Road group accident early warning method and device

Abstract

The embodiment of the invention provides a road group accident early warning method and device, computer equipment and a computer readable storage medium. The road group accident early warning method comprises the steps of obtaining a real address of a target vehicle; determining a plurality of associated vehicles based on the real addresses; obtaining associated data information of a plurality of associated vehicles; judging whether a group accident risk exists or not based on the associated data information; and under the condition that the judgment result is yes, risk prompt information is sent. According to the technical scheme provided by the embodiment of the invention, the early prediction of the accident chain of the group accident of the expressway can be realized through the fusion perception and intelligent judgment of a plurality of associated vehicles. Therefore, precious response time is provided for the target vehicle and the rear vehicle, the target vehicle and the rear vehicle are guided to take risk avoiding measures, the expansion of the accident scale is effectively blocked, and the safety level and the safety index of road traffic transportation are improved.

Description

Methods and devices for early warning of mass accidents on highways Technical Field

[0001] This application relates to the field of traffic safety monitoring technology, and in particular to a method, device, computer equipment, and computer-readable storage medium for early warning of highway group accidents. Background Technology

[0002] In the field of highway traffic safety, multi-vehicle rear-end collisions and group collisions pose serious challenges due to their high destructiveness and difficulty in early warning. The incidence and casualties of such accidents are particularly pronounced in adverse conditions such as heavy fog, nighttime, and icy roads with low visibility or low adhesion coefficients.

[0003] Current mainstream monitoring methods, such as video surveillance, radar detection, or reporting of emergency braking signals (e.g., hazard lights during sudden braking) from individual vehicles, aim to predict and warn of collision risks by analyzing the aggressive driving behaviors of individual vehicles, such as rapid acceleration and deceleration. However, this approach typically views the state of each vehicle in isolation, lacking a collaborative analysis of the interconnected behaviors of a group of vehicles in the spatiotemporal dimension. The essential characteristic of highway group accidents is the "spatiotemporal aggregation of risks," that is, the dense occurrence of abnormal states (such as emergency braking) of multiple vehicles within a very short time and at very close distances.

[0004] Therefore, relying solely on individual vehicle indicators, lacking spatiotemporal correlation, makes it difficult to provide sufficiently early warning information for vehicles in more distant areas, resulting in insufficient systemic warning capabilities.

[0005] It should be noted that the above content is not necessarily prior art, nor is it intended to limit the scope of patent protection of this application. Summary of the Invention

[0006] This application provides a method, apparatus, computer device, and computer-readable storage medium for early warning of highway group accidents, in order to solve or alleviate one or more of the technical problems mentioned above.

[0007] As a first aspect of the embodiments of this application, this application provides a method for early warning of highway group accidents, the method comprising:

[0008] Obtain the target vehicle's real address;

[0009] Multiple associated vehicles were identified based on their real addresses;

[0010] Obtain associated data information for multiple related vehicles;

[0011] Based on related data, determine whether there is a risk of mass incidents;

[0012] If the judgment result is yes, a risk warning message will be sent.

[0013] In one implementation, obtaining the real address of the target vehicle includes:

[0014] Upon receiving a connection request, obtain the target vehicle's real address at a preset frequency;

[0015] The connection establishment request is sent by the target vehicle when the target vehicle is started.

[0016] In one implementation, multiple associated vehicles are identified based on their real addresses, including:

[0017] Based on the real address, multiple vehicles located within a preset length preceding the real address are identified as associated vehicles; the preset length is 25m-150m.

[0018] In one implementation, determining whether there is a risk of a mass incident based on associated data information includes:

[0019] Based on the associated data, determine whether the associated vehicles are experiencing any abnormalities;

[0020] If the number of abnormally associated vehicles exceeds the first threshold, it is judged that there is a risk of a mass accident.

[0021] In one implementation, the associated data information includes the speed information of the associated vehicles. Based on the associated data information, determining whether the associated vehicles are experiencing an anomaly includes:

[0022] Based on the speed information, obtain the acceleration of the associated vehicles;

[0023] If the acceleration exceeds the second threshold, the associated vehicle is determined to be abnormal.

[0024] In one implementation, the associated data information includes the associated address of the associated vehicle, which is the address information of the associated vehicle obtained at a preset frequency; based on the associated data information, determining whether the associated vehicle is abnormal includes:

[0025] Based on the associated address, obtain the movement path of the associated vehicle;

[0026] If the deviation angle between the moving path and the actual route is greater than the third threshold and / or the deviation length is greater than the fourth threshold, the associated vehicle is determined to be abnormal.

[0027] In one implementation, determining whether a related vehicle has exhibited an anomaly based on associated data information further includes:

[0028] Obtain the true height of the associated vehicle;

[0029] If the actual height decreases continuously multiple times, or if the actual height is lower than the general height of the associated address and the height difference is greater than the fifth threshold, the associated vehicle is determined to be abnormal.

[0030] In one implementation, if the determination result is yes, a risk warning message is sent, including:

[0031] Based on the associated address, obtain the communication address information of the electronic display boards within a preset range of the associated address;

[0032] Send risk warning messages to electronic display boards, associated vehicles, and target vehicles.

[0033] As a second aspect of the embodiments of this application, this application provides a highway group accident early warning device, the device comprising:

[0034] The real address acquisition unit is used to obtain the real address of the target vehicle;

[0035] The associated vehicle determination unit is used to determine multiple associated vehicles based on their real addresses.

[0036] The associated data acquisition unit is used to obtain associated data information of multiple associated vehicles;

[0037] The risk assessment unit is used to determine whether there is a risk of a group accident based on related data information;

[0038] The risk alert unit is used to send a risk alert message when the judgment result is yes.

[0039] As a third aspect of the embodiments of this application, the embodiments of this application provide a computer device, including:

[0040] At least one processor; and

[0041] Memory that is communicatively connected to at least one processor;

[0042] Wherein: the memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor to enable at least one processor to perform the method described above.

[0043] As a fourth aspect of the present application, the present application provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the method described above.

[0044] The embodiments of this application employ the above-described technical solution to achieve early prediction of the "accident chain" in mass accidents on highways through the fusion perception and intelligent judgment of multiple related vehicles. This provides valuable reaction time for the target vehicle and vehicles following behind, guiding them to take evasive measures, effectively preventing the escalation of the accident, and improving the safety level and safety index of road transportation. Attached Figure Description

[0045] The accompanying drawings exemplify embodiments and form part of the specification, serving together with the textual description to explain exemplary implementations of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0046] Figure 1 shows a schematic operating environment diagram of the highway group accident early warning method according to an embodiment of this application;

[0047] Figure 2 shows a flowchart of a highway group accident early warning method according to an embodiment of this application.

[0048] Figure 3 shows a flowchart of the sub-steps of step S240 in Figure 2;

[0049] Figure 4 shows a schematic structural block diagram of a highway group accident early warning device according to an embodiment of this application; and

[0050] Figure 5 shows a schematic block diagram of the hardware architecture of a computer device according to an embodiment of this application. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0052] It should be noted that the descriptions involving "first," "second," etc., in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0053] In the description of this application, it should be understood that the numerical labels before the steps do not indicate the order of the steps, but are only used to facilitate the description of this application and to distinguish each step, and therefore should not be construed as a limitation of this application.

[0054] First, a definition of the terminology used in this application is provided:

[0055] Group accident risk: This refers to the potential danger and likelihood of traffic accidents involving multiple vehicles or a large number of people on highways. These accidents typically have the following characteristics:

[0056] 1. Wide scope: It may involve multiple vehicles such as cars, buses, and trucks, and may even affect pedestrians and non-motorized vehicles.

[0057] 2. Serious casualties and property damage: Due to the large number of vehicles involved, accidents often result in a large number of casualties, including serious injuries and deaths. At the same time, property damage such as vehicle damage and road infrastructure damage is also quite serious.

[0058] 3. Traffic congestion and disruption: Mass accidents can easily cause traffic jams on highways, and may even lead to long-term road closures, affecting normal traffic order and logistics transportation.

[0059] 4. High difficulty in rescue: It requires coordination among multiple departments (such as traffic police, fire department, medical personnel, etc.) for joint rescue, and the rescue work is complex and time-consuming.

[0060] 5. Significant social impact: Such accidents easily attract public attention and have a significant impact on social stability and the image of highway operations.

[0061] Assessing and preventing risks of mass accidents on highways is an important part of highway safety management. If early warning information can be obtained early enough, the probability of accidents can be reduced and the consequences of accidents can be mitigated.

[0062] This application provides a method for early warning of highway group accidents, aiming to obtain early warning information earlier, reduce the probability of accidents and mitigate their consequences.

[0063] Secondly, for ease of understanding, an exemplary operating environment is provided below.

[0064] As shown in Figure 1, the schematic diagram of this operating environment includes a service platform 2, a network 4, and a client 6. The service platform 2 can be a highway management system composed of one or more computing devices. These multiple computing devices can include virtualized computing instances. Virtualized computing instances can include virtual machines, such as simulations of computer systems, operating systems, servers, etc. The computing devices can load virtual machines based on virtual images and / or other data that define specific software (e.g., operating systems, dedicated applications, servers) used for simulation. As the demand for different types of processing services changes, different virtual machines can be loaded and / or terminated on one or more computing devices. A hypervisor can be implemented to manage the use of different virtual machines on the same computing device.

[0065] Service platform 2 can be configured to communicate with clients 6, etc., via network 4. Network 4 includes various network devices, such as routers, switches, multiplexers, hubs, modems, bridges, repeaters, firewalls, proxy devices, and / or similar devices. Network 4 may include physical links, such as coaxial cable links, twisted pair cable links, fiber optic links, or combinations thereof, or wireless links, such as cellular links, satellite links, Wi-Fi links, etc.

[0066] Service platform 2 can provide services such as storage, reading, writing, querying, and deletion, and provide information prompt services to clients (such as target vehicles and associated vehicles).

[0067] Client 6 can be an in-vehicle terminal running operating systems such as Windows, Android™, or iOS. Client 6 can provide / configure user access pages for controlling service platform 2 or uploading data information, etc.

[0068] It should be noted that the above-mentioned equipment is exemplary, and the number and type of equipment can be adjusted in different scenarios or according to different needs.

[0069] Figure 2 shows a flowchart of a highway group accident early warning method according to an embodiment of this application. As shown in Figure 2, the highway group accident early warning method includes:

[0070] S210, obtain the real address of the target vehicle.

[0071] The target vehicle can be understood as any vehicle traveling on the road, capable of communicating with service platform 2 and receiving warning information upon startup or after being configured. The target vehicle obtains its precise geographical location in real time through an onboard positioning module (such as a BeiDou / GPS dual-mode positioning chip). Service platform 2 can obtain the target vehicle's real address by reading this information. As the target vehicle travels, its real address changes over time, and service platform 2 can obtain the target vehicle's current real address in real time at a preset frequency. This real address may include longitude, latitude, altitude, and a positioning timestamp.

[0072] S220 identifies multiple associated vehicles based on their real addresses.

[0073] The system uses the target vehicle's real address as a reference point and delineates a dynamic monitoring area forward along the driving direction (determined by heading angle or navigation path). All networked vehicles within this dynamic monitoring area are identified as associated vehicles.

[0074] In one example, the target vehicle can be used as the starting point, and multiple connected vehicles located in front of the target vehicle can be designated as associated vehicles. For example, 6-20 connected vehicles in front of the target vehicle can be designated as associated vehicles. By monitoring these 6-20 connected vehicles in real time, a sufficiently early warning of the risk of a group accident can be provided to the target vehicle.

[0075] In one example, the driver can visually see the vehicles in front of the target vehicle. In a highway control system, vehicles further ahead of the target vehicle can be monitored. For instance, if the associated vehicles are eight vehicles in front of the target vehicle, then the second to ninth vehicles in front of the target vehicle can be considered as associated vehicles. This allows for sufficient advance warning of group accident risks without increasing the amount of data processed, thus improving data processing efficiency.

[0076] In this embodiment, step S220 selects a set of vehicles from all networked vehicles that are spatially close to the target vehicle and may be involved in the same group of accident risk scenarios.

[0077] S230, obtains associated data information for multiple related vehicles.

[0078] The highway control system is based on a communication network to obtain related data information of associated vehicles, including but not limited to: real-time speed, acceleration, heading angle, geographical location trajectory, vehicle attitude (pitch angle, roll angle), etc.

[0079] S240, based on associated data information, determines whether there is a risk of mass incidents.

[0080] The system integrates and analyzes the associated data of multiple related vehicles to identify the abnormal status of each vehicle and count the number of abnormal vehicles. For example, when the number of abnormal vehicles exceeds a preset first threshold, it is determined that there is a risk of a group accident. This "group anomaly" pattern often indicates that there is a dangerous situation on the road ahead (such as icy road surface, sudden fog, or a chain collision that has started but has not yet been detected by the target vehicles).

[0081] S250: If the judgment result is yes, a risk warning message is sent.

[0082] Once a risk of a group accident is determined, the system sends warnings through multiple channels, such as pushing audible and visual alarms to the onboard terminals of the target vehicles; sending emergency braking suggestions to related vehicles; and sending accident warning information to roadside electronic information boards to remind vehicles approaching from behind.

[0083] This application's embodiments utilize a "multi-vehicle collaborative perception" mechanism to extend the perception capabilities of a single vehicle to a collaborative perception network of a group of vehicles. The target vehicle does not need to visually witness the accident ahead; it can anticipate the potential for a group accident by observing the collective abnormal behavior of multiple related vehicles (such as simultaneous sudden braking, trajectory deviation, or abnormal vehicle posture). This fusion perception approach overcomes line-of-sight limitations, advances warning time, and provides drivers with valuable emergency response time, significantly reducing the probability of chain-reaction collisions and improving overall road safety.

[0084] The following examples illustrate different scenarios:

[0085] Example 1: Fog scene on a highway

[0086] Assume target vehicle A is traveling on a section of highway at a speed of 120 km / h. The system obtains A's real address as (longitude 116.397, latitude 39.916, altitude 45m). The system identifies three related vehicles, B, C, and D, within a 100m radius ahead.

[0087] Vehicle B (30m ahead): Speed ​​drops suddenly from 120km / h to 60km / h, with an acceleration of -1.5g (emergency braking).

[0088] Vehicle C (80m ahead): Speed ​​decreases from 110km / h to 50km / h, and at the same time, the trajectory deviates from the lane line to the left by 1.2m;

[0089] Vehicle D (120m ahead): Speed ​​drops from 115km / h to 40km / h, and the vehicle pitch angle increases abnormally (possibly colliding with the guardrail).

[0090] The system determined that all three associated vehicles were malfunctioning, exceeding the first threshold (e.g., set to two vehicles), indicating a risk of a group accident. An immediate warning was sent to vehicle A: "Multiple vehicles ahead are malfunctioning, suspected fog or accident; it is recommended to reduce speed to below 60 km / h." The driver of vehicle A slowed down in advance, avoiding a rear-end collision caused by a sudden drop in visibility after entering the fog area.

[0091] Example 2: Braking failure scenario on a long downhill slope

[0092] The target vehicle E is traveling on a long downhill section of highway. The system identifies related vehicles F, G, and H ahead.

[0093] Vehicle F (20m ahead): Speed ​​continues to increase (acceleration on the slope), but the brake lights remain on (the driver is continuously applying the brakes, which may lead to brake failure).

[0094] Vehicle G (50m ahead): Abnormal speed fluctuation (sudden braking-acceleration-sudden braking, possibly avoiding vehicle F);

[0095] Vehicle H (90m ahead): Speed ​​drops suddenly and deviates from the main lane (emergency avoidance to the emergency lane);

[0096] The system detected three vehicles malfunctioning, posing a risk of a chain-reaction accident (braking failure leading to a chain-reaction rear-end collision). An emergency warning was sent to vehicle E: "Multiple vehicles are malfunctioning on the long downhill section ahead, suspected of braking failure. It is recommended to immediately move to the right-hand emergency lane." The driver of vehicle E took timely evasive action, avoiding a rear-end collision with the out-of-control vehicles.

[0097] In one implementation, step S210 includes: upon receiving a connection establishment request, obtaining the real address of the target vehicle at a preset frequency; wherein the connection establishment request is sent by the target vehicle when the target vehicle is started.

[0098] When the target vehicle is started (ignition switch ON, engine or drive system ready), the onboard T-BOX (remote communication terminal) is automatically activated and sends a connection request to the early warning service platform via 4G / 5G network. This request includes the vehicle's VIN code, vehicle model information, and initial location data.

[0099] After the connection is established, the system maintains a persistent connection with the target vehicle. The target vehicle continuously reports its real address at a preset frequency (e.g., once every 100ms, or 10Hz). This frequency can be dynamically adjusted according to the vehicle speed: it increases to 20Hz (once every 50ms) when the vehicle speed is >100km / h, and decreases to 2Hz (once every 500ms) when the vehicle speed is <30km / h, in order to balance data real-time performance and communication load.

[0100] This application embodiment uses a "connect upon startup" mechanism to ensure that the vehicle is in a monitored and protected state from the moment it departs, thus avoiding missed alarms. The dynamic frequency adjustment mechanism ensures both the positional accuracy at high speeds (the vehicle moves approximately 2.8m within 100ms at high speeds, requiring high-frequency updates to accurately locate associated vehicles) and avoids invalid communication during low-speed congestion, reducing system load and vehicle energy consumption.

[0101] For example, when driver Zhang starts his vehicle at 8:00 AM, the onboard system automatically sends a connection request to the platform. After confirming the connection, the platform sets the initial frequency to 10Hz. Once Zhang enters the urban expressway and his speed increases to 80km / h, the system maintains the 10Hz frequency, acquiring location data every 100ms. When Zhang enters the highway and his speed reaches 120km / h, the system automatically increases the frequency to 20Hz (every 50ms). At this point, changes in vehicle positions within a 150m range ahead can be accurately captured, ensuring the accuracy of vehicle identification. When Zhang slows down to 20km / h due to congestion, the frequency automatically drops to 2Hz to reduce unnecessary communication traffic.

[0102] In one implementation, step S220 includes: determining multiple vehicles whose locations are within a preset length preceding the real address as associated vehicles based on the real address; the preset length is 25m-150m.

[0103] The preset length range of 25m-150m has been optimized. The lower limit of 25m ensures that it includes the distance of at least 1-2 vehicles ahead (the safe following distance on highways is usually >50m), avoiding insufficient statistical samples due to too few associated vehicles. The upper limit of 150m balances the warning lead time and data correlation (vehicle anomalies that are too far away may not be related to the current road section risk). This range can be dynamically adjusted according to road type: 25-50m for ordinary roads and 100-150m for expressways.

[0104] In one embodiment, as shown in FIG3, step S240 includes:

[0105] S241, Based on the associated data information, determine whether the associated vehicle is abnormal.

[0106] The system analyzes the associated data of each vehicle and uses a multi-dimensional anomaly detection algorithm to output an anomaly status identifier for each vehicle (0 = normal, 1 = abnormal).

[0107] S242, if the number of abnormally associated vehicles exceeds the first threshold, it is judged that there is a risk of a mass accident.

[0108] The system iterates through the abnormal states of all associated vehicles, accumulates the abnormal identifier values, and obtains the total number of abnormal vehicles among the multiple associated vehicles.

[0109] This application's embodiments effectively distinguish between "single-vehicle sporadic anomalies" and "group systemic risks" through a "multi-vehicle anomaly counting" mechanism. Single-vehicle anomalies may be caused by individual factors such as driver error or vehicle malfunction; while simultaneous anomalies in multiple vehicles strongly indicate the presence of a common hazard source affecting multiple vehicles ahead (such as road defects, severe weather, or sudden accidents). This judgment based on group behavior significantly reduces the false alarm rate and improves the accuracy and reliability of early warnings.

[0110] In one implementation, the associated data information includes the speed information of the associated vehicle, and step S241 includes:

[0111] Based on the speed information, obtain the acceleration of the associated vehicles;

[0112] The highway control system obtains the speed sequences {v1,v2,...,v} reported by associated vehicles at a preset frequency. n}, instantaneous acceleration is calculated using differential methods: a t =(v t -v t-1 ) / Δt, where Δt is the sampling interval.

[0113] If the acceleration exceeds the second threshold, the associated vehicle is determined to be abnormal.

[0114] Set a second threshold a threshold (Normal driving acceleration is typically <0.3g, while emergency braking can reach 0.8-1.0g). When |a t |>a threshold When this occurs, it is determined that the associated vehicle is malfunctioning (violent acceleration or violent deceleration).

[0115] Acceleration is a key indicator reflecting sudden changes in a vehicle's motion. During normal driving, drivers try to maintain smooth acceleration and deceleration; however, accidents are often preceded by extreme acceleration (such as sudden braking to avoid obstacles, deceleration after a collision, or uncontrolled acceleration). By monitoring acceleration exceeding limits, accident signs can be quickly detected, with a response time of <100ms.

[0116] The following examples illustrate different scenarios:

[0117] Example 1: Scenario of the vehicle in front braking suddenly

[0118] The speed sequence of the associated vehicle A1 is: 120km / h→118km / h→115km / h→80km / h→50km / h (time interval 100ms).

[0119] Calculate acceleration:

[0120] Point 3: (115-118) / 0.1 = -30km / h / s ≈ -0.85g;

[0121] Point 4: (80-115) / 0.1 = -350km / h / s ≈ -9.9g (far exceeding the second threshold)

[0122] If the absolute value of the acceleration at point 4 far exceeds the second threshold (set to 0.8g), an anomaly (emergency braking) is determined in vehicle A1. If vehicles A2 and A3 also detect similar emergency braking at the same time, a group accident warning is triggered.

[0123] Example 2: Vehicle loss of control scenario after collision

[0124] Related vehicle B1 speed sequence: 100km / h→95km / h→60km / h→30km / h→50km / h→70km / h...

[0125] The vehicle was deemed abnormal when its deceleration exceeded the limit at the moment of impact (100→60km / h); subsequent speed fluctuations (30→50→70km / h) indicated that the vehicle might lose control and rotate or rebound, indicating a continuous abnormal state, and the system continuously marked it as an abnormal vehicle.

[0126] In one embodiment, the associated data information includes the associated address of the associated vehicle, which is the address information of the associated vehicle obtained at a preset frequency; step S241 includes:

[0127] Based on the associated address, obtain the movement path of the associated vehicle;

[0128] The highway control system uses the geographic location sequence {(x1,y1),(x2,y2),..., ... n ,y n )}, construct the actual movement path.

[0129] If the deviation angle between the moving path and the actual route is greater than the third threshold and / or the deviation length is greater than the fourth threshold, the associated vehicle is determined to be abnormal.

[0130] The actual movement path is compared with standard lane lines (from a high-precision map) or the expected direction of travel (based on road topology calculations):

[0131] Deviation angle θ: The angle between the actual heading and the lane direction;

[0132] Deviation length d: The vertical distance between the actual position and the lane centerline;

[0133] Set a third threshold θ threshold (e.g., 15°) and the fourth threshold d threshold (e.g., lane width of 0.5m). When θ > θ threshold and / or d>d threshold When this occurs, it is determined that the associated vehicle has an anomaly (deviated from the normal driving trajectory).

[0134] Trajectory deviation reflects a vehicle's lateral stability. During normal driving, a vehicle should remain within its lane and its heading should align with the road direction; however, accidents are often preceded by trajectory deviation (such as when avoiding obstacles, skidding, tire blowouts leading to loss of control, or panicked driving). This indicator is particularly effective in predicting lateral collisions (such as side impacts or veering off the road).

[0135] Example 1: Obstacle Avoidance Scenario

[0136] The trajectory points of vehicle C1 on the straight road segment:

[0137] Standard lane centerline: y=0 (straight line)

[0138] Actual trajectory of C1: y = 0.1m → 0.2m → 0.5m → 1.0m → 0.8m → 0.3m...

[0139] Calculate the deviation:

[0140] The deviation length at point 4 is d = 1.0m > d threshold (0.5m), deemed abnormal;

[0141] Change in heading angle: from 0° to approximately 20° > θ threshold (15°), further confirm the abnormality;

[0142] Car C1 is veering left to avoid an obstacle. If cars C2 and C3 also exhibit similar left or right veer trajectories, it is considered a risk of a group accident (debris covering multiple lanes on the road).

[0143] Example 2: Sideslip scenario on a curve

[0144] The trajectory of vehicle D1 on the curve (radius of curvature 500m):

[0145] Expected trajectory: travel along a smooth curve.

[0146] Actual trajectory: Gradually shifts outwards when entering the curve, with the tangent angle being 25° larger than expected;

[0147] Deviation angle θ=25°>θ threshold (15°), indicating an abnormal sideslip at D1. If multiple vehicles entering the curve exhibit outward deviation, it indicates insufficient road surface friction coefficient (e.g., water accumulation, ice), posing a risk of a group sideslip accident.

[0148] In one implementation, step S241 further includes: obtaining the true height of the associated vehicle; and determining that the associated vehicle is abnormal when the true height decreases continuously multiple times, or when the true height is lower than the general height of the associated address and the height difference is greater than a fifth threshold.

[0149] The true height h of the associated vehicle is obtained by matching data from the vehicle's barometer, GPS elevation data, or high-precision maps. real .

[0150] Analysis of height sequences :

[0151] Continuous reduction mode: If If the incident occurs repeatedly (e.g., more than 3 times in a row), it is determined that the vehicle may fall into a low-lying area (e.g., run off the roadbed or fall off the bridge).

[0152] High-variance constant pattern: Calculate and associate the general height h of the address normal The difference Δh = h (the historical average elevation or map elevation of this road section) real -h normal .

[0153] When the number of consecutive decreases exceeds, for example, 3 times, or |Δh| > the fifth threshold h threshold (e.g., 1.5m) and h real <h normal When an abnormality is detected in the associated vehicle (falling or severe bumping), it is determined that the vehicle has experienced an anomaly.

[0154] Height information is a key indicator for identifying longitudinal hazards (vehicle falls, vehicle jumps, road collapses). While horizontal position may indicate a vehicle is on the road, a sudden drop in height suggests the vehicle has veered off the road or the road surface has collapsed. This indicator is particularly valuable for predicting accidents involving vehicles crashing through guardrails.

[0155] Example 1: Scenario of a vehicle running off the roadbed

[0156] The altitude sequence of associated vehicle E1 (sampling frequency 10Hz):

[0157] =45.2m (normal road surface height)

[0158] =44.8m (slight descent, possibly downhill)

[0159] =43.5m (sudden drop of 1.3m)

[0160] =42.1m (Continuing to decrease)

[0161] =40.8m (continuously decreasing)

[0162] It decreased three times in a row, and was consistent with h. normal The height difference (45m) reaches 4.2m>h threshold(1.5m), it is determined that E1 has gone off the road and fallen. Immediately send a warning to the target vehicle: "Vehicle ahead has fallen off the road, it is recommended to slow down and drive on the left."

[0163] Example 2: Road collapse scenario

[0164] Related vehicles F1, F2, and F3 pass through a certain road segment in sequence:

[0165] F1 passing height: 50.0m→49.8m→49.5m (slight subsidence, threshold not triggered)

[0166] The elevation changes during F2's passage were as follows: 49.5m → 48.0m → 46.5m (significant collapse).

[0167] The height of F3 during its passage: 46.5m → 45.0m → 43.5m (the collapse intensified)

[0168] Both F2 and F3 vehicles triggered an abnormal height detection (continuous descent + excessive height difference), indicating a risk of a group accident (road subsidence expanding). An emergency warning was sent to the target vehicles: "Road subsidence ahead, stop immediately."

[0169] In one implementation, step S250 includes:

[0170] Based on the associated address, obtain the communication address information of the electronic display boards within a preset range of the associated address;

[0171] Send risk warning messages to electronic display boards, associated vehicles, and target vehicles.

[0172] Based on the associated address (location of the risk of a group accident), query the roadside infrastructure database to obtain the communication address information (IP address or dedicated communication ID) of all electronic display boards (variable information signs, lane indicators, etc.) within a preset range (e.g., 500m before and after) of that location.

[0173] Construct structured risk warning information, including: risk type (accident / severe weather / road defect), risk location (mileage marker or relative distance), and suggested actions (slow down / change lanes / stop). Send it to: via different communication protocols.

[0174] Electronic display boards: Update the displayed content (such as "Accident ahead, slow down") via roadside units (RSUs) or traffic networks.

[0175] Associated Vehicles: Send a notification to the abnormal vehicle and surrounding vehicles via V2X direct communication (PC5 interface), indicating that they are in a risk area.

[0176] Target vehicle: Sends warnings to the target vehicle requesting the warning via cellular network (Uu interface) or V2X to enable early avoidance.

[0177] By employing a multi-channel dissemination mechanism combining roadside displays and in-vehicle warnings, risk information coverage is maximized. Electronic display boards warn traditional vehicles not equipped with warning systems; warnings for associated vehicles help vehicles already in the risk zone take self-rescue measures; and warnings for target vehicles enable proactive avoidance. This three-tiered protection system significantly reduces the casualty rate in group accidents.

[0178] Example 1: Multi-vehicle rear-end collision scenario on a highway

[0179] The system has determined that there is a risk of a group accident at a certain location (3 related vehicles exhibiting abnormal behavior: sudden braking + deviation + collision). The following deployment will be executed:

[0180] Electronic display board: The gantry-type VMS 500m ahead immediately switches to display "Accident ahead, slow down to 60km / h", and the VMS 1km behind displays "Congestion ahead, prepare to slow down", forming a tiered warning system.

[0181] Related vehicles: Send the message "Accident warning triggered. It is recommended to turn on hazard lights and evacuate personnel to outside the guardrail" to the G1 vehicle that has been involved in the collision.

[0182] Send a message to the G2 vehicle that is braking suddenly: "Accident ahead, it is recommended to change lanes to the left lane."

[0183] Send the message "Vehicle has deviated from lane, please correct your direction or move into the emergency lane" to vehicle G3 that has drifted out of lane.

[0184] Target vehicle H (vehicle approaching from behind): Received "3-vehicle accident 300m ahead, it is recommended to immediately reduce speed to 60km / h and turn on the left turn signal to change lanes". The driver of vehicle H reduced speed and changed lanes in advance to avoid joining the rear-end collision chain.

[0185] Example 2: Mass Accident Scenario Inside a Tunnel

[0186] Communication within the tunnel is limited, so the system disseminates information through Roadside Units (RSUs) deployed within the tunnel.

[0187] Electronic sign at the tunnel entrance: Displays "Entry prohibited due to accident inside the tunnel" (to prevent more vehicles from entering).

[0188] For vehicles inside the tunnel: Warnings are relayed via RSU (Roadside Unit) to remind drivers to turn on their lights, maintain a safe distance, and prepare to stop.

[0189] Target vehicle (about to enter the tunnel): Receive warning 500m before the tunnel entrance, choose to detour or stop and wait in advance to avoid being trapped inside the tunnel.

[0190] The following example illustrates the early warning system for group accidents on highways during rain and snow weather.

[0191] Start-up phase: After multiple vehicles start up, they automatically connect to the early warning platform and begin high-frequency location reporting (20Hz).

[0192] Association confirmed: Target vehicle I is traveling at a speed of 100km / h. The system determines that the associated vehicles within 150m ahead are four vehicles: J, K, L, and M.

[0193] Data acquisition: Real-time acquisition of speed, position, and altitude data for the four vehicles.

[0194] Anomaly detection:

[0195] Car J (40m ahead): Acceleration -1.2g (emergency braking), trajectory deviates 1.0m to the right (avoidance).

[0196] Car K (80m ahead): Acceleration -0.9g (emergency braking), trajectory deviates 0.8m to the left.

[0197] L-vehicle (120m ahead): Height continuously decreases by 0.5m (sideslips off the roadbed)

[0198] M car (140m ahead): Speed ​​drops suddenly but there is no emergency braking (possible rear-end collision).

[0199] Risk assessment: 4 out of 4 related vehicles are abnormal (100%), far exceeding the first threshold (2 vehicles), and are judged to be at serious risk of group accident.

[0200] Warning issued:

[0201] Roadside sign: "Serious accident ahead, slow down and stop immediately"

[0202] Vehicles J, K, L, and M: "You are now in an accident risk zone. We recommend turning on your hazard lights and evacuating the area."

[0203] Vehicle I: "A four-vehicle pileup has occurred within 150 meters ahead. Emergency braking and lane change to the right emergency lane are advised." Vehicle I received the warning in advance and safely decelerated from 100 km / h to a stop, avoiding becoming the fifth vehicle involved in the accident. Subsequent vehicles stopped gradually based on roadside signs and warnings from the vehicle in front, breaking the chain of accidents.

[0204] Figure 4 shows a schematic structural block diagram of a highway group accident early warning device according to an embodiment of this application. The highway group accident early warning device can be divided into one or more program modules. One or more program modules are stored in a storage medium and executed by one or more processors to complete the embodiment of this application. The program module referred to in the embodiment of this application refers to a series of computer program instruction segments capable of performing specific functions. The following description will specifically introduce the functions of each program module in this embodiment.

[0205] As shown in Figure 4, the highway group accident early warning device 400 may include:

[0206] Real address acquisition unit 401 is used to obtain the real address of the target vehicle;

[0207] The associated vehicle determination unit 402 is used to determine multiple associated vehicles based on their real addresses;

[0208] The associated data acquisition unit 403 is used to obtain associated data information of multiple associated vehicles;

[0209] Risk assessment unit 404 is used to determine whether there is a risk of a group accident based on related data information;

[0210] Risk alert unit 405 is used to send risk alert information when the judgment result is yes.

[0211] In one implementation, the real address acquisition unit 401 is used for:

[0212] Upon receiving a connection request, obtain the target vehicle's real address at a preset frequency;

[0213] The connection establishment request is sent by the target vehicle when the target vehicle is started.

[0214] In one embodiment, the associated vehicle determination unit 402 is used for:

[0215] Based on the real address, multiple vehicles located within a preset length preceding the real address are identified as associated vehicles; the preset length is 25m-150m.

[0216] In one implementation, the risk assessment unit 404 is used for:

[0217] Based on the associated data, determine whether the associated vehicles are experiencing any abnormalities;

[0218] If the number of abnormally associated vehicles exceeds the first threshold, it is judged that there is a risk of a mass accident.

[0219] In one implementation, the associated data information includes the speed information of the associated vehicles. Based on the associated data information, determining whether the associated vehicles are experiencing an anomaly includes:

[0220] Based on the speed information, obtain the acceleration of the associated vehicles;

[0221] If the acceleration exceeds the second threshold, the associated vehicle is determined to be abnormal.

[0222] In one implementation, the associated data information includes the associated address of the associated vehicle, which is the address information of the associated vehicle obtained at a preset frequency; based on the associated data information, determining whether the associated vehicle is abnormal includes:

[0223] Based on the associated address, obtain the movement path of the associated vehicle;

[0224] If the deviation angle between the moving path and the actual route is greater than the third threshold and / or the deviation length is greater than the fourth threshold, the associated vehicle is determined to be abnormal.

[0225] In one implementation, determining whether a related vehicle has exhibited an anomaly based on associated data information further includes:

[0226] Obtain the true height of the associated vehicle;

[0227] If the actual height decreases continuously multiple times, or if the actual height is lower than the general height of the associated address and the height difference is greater than the fifth threshold, the associated vehicle is determined to be abnormal.

[0228] In one implementation, the risk warning unit 405 is used to:

[0229] Based on the associated address, obtain the communication address information of the electronic display boards within a preset range of the associated address;

[0230] Send risk warning messages to electronic display boards, associated vehicles, and target vehicles.

[0231] Figure 5 shows a schematic block diagram of the hardware architecture of a computer device 10000 suitable for implementing a highway group accident early warning method according to an embodiment of this application. In some embodiments, the computer device 10000 may be a terminal device such as a smartphone, wearable device, tablet computer, personal computer, vehicle terminal, game console, virtual device, workbench, digital assistant, set-top box, robot, etc. In other embodiments, the computer device 10000 may be a rack server, blade server, tower server, or cabinet server (including independent servers or server clusters composed of multiple servers). As shown in Figure 5, the computer device 10000 includes, but is not limited to: a memory 10010, a processor 10020, and a network interface 10030 that can communicate and be linked with each other via a system bus. Wherein:

[0232] The memory 10010 includes at least one type of computer-readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 10010 may be an internal storage module of a computer device 10000, such as the hard disk or memory of the computer device 10000. In other embodiments, the memory 10010 may also be an external storage device of the computer device 10000, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 10000. Of course, the memory 10010 may also include both the internal storage module and the external storage device of the computer device 10000. In this embodiment, the memory 10010 is typically used to store the operating system and various application software installed on the computer device 10000, such as the program code for a highway group accident early warning method. In addition, the memory 10010 can also be used to temporarily store various types of data that have been output or will be output.

[0233] In some embodiments, processor 10020 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other chip. Processor 10020 is typically used to control the overall operation of computer device 10000, such as performing control and processing related to data interaction or communication with computer device 10000. In this embodiment, processor 10020 is used to run program code stored in memory 10010 or process data.

[0234] Network interface 10030 may include a wireless network interface or a wired network interface, which is typically used to establish a communication link between computer device 10000 and other computer devices. For example, network interface 10030 is used to connect computer device 10000 to an external terminal via a network, establishing a data transmission channel and communication link between computer device 10000 and the external terminal. The network may be an intranet, the Internet, Global System for Mobile Communication (GSM), Wideband Code Division Multiple Access (WCDMA), 4G network, 5G network, Bluetooth, Wi-Fi, or other wireless or wired networks.

[0235] It should be noted that Figure 5 only shows a computer device with components 10010-10030, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0236] In this embodiment, the highway group accident early warning method stored in memory 10010 can also be divided into one or more program modules and executed by one or more processors (such as processor 10020) to complete the embodiment of this application.

[0237] This application also provides a computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the highway group accident early warning method in the embodiments.

[0238] In this embodiment, the computer-readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the computer-readable storage medium can be an internal storage unit of a computer device, such as the hard disk or memory of the computer device. In other embodiments, the computer-readable storage medium can also be an external storage device of the computer device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device. Of course, the computer-readable storage medium can also include both the internal storage unit and the external storage device of the computer device. In this embodiment, the computer-readable storage medium is typically used to store the operating system and various application software installed on the computer device, such as the program code of the highway group accident early warning method in this embodiment. In addition, the computer-readable storage medium can also be used to temporarily store various types of data that have been output or will be output.

[0239] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the methods described in the above embodiments.

[0240] Obviously, those skilled in the art should understand that the modules or steps of the embodiments of this application described above can be implemented using general-purpose computer devices. They can be centralized on a single computer device or distributed across a network of multiple computer devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computer device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the embodiments of this application are not limited to any particular combination of hardware and software.

[0241] It should be noted that the above are merely preferred embodiments of this application and do not limit the scope of patent protection of this application. Any equivalent structural or procedural changes made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of this application.

Claims

1. A method for early warning of group accidents on highways, characterized in that, include: Obtain the target vehicle's real address; Multiple associated vehicles were identified based on the real addresses; Obtain association data information for multiple associated vehicles; Based on the associated data, determine whether there is a risk of a group accident; if the determination result is yes, send a risk warning message.

2. The method for early warning of highway group accidents according to claim 1, characterized in that, Obtaining the real address of the target vehicle includes: obtaining the real address of the target vehicle at a preset frequency when a connection establishment request is received; wherein the connection establishment request is sent by the target vehicle when the target vehicle is started.

3. The method for early warning of highway group accidents according to claim 1, characterized in that, The step of determining multiple associated vehicles based on the real address includes: determining multiple vehicles whose locations are within a preset length preceding the real address as associated vehicles based on the real address; the preset length is 25m-150m.

4. The method for early warning of highway group accidents according to claim 1, characterized in that, The step of determining whether there is a risk of a group accident based on the associated data information includes: determining whether the associated vehicles are abnormal based on the associated data information; and determining that there is a risk of a group accident if the number of abnormal associated vehicles exceeds a first threshold.

5. The method for early warning of highway group accidents according to claim 4, characterized in that, The associated data information includes the speed information of the associated vehicle. The step of determining whether the associated vehicle is abnormal based on the associated data information includes: obtaining the acceleration of the associated vehicle based on the speed information; and determining that the associated vehicle is abnormal if the acceleration exceeds a second threshold.

6. The method for early warning of highway group accidents according to claim 4, characterized in that, The associated data information includes the associated address of the associated vehicle, which is the address information of the associated vehicle obtained at a preset frequency; the step of determining whether the associated vehicle is abnormal based on the associated data information includes: obtaining the movement path of the associated vehicle based on the associated address; and determining that the associated vehicle is abnormal if the deviation angle between the movement path and the actual route is greater than a third threshold and / or the deviation length is greater than a fourth threshold.

7. The method for early warning of highway group accidents according to claim 6, characterized in that, If the judgment result is yes, sending risk warning information includes: obtaining the communication address information of the electronic display board within a preset range of the associated address based on the associated address; and sending the risk warning information to the electronic display board, the associated vehicle, and the target vehicle.

8. A highway group accident early warning device, characterized in that, The device includes: a real address acquisition unit for acquiring the real address of the target vehicle; an associated vehicle determination unit for determining multiple associated vehicles based on the real address; an associated data acquisition unit for acquiring associated data information of the multiple associated vehicles; a risk judgment unit for judging whether there is a risk of a group accident based on the associated data information; and a risk warning unit for sending risk warning information if the judgment result is yes.

9. A computer device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; wherein: the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method as described in any one of claims 1 to 7.

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