Hybrid vehicle anti-collision early warning method based on variable vehicle spacing

Abstract

The invention relates to a hybrid vehicle anti-collision early warning method based on variable vehicle spacing, and belongs to the technical field of automatic driving. The method comprises the following steps: setting a mixed traffic scene including a traditional person driving vehicle, an automatic driving vehicle and a network connection automatic vehicle, and obtaining vehicle state information of a current vehicle and a front vehicle at the current moment; calculating driving safety distances of different types of vehicles; a traditional person driving following model based on a cellular automaton is established, a variable vehicle spacing control model based on an intelligent driver model is established, and a variable vehicle spacing control model based on a cooperative adaptive cruise control model is established; respectively controlling a traditional person driving vehicle, an automatic driving vehicle and a network connection automatic vehicle through the established model; and the actual vehicle distance between the current vehicle and the front vehicle is obtained, the proportion between the actual vehicle distance and the driving safety distance is judged, and if the collision early warning triggering condition is met, different collision early warning behaviors are triggered according to different levels. According to the invention, the effectiveness of anti-collision early warning in a mixed vehicle scene can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of automatic driving and relates to a mixed traffic vehicle anti-collision warning method based on variable vehicle spacing. Background Art

[0002] With the increasing development of vehicle automation technology, various types of self-driving vehicles have begun to appear in the modern traffic environment. However, there is still a huge number of traditional fuel vehicles in use, and it is difficult to completely eliminate them in the short term. At the same time, it will take time for self-driving technology to be fully popularized. These factors have jointly led to the increasingly complex modern traffic environment. From traditional vehicles without automation to fully autonomous driving test models, vehicles with significantly different levels of automation will continue to coexist in the traffic environment for a long time.

[0003] However, this mixed traffic situation can also pose risks. When vehicles at different levels of automation interact, communication breakdowns or conflicting predictions can occur. Human drivers' unfamiliarity with the behavior of autonomous vehicles can lead to misjudgment, all of which can increase the likelihood of accidents. Vehicle collision warning technology can address these shortcomings, significantly improving driving safety and reducing accident casualties and property damage. It also helps promote the development of intelligent transportation systems and autonomous driving technologies.

[0004] However, existing collision warning technologies primarily focus on external environmental factors and driver-specific factors, paying less attention to the characteristics of mixed traffic flows and the level of vehicle intelligence. This approach fails to consider the varying intelligence levels of heterogeneous vehicles within mixed traffic flows. Therefore, the present invention provides a mixed traffic collision warning method based on variable vehicle spacing, which improves the effectiveness and practicality of collision warnings for mixed traffic conditions involving traditional human drivers, autonomous vehicles, and connected autonomous vehicles. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a mixed vehicle collision avoidance warning method based on variable vehicle spacing, which can improve the effectiveness and practicality of collision avoidance warning in situations where traditional human driving, self-driving vehicles and networked automatic vehicles are mixed.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A mixed traffic vehicle anti-collision warning method based on variable vehicle spacing, the method comprising:

[0008] Set up a mixed traffic scenario including traditional human driving, self-driving cars, and connected autonomous vehicles, obtain the current vehicle status information of the vehicle and the preceding vehicle; calculate the safe driving distance of different types of vehicles;

[0009] Establish a traditional human-driven car-following model based on cellular automata, a variable vehicle-to-vehicle distance control model based on an intelligent driver model, and a variable vehicle-to-vehicle distance control model based on a collaborative adaptive cruise control model; use these established models to control traditional human-driven vehicles, autonomous vehicles, and connected autonomous vehicles.

[0010] Obtain the actual distance between the current vehicle and the vehicle in front, determine the ratio between the actual distance and the current vehicle's safe driving distance, and trigger different collision warning behaviors according to different levels if the collision warning trigger conditions are met.

[0011] Furthermore, the safe driving distances for different types of vehicles are calculated:

[0012] For traditional driving, the safe driving distance is expressed as:

[0013]

[0014] Where, d i,brake (k) is the braking distance of vehicle i at time k; is the reaction distance of vehicle i at time k; v i (k) is the speed of vehicle i at time k; a d is the emergency braking deceleration; l is the vehicle length; Reaction delay for traditional people driving;

[0015] For autonomous vehicles, the safe driving distance is expressed as:

[0016]

[0017] Where, is the reaction distance of vehicle i at time k; Response delay for self-driving cars;

[0018] For connected autonomous vehicles, the safe driving distance is expressed as:

[0019]

[0020] Where, is the reaction distance of vehicle i at time k; The response delay of connected autonomous vehicles.

[0021] Furthermore, for traditional human driving, considering the impact of driver aggressiveness and collision warning on the vehicle, a traditional human driving following model based on cellular automation is established. This model follows the following evolutionary rules:

[0022] Rule 1: The driver tends to accelerate to the maximum speed. When the speed of the preceding vehicle is greater than that of the self-driving vehicle, the greater the driver's aggressiveness coefficient, the more likely the driver is to accelerate more aggressively based on the speed difference between the self-driving vehicle and the preceding vehicle. The speed of a traditional driver at a future time in Rule 1 is expressed as:

[0023]

[0024] Where, v i (k) is the speed of vehicle i at time k; v i-1 (k) is the speed of vehicle i-1 at time k; v i (k+Δk) is the speed of vehicle i at time k+Δk; Δv i is the speed difference between the preceding vehicle and vehicle i; v max is the maximum speed of vehicle i; σ is the driver's aggressiveness coefficient; Δk is the simulation time step; the speed value is rounded down;

[0025] Rule 2: The vehicle will randomly slow down with a probability of ρ, which is used to characterize the impact of environmental factors on the vehicle's driving process. In Rule 2, the speed of a traditional driver at a future moment is expressed as:

[0026] v i (k+Δk)=max(v i (k)-1,0),if:P≤ρ

[0027] Where P is the probability of vehicle deceleration;

[0028] Rule 3: When the ratio of the distance between the vehicle and the preceding vehicle is less than τ2, emergency braking will be triggered. In Rule 3, the speed of a traditional driver at the future moment is expressed as:

[0029]

[0030] Where, is the distance between vehicle i and the preceding vehicle at time k; τ2 is the proportional coefficient of the second-level driving safety distance; τ3 is the proportional coefficient of the third-level driving safety distance; is the safe driving distance of a traditional driver at time k; For traditional people driving the second level warning deceleration, a d is the emergency braking deceleration; the speed value is rounded down;

[0031] Rule 4: Update the position of the traditional human driver on the road based on the speed output from Rules 1 to 3:

[0032] X i (k+Δk)=X i (k)+v i (k+Δk)·Δk

[0033] Among them, X i (k) is the position of vehicle i at time k; X i (k+Δk) is the position of vehicle i at time k+Δk.

[0034] Furthermore, the variable vehicle spacing control model based on the intelligent driver model is expressed as:

[0035]

[0036] Where a max is the maximum acceleration of the vehicle; v i (k) is the speed of vehicle i at time k; v0 is the desired speed of the vehicle; δ is the acceleration index; λ is the variable vehicle spacing coefficient of heterogeneous vehicles, and the value of λ is determined by the type of the preceding vehicle; Δv i is the speed difference between the vehicle and the preceding vehicle; is the distance between vehicle i and the preceding vehicle at time k; is the safe driving distance of vehicle i at time k; d * (v i (k),Δv i ) is the expected following distance; a d is the emergency braking deceleration; τ2 is the second-level driving safety distance proportional coefficient; τ3 is the third-level driving safety distance proportional coefficient; is the reaction delay of the autonomous vehicle; b is the comfortable deceleration.

[0037] Furthermore, the variable vehicle distance control model based on the cooperative adaptive cruise control model is expressed as:

[0038]

[0039] Where, e is the error between the distance between the preceding vehicle and the preceding vehicle and the desired following distance; is the distance between vehicle i and the preceding vehicle at time k; is the safe driving distance of vehicle i at time k; λ is the variable vehicle spacing coefficient of heterogeneous vehicles, and the value of λ is determined according to the type of the preceding vehicle; T c is the expected safe headway distance; v i (k) is the speed of vehicle i at time k; v j (k) is the speed of vehicle j in front of vehicle i at time k; k c 、k d is the control coefficient; Δt is the control interval; τ2 is the proportional coefficient of the second-level driving safety distance; τ3 is the proportional coefficient of the third-level driving safety distance; a d This is the deceleration for emergency braking.

[0040] Furthermore, the actual distance between the current vehicle and the preceding vehicle is obtained. For traditional human driving, the driving state information of the preceding vehicle is obtained through driver perception. The distance between the preceding vehicle and the preceding vehicle at time k is is the estimated value obtained by the driver's vision;

[0041] For an autonomous vehicle, the vehicle relies on the onboard sensor to obtain the driving status information of the preceding vehicle. The distance between the autonomous vehicle i and the preceding vehicle at time k is The driving status information of the vehicle and the preceding vehicle obtained by the on-board sensors is calculated as follows:

[0042]

[0043] Where, is the position of vehicle i at time k; is the position of vehicle i-1 at time k, Expressed as:

[0044]

[0045] For connected autonomous vehicles, the vehicle-mounted sensors, roadside sensors, and vehicle communication technology are used to obtain real-time driving status information of the preceding vehicle and the same type of vehicles in front. The distance between connected autonomous vehicle i and the preceding vehicle at time k is The following is calculated by integrating the driving status information of the vehicle and the preceding vehicle obtained in real time by integrating on-board sensors, roadside sensors, and vehicle communication technology:

[0046]

[0047] Where, is the position of vehicle i at time k; is the position of vehicle j at time k, Expressed as:

[0048]

[0049] Furthermore, the distance between vehicle i and the preceding vehicle at time k is Safe distance from vehicles Compare and judge the vehicle collision warning level as shown in the following formula:

[0050]

[0051] Wherein, τ1 is the first-level driving safety distance proportional coefficient; τ2 is the second-level driving safety distance proportional coefficient; τ3 is the third-level driving safety distance proportional coefficient; Determined by vehicle i type:

[0052]

[0053] Determined by the following formula;

[0054]

[0055] Where, The safe driving distance for traditional people driving i, is the safe driving distance of the autonomous vehicle i, is the safe driving distance of the connected autonomous vehicle i;

[0056] When the first level collision warning is triggered, a first level warning alarm is issued to the driver to remind the driver of the collision risk, and vehicle i is controlled through the established control model. The position and velocity of vehicle i at time k+Δk are expressed as:

[0057]

[0058] Where, They represent the position, velocity and acceleration of vehicle i at time k respectively; They represent the position and speed of vehicle i at time k+Δk respectively; Δk is the simulation time step; where, and Calculated by the control model of the vehicle type corresponding to vehicle i;

[0059] When the second level collision warning is triggered, a second level warning alarm is issued to the driver to remind the driver of the collision risk, and the vehicle is Braking is performed until the vehicle releases the second-level collision warning or triggers the third-level collision warning; the vehicle acceleration during the second-level collision warning Calculated by the following formula:

[0060]

[0061] Where, is the distance between vehicle i and the preceding vehicle at time k; is the safe driving distance of vehicle i at time k; a d is the emergency braking deceleration; vehicle i is controlled by the established control model, and the position and velocity of vehicle i at time k+Δk are expressed as:

[0062]

[0063] in, Calculated by the control model of the vehicle type corresponding to vehicle i;

[0064] When the third level collision warning is triggered, a third level warning alarm is issued to the driver to remind the driver of the collision risk, and the vehicle is decelerated by emergency braking.d Emergency braking is performed until the vehicle is released from the third-level collision warning. Vehicle i is controlled by the established control model. The position and velocity of vehicle i at time k+Δk are expressed as: in, Calculated by the control model of the vehicle type corresponding to vehicle i.

[0065] The beneficial effects of the present invention are as follows: The present invention addresses the issues that current anti-collision warning methods pay little attention to mixed traffic flow characteristics and vehicle intelligence levels, and fail to consider the actual traffic conditions in which heterogeneous vehicles in mixed traffic have varying intelligence levels. The present invention proposes a mixed traffic vehicle anti-collision warning method based on variable vehicle spacing. The present invention considers mixed traffic scenarios involving traditional human drivers, autonomous vehicles, and connected autonomous vehicles. Based on the characteristics of these three types of vehicles, control models are designed for each of these three types of vehicles. These control models are used to control the driving of each type of vehicle in mixed traffic scenarios, achieving targeted vehicle control and improving control accuracy and response speed. Furthermore, to address complex mixed traffic conditions, the present invention introduces variable spacing to construct control models for autonomous and connected autonomous vehicles. This allows autonomous and connected autonomous vehicles to adaptively adjust their following distance based on the type of preceding vehicle, thereby adapting to the driving styles of heterogeneous vehicles. For example, traditional human drivers are more prone to sudden changes in driving state (such as sudden acceleration and deceleration) compared to other types of vehicles. This improves the generalizability of the control model in mixed traffic scenarios.

[0066] In summary, the present invention can effectively meet the collision warning needs in common mixed vehicle scenarios, so that the anti-collision warning function can play a correct effect in real traffic scenarios.

[0067] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0069] Figure 1 A flow chart of a method for collision avoidance and early warning of mixed traffic based on variable vehicle spacing provided by one embodiment of the present invention;

[0070] Figure 2 This is a flowchart for the three-level collision warning judgment;

[0071] Figure 3 This is a schematic diagram of collision warning. DETAILED DESCRIPTION

[0072] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0073] like Figure 1 and Figure 2 As shown, a mixed traffic vehicle anti-collision warning method based on variable vehicle spacing is provided in one embodiment of the present invention. The method is as follows:

[0074] S1: Set up a mixed traffic scenario, collect heterogeneous vehicle data, and obtain the current vehicle status information of the vehicle and the preceding vehicle.

[0075] Specifically, a single-lane highway scenario is set with a lane length of w. Roadside sensors are installed on both sides of the road. Traditional human driving obtains the driving status information of the vehicle in front through driver perception, and autonomous driving vehicles rely on on-board sensors to obtain the driving status information of the vehicle in front. Connected autonomous vehicles can rely on on-board sensors, roadside sensors and vehicle communication technology to obtain the driving status information of the vehicle in front in real time.

[0076] Assume that there are N vehicles in a single lane of the highway, including l mixed vehicles, including m self-driving vehicles and n connected autonomous vehicles, l+m+n=N.

[0077] S2: Based on the scenario set in S1, vehicles are divided into three categories according to their degree of automation: traditional human-driven vehicles that are fully manually driven, self-driving vehicles that have self-driving capabilities but cannot communicate with other vehicles, and connected autonomous vehicles that have both self-driving capabilities and can communicate and coordinate with vehicles of the same type. Calculate the safe driving distance for traditional human-driven vehicles by vehicle type Safe driving distance for autonomous vehicles Safe driving distance from connected autonomous vehicles

[0078] S21: The traditional driving safety distance is expressed as follows:

[0079]

[0080] Among them, d i,brake(k) is the braking distance of vehicle i at time k; is the reaction distance of vehicle i at time k; v i (k) is the speed of vehicle i at time k; a d is the emergency braking deceleration adopted by the vehicle anti-collision warning system; l is the vehicle length; Reaction delay for traditional people driving.

[0081] S22: The safe driving distance of an autonomous vehicle is expressed as follows:

[0082]

[0083] Among them, d i,brake (k0 is the braking distance of vehicle i at time k; is the reaction distance of vehicle i at time k; v i (k0 is the speed of vehicle i at time k; a d is the emergency braking deceleration adopted by the vehicle anti-collision warning system; l is the vehicle length; The response delay of the self-driving car.

[0084] S23: The safe driving distance of a connected automated vehicle is expressed as follows:

[0085]

[0086] Among them, d i,brake (k) is the braking distance of vehicle i at time k; is the reaction distance of vehicle i at time k; v i (k) is the speed of vehicle i at time k; a d is the emergency braking deceleration adopted by the vehicle anti-collision warning system; l is the vehicle length; The response delay of connected autonomous vehicles.

[0087] S3: Establish a traditional human-driving following model based on cellular automata, as well as a collaborative control model for autonomous vehicles and connected autonomous vehicles based on variable vehicle spacing.

[0088] Among them, for traditional human driving, an improved cellular automaton model is established considering the impact of the driver's aggressiveness and collision warning on the vehicle; for autonomous driving vehicles, a variable vehicle distance control model based on the intelligent driver model is established; for connected autonomous vehicles, a variable vehicle distance control model based on the collaborative adaptive cruise control model is established.

[0089] S31: The evolutionary rules of the traditional human-driven car-following model are as follows:

[0090] Rule 1: Rule 1 describes the acceleration process. To improve traffic efficiency, the driver tends to accelerate to the maximum speed. A driver aggressiveness coefficient is introduced to reflect the impact of driver aggressiveness on this process. When the leading vehicle's speed is greater than the ego vehicle's, the larger the driver aggressiveness coefficient, the more aggressive the driver's tendency to accelerate based on the speed difference between the ego vehicle and the leading vehicle. Furthermore, given the discrete nature of the cellular automaton model, the speed value is rounded down.

[0091]

[0092] Among them, v i (k) is the speed of vehicle i at time k; v i-1 (k) is the speed of vehicle i-1 at time k; v i (k+Δk) is the speed of vehicle i at time k+Δk; Δv i is the speed difference between the preceding vehicle and vehicle i; v max is the maximum speed of vehicle i; σ is the driver's aggressiveness coefficient, which ranges from 0 to 1; Δk is the simulation time step.

[0093] Rule 2: Rule 2 is a random slowing process. The vehicle will slow down randomly with a probability ρ to reflect the impact of various environmental factors on the vehicle's driving process.

[0094] v i (k+Δk)=max(v i (k)-1,0),if:P≤ρ

[0095] Where P is the probability of vehicle deceleration; Δk is the simulation time step.

[0096] Rule 3: Rule 3 describes the collision warning process. To avoid a collision, the collision warning system triggers emergency braking when the distance between the ego vehicle and the preceding vehicle falls below τ2. Furthermore, given the discrete nature of the cellular automation model, the speed is rounded down.

[0097]

[0098] in, is the distance between vehicle i and the preceding vehicle at time k; τ2 is the proportional coefficient of the second-level driving safety distance; τ3 is the proportional coefficient of the third-level driving safety distance; is the safe driving distance of a traditional driver at time k; Secondary warning deceleration for traditional human driving collision warning system, a d is the emergency braking deceleration adopted by the vehicle collision warning system; Δk is the simulation time step.

[0099] Rule 4: Rule 4 is the process of updating the position. After obtaining the latest speed output by the first three rules, the position of the traditional human driver on the road is updated based on this.

[0100] X i (k+Δk)=X i (k)+v i (k+Δk)·Δk

[0101] Among them, X i (k) is the position of vehicle i at time k; X i (k+Δk) is the position of vehicle i at time k+Δk; Δk is the simulation time step.

[0102] S32: Since the autonomous vehicle does not have vehicle-to-vehicle communication, and considering the impact of the collision warning method on the vehicle, a control model for the variable vehicle distance of the autonomous vehicle based on the intelligent driver model is selected as follows:

[0103]

[0104] Among them, a max is the maximum acceleration of the vehicle; v i (k) is the speed of vehicle i at time k; v0 is the desired speed of the ego vehicle; δ is the acceleration index; λ is the variable inter-vehicle distance coefficient of heterogeneous vehicles. Since the different driving styles of heterogeneous vehicles will also affect the following behavior of the following vehicle, for example, traditional human driving is more prone to sudden changes in driving state (sudden acceleration, sudden deceleration, etc.) compared with other types of vehicles, the following vehicle needs to consider the impact of the type of the leading vehicle on the inter-vehicle distance, so this coefficient is introduced. The specific value of the coefficient varies according to the type of the leading vehicle; Δv i is the speed difference between the vehicle and the preceding vehicle; is the distance between vehicle i and the preceding vehicle at time k; is the safe driving distance of vehicle i at time k; d * (v i (k),Δv i ) is the expected following distance; a d is the emergency braking deceleration adopted by the vehicle-mounted anti-collision warning system; τ2 is the proportional coefficient of the second-level driving safety distance; τ3 is the proportional coefficient of the third-level driving safety distance; is the reaction delay of the autonomous vehicle; b is the comfortable deceleration.

[0105] S33: Because connected automated vehicles (CAVs) have vehicle-to-vehicle communication capabilities, they can use sensors to obtain status information from the preceding vehicle while simultaneously communicating with the preceding vehicle and the leading vehicle. This creates a communication topology. To describe this interconnection and communication between CAVs, a leading-leader-following communication topology is selected and a cooperative control model with variable inter-vehicle spacing is established based on the cooperative adaptive cruise control model as follows:

[0106]

[0107]

[0108] Where e is the error between the distance between the vehicle and the preceding vehicle and the desired following distance; is the distance between vehicle i and the preceding vehicle at time k; is the safe driving distance of vehicle i at time k; λ is the variable vehicle spacing coefficient of heterogeneous vehicles; T c is the expected safe headway distance; v j (k) is the speed of vehicle j in front of vehicle i at time k; k c 、k d is the control coefficient; Δt is the control interval.

[0109] S4: Determine the actual distance between vehicles Safe driving distance from vehicles If the collision warning triggering conditions are met, different collision warning behaviors will be triggered according to different levels.

[0110] S41: Determine the vehicle type and the distance between vehicle i and the preceding vehicle at time k based on the vehicle status information obtained in S1

[0111] Since the degree of automation of heterogeneous vehicles varies, the method for obtaining the inter-vehicle distance varies according to the vehicle type. The specific methods are as follows:

[0112] Traditional human driving obtains the driving status information of the preceding vehicle through driver perception, so the distance between the traditional human driving i and the preceding vehicle at time k is It is an estimated value obtained by the driver's vision.

[0113] The autonomous vehicle can obtain the driving status information of the vehicle in front of it through the onboard sensors, so the distance between the autonomous vehicle i and the vehicle in front at time k is It is calculated based on the driving status information of the vehicle and the preceding vehicle obtained by the on-board sensors. The calculation formula is as follows:

[0114]

[0115] in, is the position of vehicle i at time k; is the position of vehicle i-1 at time k:

[0116]

[0117] The connected autonomous vehicle can obtain the driving status information of the preceding vehicle and the same type of vehicles in front in real time by relying on the onboard sensors, roadside sensors and vehicle communication technology. Therefore, the distance between the connected autonomous vehicle i and the preceding vehicle at time k is It is calculated by integrating the driving status information of the vehicle and the preceding vehicle obtained in real time by on-board sensors, roadside sensors, and vehicle communication technology:

[0118]

[0119] in, is the position of vehicle i at time k; is the position of vehicle j at time k:

[0120]

[0121] S42: The distance between vehicle i and the preceding vehicle at time k is The safe driving distance from the vehicle calculated in S2 Compare and get the ratio to determine the vehicle collision warning level. The formula is as follows:

[0122]

[0123] in, is the distance between vehicle i and the preceding vehicle at time k; τ1 is the first-level driving safety distance proportional coefficient; τ2 is the second-level driving safety distance proportional coefficient; τ3 is the third-level driving safety distance proportional coefficient; is the safe driving distance of vehicle i at time k, Determined by the following formula;

[0124]

[0125] S43: Different warning actions are taken according to the collision warning level triggered by the vehicle to ensure driving safety. The specific warning actions are as follows:

[0126] Level 1 collision warning: Level 1 collision warning is the lowest level collision warning, which is mainly used to remind the driver of the possible collision risk. When the distance between vehicle i and the vehicle in front is at time k, Safe distance from vehicles The ratio between At this time, the vehicle collision warning system will issue various alarms to the driver, including vehicle radar alarms and vehicle central control picture alarms, as a first-level warning alarm to remind the driver of the collision risk. The specific alarm form varies depending on the vehicle.

[0127] At this time, the position and velocity of vehicle i at time k+Δk can be described as:

[0128]

[0129] in, They represent the position, velocity and acceleration of vehicle i at time k respectively; They represent the position and velocity of vehicle i at time k+Δk respectively; Δk is the simulation time step.

[0130] Second level collision warning: When vehicle i is at the distance from the preceding vehicle at time k Safe distance from vehicles The ratio between The second level collision warning will be triggered when the collision occurs. At this time, the vehicle collision warning system will send various warnings including vehicle radar warnings and vehicle central control picture warnings to the driver as second level warnings to remind the driver of the collision risk. The specific warning form varies depending on the vehicle. At the same time, the vehicle collision warning system will also Braking is performed until the vehicle is released from the second level collision warning or the third level collision warning is triggered. The calculation formula is as follows:

[0131]

[0132] in, is the distance between vehicle i and the preceding vehicle at time k; τ3 is the third-level driving safety distance proportional coefficient; is the safe driving distance of the vehicle at time k; a d This is the emergency braking deceleration adopted by the vehicle's anti-collision warning system.

[0133] At this time, the position and velocity of vehicle i at time k+Δk can be described as:

[0134]

[0135] in, They represent the position, velocity and acceleration of vehicle i at time k respectively; They represent the position and velocity of vehicle i at time k+Δk respectively; Δk is the simulation time step.

[0136] Level 3 collision warning: Level 3 collision warning is the highest level of collision warning. When the distance between vehicle i and the preceding vehicle is Safe distance from vehicles The ratio between At this time, the vehicle collision warning system will send various warnings to the driver, including vehicle radar warnings and vehicle central control picture warnings, as three-level warnings to remind the driver of the risk of collision. The specific warning form varies depending on the vehicle. At the same time, the vehicle collision warning system will also use emergency braking deceleration a d Apply emergency braking until the vehicle is released from the third level collision warning.

[0137] At this time, the position and velocity of vehicle i at time k+Δk can be described as:

[0138]

[0139] in, They represent the position, velocity and acceleration of vehicle i at time k respectively; They represent the position and velocity of vehicle i at time k+Δk respectively; Δk is the simulation time step.

[0140] by Figure 3 For example, vehicle i is a connected autonomous vehicle, the preceding vehicle i-1 is a traditional human-driven vehicle, and the leading vehicle is also a connected autonomous vehicle. Then, according to the formula Calculating safe driving distance for connected autonomous vehicles Based on the cooperative adaptive cruise control model, a variable vehicle spacing cooperative control model is established and the vehicle spacing coefficient value is determined according to the type of the preceding vehicle; then the actual spacing is determined. Safe driving distance Since the connected automatic vehicle can obtain the driving status information of the preceding vehicle and the same type of vehicles in front in real time by relying on the onboard sensors, roadside sensors and vehicle communication technology, the distance between vehicle i and the preceding vehicle at time k is It is calculated by integrating the driving status information of the vehicle and the preceding vehicle obtained in real time by on-board sensors, roadside sensors, and vehicle communication technology. The calculation formula is as follows:

[0141]

[0142] in is the position of vehicle i at time k; is the position of the lead vehicle at time k.

[0143] Finally, different collision warning behaviors are triggered at different levels according to the collision warning triggering conditions. The specific multi-level warning strategy can be referred to Table 1:

[0144] Table 1

[0145]

[0146] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A mixed vehicle collision warning method based on variable vehicle spacing, characterized in that: The method includes: Set up a mixed traffic scenario including traditional human driving, self-driving cars, and connected autonomous vehicles, obtain the current vehicle status information of the vehicle and the preceding vehicle; calculate the safe driving distance of different types of vehicles; Establish a traditional human-driven car-following model based on cellular automata, a variable vehicle-to-vehicle distance control model based on an intelligent driver model, and a variable vehicle-to-vehicle distance control model based on a collaborative adaptive cruise control model; use these established models to control traditional human-driven vehicles, autonomous vehicles, and connected autonomous vehicles. Obtain the actual distance between the current vehicle and the vehicle in front, determine the ratio between the actual distance and the current vehicle's safe driving distance, and trigger different collision warning behaviors according to different levels if the collision warning trigger conditions are met.

2. The method according to claim 1, characterized in that For traditional driving, the safe driving distance is expressed as: Where, d i,brake (k) is the braking distance of vehicle i at time k; is the reaction distance of vehicle i at time k; v i (k) is the speed of vehicle i at time k; a d is the emergency braking deceleration; l is the vehicle length; Reaction delay for traditional people driving; For autonomous vehicles, the safe driving distance is expressed as: Where, is the reaction distance of vehicle i at time k; Response delay for self-driving cars; For connected autonomous vehicles, the safe driving distance is expressed as: Where, is the reaction distance of vehicle i at time k; The response delay of connected autonomous vehicles.

3. The method according to claim 1, characterized in that For traditional human driving, considering the impact of driver aggressiveness and collision warning on the vehicle, a traditional human driving following model based on cellular automation is established. The model follows the following evolution rules: Rule 1: The driver tends to accelerate to the maximum speed. When the speed of the preceding vehicle is greater than that of the self-driving vehicle, the greater the driver's aggressiveness coefficient, the more likely the driver is to accelerate more aggressively based on the speed difference between the self-driving vehicle and the preceding vehicle. The speed of a traditional driver at a future time in Rule 1 is expressed as: Where, v i (k) is the speed of vehicle i at time k; v i-1 (k) is the speed of vehicle i-1 at time k; v i (k+Δk) is the speed of vehicle i at time k+Δk; Δv i is the speed difference between the preceding vehicle and vehicle i; v max is the maximum speed of vehicle i; σ is the driver's aggressiveness coefficient; Δk is the simulation time step; the speed value is rounded down; Rule 2: The vehicle will randomly slow down with a probability of ρ, which is used to characterize the impact of environmental factors on the vehicle's driving process. In Rule 2, the speed of a traditional driver at a future moment is expressed as: v i (k+Δk)=max(v i (k)-1,0),if:P≤ρ Where P is the probability of vehicle deceleration; Rule 3: When the ratio of the distance between the vehicle and the preceding vehicle is less than τ2, emergency braking will be triggered. In Rule 3, the speed of a traditional driver at the future moment is expressed as: Where, is the distance between vehicle i and the preceding vehicle at time k; τ2 is the proportional coefficient of the second-level driving safety distance; τ3 is the proportional coefficient of the third-level driving safety distance; is the safe driving distance of a traditional driver at time k; For traditional people driving the second level warning deceleration, a d is the emergency braking deceleration; the speed value is rounded down; Rule 4: Update the position of the traditional human driver on the road based on the speed output from Rules 1 to 3: X i (k+Δk)=X i (k)+v i (k+Δk)·Δk Among them, X i (k) is the position of vehicle i at time k; X i (k+Δk) is the position of vehicle i at time k+Δk.

4. The method according to claim 1, wherein The variable vehicle spacing control model based on the intelligent driver model is expressed as: Where a max is the maximum acceleration of the vehicle; v i (k) is the speed of vehicle i at time k; v0 is the desired speed of the vehicle; δ is the acceleration index; λ is the variable vehicle spacing coefficient of heterogeneous vehicles, and the value of λ is determined by the type of the preceding vehicle; Δv i is the speed difference between the vehicle and the preceding vehicle; is the distance between vehicle i and the preceding vehicle at time k; is the safe driving distance of vehicle i at time k; d * (v i (k),Δv i ) is the expected following distance; a d is the emergency braking deceleration; τ2 is the second-level driving safety distance proportional coefficient; τ3 is the third-level driving safety distance proportional coefficient; is the reaction delay of the autonomous vehicle; b is the comfortable deceleration.

5. The method according to claim 1, wherein The variable vehicle distance control model based on the cooperative adaptive cruise control model is expressed as: Where, e is the error between the distance between the preceding vehicle and the preceding vehicle and the desired following distance; is the distance between vehicle i and the preceding vehicle at time k; is the safe driving distance of vehicle i at time k; λ is the variable vehicle spacing coefficient of heterogeneous vehicles, and the value of λ is determined according to the type of the preceding vehicle; T c is the expected safe headway distance; v i (k) is the speed of vehicle i at time k; v j (k) is the speed of vehicle j in front of vehicle i at time k; k c 、k d is the control coefficient; Δt is the control interval; τ2 is the proportional coefficient of the second-level driving safety distance; τ3 is the proportional coefficient of the third-level driving safety distance; a d This is the deceleration for emergency braking.

6. The method according to claim 1, characterized in that Get the actual distance between the current vehicle and the preceding vehicle. For traditional human driving, the driving status information of the preceding vehicle is obtained through driver perception. The distance between the preceding vehicle and the preceding vehicle at time k is is the estimated value obtained by the driver's vision; For an autonomous vehicle, the vehicle relies on the onboard sensor to obtain the driving status information of the preceding vehicle. The distance between the autonomous vehicle i and the preceding vehicle at time k is The driving status information of the vehicle and the preceding vehicle obtained by the on-board sensors is calculated as follows: Where, is the position of vehicle i at time k; is the position of vehicle i-1 at time k, Expressed as: For connected autonomous vehicles, the vehicle-mounted sensors, roadside sensors, and vehicle communication technology are used to obtain real-time driving status information of the preceding vehicle and the same type of vehicles in front. The distance between connected autonomous vehicle i and the preceding vehicle at time k is The following is calculated by integrating the driving status information of the vehicle and the preceding vehicle obtained in real time by integrating on-board sensors, roadside sensors, and vehicle communication technology: Where, is the position of vehicle i at time k; is the position of vehicle j at time k, Expressed as:

7. The method according to claim 6, characterized in that The distance between vehicle i and the preceding vehicle at time k is Safe distance from vehicles Compare and judge the vehicle collision warning level as shown in the following formula: Wherein, τ1 is the first-level driving safety distance proportional coefficient; τ2 is the second-level driving safety distance proportional coefficient; τ3 is the third-level driving safety distance proportional coefficient; Determined by vehicle i type: Determined by the following formula; Where, The safe driving distance for traditional people driving i, is the safe driving distance of the autonomous vehicle i, is the safe driving distance of the connected autonomous vehicle i; When the first level collision warning is triggered, a first level warning alarm is issued to the driver to remind the driver of the collision risk, and vehicle i is controlled through the established control model. The position and velocity of vehicle i at time k+Δk are expressed as: Where, They represent the position, velocity and acceleration of vehicle i at time k respectively; They represent the position and speed of vehicle i at time k+Δk respectively; Δk is the simulation time step; where, and Calculated by the control model of the vehicle type corresponding to vehicle i; When the second level collision warning is triggered, a second level warning alarm is issued to the driver to remind the driver of the collision risk, and the vehicle is Braking is performed until the vehicle releases the second-level collision warning or triggers the third-level collision warning; the vehicle acceleration during the second-level collision warning Calculated by the following formula: Where, is the distance between vehicle i and the preceding vehicle at time k; is the safe driving distance of vehicle i at time k; a d is the emergency braking deceleration; vehicle i is controlled by the established control model, and the position and velocity of vehicle i at time k+Δk are expressed as: in, Calculated by the control model of the vehicle type corresponding to vehicle i; When the third level collision warning is triggered, a third level warning alarm is issued to the driver to remind the driver of the collision risk, and the vehicle is decelerated by emergency braking. d Emergency braking is performed until the vehicle is released from the third-level collision warning. Vehicle i is controlled by the established control model. The position and velocity of vehicle i at time k+Δk are expressed as: in, Calculated by the control model of the vehicle type corresponding to vehicle i.

Images