An online rollover risk assessment method based on vehicle status information

Abstract

This paper proposes an online rollover risk assessment method based on vehicle state information. The method first collects vehicle rollover state variables and safety threshold intervals. The method then calculates the system's safe allowable time, the alienation time of the state variables, and the number of alienation cycles. This method then calculates the alienation scale coefficient, alienation interval division, and alienation rate corresponding to the vehicle state parameters, thereby deriving the overall system state parameter alienation rate. Finally, the vehicle's rollover risk is determined based on the obtained parameter characteristics, providing effective early warning and control reference signals for the driver and semi-active / active control systems. This method is capable of collaboratively integrating multiple chassis subsystems and rapidly processing large amounts of real-time data. Based on multiple state variable inputs, it effectively determines vehicle rollover risk and provides early warnings from the perspectives of system parameters and time scales, providing a decision-making basis for proactive intervention by the driver and control system.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle stability control, and in particular relates to an online rollover risk assessment method based on vehicle state information. Background Art

[0002] Rollover accidents are a significant safety issue worldwide. While they represent a relatively small proportion of total traffic accidents, they contribute significantly to fatalities, and the resulting casualties and economic losses are increasing year by year. Therefore, the design and development of early warning signals and corresponding systems for rollover accidents has become a research hotspot and trend.

[0003] The rapid development of electronics and information technology has posed new requirements and challenges for improving and enhancing vehicle performance. Initial real-time monitoring of single static indicators is no longer sufficient to meet the dynamic performance requirements of harsh and variable driving conditions. These indicators and monitoring systems often treat the vehicle as a rigid body, focusing solely on its design parameters while ignoring the dynamic and nonlinear changes in vehicle parameters during driving. Consequently, they fail to effectively reflect the dynamic characteristics of the system.

[0004] With the modularization, sophistication, and intelligence of vehicle chassis systems, the amount of information transmitted by multiple onboard sensors and data is rapidly increasing. Chassis digitization and informatization are becoming a future development trend. Consequently, the detection, warning, and prevention of vehicle rollover accidents are evolving into a process based on the fusion of dynamic multi-sensor signals and indicators, combined with rollover risk assessment. Accurately and rationally determining a vehicle's current rollover risk based on available vehicle state variables and data remains an unavoidable challenge in practical research.

[0005] Under these conditions, traditional rollover risk assessment methods based on a single variable have limited applicability and are less adaptable to nonlinear vehicle systems. Extracting effective vehicle stability characteristics and information from complex multi-state information to determine a vehicle's rollover risk has become a critical issue that needs to be addressed. Summary of the Invention

[0006] (1) Technical issues to be solved

[0007] The present invention proposes an online rollover risk assessment method based on vehicle state information to solve the technical problem of how to calculate vehicle rollover parameters under multi-state variable input, so as to reasonably determine the vehicle rollover risk according to the calculated parameters.

[0008] (2) Technical solution

[0009] In order to solve the above technical problems, the present invention proposes an online rollover risk assessment method based on vehicle status information, which includes the following steps:

[0010] S1. Select vehicle rollover state parameters

[0011] Select the vehicle's body roll angle Lateral acceleration a y The five state variables, yaw rate ω, lateral load transfer rate LTR and center of mass sideslip angle β, constitute the vehicle state information matrix The data dimension of each state quantity xi (i = 1, 2, ..., 5) in the matrix is ​​n, which corresponds to the vehicle state data value at n time points from the initial time 0 to time T. The overall dimension of the matrix x is 5 × n:

[0012] The sampling time step Sam of the system data is calculated according to the following formula:

[0013]

[0014] When the vehicle is in a stable state, different state variables xi (i = 1, 2, ..., 5) fluctuate within their corresponding safety threshold ranges. The safety threshold ranges for different variables are as follows:

[0015]

[0016] S2. Dynamic calculation of system safety tolerance time

[0017] S2-1. According to the following formula, calculate the vehicle state from the safety threshold when the vehicle rolls over a y_max 、ω max , LTR max , β max Threshold value when changing to rollover state a y_roll 、ω roll , LTR roll and β roll Time required:

[0018]

[0019] Where, and They represent the time values ​​corresponding to the vehicle status threshold when the system rolls over, and The time values ​​corresponding to the vehicle status safety thresholds respectively;

[0020] S2-2. Calculate the system safety tolerance time T according to the following formula: R:

[0021]

[0022] Where Ξ is the vehicle state risk factor, which is related to the vehicle's speed v and is calculated as follows:

[0023]

[0024] S2-3. Define system alienation time T D and the number of alienation cycles N DP , alienation time T D Refers to the state variable x ij ≥x i_max (i=1,2,…,5;j=1,2,…,n), that is, the state variable x ij The duration of reaching and exceeding the safety threshold interval; the number of alienation cycles N at the initial moment DP Set to 0, the vehicle is in a stable and safe state by default. If the alienation time T D Every time it lasts for a certain period of time and exceeds the safety allowable time T R Once, the number of alienation cycles corresponding to the state variable is N DP The cumulative count is increased once on the original basis;

[0025] S3. Calculate the alienation rate of system state parameters

[0026] S3-1. Screening vehicle status parameters

[0027] According to the information matrix x, the ones that meet the condition |x ij |≥x i_max The variables (i=1,2,…,5;j=1,2,…,n) are reorganized into new parameter alienation matrices x'1, x'2, x'3, x'4 and x'5 in order, and their corresponding data dimensions are k1, k2, k3, k4 and k5 respectively, and at the same time meet the conditions: k i ≤n(i=1,2,…,5);

[0028] S3-2. Calculate each vehicle state parameter x according to the following formula: i (i=1,2,…,5) corresponding alienation scale coefficient Λ i :

[0029]

[0030] According to the following alienation interval division criteria, the vehicle state parameter alienation matrix x is obtained: i '(i=1,2,…,5), the state value dimension (k 11 ,k12 )、(k 21 ,k 22 )、(k 31 ,k 32 )、(k 41 ,k 42 ) and (k 51 ,k 52 ), where k1 = k 11 +k 12 、k2=k 21 +k 22 、k3=k 31 +k 32 、k4=k 41 +k 42 and k5=k 51 +k 52 :

[0031] The criteria for dividing the first-level critical alienation interval are:

[0032]

[0033] The criteria for dividing the secondary extreme alienation interval are:

[0034]

[0035] S3-3. Calculate the alienation rate η of different state parameters according to the following formula i :

[0036]

[0037] According to the following formula, the system overall parameter alienation rate η is calculated:

[0038]

[0039] S4. Vehicle rollover risk level warning determination

[0040] Alienation time T according to vehicle status D and the number of alienation cycles N DP , and the system overall parameter alienation rate η, to determine the vehicle's rollover risk level.

[0041] 2. The rollover risk online assessment method according to claim 1, wherein in step S2-3, if the alienation time T D Every 5 seconds that the safety time T is exceeded R Once, the number of alienation cycles corresponding to the state variable is N DP The cumulative count is increased once based on the original one.

[0042] (3) Beneficial effects

[0043] This paper proposes an online rollover risk assessment method based on vehicle state information. The method first collects vehicle rollover state variables and safety threshold intervals. The method then calculates the system's safe allowable time, the alienation time of the state variables, and the number of alienation cycles. This method then calculates the alienation scale coefficient, alienation interval division, and alienation rate corresponding to the vehicle state parameters, thereby deriving the overall system state parameter alienation rate. Finally, the vehicle's rollover risk is determined based on the obtained parameter characteristics, providing effective early warning and control reference signals for the driver and semi-active / active control systems. This method is capable of collaboratively integrating multiple chassis subsystems and rapidly processing large amounts of real-time data. Based on multiple state variable inputs, it effectively determines vehicle rollover risk and provides early warnings from the perspectives of system parameters and time scales, providing a decision-making basis for proactive intervention by the driver and control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is the online rollover risk assessment process of an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, content and advantages of the present invention more clear, the specific implementation methods of the present invention are further described in detail below with reference to the accompanying drawings and examples.

[0046] This embodiment proposes an online rollover risk assessment method based on vehicle status information, the main process of which is as follows: Figure 1 As shown, it mainly includes the following steps:

[0047] S1. Select vehicle rollover state parameters

[0048] Select the vehicle's body roll angle Lateral acceleration a y The five state variables, yaw rate ω, lateral load transfer rate LTR and center of mass sideslip angle β, constitute the vehicle state information matrix Among them, each state quantity x in the matrix i The data dimension of (i=1,2,…,5) is n, which corresponds to the vehicle status data values ​​at n time points from the initial time 0 to time T. The overall dimension of the matrix x is 5×n.

[0049] According to the following formula, the sampling time step S of the system data is calculated am :

[0050]

[0051] The vehicle is in a stable state, different state variables x i (i=1,2,…,5) all fluctuate within their corresponding safety threshold ranges. The safety threshold ranges of different variables are as follows:

[0052]

[0053] S2. Dynamic calculation of system safety tolerance time

[0054] S2-1. According to the following formula, calculate the vehicle state from the safety threshold when the vehicle rolls over a y_max 、ω max , LTR max , β max Threshold value when changing to rollover state a y_roll 、ω roll , LTR roll and β roll Time required:

[0055]

[0056] Where, and They represent the time values ​​corresponding to the vehicle status threshold when the system rolls over, and The time values ​​corresponding to the vehicle status safety thresholds respectively.

[0057] S2-2. Calculate the system safety tolerance time T according to the following formula: R :

[0058]

[0059] Where Ξ is the vehicle state risk factor, which is related to the vehicle's speed v and is calculated as follows:

[0060]

[0061] S2-3. Define system alienation time T D and the number of alienation cycles N DP To avoid risk misdiagnosis and assessment caused by transient fluctuations of state variables. D Refers to the state variable x ij ≥x i_max (i=1,2,…,5;j=1,2,…,n), that is, the state variable x ij The duration of reaching and exceeding the safety threshold interval. The number of alienation cycles N at the initial moment DP Set to 0, the vehicle is in a stable and safe state by default. If the alienation time T D Every 5 seconds that the safety time T is exceeded R Once, the number of alienation cycles corresponding to the state variable is NDP The cumulative count is increased once based on the original one.

[0062] S3. Calculate the alienation rate of system state parameters

[0063] S3-1. Screening vehicle status parameters

[0064] According to the information matrix x, the ones that meet the condition |x ij |≥x i_max The variables (i=1,2,…,5;j=1,2,…,n) are reorganized into new parameter alienation matrices x'1, x'2, x'3, x'4 and x'5 in order, and their corresponding data dimensions are k1, k2, k3, k4 and k5 respectively, and at the same time meet the conditions: k i ≤n(i=1,2,…,5).

[0065] S3-2. Calculate each vehicle state parameter x according to the following formula: i (i=1,2,…,5) corresponding alienation scale coefficient Λ i :

[0066]

[0067] According to the following alienation interval division criteria, the vehicle state parameter alienation matrix x is obtained: i '(i=1,2,…,5), the state value dimension (k 11 ,k 12 )、(k 21 ,k 22 )、(k 31 ,k 32 )、(k 41 ,k 42 ) and (k 51 ,k 52 ), where k1 = k 11 +k 12 、k2=k 21 +k 22 、k3=k 31 +k 32 、k4=k 41 +k 42 and k5=k 51 +k52:

[0068] The criteria for dividing the first-level critical alienation interval are:

[0069]

[0070] The criteria for dividing the secondary extreme alienation interval are:

[0071]

[0072] S3-3. Calculate the alienation rate ηi of different state parameters according to the following formula:

[0073]

[0074] According to the following formula, the system overall parameter alienation rate η is calculated:

[0075]

[0076] S4. Vehicle rollover risk level warning determination

[0077] The rollover risk warning system has four response levels: no warning, level 1 warning, level 2 warning, and level 3 warning. The corresponding rollover risk probability intervals are [0, 40%), [40%, 60%), [60%, 80%), and [80%, 100%), respectively. The corresponding rollover risk levels are: no rollover risk, low rollover risk, medium rollover risk, and high rollover risk. The specific results for different rollover risk levels are shown in the table below.

[0078] Table 1 Vehicle rollover risk level warning judgment results

[0079]

[0080] Alienation time T according to vehicle status D and the number of alienation cycles N DP , and the system overall parameter alienation rate η, to determine the vehicle's rollover risk level.

[0081] At this time, the driver and semi-active / active control system can activate the corresponding controller to perform assisted driving and active intervention control based on the system warning information.

[0082] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An online rollover risk assessment method based on vehicle status information, characterized in that: The rollover risk online assessment method comprises the following steps: S1. Select vehicle rollover state parameters Select the vehicle's body roll angle Lateral acceleration a y The five state variables, yaw rate ω, lateral load transfer rate LTR and center of mass sideslip angle β, constitute the vehicle state information matrix Among them, each state quantity x in the matrix i , the data dimension of i=1,2,…,5 is n, which corresponds to the vehicle state data values ​​at n time points from the initial time 0 to time T. The overall dimension of the matrix x is 5×n: According to the following formula, the sampling time step S of the system data is calculated am : The vehicle is in a stable state, different state variables x i , i=1,2,…,5 all fluctuate within their corresponding safety threshold ranges. The safety threshold ranges of different variables are as follows: S2. Dynamic calculation of system safety tolerance time S2-1. According to the following formula, calculate the vehicle state from the safety threshold when the vehicle rolls over a y_max 、ω max , LTR max , β max Threshold value when changing to rollover state a y_roll 、ω roll , LTR roll and β roll Time required: Where, and They represent the time values ​​corresponding to the vehicle status threshold when the system rolls over, and The time values ​​corresponding to the vehicle status safety thresholds respectively; S2-2. Calculate the system safety tolerance time T according to the following formula: R : Where Ξ is the vehicle state risk factor, which is related to the vehicle's speed v and is calculated as follows: S2-3. Define system alienation time T D and the number of alienation cycles N DP , alienation time T D Refers to the state variable x ij ≥x i_max , i=1,2,…,5;j=1,2,…,n, i.e. state variable x ij The duration of reaching and exceeding the safety threshold interval; the number of alienation cycles N at the initial moment DP Set to 0, the vehicle is in a stable and safe state by default. If the alienation time T D Every time it lasts for a certain period of time and exceeds the safety allowable time T R Once, the number of alienation cycles corresponding to the state variable is N DP The cumulative count is increased once on the original basis; S3. Calculate the alienation rate of system state parameters S3-1. Screening vehicle status parameters According to the information matrix x, the ones that meet the condition |x ij |≥x i_max , i=1,2,…,5;j=1,2,…,n,the variables are reorganized into new parameter alienation matrices x'1, x'2, x'3, x'4 and x'5 in order, and the corresponding data dimensions are k1, k2, k3, k4 and k5 respectively, and at the same time meet the conditions: k i ≤n, i=1,2,…,5; S3-2. Calculate each vehicle state parameter x according to the following formula: i , i=1,2,…,5 corresponding to the alienation scale coefficient Λ i : According to the following alienation interval division criteria, the vehicle state parameter alienation matrix x is obtained: i ', i = 1, 2, ..., 5, the state value dimension (k 11 ,k 12 )、(k 21 ,k 22 )、(k 31 ,k 32 )、(k 41 ,k 42 ) and (k 51 ,k 52 ), where k1 = k 11 +k 12 、k2=k 21 +k 22 、k3=k 31 +k 32 、k4=k 41 +k 42 and k5=k 51 +k 52 : The criteria for dividing the first-level critical alienation interval are: The criteria for dividing the secondary extreme alienation interval are: S3-3. Calculate the alienation rate η of different state parameters according to the following formula i : According to the following formula, the system overall parameter alienation rate η is calculated: S4. Vehicle rollover risk level warning determination Alienation time T according to vehicle status D and the number of alienation cycles N DP , and the system overall parameter alienation rate η, to determine the vehicle's rollover risk level.

2. The rollover risk online assessment method according to claim 1, characterized in that: In step S2-3, if the alienation time T D Every 5 seconds that the safety time T is exceeded R Once, the number of alienation cycles corresponding to the state variable is N DP The cumulative count is increased once based on the original one.

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