehicle, onboard sensors, such as wheel speed sensors, acceleration sensors, and vehicle attitude sensors, collect real-time braking status data. Wheel speed sensors monitor wheel speed changes to determine if the vehicle locks up; acceleration sensors measure deceleration to assess braking intensity and effectiveness; and vehicle attitude sensors detect changes in vehicle attitude during braking, such as skidding or fishtailing. This sensor data is then transmitted back to the server in real-time via vehicle-to-everything (V2X) technology. The server analyzes and processes the data to evaluate the braking effect of each target vehicle. The evaluation includes multiple indicators such as whether the braking distance meets expectations, whether the braking process is smooth (e.g., uniform deceleration), whether the vehicle remains stable (e.g., whether skidding or fishtailing occurs), and whether a safe distance from the vehicle in front is maintained. If the braking effect of the target vehicle is unsatisfactory, such as excessive braking distance or instability, the server promptly adjusts the braking strategy, recalculates the target braking distance, and sends a corrected braking command to the target vehicle to ensure a safe and smooth stop.

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

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

[0089] Step S702: Obtain at least one adjacent lane driving data for 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 situation of vehicles in adjacent lanes.

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

[0091] Step S704: Based on a target driving lanes and a vehicle speeds corresponding to a first vehicles, determine the driving parameters of each of the a first vehicles to obtain a driving parameters, including a steering wheel angle sequence and a steering time sequence;

[0092] Step S705: Send a driving parameters to a first vehicles so that each of the a first vehicles drives based on the corresponding driving parameters in the a driving parameters.

[0093] In one possible embodiment, if the candidate braking distance of the target vehicle is less than its minimum braking distance, then forcibly braking the target vehicle will not achieve the desired effect. The target vehicle may enter the danger zone corresponding to the road collapse event or collide with other vehicles. In this case, the target vehicle can be controlled to change its driving lane without affecting the driving of other vehicles. For example, it can temporarily enter the oncoming lane when there are no vehicles driving in the oncoming lane, or enter the emergency lane, etc., in order to reduce the losses caused by the road collapse accident.

[0094] In one possible embodiment, the target lane for the first vehicle is determined based on *a* adjacent lane driving data and *a* minimum braking distances corresponding to the first vehicle. First, a comprehensive assessment of the traffic conditions in the adjacent lanes is performed, including factors such as lane congestion levels, average vehicle speed, and vehicle spacing. Then, based on the first vehicle's minimum braking distance, the potential collision risk at the current speed if braking in the current lane and the risks of switching to an adjacent lane are analyzed. Collision risk can be determined based on collision probability and severity. For example, if the adjacent lanes are clear and vehicle spacing is large, but there is congestion or potential braking risk ahead in the current lane, the first vehicle is switched to a suitable adjacent lane. The driving parameters of the first vehicle, including steering wheel angle sequences and steering time sequences, are calculated based on the determined *a* target driving lanes and *a* vehicle speeds corresponding to the first vehicles to ensure vehicle stability and safety during lane changes. For example, based on the vehicle's current speed and the angle between the target lane and the current lane, a suitable initial steering wheel angle is calculated. Then, based on the vehicle's steering response characteristics and the desired lane change trajectory, the subsequent steering wheel angle change sequence is gradually determined. At the same time, combined with the vehicle's speed and road conditions, a reasonable steering time sequence is calculated to control the rhythm of the lane change process and avoid loss of vehicle control or impact on other vehicles due to excessively fast or slow steering.

[0095] In one possible embodiment, after the first vehicle receives the driving parameter command, the onboard electronic control unit controls the vehicle's steering system to perform a lane change operation according to the command. During the operation, the vehicle's driving status is monitored in real time by onboard sensors, including information such as steering wheel angle, lateral acceleration, and distance from vehicles in adjacent lanes, and this 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 status of the vehicle does not match the expectations, such as excessive steering wheel angle deviation or abnormal lateral acceleration, the server recalculates the driving parameters based on the actual situation of the vehicle, generates new commands, and issues them to the vehicle to adjust the vehicle's driving process in a timely manner. During the lane change operation, the onboard human-machine interaction system provides the driver with relevant information prompts, such as lane change intention prompts and steering operation prompts, so that the driver understands the vehicle's driving intention and improves the driver's sense of safety and participation. If the driver believes that the vehicle's automatic lane-changing 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-machine interaction system to pause or cancel the automatic lane-changing operation. While ensuring the safety of the vehicle's automatic driving, it fully combines the driver's subjective judgment and driving experience, realizing human-machine collaborative driving and improving driving comfort and flexibility.

[0096] In one possible embodiment, the method for handling road collapse events in a highway scenario is implemented using detection equipment, display equipment, vehicle audio system, server, auxiliary braking system, auxiliary parking system, and diagnostic equipment. The detection equipment may include high-definition cameras, thermal imaging cameras, dashcams, etc., for detecting road conditions and the condition of vehicles ahead. The display equipment is used to display abnormal situations. The vehicle audio system is used to broadcast abnormal situations and guide drivers. The server is used to issue control commands and interact with other devices or systems. The auxiliary braking system assists the vehicle in braking after detecting an abnormal situation. The auxiliary parking system assists the vehicle in stopping after detecting an abnormal situation. The diagnostic equipment diagnoses vehicle speed and abnormal situations.

[0097] Specifically, the system performs real-time detection of road conditions during vehicle operation, diagnoses the vehicle, and activates the detection equipment when the vehicle reaches a preset speed. Based on this equipment, the system uses one or more methods, including visual sensors, LiDAR, millimeter-wave radar, vehicle dynamic data, satellite positioning and map data, and communication and network technologies, to detect road conditions in front of the vehicle in real time. For example, visual sensors can be used to detect road conditions by mounting multiple high-definition cameras on the front, top, or sides of the vehicle to capture images of the road surface in real time. Then, computer vision and image processing algorithms are used to analyze the captured images to identify features such as road surface depressions, the shape and length of cracks, and road deformation caused by roadbed loosening. For example, the system compares detection results with satellite positioning and map data, using a satellite positioning system to obtain the vehicle's precise location. Comparing the real-time location with pre-stored high-precision map data, if significant differences are found between the actual road surface and the information on the map regarding height, smoothness, etc., it indicates a potential road surface anomaly. For example, vehicle-to-vehicle or vehicle-to-infrastructure communication is carried out based on communication and network technologies, allowing vehicles to share road condition information with each other and to obtain road condition information from roadside infrastructure.

[0098] When anomalies are detected, they are assessed. These anomalies can include road subsidence, crack expansion, roadbed loosening, and traffic disruption. If one or more of these anomalies are detected, a potential highway collapse is assessed. Furthermore, when anomalies are detected, geological survey data is used to make a more precise and comprehensive assessment. This geological survey data can be road surface geological data obtained through intelligent monitoring equipment such as drones, video surveillance, and slope early warning systems. Further, anomalies can be warning signals from vehicles ahead. These signals can be emitted by the vehicle ahead or are signals detected by the vehicle itself after detecting a hazard. Detection of vehicles ahead can be performed using equipment such as thermal imaging cameras. Generally, thermal imaging cameras can detect the thermal radiation of people or objects at distances of tens of meters or even longer and measure their surface temperature. For example, some thermal imaging sensors can detect pedestrians, animals, and other vehicles at distances up to four times that of traditional headlights. For instance, if a thermal imaging camera detects a large number of vehicles gathering ahead, or if a vehicle ahead suddenly accelerates, decelerates, or undergoes excessive vertical displacement, a potential highway collapse is assessed.

[0099] When an anomaly is detected as a highway collapse, the system automatically slows the vehicle to a stop and issues an anomaly warning to the driver. Upon confirmation of a highway collapse, the server first sends a command to gradually increase the vehicle's auxiliary braking system, reducing speed smoothly and safely. During deceleration, warning lights inside the vehicle flash rapidly to alert the driver, and the hazard lights are activated to warn other drivers. Throughout the deceleration process, the system continuously monitors the distance to vehicles in front and behind, intelligently adjusting the deceleration force and pace based on surrounding traffic conditions to minimize the risk of collisions. Once the speed has decreased to a safe level, the auxiliary parking system automatically finds a suitable location to pull over to the side of the road, ensuring the safety of the driver and vehicle.

[0100] Optionally, the abnormal situation can be displayed to the driver via a display device, such as "There is a collapse on the highway ahead," and an abnormal warning can be broadcast to the driver via the vehicle's audio system: "Emergency warning! There is a collapse on the highway ahead. Please prepare to stop immediately, remain calm, and operate with caution."

[0101] Optionally, for vehicles that are traveling on the collapsed road surface, the vehicle can be accelerated to move 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 to develop detour plans for other vehicles that may pass by, and timely warnings are sent so that relevant rescue personnel can take further action.

[0103] In summary, in this embodiment, road detection data of the target road is first acquired, and based on the road detection data, it is determined whether a road collapse event has occurred on the target road. If a road collapse event occurs, vehicle information of each of the j vehicles traveling on the target road is acquired, resulting in j vehicle information entries. These j vehicle information entries include j vehicle positions and j vehicle speeds, where j is a natural number. 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 among the n target vehicles are controlled to perform appropriate braking operations, so that each of the n target vehicles safely stops before the danger zone corresponding to the road collapse event. Therefore, by determining whether a road collapse event has occurred on the target road using road detection data and acquiring multiple vehicle information entries when a road collapse event occurs, braking operations are performed on the corresponding vehicles based on the vehicle information. This allows for timely judgment and handling of relevant vehicles in the event of a collapse event on a highway, ensuring the personal safety of relevant personnel.

[0104] The methods of the embodiments of the present invention have been described in detail above, and the apparatus of the embodiments of the present invention is provided below.

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

[0106] Acquisition unit 801 is used to acquire road detection data of the target road;

[0107] Processing unit 802 is used to determine whether a road collapse event has occurred on the target road based on road detection data;

[0108] If a road collapse occurs, obtain the vehicle information of each of the j vehicles traveling on the target road, resulting in j vehicle information entries. The j vehicle information entries include the j vehicle positions and j vehicle speeds; j is a natural number; the server maintains a communication connection with the j vehicles.

[0109] Identify n target vehicles from j moving vehicles based on j vehicle locations; n is a natural number less than or equal to j.

[0110] Based on the positions and speeds of n target vehicles corresponding to 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 safely stop in front of the danger zone corresponding to the road collapse event.

[0111] In some possible embodiments, the acquisition unit 801 is specifically used for: acquiring road detection data of the target road.

[0112] Obtain road information for the target road, including at least one of the following: road age, road location, road type, and road load.

[0113] The road inspection level of the target road is determined based on road information. The road inspection level is used to reflect the degree of inspection needs of the target road.

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

[0115] The detection frequency of the target road is determined based on the road detection level and climate data;

[0116] The first detection data of the target road is obtained based on the detection frequency. The first detection data includes settlement distance and road sound.

[0117] The collapse risk score of the target road is determined based on the first detection data;

[0118] When the collapse risk score is higher than the preset risk score threshold, m detected vehicles that have passed through the target road within a preset historical time period are identified; m is a positive integer greater than 1.

[0119] Second detection data of the target road is obtained by using m detection vehicles. The second detection data is used to reflect the road surface condition of the target road.

[0120] By integrating the first and second inspection data, road inspection data is obtained.

[0121] In some possible embodiments, in determining whether a road collapse event has occurred on a target road based on road detection data, the processing unit 802 is specifically used for:

[0122] The i bump locations on the target road are determined based on road detection data; i is a natural number.

[0123] Based on road detection data, the trends of road height change, road gap change, and road sound characteristics are determined. The trends of road gap change include the trends of change in the number, length, and width of road gaps.

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

[0125] The bump coefficient of the target road is determined based on i bump locations and 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 characteristics are consistent with the preset sound characteristics, or the bump coefficient is higher than the preset bump coefficient threshold, then it is determined that a road collapse event has occurred on the target road.

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

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

[0129] The location of the road collapse was determined based on road detection data;

[0130] Based on the location of the incident and the locations of j vehicles, determine the first relative positional relationship between each of the j vehicles and the location of the incident, thus obtaining j first relative positional relationships;

[0131] Among the j first relative position relationships, the vehicles that are consistent with the preset relative position relationship and whose vehicle speed is greater than the preset speed threshold among the j vehicle information are selected as n target vehicles.

[0132] In some possible embodiments, in 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 n target vehicles in j vehicle information, the processing unit 802 is specifically used for:

[0133] Based on the current road surface conditions of the target road and the speeds of n vehicles, determine the minimum braking distance for each of the n target vehicles, thus obtaining n minimum braking distances;

[0134] Based on the positions of n vehicles, determine the second relative positional relationship between each of the n target vehicles and its adjacent vehicles, and obtain n second relative positional relationships;

[0135] Based on the n first relative positional relationships, determine the first distance between each of the n target vehicles and the location of the incident, thus obtaining the n first distances;

[0136] Based on the n second relative positional relationships, determine the second distance between each of the n target vehicles and the vehicle in front, thus obtaining n second distances;

[0137] Based on n first distances and n second distances, candidate braking distances are determined for each of the n target vehicles, resulting in n candidate braking distances;

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

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

[0140] Based on n preset distances, n vehicle positions, n candidate braking distances, and n minimum braking distances, determine the target braking distance for each of the n target vehicles, resulting in n target braking distances, such that the distance between adjacent vehicles among the n target vehicles is greater than the corresponding preset distance among the n preset distances; and the n target braking distances are greater than or equal to the corresponding minimum braking distance among the n minimum braking distances.

[0141] Based on n target braking distances and n vehicle speeds, n braking commands are determined. The n braking commands include n braking parameter information, which includes n braking pressure value sequences and n braking time sequences.

[0142] n braking commands are issued to n target vehicles so that the n target vehicles brake based on the n braking commands.

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

[0144] Among the n candidate braking distances, the target vehicle whose braking distance is less than the corresponding minimum braking distance among the n minimum braking distances is taken as the first vehicle, resulting in a first vehicles; a is a natural number less than or equal to n.

[0145] Obtain at least one adjacent lane driving data for each of the a first vehicles to obtain a adjacent lane driving data set; the adjacent lane driving data is used to reflect the driving situation of vehicles in adjacent lanes.

[0146] Based on driving data of a adjacent lanes and a minimum braking distances corresponding to a first vehicles, the target driving lane of each of the a first vehicles is determined, resulting in a target driving lanes;

[0147] Based on a target driving lanes and a vehicle speeds corresponding to a first vehicles, determine the driving parameters of each of the a first vehicles, and obtain a driving parameters, including a steering wheel angle sequence and a steering time sequence;

[0148] A driving parameters are sent to a first vehicles so that each of the a first vehicles drives based on the corresponding driving parameters in the a driving parameters.

[0149] See Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. For example... 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 stores computer programs and data, and can transmit the data stored in the memory 903 to the processor 902. This electronic device can be the aforementioned road collapse event handling device, and the processor 902 can be the aforementioned acquisition unit 801 and processing unit 802.

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

[0151] Obtain road detection data for the target road;

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

[0153] If a road collapse occurs, obtain the vehicle information of each of the j vehicles traveling on the target road, resulting in j vehicle information entries. The j vehicle information entries include the j vehicle positions and j vehicle speeds; j is a natural number; the server maintains a communication connection with the j vehicles.

[0154] Identify n target vehicles from j moving vehicles based on j vehicle locations; n is a natural number less than or equal to j.

[0155] Based on the positions and speeds of n target vehicles corresponding to 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 safely stop in front of the danger zone corresponding to the road collapse event.

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

[0157] Obtain road information for the target road, including at least one of the following: road age, road location, road type, and road load.

[0158] The road inspection level of the target road is determined based on road information. The road inspection level is used to reflect the degree of inspection needs of the target road.

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

[0160] The detection frequency of the target road is determined based on the road detection level and climate data;

[0161] The first detection data of the target road is obtained based on the detection frequency. The first detection data includes settlement distance and road sound.

[0162] The collapse risk score of the target road is determined based on the first detection data;

[0163] When the collapse risk score is higher than the preset risk score threshold, m detected vehicles that have passed through the target road within a preset historical time period are identified; m is a positive integer greater than 1.

[0164] Second detection data of the target road is obtained by using m detection vehicles. The second detection data is used to reflect the road surface condition of the target road.

[0165] By integrating the first and second inspection data, road inspection data is obtained.

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

[0167] The i bump locations on the target road are determined based on road detection data; i is a natural number.

[0168] Based on road detection data, the trends of road height change, road gap change, and road sound characteristics are determined. The trends of road gap change include the trends of change in the number, length, and width of road gaps.

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

[0170] The bump coefficient of the target road is determined based on i bump locations and 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 characteristics are consistent with the preset sound characteristics, or the bump coefficient is higher than the preset bump coefficient threshold, then it is determined that a road collapse event has occurred on the target road.

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

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

[0174] The location of the road collapse was determined based on road detection data;

[0175] Based on the location of the incident and the locations of j vehicles, determine the first relative positional relationship between each of the j vehicles and the location of the incident, thus obtaining j first relative positional relationships;

[0176] Among the j first relative position relationships, the vehicles that are consistent with the preset relative position relationship and whose vehicle speed is greater than the preset speed threshold among the j vehicle information are selected as n target vehicles.

[0177] In some possible embodiments, in 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 n target vehicles in j vehicle information, the processor 902 is specifically configured to perform the following operations:

[0178] Based on the current road surface conditions of the target road and the speeds of n vehicles, determine the minimum braking distance for each of the n target vehicles, thus obtaining n minimum braking distances;

[0179] Based on the positions of n vehicles, determine the second relative positional relationship between each of the n target vehicles and its adjacent vehicles, and obtain n second relative positional relationships;

[0180] Based on the n first relative positional relationships, determine the first distance between each of the n target vehicles and the location of the incident, thus obtaining the n first distances;

[0181] Based on the n second relative positional relationships, determine the second distance between each of the n target vehicles and the vehicle in front, thus obtaining n second distances;

[0182] Based on n first distances and n second distances, candidate braking distances are determined for each of the n target vehicles, resulting in n candidate braking distances;

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

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

[0185] Based on n preset distances, n vehicle positions, n candidate braking distances, and n minimum braking distances, determine the target braking distance for each of the n target vehicles, resulting in n target braking distances, such that the distance between adjacent vehicles among the n target vehicles is greater than the corresponding preset distance among the n preset distances; and the n target braking distances are greater than or equal to the corresponding minimum braking distance among the n minimum braking distances.

[0186] Based on n target braking distances and n vehicle speeds, n braking commands are determined. The n braking commands include n braking parameter information, which includes n braking pressure value sequences and n braking time sequences.

[0187] n braking commands are issued to n target vehicles so that the n target vehicles brake based on the n braking commands.

[0188] In some possible embodiments, 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 processor 902 is also configured to perform the following operations:

[0189] Among the n candidate braking distances, the target vehicle whose braking distance is less than the corresponding minimum braking distance among the n minimum braking distances is taken as the first vehicle, resulting in a first vehicles; a is a natural number less than or equal to n.

[0190] Obtain at least one adjacent lane driving data for each of the a first vehicles to obtain a adjacent lane driving data set; the adjacent lane driving data is used to reflect the driving situation of vehicles in adjacent lanes.

[0191] Based on driving data of a adjacent lanes and a minimum braking distances corresponding to a first vehicles, the target driving lane of each of the a first vehicles is determined, resulting in a target driving lanes;

[0192] Based on a target driving lanes and a vehicle speeds corresponding to a first vehicles, determine the driving parameters of each of the a first vehicles, and obtain a driving parameters, including a steering wheel angle sequence and a steering time sequence;

[0193] A driving parameters are sent to a first vehicles so that each of the a first vehicles drives based on the corresponding driving parameters in the a driving parameters.

[0194] This application also provides a computer-readable storage medium storing a computer program that is executed by a processor to implement some or all of the steps of any of the road collapse event handling methods described in the above method embodiments.

[0195] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the road collapse event handling methods described in the above method embodiments.

[0196] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0197] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0198] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or modules may be electrical or other forms.

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

[0200] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software program modules.

[0201] If the integrated module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this 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. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0202] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method of handling a road collapse event, characterized by, The method is applied to a server and comprises the following steps: obtaining 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, obtaining vehicle information of each of j traveling vehicles corresponding to the target road, obtaining j pieces of vehicle information, the j pieces of vehicle information comprising j vehicle positions and j vehicle speeds; j is a natural number; the server and the j traveling vehicles are in communication connection; 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; controlling corresponding vehicles in the n target vehicles to perform corresponding braking operations based on n vehicle positions and n vehicle speeds corresponding to the n target vehicles in the j pieces of vehicle information, so that each vehicle in the n target vehicles is parked safely in front of a dangerous area corresponding to the road collapse event; wherein the controlling of the corresponding vehicles in the n target vehicles to perform the corresponding braking operations based on the n vehicle positions and the n vehicle speeds corresponding to the n target vehicles in the j pieces of vehicle information specifically comprises: determining n minimum braking distances of each target vehicle in the n target vehicles based on current road surface conditions of the target road and the n vehicle speeds, obtaining n minimum braking distances; determining n second relative position relationships between each target vehicle in the n target vehicles and an adjacent vehicle based on the n vehicle positions, obtaining n second relative position relationships; determining n first distances between each target vehicle in the n target vehicles and a position where the road collapse event occurs based on n first relative position relationships, obtaining n first distances; the n first relative position relationships are relative position relationships between each target vehicle in the n target vehicles and the position where the road collapse event occurs; determining n second distances between each target vehicle in the n target vehicles and a preceding vehicle based on the n second relative position relationships, obtaining n second distances; determining n candidate braking distances of each target vehicle in the n target vehicles based on the n first distances and the n second distances, obtaining n candidate braking distances; the n candidate braking distances are maximum braking distances that can be selected by each target vehicle in the n target vehicles in a current traveling state; controlling corresponding target vehicles in the n target vehicles to brake based on the n candidate braking distances and the n minimum braking distances; wherein the controlling of the corresponding target vehicles in the n target vehicles to brake based on the n candidate braking distances and the n minimum braking distances specifically comprises: determining a target braking distance of 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, to obtain n target braking distances, so that the distance between adjacent vehicles of the n target vehicles is greater than a corresponding preset distance in the n preset distances; the n target braking distances are greater than or equal to a corresponding minimum braking distance in the n minimum braking distances; the n preset distances are minimum distances required to be maintained between each of the n target vehicles and adjacent vehicles; 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; issuing the n braking instructions to the n target vehicles, so that the n target vehicles brake based on the n braking instructions.

2. The method of claim 1, wherein, The road detection data of the target road is obtained, including: obtaining road information of the target road, the road information including at least one of the following: road age, road position, road type, road load condition; determining a road detection level of the target road based on the road information, the road detection level reflecting the detection demand degree of the target road; obtaining 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; obtaining first detection data of the target road based on the detection frequency, the first detection data including settlement distance and 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, determining m detection vehicles passing through the target road in a preset historical time period; m is a positive integer greater than 1; obtaining second detection data of the target road through the m detection vehicles, the second detection data reflecting the road surface condition of the target road; integrating the first detection data and the second detection data to obtain the road detection data.

3. The method of claim 2, wherein, The determination of whether the target road has a road collapse event based on the road detection data includes: determining i bump positions of the target road based on the road detection data; i is a natural number; determining road height change trend, road gap change trend and road sound characteristics based on the road detection data, the road gap change trend including change trends of the number, length and width of road gaps; obtaining 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 a preset first change trend, or the road gap change trend is consistent with a preset second change trend, or the road sound characteristics are consistent with a preset sound characteristics, or the bump coefficient is higher than a preset bump coefficient threshold, it is determined that the target road has the road collapse event. Otherwise, it is determined that the road collapse event did not occur on the target road.

4. The method of claim 3, wherein, The step of determining n target vehicles from the j moving vehicles based on the j vehicle positions includes: The location of the road collapse event was determined based on the road detection data; Based on the location of the incident and the locations of the j vehicles, a first relative positional relationship between each of the j vehicles and the location of the incident is determined, resulting in j first relative positional relationships; The vehicles that have the same relative position relationship as the preset relative position relationship among the j first relative position relationships and whose vehicle speed is greater than the preset speed threshold among the j vehicle information are selected as the n target vehicles.

5. The method of claim 1, wherein, 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: The target vehicle whose braking distance is less than the corresponding minimum braking distance among the n candidate braking distances is selected as the first vehicle, resulting in a first vehicles; where a is a natural number less than or equal to n. Acquire at least one adjacent lane driving data for each of the a first vehicles to obtain a adjacent lane driving data set; the adjacent lane driving data is used to reflect the driving situation of vehicles in adjacent lanes; Based on the driving data of the a adjacent lanes and the a minimum braking distances corresponding to the a first vehicles, the target driving lane of each of the a first vehicles is determined, resulting in a target driving lanes; Based on the a target driving lanes and the a vehicle speeds corresponding to the a first vehicles, the driving parameters of each of the a first vehicles are determined to obtain a driving parameters, which 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 drives based on the corresponding driving parameters among the a driving parameters.

6. A road collapse event handling apparatus characterized by comprising: Applied to a server, the device includes 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 has occurred on the target road based on the road detection data; If the road collapse event occurs, the vehicle information of each of the j vehicles traveling on the target road is obtained, resulting in j vehicle information, which includes j vehicle positions and j vehicle speeds; j is a natural number; the server maintains a communication connection with the j vehicles. Based on the positions of the j vehicles, determine n target vehicles from the j moving vehicles; 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, control the corresponding vehicles among the n target vehicles to perform corresponding braking operations, so that each of the n target vehicles can safely stop in front of the danger zone corresponding to the road collapse event; The processing unit is specifically configured to: determine the minimum braking distance of each target vehicle in the n target vehicles based on the current road surface condition of the target road and the n vehicle speeds, to obtain n minimum braking distances; determine the second relative position relationship between each target vehicle in the n target vehicles and the adjacent vehicle based on the n vehicle positions, to obtain n second relative position relationships; determine the first distance between each target vehicle in the n target vehicles and the occurrence position of the road collapse event based on n first relative position relationships, to obtain n first distances; the n first relative position relationships are the relative position relationships between each target vehicle in the n target vehicles and the occurrence position; determine the second distance between each target vehicle in the n target vehicles and the preceding vehicle based on the n second relative position relationships, to obtain n second distances; determine the candidate braking distance of each target vehicle in the n target vehicles based on the n first distances and the n second distances, to obtain n candidate braking distances; the n candidate braking distances are the maximum braking distances that can be selected by each target vehicle in the n target vehicles under the current driving state; control the corresponding target vehicle in the n target vehicles to brake based on the n candidate braking distances and the n minimum braking distances; The processing unit is specifically configured to: determine the target braking distance of each target vehicle in the n target vehicles based on n preset distances, the n vehicle positions, the n candidate braking distances, and the n minimum braking distances, to obtain n target braking distances, so that the distance between the adjacent vehicles of the n target vehicles is greater than the corresponding preset distance in the n preset distances; the n target braking distances are greater than or equal to the corresponding minimum braking distance in the n minimum braking distances; the n preset distances are the minimum distances that need to be maintained between each target vehicle in the n target vehicles and the adjacent vehicle; determine 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; issue the n braking instructions to the n target vehicles, so that the n target vehicles brake based on the n braking instructions.

7. An electronic device, comprising: The electronic device comprises: a processor and a memory, the processor being connected with the memory, the memory being used to store a computer program, and the processor being used to execute the computer program stored in the memory, so that the electronic device executes the method in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, the computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1-5.

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