High-risk scene identification and passive safety cooperation method based on critical risk avoiding state

Abstract

The invention discloses a high-risk scene recognition and passive safety cooperation method based on a critical risk avoiding state. The method comprises the steps of obtaining current target vehicle state data; determining critical risk avoiding time Tcep based on the current target vehicle state data; the successful risk avoiding time is calculated according to the current target vehicle state data and the critical risk avoiding time Tcep; high-risk scene recognition is carried out by using the successful risk avoiding time, when the high-risk scene is not recognized, the current vehicle distance is obtained again, and when the high-risk scene is recognized, passive safety constraint is carried out by stages by using a passive safety cooperation strategy; according to the method, the space-time proximity degree of the vehicle is represented by using the critical risk avoiding time, the recognition capability of the target vehicle to the high-risk scene is improved, timely passive safety cooperative response can be started in the high-risk scene, the safety coefficient of the target vehicle is improved, and the traffic accident rate is reduced.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a high-risk scene recognition and passive safety coordination method based on a critical hazard avoidance state. Background Art

[0002] Autonomous driving technology can reduce human errors to a certain extent, thereby reducing the incidence of traffic accidents and reducing personal injuries, and therefore has received increasing attention. At present, the safety performance of autonomous driving vehicles is still being improved and enhanced. Among them, accurately characterizing risks is the basis and key to improving the safety performance of autonomous driving vehicles. Previous studies have evaluated the driving risks of vehicles from different angles and formulated vehicle braking control strategies based on them. In some cases, the underlying risk expression indicators mainly include: safety distance indicators, time to collision indicators, and driver's subjective cognitive judgment indicators. The applicable scenarios of autonomous driving are also constantly enriching and developing. For example, the evaluation scenarios have gradually evolved from single longitudinal safety control evaluations such as CCRs (Car-to-Car Rear Stationary, the front vehicle is stationary) and CCRm (Car-to-Car Relative Motion, the front vehicle is slow), etc. to vehicle turning scenarios. In the past, the decision-making control algorithm with TTC (Time to collision) as the risk indicator was only sensitive to objects in the current running direction, and lacked corresponding risk estimation methods for the target vehicle turning (the target object is not on the extension line of the current running direction of the target vehicle). It is necessary to find a risk assessment method applicable to the turning scenario and conduct test analysis.

[0003] In some cases, autonomous driving methods still have inadequate representations of risks, their risk assessment methods do not take into account comprehensive factors, and the identification of high-risk scenarios is not accurate. In addition, autonomous driving technology fails to reflect the passive safety response capabilities of vehicles in the current environment. Summary of the invention

[0004] The purpose of this application is to provide a high-risk scene recognition and passive safety coordination method based on critical risk avoidance state, which can improve the target vehicle's ability to recognize high-risk scenes, initiate timely passive safety coordinated response in high-risk scenes, improve the safety factor of the target vehicle, and reduce the incidence of traffic accidents.

[0005] To achieve the above objectives, this application provides the following solutions.

[0006] The present application provides a high-risk scene recognition and passive safety coordination method based on a critical hazard avoidance state, the high-risk scene recognition and passive safety coordination method based on a critical hazard avoidance state comprises: obtaining current target vehicle status data; the current target vehicle status data at least comprises: the current target vehicle driving status, the current vehicle distance, the current target vehicle speed and the current target vehicle acceleration; the current target vehicle driving status is straight or turning; the current vehicle distance is: the distance between the target vehicle and the vehicle to be avoided; determining the critical hazard avoidance time based on the current target vehicle status data Tcep ; The critical hedging time Tcep The critical avoidance point is the time from the critical avoidance point to the collision between the target vehicle and the vehicle to be avoided; the critical avoidance point is the shortest distance required for the target vehicle to successfully avoid the vehicle to be avoided; according to the current target vehicle status data and the critical avoidance time Tcep Calculate the successful avoidance time; use the successful avoidance time to identify high-risk scenarios, and when no high-risk scenarios are identified, reacquire the current vehicle distance; when a high-risk scenario is identified, use the passive safety collaboration strategy to perform passive safety constraints in stages; the high-risk scenario is a scenario where the successful avoidance time is less than or equal to a preset time; the passive safety constraints include: seat belt constraints and / or airbag constraints.

[0007] According to the specific embodiments provided in this application, this application discloses the following technical effects.

[0008] This application determines the critical avoidance time through the current target vehicle status data Tcep , and based on the current target vehicle status data and critical avoidance time Tcep Calculate the time that can be successfully avoided; use the time that can be successfully avoided to identify high-risk scenes, and use the critical avoidance time to characterize the temporal and spatial proximity of vehicles, thereby improving the target vehicle's ability to identify high-risk scenes; when a high-risk scene is identified, use the passive safety collaborative strategy to perform passive safety constraints in stages, and be able to initiate a timely passive safety collaborative response in high-risk scenes, thereby improving the safety factor of the target vehicle and reducing the incidence of traffic accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0010] Figure 1 A schematic diagram of a high-risk scene recognition and passive safety coordination method based on a critical risk avoidance state provided in an embodiment of the present application Figure 1 .

[0011] Figure 2 A schematic diagram of a high-risk scene recognition and passive safety coordination method based on a critical risk avoidance state provided in an embodiment of the present application Figure 2 .

[0012] Figure 3 A schematic diagram of the position relationship of the risk avoidance critical points provided in an embodiment of the present application.

[0013] Figure 4 A schematic diagram of the vehicle position relationship when the target vehicle is traveling straight provided in an embodiment of the present application.

[0014] Figure 5 A schematic diagram of the vehicle position relationship when the target vehicle turns provided in an embodiment of the present application.

[0015] Figure 6 A schematic diagram of the successful hedging time provided in an embodiment of the present application.

[0016] Figure 7 A simulation scene diagram provided for an embodiment of the present application.

[0017] Figure 8 This is a graph of the straight-ahead vertical collision speed and braking force provided in an embodiment of the present application.

[0018] Figure 9 This is a graph of speed and braking force for a straight-ahead rear-end collision provided in an embodiment of the present application.

[0019] Figure 10 This is a graph of the straight-ahead collision speed and braking force provided in an embodiment of the present application.

[0020] Figure 11 This is a graph of the steering collision speed and braking force provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0022] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0023] Embodiment 1, as Figure 1 - Figure 2As shown, this embodiment provides a high-risk scene recognition and passive safety coordination method based on a critical risk avoidance state, and the method includes the following steps.

[0024] S1. Obtain the current target vehicle status data; the current target vehicle status data at least includes: the current target vehicle driving state, the current vehicle distance, the current target vehicle speed and the current target vehicle acceleration; the current target vehicle driving state is straight or turning; the current vehicle distance is: the distance between the target vehicle and the vehicle to be avoided.

[0025] Optionally, the vehicle to be avoided is a two-wheeled vehicle or a car.

[0026] S2. Figure 3 - Figure 5 As shown, the critical avoidance time is determined based on the current target vehicle status data T cep ; Critical hedging time T cep It is the time from the critical avoidance point to the collision between the target vehicle and the vehicle to be avoided; the critical avoidance point is the shortest distance required for the target vehicle to successfully avoid the vehicle to be avoided.

[0027] Optionally, the Collision Evasion Point (CEP) is when the target vehicle still does not take evasive action when the current vehicle distance is lower than a specific value. Although the driver may take a variety of evasive actions such as deceleration and steering, the collision is unavoidable. The specific value at this time is the critical point.

[0028] Step S2 specifically includes the following steps.

[0029] S21. Figure 4 As shown in the figure, when the current target vehicle is in a straight-moving state, the avoidance conditions under different straight-moving avoidance scenarios are used to calculate the avoidance time under each straight-moving avoidance scenario, and the shortest avoidance time under each straight-moving avoidance scenario is selected as the critical avoidance time T cep ; The straight-ahead avoidance scenario includes at least: the first scenario, the second scenario and the third scenario; the first scenario is a scenario in which the target vehicle slows down to avoid the vehicle to be avoided; the second scenario is a scenario in which the target vehicle turns in the same direction to avoid the vehicle to be avoided; the third scenario is a scenario in which the target vehicle turns in the opposite direction to avoid the vehicle to be avoided.

[0030] Furthermore, the hedging conditions in different straight-ahead hedging scenarios are used to calculate the hedging time in each straight-ahead hedging scenario, which specifically includes the following steps.

[0031] 1) Establish a target vehicle moving direction x 1 Axis, perpendicular to x 1 The axis direction isx 2 Axis, the first coordinate system with the current position of the target vehicle as the coordinate origin.

[0032] 2) Using the risk avoidance conditions in the first scenario with the current vehicle spacing as the minimum as the goal, determine the risk avoidance distance in the first scenario, and calculate the risk avoidance time in the first scenario based on the risk avoidance distance in the first scenario; wherein, the risk avoidance conditions in the first scenario are as follows.

[0033] v 1 2 ≤ v 2 2 ·cos 2 θ +2 a 1 · μ · g · m .

[0034] In the formula, v 1 is the target vehicle speed, v 2 is the speed of the vehicle to be avoided, θ is the velocity direction of the vehicle to be avoided and the first coordinate system x 1 The angle of the axis, a 1 For the target vehicle x 1 Maximum acceleration of the axis, μ is the ground friction coefficient, g is the acceleration due to gravity, m is the current vehicle distance.

[0035] 3) Using the risk avoidance conditions in the second scenario with the current vehicle spacing as the minimum as the goal, determine the risk avoidance distance in the second scenario, and calculate the risk avoidance time in the second scenario based on the risk avoidance distance in the second scenario; wherein, the risk avoidance conditions in the second scenario are as follows.

[0036] v 1 2 ≤ v 2 2 ·cos 2 θ +2 a 1 · μ · g · m .

[0037] Lx 1 -d 1 / 2-( d 2 ·sinθ) / 2+ a ≥ Lx 2 .

[0038] In the formula, d 1 is the target vehicle width, d 2 is the length of the vehicle to be avoided, Lx 1 is the lateral displacement of the vehicle, Lx 2 is the lateral displacement of the vehicle to be avoided, a is the distance between the vehicle to be avoided and the first coordinate system x 1 The vertical distance of the axis.

[0039] Optionally, the calculation formula for the lateral displacement of the vehicle is as follows.

[0040] Lx 1 =(t 2 ·a 2 ) / 2.

[0041] Optionally, the calculation formula for the lateral displacement of the vehicle to be avoided is as follows.

[0042] Lx 2 =v 2 ·t· sin θ .

[0043] 4) Using the risk avoidance conditions in the third scenario with the current vehicle spacing as the minimum as the goal, determine the risk avoidance distance in the third scenario, and calculate the risk avoidance time in the third scenario based on the risk avoidance distance in the third scenario; wherein, the risk avoidance conditions in the third scenario are as follows.

[0044] v 1 2 ≤ v 2 2 ·cos 2 θ +2 a 1 · μ · g · m .

[0045] Lx 1 +Lx 2 ≤a - d 1 / 2-(d 2 ·sinθ) / 2 .

[0046] S22. Figure 5 As shown in the figure, when the current target vehicle is in a turning state, the avoidance time under each turning avoidance scenario is calculated using the avoidance conditions under different turning avoidance scenarios, and the shortest avoidance time under each turning avoidance scenario is selected as the critical avoidance time T cep ; The steering avoidance scenarios include at least: the fourth scenario, the fifth scenario and the sixth scenario; the fourth scenario is a scenario in which the speed of the target vehicle in the driving direction of the vehicle to be avoided has been reduced to 0 before the target vehicle drives to the driving route of the vehicle to be avoided; the fifth scenario is a scenario in which the target vehicle crosses the driving route of the vehicle to be avoided from behind the vehicle to be avoided and keeps driving behind the vehicle to be avoided; the sixth scenario is a scenario in which the target vehicle crosses the driving route of the vehicle to be avoided from in front of the vehicle to be avoided and keeps driving in front of the vehicle to be avoided.

[0047] Furthermore, the hedging conditions in different risk-avoidance-turning scenarios are used to calculate the hedging time in each risk-avoidance-turning scenario, which specifically includes the following steps.

[0048] 1) Establish a moving direction of the vehicle to be avoided x 1 Axis, perpendicular to x 1 The axis direction is x 2 Axis, a second coordinate system with the current position of the vehicle to be avoided as the coordinate origin.

[0049] Optional, the current vehicle distance is m , project the target vehicle coverage space to the second coordinate system x 1 Calculate the distance from the center point of the vehicle to be avoided to the projection center point of the target vehicle x 0 , the distance from the center point of the target vehicle to the center point of the target vehicle projection y 0 , the angle between the speed direction of the vehicle to be avoided and the speed direction of the target vehicle γ .

[0050] 2) Using the risk avoidance conditions in the fourth scenario with the current vehicle spacing as the minimum as the goal, determine the risk avoidance distance in the fourth scenario, and calculate the risk avoidance time in the fourth scenario based on the risk avoidance distance in the fourth scenario; wherein, the risk avoidance conditions in the fourth scenario are as follows.

[0051] v y (t)=0 .

[0052] y(t) ≤ y 0 .

[0053] in, v y (t) For the target vehicle t At the second coordinate system x 1 The speed in the axis direction, y(t) For the target vehicle t At the second coordinate system x 1 The displacement in the axial direction, y 0 The target vehicle and the vehicle to be avoided are in the second coordinate system x 1 Initial spacing along the axis.

[0054] Optionally, first calculate and decompose the acceleration of the target vehicle into x 1 , x 2 Get on axis a x ( t ) with a y ( t). a x (t) The calculation formula is as follows.

[0055] a x ( t ) =a x1 (t) + a x2 (t) .

[0056] in, a x1 (t) is the maximum longitudinal acceleration of the target vehicle a 1 exist x 1 The weight, ax2 (t) The maximum lateral acceleration (deceleration) speed of the target vehicle a 2 exist x 1 The amount. a x1 (t) The calculation formula is as follows.

[0057] a x1 ( t ) =a 1 · cos γ ( t ).

[0058] in, γ ( t )for t The angle between the speed direction of the vehicle to be avoided and the speed direction of the target vehicle at any moment, and its initial value is γ . a x2 (t) The calculation formula is as follows.

[0059] a x2 (t) = a 2 · sin γ(t) .

[0060] a y (t) The calculation formula is as follows.

[0061] a y (t) = a y1 (t) + a y2 (t) .

[0062] in, a y1 (t) The maximum longitudinal acceleration (deceleration) speed of the target vehicle a 1 exist x 2 The weight, a y2 (t) The maximum lateral acceleration (deceleration) speed of the target vehicle a 2 exist x 2 The amount.a y1 (t) The calculation formula is as follows.

[0063] a y1 (t) = a 1 ·sinγ(t) .

[0064] a y2 (t) The calculation formula is as follows.

[0065] a y2 (t) = a 2 ·cosγ(t) .

[0066] Calculate interval time Δt After that, the calculation formula of the angle between the speed of the vehicle to be avoided and the speed direction of the target vehicle is as follows.

[0067] γ(t + Δt) = γ(t) - ω(t)·Δt .

[0068] in, ω(t) The calculation formula for the angular velocity of the target vehicle is as follows.

[0069] ω(t) = k·R(t) .

[0070] in k is the steering sensitivity coefficient, R(t) It's the steering wheel angle.

[0071] Calculate the target vehicle speed. Decompose the target vehicle speed into x 1 , x 2 Get on axis v x (t) and v y (t) , v x (t) The calculation formula is as follows.

[0072] v x (t) = v 2 (t)·cosγ(t) - a x (t)·Δt .

[0073] in, v 2 (t)is the target vehicle speed at time t, and its initial value is v 2 .

[0074] v y (t) The calculation formula is as follows.

[0075] v y (t) = v 2 (t)·sinγ(t) - a y (t)·Δt .

[0076] Calculate interval time Δt After that, the displacement of the target vehicle. The position of the target vehicle is decomposed into x 1 , x 2 Get on axis x ( t + Δt ) and y ( t + Δt ) 。x ( t + Δt ) is calculated as follows.

[0077] x(t + Δt) = x(t) + v x (t + Δt)·Δt .

[0078] y ( t + Δt ) is calculated as follows.

[0079] y(t + Δt) = y(t) + v y (t + Δt)·Δt .

[0080] in, x(t) , y(t) The initial value of is 0.

[0081] Finally, we determine whether the current position is a critical point for risk aversion. If any of the above three conditions are met, the current position is not a critical point for risk aversion. γ ( t ), x(t), y(t) The value of t + Δt The value of the moment, update v 2 (t) The value of If the above three conditions are not met, the current position is the risk-avoidance critical point and the algorithm iteration terminates. y cep It is between the critical point of risk avoidance and the pre-collision point. x 2 The projection distance on the axis, γ cep is the angle at which the vehicle is at the critical point of danger avoidance, and the critical distance m cep The calculation formula is as follows.

[0082] m cep =y cep / sin (γ cep ) .

[0083] 3) Using the avoidance conditions in the fifth scenario with the current vehicle spacing as the minimum as the goal, determine the avoidance distance in the fifth scenario, and calculate the avoidance time in the fifth scenario based on the avoidance distance in the fifth scenario; wherein, the avoidance conditions in the fifth scenario are as follows.

[0084] y(t) ≤ y 0 .

[0085] y(t + Δt) > y 0 .

[0086] v 1 ·t - x 0 - x(t) > 0.5·d 2 +d 1 ·cosγ .

[0087] In the formula, d 1 is the target vehicle width, d 2 is the length of the vehicle to be avoided, x 0 The target vehicle and the vehicle to be avoided are in the second coordinate system x 2 The initial spacing in the axis direction, x(t) For the target vehicle t At the second coordinate system x 2 The displacement in the axial direction, γ is the angle between the speed direction of the vehicle to be avoided and the speed direction of the target vehicle, v 1is the target vehicle speed, Δt For the interval time.

[0088] 4) Using the avoidance conditions in the sixth scenario with the current vehicle spacing as the minimum as the goal, determine the avoidance distance in the sixth scenario, and calculate the avoidance time in the sixth scenario based on the avoidance distance in the sixth scenario; wherein, the avoidance conditions in the sixth scenario are as follows.

[0089] y(t) ≤ y 0 .

[0090] y(t + Δt) > y 0 .

[0091] x(t) - v 1 ·t + x 0 >0.5·d 2 +d 1 ·cosγ .

[0092] S3. Figure 6 As shown in the figure, according to the current target vehicle status data and critical avoidance time T cep Calculate the time it takes to successfully hedge.

[0093] Furthermore, the formula for calculating the successful hedging time is as follows.

[0094] T gap =T 0 -T cep .

[0095] T cep = m cep / v 0 .

[0096] In the formula, T gap The time when risk hedging can be successful; T 0 is the total time from the current vehicle distance to the vehicle collision; T cep It is the critical hedging time; m cep The distance between the critical avoidance point and the collision point of the vehicles; v 0 is the current speed of the target vehicle.

[0097] Optional, when hedging can be successful T gap If it is less than 2 seconds, it is identified as a high-risk scenario.

[0098] In actual application, the successful avoidance time is used to characterize the risk measurement index based on the critical state of avoidance. The longer the target vehicle is away from the critical point of avoidance, the longer the time left for the autonomous driving to detect the danger and perform avoidance operations, and the lower the risk of an accident. Otherwise, the risk is higher.

[0099] S4. Use the time that can be successfully avoided to identify high-risk scenarios. When no high-risk scenario is identified, reacquire the current vehicle distance. When a high-risk scenario is identified, use the passive safety collaborative strategy to perform passive safety constraints in stages; high-risk scenarios are scenarios where the time that can be successfully avoided is less than or equal to the preset time; passive safety constraints include: seat belt constraints and / or airbag constraints.

[0100] Furthermore, as shown in Table 1, the passive safety coordination strategy is as follows.

[0101] 1) When the time available for successful hedging is reduced to T cep When the time interval is equal to the sum of the extended time slots, the seat belt is tightened with a first preload force; the first preload force is 60% of the preload force of the seat belt of the target vehicle.

[0102] 2) When the time available for successful hedging is reduced to T cep When the second preload force is used, the seat belt is tightened and the airbag is pre-inflated. The second preload force is 100% of the preload force of the seat belt of the target vehicle.

[0103] Table 1 is based on T cep Passive safety coordination strategy table

[0104] Optionally, when the acceleration change of the current target vehicle exceeds a threshold, it indicates that a collision has occurred, and the passive safety restraints at this time also include: fully deploying the airbag and dynamically deflating the seat belt.

[0105] In actual application, compared with the existing method based on critical collision time ( TTC ) high-risk scene identification method, the high-risk scene identification method of this application takes risk factors into consideration more comprehensively, and the risk indicators used T gap The randomness of the target vehicle going straight, turning left, and turning right during driving is taken into account, which has better applicability for turning scenarios. T gap Indicators can distinguish TTCThere are no differences in risk that cannot be identified, so the identification results of high-risk scenarios are more accurate.

[0106] pass T gap The risk level indicated by the value determines whether it is a high-risk scene, and the pre-collision (Pre-Crash) data set obtained by this application is used as the basis for scene recognition. This data set is based on the on-site records obtained through an in-depth investigation of road traffic accidents in a certain country. It uses accident reconstruction technology to reproduce the scene before the accident and extract the reproduced scene data. The reconstructed data mainly includes: the position of the target vehicle and related vehicles, the running trajectory, the collision position, the collision posture, the surrounding environment, etc. This application randomly selected 116 car-vehicle collision accident scenes to be avoided from the data set for research, and divided the scenes into two categories according to whether the target vehicle was going straight or turning before the accident.

[0107] Furthermore, when a high-risk scenario is identified, it also includes: performing safe braking in stages using a graded braking strategy based on current target vehicle state data, as follows.

[0108] 1) When the target vehicle speed is 0 km / h ~ 30 km / h When the time for successful avoidance is reduced to 2 T cep , braking is performed with the first braking force; the first braking force is 100% of the braking force of the target vehicle.

[0109] 2) When the target vehicle speed is 30 km / h ~50 km / h When the risk avoidance time is reduced to 4 T cep When the time to avoid danger is further reduced to 2 T cep When braking, the first braking force is used; the second braking force is 40% of the braking force of the target vehicle.

[0110] 3) When the target vehicle speed exceeds 50 km / h When the risk avoidance time is reduced to 6 T cep When the time for successful avoidance continues to decrease to 4 T cep When the time for successful avoidance continues to decrease to 2 T cep When braking, use the first braking force.

[0111] The hierarchical braking strategy of this embodiment is shown in Table 2.

[0112]

[0113] Optional, when T cep When it is less than 0.2s, the graded braking thresholds are set to 0.5s and 1s respectively, taking into account factors such as the driver's reaction time.

[0114] The calculation results show that most of the accident scenarios T gap The value is between 1.2 seconds and 2 seconds. There are some scenes where the target vehicle is driving in a straight line. T gap Less than 1 second. Target vehicle turning scene T gap The values ​​are concentrated between 1.4 seconds and 2 seconds, and in some scenes they are around 1.5 seconds. T gap If it is less than 2 seconds, it is identified as a high-risk scenario.

[0115] In actual application, the hierarchical braking strategy of this application takes into account factors such as the randomness of steering, and makes risk assessment more accurate, so that braking can be performed more timely, the speed curve is smoother, and accidents can be better avoided and damage reduced. The effect is most obvious in the steering scenario.

[0116] At the same time, in order to verify the braking control strategy of this application, an accident scenario was built in a simulation environment, and simulation experiments were carried out on different collision scenarios, as follows.

[0117] First, the steps for building a simulation scene are as follows.

[0118] (1) Import the accident reconstruction file in the collision dataset (previously generated by PC-crash) into the Prescan simulation environment to generate the basic accident scenario.

[0119] (2) Improve the static environment of the basic accident scenario in the simulation environment.

[0120] (3) Realize the connection between Simulink and Prescan, and add the braking control algorithm module and collision detection module in Simulink. Finally, build a simulation scene to perform simulation test of the braking control algorithm. The simulation scene is as follows: Figure 7 shown.

[0121] The simulation results of the accident scenario where the target vehicle collides with the side of the vehicle to be avoided are as follows: Figure 8As shown in the figure, it can be seen from the simulated speed and braking force change curves that the braking strategy of the present application makes the vehicle speed curve change more smoothly, the braking advance is larger, the braking distance is shorter, and the risk avoidance ability and riding comfort in the scene are better than the comparison strategy.

[0122] The simulation results of the target vehicle's straight-ahead rear-end collision with the vehicle to be avoided are as follows: Figure 9 As shown. Both braking strategies failed to avoid collision. However, compared with the comparison strategy, the braking strategy of the present application reduced the collision speed of the target vehicle from 39.9 km / h to 25.2 km / h, a decrease of 36.84%, reducing the damage caused by the accident.

[0123] The simulation results of the accident scenario where the target vehicle collides with the oncoming vehicle to be avoided are as follows: Figure 10 The control strategy failed to prevent the collision, and the target vehicle collided at a speed of 0.8 m / s. For this extremely dangerous scenario, the braking strategy of the present application stopped the target vehicle in the first 1.8 seconds to avoid the accident.

[0124] The simulation results of the accident scenario where the target vehicle turns are as follows: Figure 11 As shown. Both braking strategies avoid accidents. The braking strategy of the present application has a more advanced braking time and a smoother speed curve, which reduces the impact on the driver during the braking process.

[0125] It can be seen that the simulation experiments of high-risk scenarios with different collision postures, such as the target vehicle turning and the target vehicle going straight, have verified the effectiveness of the braking strategy of this application. The braking strategy of this application outperforms the comparison strategy in all four scenarios, with earlier braking time and a flatter speed curve, which can better avoid accidents and reduce damage. Especially in the turning scenario, the trigger time is set about 2s in advance, which has a significant effect.

[0126] The technical effects of this application are as follows: This application first proposed the concept of the critical state of risk avoidance, and designed a calculation method for the critical point of risk avoidance for the target vehicle in two scenarios: straight driving and turning. The critical risk avoidance time is used to characterize the temporal and spatial proximity of the vehicles. By calculating the expected running time between the target vehicle and the critical point of risk avoidance (the time it can successfully avoid risk), Tgap, realize the identification of high-risk scenes, improve the target vehicle's ability to identify high-risk scenes, and formulate corresponding passive safety coordination strategies, which can initiate timely passive safety coordination responses in high-risk scenes, improve the safety factor of the target vehicle, and reduce the incidence of traffic accidents. In addition, this application also formulates a corresponding automatic driving braking strategy. This application takes into account the risk avoidance ability of the target vehicle, which is particularly suitable for turning scenarios; it can distinguish the risk differences under different road adhesion coefficients; the formulated automatic driving braking strategy can intervene in advance, smooth the vehicle speed changes and pre-start passive response, thereby effectively avoiding accidents or reducing collision damage.

[0127] The critical avoidance time is used to characterize the temporal and spatial proximity of vehicles, which improves the target vehicle's ability to identify high-risk scenarios and can initiate timely passive safety coordinated responses in high-risk scenarios, thereby improving the safety factor of the target vehicle and reducing the incidence of traffic accidents.

[0128] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0129] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A high-risk scene recognition and passive safety coordination method based on critical risk avoidance state, characterized in that: The high-risk scene recognition and passive safety coordination method based on critical risk avoidance state includes: Acquire current target vehicle state data; the current target vehicle state data at least includes: current target vehicle driving state, current vehicle distance, current target vehicle speed and current target vehicle acceleration; the current target vehicle driving state is straight or turning; the current vehicle distance is: the distance between the target vehicle and the vehicle to be avoided; Determine critical avoidance time based on current target vehicle status data T cep ; The critical hedging time T cep The critical avoidance point is the time from when the target vehicle will collide with the vehicle to be avoided; the critical avoidance point is the shortest distance required for the target vehicle to successfully avoid the vehicle to be avoided; According to the current target vehicle status data and critical avoidance time T cep Calculate the time it takes to successfully hedge; High-risk scenarios are identified using the time that can be successfully avoided. When no high-risk scenario is identified, the current vehicle distance is reacquired. When a high-risk scenario is identified, passive safety constraints are performed in stages using a passive safety collaboration strategy. The high-risk scenario is a scenario where the time that can be successfully avoided is less than or equal to a preset time. The passive safety constraints include: seat belt constraints and / or airbag constraints.

2. The high-risk scene recognition and passive safety coordination method based on critical risk avoidance state according to claim 1 is characterized in that: Determine critical avoidance time based on current target vehicle status data T cep , specifically including: When the current target vehicle is in a straight-ahead state, the avoidance time under different straight-ahead avoidance scenarios is calculated using the avoidance conditions under different straight-ahead avoidance scenarios, and the shortest avoidance time under each straight-ahead avoidance scenario is selected as the critical avoidance time. T cep ; The straight-ahead danger avoidance scenario includes at least: a first scenario, a second scenario and a third scenario; the first scenario is a scenario in which the target vehicle decelerates to avoid the vehicle to be avoided; the second scenario is a scenario in which the target vehicle turns in the same direction to avoid the vehicle to be avoided; the third scenario is a scenario in which the target vehicle turns in the opposite direction to avoid the vehicle to be avoided; When the current target vehicle is in a turning state, the avoidance time under each turning avoidance scenario is calculated using the avoidance conditions under different turning avoidance scenarios, and the shortest avoidance time under each turning avoidance scenario is selected as the critical avoidance time. T cep ; The steering avoidance scenarios include at least: a fourth scenario, a fifth scenario and a sixth scenario; the fourth scenario is a scenario in which the speed of the target vehicle in the driving direction of the vehicle to be avoided has been reduced to 0 before the target vehicle drives to the driving route of the vehicle to be avoided; the fifth scenario is a scenario in which the target vehicle crosses the driving route of the vehicle to be avoided from behind the vehicle to be avoided and keeps driving behind the vehicle to be avoided; the sixth scenario is a scenario in which the target vehicle crosses the driving route of the vehicle to be avoided from in front of the vehicle to be avoided and keeps driving in front of the vehicle to be avoided.

3. The high-risk scene recognition and passive safety coordination method based on critical risk avoidance state according to claim 2 is characterized in that: The hedging conditions under different straight-ahead hedging scenarios are used to calculate the hedging time under each straight-ahead hedging scenario, including: Establish a target vehicle moving direction x 1 axis, perpendicular to x 1 axis direction is x 2-axis, the first coordinate system with the current position of the target vehicle as the coordinate origin; The avoidance conditions in the first scenario are used to minimize the current vehicle spacing, determine the avoidance distance in the first scenario, and calculate the avoidance time in the first scenario based on the avoidance distance in the first scenario; wherein the avoidance conditions in the first scenario are as follows: v 1 2 ≤ v 2 2 ·cos 2 θ +2· a 1· μ · g · m ; In the formula, v 1 is the target vehicle speed, v 2 is the speed of the vehicle to be avoided, θ is the velocity direction of the vehicle to be avoided and the first coordinate system x The angle of the 1 axis, a 1 is the target vehicle along x Maximum acceleration of axis 1, μ is the ground friction coefficient, g is the acceleration due to gravity, m is the current vehicle distance; The avoidance conditions in the second scenario are used to minimize the current vehicle spacing, determine the avoidance distance in the second scenario, and calculate the avoidance time in the second scenario based on the avoidance distance in the second scenario; wherein the avoidance conditions in the second scenario are as follows: v 1 2 ≤ v 2 2 ·cos 2 θ +2· a 1· μ · g · m ; Lx 1-d1 / 2-( d 2·sinθ) / 2+ a ≥ Lx 2; In the formula, d 1 is the target vehicle width, d 2 is the length of the vehicle to be avoided, Lx 1 is the lateral displacement of the car, Lx 2 is the lateral displacement of the vehicle to be avoided, a is the distance between the vehicle to be avoided and the first coordinate system x 1. The vertical distance of the axis; The avoidance conditions in the third scenario are used to minimize the current vehicle spacing, determine the avoidance distance in the third scenario, and calculate the avoidance time in the third scenario based on the avoidance distance in the third scenario; wherein, the avoidance conditions in the third scenario are as follows: v 1 2 ≤ v 2 2 ·cos 2 θ +2· a 1· μ · g · m ; Lx 1 +Lx 2 ≤ad 1 / 2-(d 2 ·sinθ) / 2 。 4. The high-risk scene recognition and passive safety coordination method based on critical risk avoidance state according to claim 2 is characterized in that: The hedging conditions under different risk-avoidance scenarios are used to calculate the hedging time under each risk-avoidance scenario, including: Establish the movement direction of the vehicle to be avoided as x 1 axis, perpendicular to x 1 axis direction is x 2-axis, a second coordinate system with the current position of the vehicle to be avoided as the coordinate origin; The avoidance conditions in the fourth scenario are used to minimize the current vehicle spacing, determine the avoidance distance in the fourth scenario, and calculate the avoidance time in the fourth scenario based on the avoidance distance in the fourth scenario; wherein, the avoidance conditions in the fourth scenario are as follows: v y (t)=0 ； y(t)≤y 0； in, v y (t) For the target vehicle t At the second coordinate system x Speed ​​in 1-axis direction, y(t) For the target vehicle t At the second coordinate system x The displacement in the 1-axis direction, y 0 is the target vehicle and the vehicle to be avoided in the second coordinate system x 1. Initial spacing in the axis direction; The avoidance conditions in the fifth scenario are used to minimize the current vehicle spacing, determine the avoidance distance in the fifth scenario, and calculate the avoidance time in the fifth scenario based on the avoidance distance in the fifth scenario; wherein, the avoidance conditions in the fifth scenario are as follows: y(t)≤y 0； y(t+Δt)>y 0； v 1 ·tx 0 -x(t)>0.5·d 2 +d 1 ·cosγ ； In the formula, d 1 is the target vehicle width, d 2 is the length of the vehicle to be avoided, x 0 is the target vehicle and the vehicle to be avoided in the second coordinate system x The initial spacing in the 2-axis direction, x(t) For the target vehicle t At the second coordinate system x Displacement in 2-axis direction, γ is the angle between the speed direction of the vehicle to be avoided and the speed direction of the target vehicle, v 1 is the target vehicle speed, Δt is the interval time; The avoidance conditions in the sixth scenario are used to minimize the current vehicle spacing, determine the avoidance distance in the sixth scenario, and calculate the avoidance time in the sixth scenario based on the avoidance distance in the sixth scenario; wherein, the avoidance conditions in the sixth scenario are as follows: y(t)≤y 0； y(t+Δt)>y 0； x(t)-v 1 ·t+x 0 >0.5·d 2 +d 1 ·cosγ 。 5. The high-risk scene recognition and passive safety coordination method based on critical risk avoidance state according to claim 4 is characterized in that: The formula for calculating the successful hedging time is as follows: T gap =T 0 -T cep ； T cep = m cep / v 0； In the formula, T gap The time when risk hedging can be successful; T 0 is the total time from the current vehicle distance to the vehicle collision; T cep It is the critical hedging time; m cep The distance between the critical avoidance point and the collision point of the vehicles; v 0 is the current speed of the target vehicle.

6. The high-risk scene recognition and passive safety coordination method based on critical risk avoidance state according to claim 1 is characterized in that: The passive safety coordination strategy specifically includes: When the time available for successful hedging is reduced to T cep When the time interval is equal to the sum of the time intervals and the extended time interval, the seat belt is tightened with the first pre-tightening force; When the time available for successful hedging is reduced to T cep When the safety belt is tightened with the second pre-tensioning force, the airbag is pre-inflated.

7. The high-risk scene recognition and passive safety coordination method based on critical risk avoidance state according to claim 6 is characterized in that: The first preload force is 60% of the preload force of the seat belt of the target vehicle; and the second preload force is 100% of the preload force of the seat belt of the target vehicle.

8. The high-risk scene recognition and passive safety coordination method based on critical risk avoidance state according to claim 1 is characterized in that: When a high-risk scenario is identified, it also includes: performing safe braking in stages using a graded braking strategy based on current target vehicle status data.

9. The high-risk scene recognition and passive safety coordination method based on critical risk avoidance state according to claim 8 is characterized in that: Based on the current target vehicle status data, a graded braking strategy is used to perform safe braking in stages, including: When the target vehicle speed is 0 km / h~ 30 km / h When the time for successful avoidance is reduced to 2 T cep When braking, the first braking force is used; When the target vehicle speed is 30 km / h ~50 km / h When the risk avoidance time is reduced to 4 T cep When the time to avoid danger is further reduced to 2 T cep When braking, use the first braking force; When the target vehicle speed exceeds 50 km / h When the risk avoidance time is reduced to 6 T cep When the time for successful avoidance continues to decrease to 4 T cep When the time for successful avoidance continues to decrease to 2 T cep When braking, use the first braking force.

10. The high-risk scene recognition and passive safety coordination method based on critical risk avoidance state according to claim 9 is characterized in that: The first braking force is 100% of the braking force of the target vehicle; the second braking force is 40% of the braking force of the target vehicle.

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