Vehicle pre-collision warning method and system based on matching of traffic accident elements

Abstract

The application discloses a vehicle pre-collision warning method and system based on traffic accident element matching, and the method comprises the following steps: extracting accident parameters from a traffic accident case; simulating and restoring a traffic accident process based on the extracted accident parameters, and extracting a multi-dimensional time sequence of traffic accident participants from the traffic accident process; importing the multi-dimensional time sequence into a DS-HDP-HMM model to obtain a corresponding hidden state sequence; marking each hidden state as a dangerous state, a sub-dangerous state and a normal state; storing a dangerous state sub-sequence and a sub-dangerous state sub-sequence into a traffic accident state sub-database which is consistent with the traffic accident; acquiring a road type where a vehicle with a warning demand is located, acquiring real-time motion parameters of the vehicle and surrounding traffic participants; calculating the similarity between a multi-dimensional time sequence of the vehicle and a sequence in the consistent sub-database, and performing pre-collision warning on the vehicle with the warning demand based on the similarity.

Description

Technical Field

[0001] The present invention belongs to the technical field of traffic warning control, and in particular relates to a vehicle pre-collision warning method and system based on traffic accident element matching. Background Art

[0002] Existing vehicle warning systems widely utilize onboard sensors and connected vehicle technologies, including radar, lidar, cameras, ultrasonic, and infrared sensors. These systems sense the vehicle's surroundings and potential hazards to issue warnings. Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies enable information sharing and coordinated warnings between vehicles and between vehicles and road infrastructure. While these technologies have played an important role in improving driving safety, they still face challenges such as difficulty in multi-sensor fusion, poor sensor performance in complex weather conditions, and computational limitations required for real-time performance. At present, the intelligent era is approaching rapidly. How to ensure the driving safety of vehicles in the complex road environment where various traffic participants exist has become a key issue. Although today's on-board equipment and safety technologies can avoid the occurrence of some accidents, there is still a lot of room for development in automobile safety. Many vehicle collision accidents occur due to late braking triggering time, sensors failing to detect surrounding vehicles or abnormal judgment of drivers. Based on such reasons, this paper extracts the motion state parameters of both vehicles at the time of the accident from the accident case as the trigger conditions for preventing safety accidents. Through early warning and braking as well as moderate intervention of the driver's operation, the safety of the vehicle and the driver is protected when it is judged to be consistent with previous accident cases. Summary of the Invention

[0003] The present invention provides a vehicle pre-collision warning method and system based on traffic accident factor matching, which solves the problem that traffic accidents may occur due to the current reliance on sensors to avoid accidents and the inability to collect movement information of all surrounding traffic participants.

[0004] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0005] A vehicle pre-collision warning method based on traffic accident factor matching, comprising:

[0006] Step 1: extracting accident parameter data for restoring the traffic accident from the traffic accident case;

[0007] Step 2: Based on the extracted accident parameter data, the process before and after the traffic accident is simulated and restored. Multiple motion parameters of the two parties involved in the traffic accident before and after the traffic accident are extracted from the restored traffic accident case to obtain a multidimensional time series describing the process before and after the traffic accident.

[0008] Step 3: Import the multidimensional time series describing the process before and after the traffic accident into the DS-HDP-HMM model to obtain the corresponding hidden state sequence; mark each hidden state as a dangerous state, a sub-dangerous state, and a normal state to obtain a dangerous state subsequence, a sub-dangerous state subsequence, and a normal state subsequence; then, store the dangerous state subsequence and the sub-dangerous state subsequence, as well as the multidimensional time series of the corresponding time period, into a sub-library of the accident state library that matches the traffic accident; the accident state library is divided into multiple sub-libraries according to the type of road on which the traffic accident occurred and the types of parties involved in the traffic accident;

[0009] Step 4: The vehicle with the warning requirement obtains the current road type and the multiple motion parameters of itself and surrounding traffic participants in the recent long time period including the current moment; wherein the motion parameters of the surrounding traffic participants are obtained through the vehicle-road-cloud integrated device; the vehicle is combined with each surrounding traffic participant to obtain multiple groups of traffic accident participants to be predicted, and each group of traffic accident participants to be predicted corresponds to a set of multidimensional time series T 实 ;

[0010] Step 5: For each group of parties involved in the traffic accident to be predicted, locate the corresponding sub-library in the accident status database according to the road type and the type of the parties involved, and convert the corresponding multidimensional time series T 实 Calculate similarity with the multidimensional time series in the sub-database;

[0011] Step 6: Based on the similarity calculation value, a pre-collision warning is issued to the vehicle that needs a warning.

[0012] Furthermore, multiple motion parameters are extracted, including: speed, position, heading angle and relative distance of both traffic participants.

[0013] Furthermore, the road types include intersections, T-junctions, and highways.

[0014] Furthermore, the types of participants in traffic accidents include passenger vehicles, two-wheeled vehicles, and pedestrians.

[0015] Furthermore, the classification criteria for marking each hidden state are as follows: the state within a preset time period before the collision is marked as a dangerous state, the state within a certain time period before the dangerous state is marked as a secondary dangerous state, and the state within a certain time period before the secondary dangerous state is marked as a normal state.

[0016] Furthermore, similarity calculation is performed through normalized cross-correlation, including:

[0017] First, the two sets of multidimensional time series involved in the calculation are normalized;

[0018] Then, the normalized cross-correlation is calculated on each dimension to calculate the shape similarity between the two time series on that dimension:

[0019]

[0020] in, Represent two normalized time series of the j-th dimension, Representing time series The shape similarity between them; τ is the time offset, and different values ​​of τ represent the translation of one sequence relative to another sequence; i represents the time series The sample point number, n represents the time series length; It means that under all possible time offsets τ, a τ is selected so that the two time series The normalized cross-correlation reaches its maximum value;

[0021] Then, the shape similarities between the two time series in all dimensions are combined to obtain the similarity between the two sets of multidimensional time series:

[0022]

[0023] Among them, T1 and T2 represent the two sets of multidimensional time series involved in the calculation, S(T1, T2) represents the similarity between the two sets of multidimensional time series T1 and T2, and w j represents the weight of the j-th dimension, and m represents the dimension of the multidimensional time series.

[0024] Furthermore, the method for performing pre-collision warning based on the similarity calculation value is as follows:

[0025] First, the vehicle's current multidimensional time series T 实 Compare the similarity between each dangerous state subsequence in the positioning sub-library. If T 实 If the similarity between the sequence and any dangerous state subsequence exceeds the threshold, a warning is issued to remind the current driver that the vehicle is in a dangerous driving state; otherwise, T 实 Compare the similarity between each sub-dangerous state subsequence in the positioning sub-library. If T 实 If the similarity between the current state and any of the sub-dangerous state subsequences exceeds a threshold, a warning is issued to remind the current driver that there is a risk of an accident.

[0026] A vehicle pre-collision warning system based on traffic accident factor matching, comprising:

[0027] The accident parameter extraction module is used to extract accident parameter data used to restore traffic accidents from traffic accident cases;

[0028] The accident reconstruction and data export module is used to simulate and restore the process before and after the traffic accident based on the extracted accident parameter data, and extract multiple motion parameters of both parties involved in the traffic accident before and after the traffic accident from the restored traffic accident case to obtain a multidimensional time series describing the process before and after the traffic accident;

[0029] The accident state library construction module is used to: import the multidimensional time series representing the process before and after the traffic accident into the DS-HDP-HMM model to obtain the corresponding hidden state sequence; mark each hidden state as a dangerous state, a sub-dangerous state, and a normal state to obtain a dangerous state subsequence, a sub-dangerous state subsequence, and a normal state subsequence; then store the dangerous state subsequence and the sub-dangerous state subsequence, as well as the multidimensional time series of the corresponding time period, in a sub-library of the accident state library that matches the traffic accident; the accident state library is divided into multiple sub-libraries according to the type of road on which the traffic accident occurred and the types of parties involved in the traffic accident;

[0030] The real-time data acquisition module is used to: for vehicles with early warning requirements, obtain the current road type, obtain the multiple motion parameters of the vehicle itself and surrounding traffic participants in the recent long time period including the current moment; wherein the motion parameters of the surrounding traffic participants are obtained through the vehicle-road-cloud integrated device; combine the vehicle itself with each surrounding traffic participant to obtain multiple groups of traffic accident participants to be predicted, and each group of traffic accident participants to be predicted corresponds to a set of multidimensional time series T 实 ;

[0031] The similarity calculation module is used to locate the corresponding sub-library in the accident status database for each group of traffic accident participants to be predicted, according to the road type and the type of both parties, and convert the corresponding multidimensional time series T 实 Calculate similarity with the multidimensional time series in the sub-database;

[0032] The warning module is used to: provide pre-collision warning to vehicles that currently need warnings based on the similarity calculation value.

[0033] Beneficial effects

[0034] This solution is different from the transmission's vehicle safety warning technology. It does not simply rely on the vehicle's on-board sensor acquisition equipment, but adds vehicle networking technology. In the future era of integrated vehicle-road-cloud traffic information sharing, it can obtain all-round movement information of surrounding traffic participants, help vehicles avoid the type of accidents that have occurred, and respond in time to emergency situations that cannot be detected by sensors, using "eyes" outside the vehicle to help improve vehicle safety while driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a schematic diagram of the vehicle pre-collision warning system of the present invention;

[0036] Figure 2 and Figure 3 It is a flow chart of the vehicle pre-collision warning method of the present invention. DETAILED DESCRIPTION

[0037] The following is a detailed description of an embodiment of the present invention. This embodiment is based on the technical solution of the present invention, provides a detailed implementation method and a specific operation process, and further explains the technical solution of the present invention.

[0038] Example 1

[0039] This embodiment provides a vehicle pre-collision warning method based on traffic accident factor matching, referring to Figure 1-3 Shown, including:

[0040] Step 1: extract accident parameter data for restoring traffic accidents from traffic accident cases.

[0041] Obtain relevant data and related information from traffic accident cases for subsequent step 2 to reconstruct the traffic accident, such as accident cases recorded in CIDAS (China Traffic Accident In-depth Investigation Data) and GIDAS (German Traffic Accident In-depth Investigation Data). The extracted accident parameters include accident scene photos and videos (panoramic photos, close-up photos of the vehicle collision point and damaged parts, brake marks, and other physical evidence), physical measurement data (brake mark length and location, precise location of the collision point, vehicle stop position), vehicle data recorder (EDR) data (vehicle speed, acceleration, brake and accelerator pedal position, yaw angular velocity, yaw rate, etc.), and other data (vehicle position and trajectory, road adhesion coefficient, accident scene base map, vehicle brand and model, vehicle inertia parameters, and tire type and size, etc.).

[0042] Step 2: Based on the extracted accident parameter data, the process before and after the traffic accident is simulated and restored, and multiple motion parameters of the two parties involved in the traffic accident before and after the traffic accident are extracted from the restored traffic accident case to obtain a multidimensional time series that describes the process before and after the traffic accident.

[0043] The simulation restoration process includes:

[0044] (1) Create the accident base map using AutoCAD or other software with similar functions, drag it into the PC-Crash work interface, and set the various parameters in the scenario based on real accident cases, including: 1. Vehicle data: vehicle size, mass, center of gravity, suspension system, tire characteristics, and braking efficiency. In addition, vehicle performance characteristics data, such as steering angle and braking force distribution, are also required; 2. Accident scene information: Data on the accident location is required, including road layout, slope, road friction coefficient (especially wet or icy roads), and brake marks; 3. Collision conditions: The initial speed, direction, and angle of the vehicle at the time of collision need to be estimated. PC-Crash uses momentum-based reduction to calculate the interaction between vehicles during and after the collision; 4. Pedestrian or motorcycle data: If the accident involves pedestrians or motorcycles, pedestrian mass and movement, motorcycle dynamic data, etc. need to be input. PC-Crash has special models for these situations, such as multi-body systems for pedestrian dynamics; 5. Post-collision movement: PC-Crash can simulate the movement of vehicles after the collision, including trajectory, rollover possibility, and final stopping position, and can also calculate secondary collisions and their consequences.

[0045] (2) Model the accident participants based on the information provided in the case, and set the optimal collision contact point, collision angle, accident participant position and other parameters. The optimal collision contact point and angle mainly refer to those parameter settings that can most realistically and physically reproduce the accident under specific accident scenarios and meet the research objectives. These parameters can be optimized through software simulation and repeated adjustments to achieve a reproduction effect that is closest to the actual accident.

[0046] (3) Accident reconstruction simulation: Run the accident simulation with the parameters set in steps (2) and (3). Compare the running results (check whether the restored vehicle motion trajectory is consistent with the physical evidence such as brake marks, tire marks, and the vehicle's final resting position at the accident scene. This is the most direct and effective verification method for accident reconstruction) to the real accident information to determine whether the restoration is consistent. If not, return to step (2) to adjust the relevant parameters until the accident is restored.

[0047] Multiple motion parameters extracted from the restored traffic accident case, including speed, acceleration, and heading angle, are exported from the simulated PC-crash software: Select "Calculate Output" from the "Calculate" menu in PC-crash software. In the "Calculate Output" window, check the "Speed," "Acceleration," and "Heading Angle" options and select the traffic participants to be exported. Export data: Select "Export Data" from the "File" menu to export and save a file containing speed, acceleration, and heading angle data.

[0048] Multiple motion parameters of traffic accident participants before and after the traffic accident are obtained to form a multidimensional time series that can describe the process before and after the traffic accident.

[0049] Step 3: Import the multidimensional time series describing the process before and after the traffic accident into the DS-HDP-HMM model to obtain the corresponding hidden state sequence; mark each hidden state as a dangerous state, a sub-dangerous state, and a normal state to obtain a dangerous state subsequence, a sub-dangerous state subsequence, and a normal state subsequence; then store the dangerous state subsequence and the sub-dangerous state subsequence, as well as the multidimensional time series of the corresponding time period, in the sub-library of the accident state library that matches the traffic accident; the accident state library is divided into multiple sub-libraries according to the type of road on which the traffic accident occurred and the types of the parties involved in the traffic accident.

[0050] In step 3.1, the multidimensional time series describing the process before and after the traffic accident is imported into the DS-HDP-HMM model to obtain the corresponding hidden state sequence.

[0051] The DS-HDP-HMM model is mainly divided into the HDP (Hierarchical Dirichlet Model) and the HMM (Hidden Markov Model). The HDP model is a statistical method used for non-parametric Bayesian clustering and topic modeling. This method is introduced to the model to expand the traditional HMM model, allowing the model to automatically determine the number of states actually required. The hierarchical Dirichlet process models the state transition matrix as a random process, eliminating the need to define the number of implicit states of traffic participants during an accident. For the HMM model, HDP is used to provide the state transition matrix in the HMM model, enabling it to capture more complex state transition structures. At the same time, the globally shared random variable in the HDP model (the basis distribution of the Dirichlet process) provides a common probability space for different states, thereby achieving sharing and constraints on state transition probabilities.

[0052] I won't go into detail about the basic components of the Hidden Markov Model (HMM). Here, we'll briefly explain how the DS-HDPHMM works. First, we use the HDP to construct the state transition matrix in the HMM. HDP is primarily divided into two types of Dirichlet processes: a global Dirichlet process (β~GEM(γ)), ​​where the GEM (Griffiths-Engen-McCloskey) distribution is used to generate hidden states. The other is a local Dirichlet process:

[0053]

[0054] k i represents the state self-sustaining probability controlled by the two hyperparameters ρ1 and ρ2; α is the centralized parameter that controls the distribution of transition probability, π' irepresents the state transition probability generated by the Dirichlet distribution with the basic metric β; δ in the formula i represents the Dirac measure on state i.

[0055] By flexibly controlling the state transition through ρ1, ρ2, and α, the model can more flexibly handle the state division of complex long sequences. At the same time, the parameter θ of the observation function is required to determine the distribution of the observation data generated in each hidden state. Assuming that the observation data is generated by a Gaussian distribution, that is, For each time point t, the observed variable x t Generated by the corresponding hidden state: where F is the observed distribution, is the hidden state s t Corresponding parameters. In the Bayesian framework, the parameters θ of the observation function are usually sampled from a prior distribution H. The choice of the prior distribution depends on the expectation of the observed data. Formally, it can be expressed as:

[0056] θ~H

[0057] Among them, H is a prior distribution, which can be a known standard distribution (such as normal distribution, Gamma distribution, etc.) or a customized distribution based on domain knowledge.

[0058] After the model framework is built, it is necessary to determine whether to infer the hidden state through Gibbs sampling. The following are the simple steps:

[0059] (1) Initialization: Initialize all variables, including the state sequence z, transition probability π, global transition probability β, and observation model parameters θ.

[0060] (2) Model parameter update:

[0061] 1. Sample state sequence. For each time step t, update the hidden state z based on the current parameters and observation data:

[0062]

[0063] Among them, z -t represents the state sequence of other time steps, p(y t |z t ,θ) represents the likelihood function of a given state of the observation model, and They represent the probability of transition between the previous and next states respectively.

[0064] 2. Sampling state transition probability:

[0065] π i ~Dir(αβ+κδ i +n i )

[0066] Among them, n k is the number of transitions from state i to other states; α and k are the hyperparameters of the model, δ k An indicator representing state i.

[0067] 3. Sampling observation model parameters:

[0068] θ i ~p(θ i |y i ,H)

[0069] Among them, y i and H represent the observed data and prior distribution under a given state i, respectively.

[0070] 4. Probability of sampling self-sustaining:

[0071]

[0072] in, Indicates that from state z t-1 The total number of transitions to other states. Resample the auxiliary variable s according to the probability calculated above t The value of , thereby determining whether the state of the current time step remains unchanged.

[0073] (3) Iterative convergence: Repeat the above steps and make the Gibbs sampling chain converge through multiple iterations to obtain the final hidden state sequence.

[0074] In step 3.2, each hidden state is marked as a dangerous state, a sub-dangerous state, and a normal state, and a dangerous state subsequence, a sub-dangerous state subsequence, and a normal state subsequence are obtained.

[0075] The marking classification standard for each hidden state is as follows: the state within a preset time length (set to 2 seconds in this embodiment) before the collision is marked as a dangerous state, the state before the dangerous state (set to 2 seconds in this embodiment) is marked as a secondary dangerous state, and the state within a certain time length before the secondary dangerous state is marked as a normal state.

[0076] In step 3.3, the dangerous state subsequence and the sub-dangerous state subsequence, as well as the multi-dimensional time series of the corresponding time period, are stored in the sub-library of the accident state library that matches the traffic accident.

[0077] The accident status database described in this embodiment includes multiple sub-databases, grouped by the road type where the accident occurred and the types of other accident participants. This allows for rapid sub-database locating during real-time warnings, reducing the computational effort required. Road types include intersections, T-junctions, and highways (but are not limited to these; all road types are included, as accidents can occur on a variety of road types). Other accident participant types include passenger vehicles, two-wheeled vehicles, and pedestrians.

[0078] Step 4: The vehicle that needs early warning obtains the current road type and the multiple motion parameters of itself and surrounding traffic participants in the last 3 seconds including the current moment.

[0079] Among them, the vehicle-road-cloud integrated device obtains real-time information about traffic participants around the vehicle, including the driving direction, heading angle, speed and position changes of traffic participants. The vehicle is combined with each traffic participant around it to obtain multiple groups of traffic accident participants to be predicted. Each group of traffic accident participants to be predicted corresponds to a set of multidimensional time series T 实 .

[0080] Step 5: For each group of parties involved in the traffic accident to be predicted, locate the corresponding sub-library in the accident status database according to the road type and the type of the parties involved, and convert the corresponding multidimensional time series T 实 Calculate the similarity with each multidimensional time series in the sub-database.

[0081] This embodiment calculates similarity by normalized cross-correlation, including:

[0082] First, the two sets of multidimensional time series involved in the calculation are normalized;

[0083] Then, the normalized cross-correlation is calculated on each dimension to calculate the shape similarity between the two time series on that dimension:

[0084]

[0085] in, Represent two normalized time series of the j-th dimension, Representing time series The shape similarity between them; τ is the time offset, and different values ​​of τ represent the translation of one sequence relative to another sequence; i represents the time series The sample point number, n represents the time series length; It means that under all possible time offsets τ, a τ is selected so that the two time series The normalized cross-correlation reaches its maximum value;

[0086] Then, the shape similarities between the two time series in all dimensions are combined to obtain the similarity between the two sets of multidimensional time series:

[0087]

[0088] Among them, T1 and T2 represent the two sets of multidimensional time series involved in the calculation, S(T1, T2) represents the similarity between the two sets of multidimensional time series T1 and T2, and w j represents the weight of the j-th dimension, and m represents the dimension of the multidimensional time series.

[0089] Step 6: Based on the similarity calculation value, a pre-collision warning is issued to the vehicle that needs a warning.

[0090] First, the multidimensional time series T of the two parties involved in each group of traffic accidents to be predicted 实 Compare the similarity between each dangerous state subsequence in the positioning sub-library. If T 实 If the similarity between the sequence and any dangerous state subsequence exceeds the threshold, a warning is issued to remind the current driver that the vehicle is in a dangerous driving state; otherwise, T 实 Compare the similarity between each sub-dangerous state subsequence in the positioning sub-library. If T 实 If the similarity between the subsequence and any of the sub-dangerous states exceeds a threshold, a warning is issued to remind the current driver of the risk of an accident. Otherwise, there is no need to issue a warning signal and the vehicle can continue to drive normally.

[0091] In a better implementation plan, while reminding the current driver that the vehicle is in a dangerous driving state, it continues to determine whether the TTC between the current vehicle and the dangerous conflicting vehicle has reached the threshold. If the TTC threshold is not reached, the AEB emergency braking system is intervened; if the TTC threshold is exceeded, it is considered that braking cannot avoid the occurrence of the accident, and then a certain degree of intervention is made in the control of the vehicle to reduce the possibility of an accident.

[0092] Example 2

[0093] This embodiment provides a vehicle pre-collision warning system based on traffic accident factor matching, which is characterized by including:

[0094] The accident parameter extraction module is used to extract accident parameter data used to restore traffic accidents from traffic accident cases;

[0095] The accident reconstruction and data export module is used to simulate and restore the process before and after the traffic accident based on the extracted accident parameter data, and extract multiple motion parameters of the two parties involved in the traffic accident before and after the traffic accident from the restored traffic accident case to obtain a multidimensional time series that describes the process before and after the traffic accident.

[0096] The accident state library construction module is used to: import the multidimensional time series representing the process before and after the traffic accident into the DS-HDP-HMM model to obtain the corresponding hidden state sequence; mark each hidden state as a dangerous state, a sub-dangerous state, and a normal state to obtain a dangerous state subsequence, a sub-dangerous state subsequence, and a normal state subsequence; then store the dangerous state subsequence and the sub-dangerous state subsequence, as well as the multidimensional time series of the corresponding time period, in the sub-library of the accident state library that matches the traffic accident; the accident state library is divided into multiple sub-libraries according to the type of road on which the traffic accident occurred and the types of parties involved in the traffic accident;

[0097] The real-time data acquisition module is used to: for vehicles with early warning requirements, obtain the current road type, obtain the multiple motion parameters of the vehicle itself and surrounding traffic participants in the recent long time period including the current moment; wherein the motion parameters of the surrounding traffic participants are obtained through the vehicle-road-cloud integrated device; combine the vehicle itself with each surrounding traffic participant to obtain multiple groups of traffic accident participants to be predicted, and each group of traffic accident participants to be predicted corresponds to a set of multidimensional time series T 实 ;

[0098] The similarity calculation module is used to locate the corresponding sub-library in the accident status database for each group of traffic accident participants to be predicted, according to the road type and the type of both parties, and convert the corresponding multidimensional time series T 实 Calculate similarity with the multidimensional time series in the sub-database;

[0099] The warning module is used to: provide pre-collision warning to vehicles that currently need warnings based on the similarity calculation value.

[0100] The working mode of each module in this embodiment is the same as that in embodiment 1 and will not be repeated.

[0101] The above embodiments are preferred embodiments of the present application. Ordinary technicians in this field can also make various changes or improvements on this basis. Without departing from the overall concept of the present application, these changes or improvements should fall within the scope of protection required by the present application.

Claims

1. A vehicle pre-collision warning method based on traffic accident factor matching, characterized in that: include: Step 1: extracting accident parameter data for restoring the traffic accident from the traffic accident case; Step 2: Based on the extracted accident parameter data, the process before and after the traffic accident is simulated and restored. Multiple motion parameters of the two parties involved in the traffic accident before and after the traffic accident are extracted from the restored traffic accident case to obtain a multidimensional time series describing the process before and after the traffic accident. Step 3: Import the multidimensional time series describing the process before and after the traffic accident into the DS-HDP-HMM model to obtain the corresponding hidden state sequence; mark each hidden state as a dangerous state, a sub-dangerous state, and a normal state to obtain a dangerous state subsequence, a sub-dangerous state subsequence, and a normal state subsequence; then, store the dangerous state subsequence and the sub-dangerous state subsequence, as well as the multidimensional time series of the corresponding time period, into a sub-library of the accident state library that matches the traffic accident; the accident state library is divided into multiple sub-libraries according to the type of road on which the traffic accident occurred and the types of parties involved in the traffic accident; Step 4: The vehicle with the warning requirement obtains the current road type and the multiple motion parameters of itself and surrounding traffic participants in the recent long time period including the current moment; wherein the motion parameters of the surrounding traffic participants are obtained through the vehicle-road-cloud integrated device; the vehicle is combined with each surrounding traffic participant to obtain multiple groups of traffic accident participants to be predicted, and each group of traffic accident participants to be predicted corresponds to a set of multidimensional time series T 实 ; Step 5: For each group of parties involved in the traffic accident to be predicted, locate the corresponding sub-library in the accident status database according to the road type and the type of the parties involved, and convert the corresponding multidimensional time series T 实 Calculate similarity with the multidimensional time series in the sub-database; Step 6: Based on the similarity calculation value, a pre-collision warning is issued to the vehicle that needs a warning.

2. The vehicle pre-collision warning method based on traffic accident factor matching according to claim 1 is characterized in that: Multiple motion parameters are extracted, including: speed, position, heading angle and relative distance of both traffic participants.

3. The vehicle pre-collision warning method based on traffic accident factor matching according to claim 1, characterized in that: The road types include intersections, T-junctions, and highways.

4. The vehicle pre-collision warning method based on traffic accident factor matching according to claim 1 is characterized in that: Types of traffic accident participants, including passenger vehicles, two-wheeled vehicles, and pedestrians.

5. The vehicle pre-collision warning method based on traffic accident factor matching according to claim 1 is characterized in that: The classification criteria for marking each hidden state are as follows: the state within a preset time period before the collision is marked as a dangerous state, the state within a certain time period before the dangerous state is marked as a secondary dangerous state, and the state within a certain time period before the secondary dangerous state is marked as a normal state.

6. The vehicle pre-collision warning method based on traffic accident factor matching according to claim 1 is characterized in that: Similarity calculation is performed through normalized cross-correlation, including: First, the two sets of multidimensional time series involved in the calculation are normalized; Then, the normalized cross-correlation is calculated on each dimension to calculate the shape similarity between the two time series on that dimension: in, Represent two normalized time series of the j-th dimension, Representing time series The shape similarity between them; τ is the time offset, and different values ​​of τ represent the translation of one sequence relative to another sequence; i represents the time series The sample point number, n represents the time series length; It means that under all possible time offsets τ, a τ is selected so that the two time series The normalized cross-correlation reaches its maximum value; Then, the shape similarities between the two time series in all dimensions are combined to obtain the similarity between the two sets of multidimensional time series: Among them, T1 and T2 represent the two sets of multidimensional time series involved in the calculation, S(T1, T2) represents the similarity between the two sets of multidimensional time series T1 and T2, and w j represents the weight of the j-th dimension, and m represents the dimension of the multidimensional time series.

7. The vehicle pre-collision warning method based on traffic accident factor matching according to claim 1 is characterized in that: The method for performing pre-collision warning based on the similarity calculation value is as follows: First, the vehicle's current multidimensional time series T 实 Compare the similarity between each dangerous state subsequence in the positioning sub-library. If T 实 If the similarity between the vehicle and any dangerous state subsequence exceeds a threshold, a warning is issued to remind the current driver that the vehicle is in a dangerous driving state; Otherwise, T 实 Compare the similarity between each sub-dangerous state subsequence in the positioning sub-library. If T 实 If the similarity between the current state and any of the sub-dangerous state subsequences exceeds a threshold, a warning is issued to remind the current driver that there is a risk of an accident.

8. A vehicle pre-collision warning system based on traffic accident factor matching, characterized in that: include: The accident parameter extraction module is used to extract accident parameter data used to restore traffic accidents from traffic accident cases; The accident reconstruction and data export module is used to simulate and restore the process before and after the traffic accident based on the extracted accident parameter data, and extract multiple motion parameters of both parties involved in the traffic accident before and after the traffic accident from the restored traffic accident case to obtain a multidimensional time series describing the process before and after the traffic accident; The accident state library construction module is used to: import the multidimensional time series representing the process before and after the traffic accident into the DS-HDP-HMM model to obtain the corresponding hidden state sequence; mark each hidden state as a dangerous state, a sub-dangerous state, and a normal state to obtain a dangerous state subsequence, a sub-dangerous state subsequence, and a normal state subsequence; then store the dangerous state subsequence and the sub-dangerous state subsequence, as well as the multidimensional time series of the corresponding time period, in a sub-library of the accident state library that matches the traffic accident; the accident state library is divided into multiple sub-libraries according to the type of road on which the traffic accident occurred and the types of parties involved in the traffic accident; The real-time data acquisition module is used to: for vehicles with early warning requirements, obtain the current road type, obtain the multiple motion parameters of the vehicle itself and surrounding traffic participants in the recent long time period including the current moment; wherein the motion parameters of the surrounding traffic participants are obtained through the vehicle-road-cloud integrated device; combine the vehicle itself with each surrounding traffic participant to obtain multiple groups of traffic accident participants to be predicted, and each group of traffic accident participants to be predicted corresponds to a set of multidimensional time series T 实 ; The similarity calculation module is used to locate the corresponding sub-library in the accident status database for each group of traffic accident participants to be predicted, according to the road type and the type of both parties, and convert the corresponding multidimensional time series T 实 Calculate similarity with the multidimensional time series in the sub-database; The warning module is used to: provide pre-collision warning to vehicles that currently need warnings based on the similarity calculation value.

9. The vehicle pre-collision warning system based on traffic accident factor matching according to claim 8, characterized in that: Multiple motion parameters are extracted, including: speed, position, heading angle and relative distance of both traffic participants.

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