Safe vehicle distance planning method based on risk coupling potential in connected mixed traffic environment

Abstract

The method for planning safe vehicle distance based on risk coupling potential in a networked mixed traffic environment belongs to the field of vehicle intelligent interaction and active safety technology, including: obtaining information about the vehicle ahead of the vehicle through the cloud database of the Internet of Vehicles and the vehicle-mounted sensors of the vehicle to determine the safe headway between the two vehicles; inputting the safe headway and the vehicle speed into the longitudinal risk correction distance formula; calculating the speed difference between the two vehicles and the vehicle state risk factor of the leading vehicle based on the vehicle speed, the leading vehicle speed and the mass; and uniformly inputting the above-mentioned parts into the expression of the risk coupling potential value; finally, combining the potential value safety threshold to reversely infer the critical safe following distance of the vehicle. The risk coupling potential model proposed in the present invention can accurately analyze the risk differences of the leading vehicle to the vehicle in a networked mixed traffic environment caused by vehicle attributes, motion state and driver type, and thus provide a reasonable planning method for the safe following distance of vehicles of different types and motion states.

Description

Technical Field

[0001] The present invention belongs to the field of vehicle intelligent interaction and active safety technology, and in particular relates to a safe vehicle distance planning method based on risk coupling potential in a networked mixed traffic environment. Background Art

[0002] With the rapid development of science and technology, connected vehicles (CAVs) and autonomous driving technologies will lead the future of transportation. They can significantly improve the accuracy of vehicle perception, response, and decision-making speed, playing a significant role in reducing traffic accidents and improving road capacity. However, due to limitations in technology, application scenarios, and consumer preferences, the traffic environment will likely be a mix of connected autonomous vehicles (CAVs) and connected manually driven vehicles (CMVs) for a considerable period of time.

[0003] An important criterion for measuring vehicle driving safety is whether it can maintain a safe following distance from the vehicle in front. This distance is affected by multiple vehicle safety factors. Existing safety distance models not only fail to accurately consider vehicle status factors, but also lack effective differentiation between the risk differences between CAVs and CMVs in connected mixed traffic environments, as well as the risk differences between different types of drivers.

[0004] Therefore, the existing technology urgently needs a risk coupling potential model that includes multiple vehicle safety factors, which can accurately analyze the risk differences of the preceding vehicle to the vehicle caused by vehicle attributes, motion state and driver type in a connected mixed traffic environment, and then provide a reasonable planning method for the safe following distance of the vehicle of different types and motion states. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a safe vehicle distance planning method based on risk coupling potential in a connected mixed traffic environment, so as to solve the problems in the existing vehicle safety distance model that the consideration of vehicle status factors is not accurate enough, and there is a lack of effective distinction between the risk differences between CAVs and CMVs, as well as the risk differences between different types of drivers.

[0006] A safe vehicle distance planning method based on risk coupling potential in a connected mixed traffic environment includes the following steps, which are performed in sequence:

[0007] Step 1: Use the Internet of Vehicles cloud database and on-board sensors to obtain the attribute information and motion information of the preceding vehicle, including the driving type and mass m of the preceding vehicle. j , the motion information includes the front vehicle speed v j and the historical acceleration a within the time window j(H) ;According to the type of vehicle pair between the vehicle and the preceding vehicle, obtain the safe headway time THW between the two vehicles S ;

[0008] Step 2: Establish the longitudinal risk correction distance formula r ji(L) ', the safe headway time THW obtained in step 1 is S and the vehicle's speed v i Input into the longitudinal risk correction distance formula to make corrections for longitudinal risk differences;

[0009] Step 3: Pass the vehicle speed v i and the speed of the preceding vehicle v j Calculate the speed difference Δv between the two vehicles; when the driving type of the front vehicle is CAV, the speed v of the front vehicle is j and the mass of the preceding vehicle m j Get the vehicle status risk factor R of the preceding vehicle V(j) ; When the current vehicle driving type is manual driving CMV, through the historical acceleration a in the time window j(H) The four sampling values are used to obtain the risk factor R of the driver of the preceding vehicle D(j) ;

[0010] Step 4: Modify the distance formula r using the longitudinal risk ji(L) ′, the speed difference between the two vehicles Δv, and the risk factor of the vehicle status of the leading vehicle R V(j) and the driver's risk factor R D(j) Establish the risk coupling potential value PV j-i ;

[0011] Step 5: Use the risk coupling potential value PV established in step 4 j-i Combined potential value safety threshold |PV j-i(s) |Reverse calculation of the critical safe following distance D of the vehicle Cs(i-j) .

[0012] The preceding vehicle driving type in the step includes automatic driving CAV and manual driving CMV; when both the vehicle and the preceding vehicle are CAV, THW S(CAV-CAV) = 0.9s; when the vehicle is a CAV and the preceding vehicle is a CMV, THW S(CAV-CMV) = 1.2s; When the vehicle is a CMV and the preceding vehicle is a CAV, or both the vehicle and the preceding vehicle are CMV, THW S(CMV-CAV) =THW S(CMV-CMV) =1.5s.

[0013] The longitudinal risk correction distance formula r in step 2 ji(L) 'for:

[0014]

[0015] Among them, |r ji ′| is the longitudinal risk effect correction distance, d ji(min)is the vector corresponding to the shortest distance between the outer contour of the preceding vehicle j and the outer contour of the vehicle i, s S is the minimum longitudinal safety distance for parking, which is 3m, η is the longitudinal risk correction coefficient, which is 0.015, v i is the speed of vehicle i (km / h).

[0016] In step 3, the speed difference Δv between the two vehicles is Δv=v i -v j .

[0017] Step 3: The risk factor R of the vehicle status of the preceding vehicle V(j) for,

[0018]

[0019] Where m E is the mass of a standard small car, 1500kg, m j is the mass of the preceding vehicle j (kg).

[0020] Step 3: The risk factor R of the driver of the preceding vehicle D(j) for

[0021]

[0022] Where, is the historical acceleration a of the preceding vehicle j within the 3s time window j(H) (m / s 2 ) The average of the absolute values of four sampling values every 1s, for a j(H) The standard deviation of the four sampling values, γ1 and γ2 are coefficients, both are set to 20.

[0023] Step 4: Risk coupling potential value PV j-i The expression is:

[0024] When the vehicle ahead is a CAV,

[0025]

[0026] When the vehicle ahead is a CMV,

[0027]

[0028] Among them, λ is the coefficient, which is 0.0222, and k is the relative speed risk coefficient, which is 0.011.

[0029] The critical safety following distance D in step 5 Cs(i-j) The expression is:

[0030] When the vehicle ahead is a CAV,

[0031]

[0032] When the vehicle ahead is a CMV,

[0033]

[0034] Among them, |PV j-i(S) | is the safety threshold of the risk coupling potential value, which is 500.

[0035] Through the above design scheme, the present invention can bring the following beneficial effects:

[0036] 1. The risk coupling potential model proposed in this paper incorporates multiple vehicle safety factors and can accurately analyze the differences in risk posed by preceding vehicles to the vehicle in connected mixed traffic environments due to vehicle attributes, motion state, and driver type. This is of great significance for risk warning and safety decision-making for vehicles of different types and motion states.

[0037] 2. The safe distance planning method based on risk coupling potential solves the problems of existing vehicle safety distance models that do not accurately consider vehicle status factors, lack effective differentiation between the risk differences between CAVs and CMVs, and lack effective differentiation between different types of drivers. It provides important theoretical and technical support for planning reasonable safe following distances for vehicles of different types and motion states in a connected mixed traffic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0039] Figure 1 This is a flow chart of the safe vehicle distance planning method based on risk coupling potential in a networked mixed traffic environment according to the present invention.

[0040] Figure 2 This is a schematic diagram of planning the critical safety states of two vehicles using the safe vehicle distance planning method based on risk coupling potential in a networked mixed traffic environment according to the present invention. DETAILED DESCRIPTION

[0041] A safe distance planning method based on the risk coupling potential in a connected mixed traffic environment acquires various attribute and motion state information of the first vehicle ahead of the vehicle through the connected vehicle cloud database and the vehicle's onboard sensors. The safe headway between the two vehicles is determined based on the vehicle pair type. The safe headway and the vehicle's speed are input into the longitudinal risk-corrected distance formula. The speed difference between the two vehicles and the vehicle state risk factor of the preceding vehicle are calculated based on the vehicle's speed, the preceding vehicle's speed, and the preceding vehicle's mass. If the preceding vehicle is a manned vehicle (CMV), its driver's inclination risk factor is calculated based on historical acceleration samples within its time window. All of these components are then integrated into the expression for the risk coupling potential value. Finally, the critical safe following distance for the vehicle is derived by combining the potential safety threshold. This method addresses the existing vehicle safety distance model's inaccurate consideration of vehicle state factors and its lack of effective differentiation between the risk differences between CAVs and CMVs, as well as between different types of drivers. This method provides important theoretical and technical support for planning reasonable safe following distances for vehicles of different types and motion states in connected mixed traffic environments.

[0042] To make the objects, features, and advantages of the present invention more apparent and understandable, the technical solutions of the present invention are described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the present invention is not limited to the following embodiments, and specific implementation methods can be determined based on the technical solutions of the present invention and actual conditions. To avoid obscuring the essence of the present invention, well-known methods, processes, and procedures are not described in detail.

[0043] like Figure 1 As shown in the figure, the safe vehicle distance planning method based on risk coupling potential in a connected mixed traffic environment includes the following steps:

[0044] Step S1: Obtain various attribute information and motion status information of the first vehicle in front of the vehicle through the Internet of Vehicles cloud database and the vehicle-mounted sensors. The attribute information includes the vehicle type (autonomous driving vehicle CAV or manual driving vehicle CMV) and mass m j , the motion state information includes the instantaneous speed v of the preceding vehicle j and historical acceleration a within the time window (3s) j(H) The four sampling values (sampling every 1s) are used to determine the safe headway time THW between the vehicle and the preceding vehicle based on the vehicle pair type. S ;

[0045] Specifically, considering the integrity and accuracy of vehicle information obtained by the Internet of Vehicles and the vehicle's onboard sensors, the rapidity of the driver's reaction ability in CAV compared to CMV, and the better decision synchronization between CAVs, the safe headway time THW between the vehicle and the preceding vehicle in different types of vehicle encounters is: SThe value is: When both the vehicle and the preceding vehicle are CAV, THW S(CAV-CAV) = 0.9s; when the vehicle is a CAV and the preceding vehicle is a CMV, THW S(CAV-CMV) = 1.2s; When the vehicle is a CMV and the preceding vehicle is a CAV, or both the vehicle and the preceding vehicle are CMV, THW S(CMV-CAV) =THW S(CMV-CMV) =1.5s;

[0046] Step S2: The safe headway time THW obtained in step S1 is converted to S and the vehicle's speed v i Input to the longitudinal risk correction distance formula r ji(L) 'middle;

[0047] Specifically, considering the differences in the effects of different types of vehicles on the longitudinal risk represented by the safe headway and the speed of the following vehicle during the vehicle movement, the longitudinal risk correction distance formula r in step S2 is ji(L) 'for:

[0048]

[0049] Among them, |r ji ′| is the risk effect correction distance, d ji(min) is the vector corresponding to the shortest distance between the outer contour of the preceding vehicle j and the outer contour of the vehicle i, s S is the minimum longitudinal safety distance for parking, which is 3m, η is the longitudinal risk correction coefficient, which is 0.015, THW S is the safe headway of different types of vehicle pairs, v i is the speed of vehicle i (km / h).

[0050] Step S3: Based on the vehicle speed v i and the preceding vehicle speed v obtained in step S1 j Calculate the speed difference Δv between the two vehicles based on the preceding vehicle speed v obtained in step S1 j and mass m j Calculate the vehicle status risk factor R of the preceding vehicle V(j) , when the current vehicle is a CMV, based on the historical acceleration a within the time window obtained in step S1 j(H) The four sampling values are used to calculate the driver's tendency risk factor R D(j) ;

[0051] Specifically, the calculation methods of the three indicators in step S3 are:

[0052] Step S301: Considering the relative risk difference between the vehicle and the preceding vehicle caused by the speed difference between the vehicle and the preceding vehicle, the speed difference Δv between the two vehicles is calculated: Δv = v i -v j

[0053] Among them, v j is the speed of the preceding vehicle j (km / h);

[0054] In step S302, the absolute risk of a moving vehicle is related to its mass and speed: the greater the mass, the greater the risk, for example, a large vehicle has a greater risk than a small vehicle; the greater the speed, the greater the risk, and at high speeds, the risk of a vehicle increases rapidly as the speed increases. Considering the absolute risk differences caused by different vehicle masses and speeds, the vehicle state risk factor R of the preceding vehicle j is calculated. V(j) :

[0055]

[0056] Among them, m E is the mass of a standard small car, 1500kg, m j is the mass of the preceding vehicle j (kg);

[0057] Step S303: There are significant differences in risk between individual drivers. Drivers with high risk tend to have a strong tendency to drive aggressively. Aggressive tendency can be measured by the driver's manipulation characteristics of the accelerator and brake pedals. The CMV driven by a driver with a stronger aggressive tendency tends to have more rapid acceleration and deceleration behaviors, which is characterized by large acceleration values and fluctuations in the vehicle motion state parameters. Based on the above discussion, the historical acceleration of the CMV within the time window is used as the basic parameter to characterize the individual risk differences between different drivers, and the driver tendency risk factor R of the front vehicle j is calculated. D(j) :

[0058]

[0059] in, is the historical acceleration a of the preceding vehicle j within the time window (3s) j(H) (m / s 2 ) The average of the absolute values of four sampling values (sampling every 1s), for a j(H) The standard deviation of the four sampling values, γ1 and γ2 are coefficients, both are set to 20.

[0060] Step S4: When the vehicle and the preceding vehicle have different speed combinations at the same distance, the risk of the preceding vehicle to the host vehicle is also different. For example, in speed combination 1, the vehicle's speed is 60 km / h and the preceding vehicle's speed is 80 km / h; in speed combination 2, the vehicle's speed is 100 km / h and the preceding vehicle's speed is 80 km / h. Obviously, the tendency of the two vehicles to approach each other in speed combination 2 is stronger than in speed combination 1, so the risk of the preceding vehicle to the vehicle in speed combination 2 is higher than in speed combination 1. Based on the above discussion, the speed difference Δv between the two vehicles obtained in step S3 is processed and compared with the longitudinal risk correction distance formula r obtained in step S2. ji(L) ′, the vehicle status risk factor R of the preceding vehicle obtained in step S3 V(j) and the driver's tendency risk factor R when the preceding vehicle is a CMV D(j) Unified input to the risk coupling potential value PV j-i In the expression of;

[0061] Specifically, the risk coupling potential value PV in step S4 j-i The expression is:

[0062] When the vehicle ahead is a CAV,

[0063]

[0064] When the vehicle ahead is a CMV,

[0065]

[0066] Among them, λ is the coefficient, which is 0.0222, and k is the relative speed risk coefficient, which is 0.011.

[0067] Step S5: Based on the risk coupling potential value PV obtained in step S4 j-i The expression of the potential safety threshold |PV j-i(s) |Reversely calculate the critical safe following distance of the vehicle;

[0068] Specifically, the diagram of the critical safety state of following a vehicle is as follows: Figure 2 As shown, the critical safety following distance D in step S5 Cs(i-j) The expression is:

[0069] When the vehicle ahead is a CAV,

[0070]

[0071] When the vehicle ahead is a CMV,

[0072]

[0073] Among them, |PV j-i(S) | is the safety threshold of the risk coupling potential value, which is 500.

[0074] The present invention characterizes the multiple risks imposed on the host vehicle by the preceding vehicle as the risk coupling potential of the preceding vehicle on the host vehicle, and can accurately analyze the risk differences of the preceding vehicle on the host vehicle caused by vehicle attributes, motion state and driver type in a connected mixed traffic environment.

Claims

1. A safe vehicle distance planning method based on risk coupling potential in a connected mixed traffic environment is characterized by: The method comprises the following steps, and the following steps are performed in sequence: Step 1: Use the Internet of Vehicles cloud database and on-board sensors to obtain the attribute information and motion information of the preceding vehicle, including the driving type and mass m of the preceding vehicle. j , the motion information includes the front vehicle speed v j and the historical acceleration a within the time window j(H) ;According to the type of vehicle pair between the vehicle and the preceding vehicle, obtain the safe headway time THW between the two vehicles S ; Step 2: Establish the longitudinal risk correction distance formula r ji(L) ', the safe headway time THW obtained in step 1 is S and the vehicle's speed v i Input into the longitudinal risk correction distance formula to make corrections for longitudinal risk differences; The longitudinal risk correction distance formula r in step 2 ji(L) 'for: Among them, |r ji '| is the longitudinal risk effect correction distance, d ji(min) is the vector corresponding to the shortest distance between the outer contour of the preceding vehicle j and the outer contour of the vehicle i, s S is the minimum longitudinal safety distance for parking, which is 3m, η is the longitudinal risk correction coefficient, which is 0.015, v i is the speed of vehicle i (km / h); Step 3: Pass the vehicle speed v i and the speed of the preceding vehicle v j Calculate the speed difference Δv between the two vehicles; when the driving type of the front vehicle is CAV, the speed v of the front vehicle is j and the mass of the preceding vehicle m j Get the vehicle status risk factor R of the preceding vehicle V(j) ; When the current vehicle driving type is manual driving CMV, through the historical acceleration a in the time window j(H) The four sampling values are used to obtain the risk factor R of the driver of the preceding vehicle D(j) ; Step 4: Modify the distance formula r using the longitudinal risk ji(L) ′, the speed difference between the two vehicles Δv, and the risk factor of the vehicle status of the leading vehicle R V(j) and the driver's risk factor R D(j) Establish the risk coupling potential value PV j-i ; Step 4: Risk coupling potential value PV j-i The expression is: When the vehicle ahead is a CAV, When the vehicle ahead is a CMV, Among them, λ is the coefficient, which is 0.0222, and k is the relative speed risk coefficient, which is 0.011; Step 5: Use the risk coupling potential value PV established in step 4 j-i Combined potential value safety threshold |PV j-i(s) |Reverse calculation of the critical safe following distance D of the vehicle Cs(i-j) .

2. The method for planning safe vehicle distance based on risk coupling potential in a connected mixed traffic environment according to claim 1 is characterized by: The preceding vehicle driving type in the step includes automatic driving CAV and manual driving CMV; when both the vehicle and the preceding vehicle are CAV, THW S(CAV-CAV) =0.9s; when the vehicle is a CAV and the preceding vehicle is a CMV, THW S(CAV-CMV) = 1.2s; When the vehicle is a CMV and the preceding vehicle is a CAV, or both the vehicle and the preceding vehicle are CMV, THW S(CMV-CAV) =THW S(CMV-CMV) =1.5s.

3. The safe vehicle distance planning method based on risk coupling potential in a connected mixed traffic environment according to claim 1 is characterized by: In step 3, the speed difference Δv between the two vehicles is Δv=v i -v j .

4. The method for planning safe vehicle distance based on risk coupling potential in a connected mixed traffic environment according to claim 1 is characterized by: Step 3: The risk factor R of the vehicle status of the preceding vehicle V(j) for, Where m E is the mass of a standard small car, 1500kg, m j is the mass of the preceding vehicle j (kg).

5. The safe vehicle distance planning method based on risk coupling potential in a connected mixed traffic environment according to claim 4 is characterized by: Step 3: The risk factor R of the driver of the preceding vehicle D(j) for Where, is the historical acceleration a of the preceding vehicle j within the 3s time window j(H) (m / s 2 ) The average of the absolute values of four sampling values every 1s, for a j(H) The standard deviation of the four sampling values, γ1 and γ2 are coefficients, both are set to 20.

6. The method for planning safe vehicle distance based on risk coupling potential in a connected mixed traffic environment according to claim 5 is characterized by: Step 4: Risk coupling potential value PV j-i The expression is: When the vehicle ahead is a CAV, When the vehicle ahead is a CMV, Among them, λ is the coefficient, which is 0.0222, and k is the relative speed risk coefficient, which is 0.

011.

7. The method for planning safe vehicle distance based on risk coupling potential in a connected mixed traffic environment according to claim 6 is characterized by: The critical safety following distance D in step 5 Cs(i-j) The expression is: When the vehicle ahead is a CAV, When the vehicle ahead is a CMV, Among them, |PV j-i(S) | is the safety threshold of the risk coupling potential value, which is 500.

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