An active collision avoidance method based on multi-risk fusion potential field and vehicle-mounted electronic equipment

Abstract

The present invention discloses an active collision avoidance method and vehicle-mounted electronic equipment based on a multi-level risk fusion potential field, comprising the following steps: 1: obtaining information about the vehicle and the surrounding environment; 2: predicting the driving trajectory of the vehicle and other vehicles; 3: judging the positional relationship between the trajectories of the vehicle and other vehicles; 4: if the trajectories intersect, calculating the intersection point in a fixed coordinate system; if the trajectories are parallel and there is a possibility of collision, calculating the rear-end collision point; 5: designing and constructing a risk domain of lane boundaries, static obstacles, dynamic vehicles, estimated trajectory intersection points and collision points in the surrounding environment; 6: performing real-time curvature constraints on the collision avoidance path, and planning a virtual force range within a small time period; 7: determining the optimal virtual force within the current time period based on the principle of low driving risk, and planning a real-time optimal local path; 8: obtaining continuous collision avoidance trajectories based on the optimal virtual forces planned in real time in different time periods; 9: repeating the above steps until the vehicle reaches a safe position.

Description

Technical Field

[0001] The present invention relates to an active collision avoidance method based on a multi-risk fusion potential field and corresponding vehicle-mounted electronic equipment, belonging to the field of intelligent driving safety technology. Background Art

[0002] In recent years, with China's rapid economic growth, the number of vehicles on the road has increased rapidly. Therefore, quantifying and assessing vehicle driving risks is a crucial prerequisite for ensuring safe driving. Existing active collision avoidance algorithms do not fully consider road risk factors and fail to adequately consider the safety of collision avoidance paths. The rationality of local path planning methods determines whether a vehicle can operate efficiently, comfortably, and safely during lane changes.

[0003] In the local path planning process of intelligent vehicles, the path curvature obtained by trajectory planning using polynomials is bounded and continuous, meeting the general constraints of vehicle dynamics. Its advantages lie in its flexibility and adaptability. However, for most currently studied polynomial lane-changing trajectories, their planning layer requires advance knowledge of the vehicle's lane-changing starting and target state parameters. This limits the ideal vehicle trajectory to a mathematical expression, and the prerequisites required to solve this expression are complex. The lane-changing time in the expression of such a trajectory is obtained based on experience and has significant uncertainty. Furthermore, such a trajectory is more suitable for situations where the longitudinal vehicle speed remains constant, and its adaptability and maneuverability in steering and braking conditions are poor.

[0004] The traditional artificial potential field method uses various on-board sensors to detect various obstacles in the environment, attaching a repulsive potential field corresponding to each obstacle, and setting a target point to provide a gravitational potential field. The vehicle achieves collision avoidance through the combined effects of gravity and repulsion. However, its shortcomings are also very obvious: the path planned using the traditional artificial potential field method is prone to local minima and is prone to local minima. When unexpected external obstacles are detected, the planned path may oscillate and experience dangerous phenomena. Furthermore, the traditional artificial potential field method requires the introduction of a target point to provide virtual gravity, but the actual driving process is complex, and the ideal target gravitational point position cannot be determined at the beginning of the lane change.

[0005] The ant colony algorithm is also a commonly used path optimization algorithm. Ants with shorter paths release more pheromones. Over time, the accumulated pheromone concentration on shorter paths gradually increases, and the number of ants choosing that path also increases. Ultimately, through continuous positive feedback, the optimal path is determined. However, excessive diversity in the ant colony can lead to excessive random movements, causing the colony to fall into a chaotic state and unable to find the optimal solution. Insufficient diversity can lead to low search accuracy, making the colony unable to adjust to environmental changes, and the planned path less adaptable to dynamic conditions.

[0006] In summary, current active collision avoidance methods have poor real-time performance and fail to account for dangerous instability conditions that may occur during the active collision avoidance process. This invention addresses these two deficiencies and proposes a new active collision avoidance method. Summary of the Invention

[0007] This invention aims to avoid dangerous driving conditions during vehicle operation to improve driving safety. The multivariate risk potential field model proposed by the invention can predict possible future dangerous intersections or collision points and include them in the risk source category, while also considering other different types of risk sources on the road. It also considers skidding and rollover phenomena, incorporates curvature constraints into driving safety, and uses this as a basis to plan real-time virtual forces to achieve collision avoidance. The specific solution is as follows:

[0008] An active collision avoidance method based on a multi-risk fusion potential field comprises the following steps:

[0009] Step 1: Obtain information about the vehicle's own driving parameters and surrounding environment;

[0010] Step 2: Predict and estimate the trajectories of the vehicle and other vehicles in the surrounding environment;

[0011] Step 3: Determine the trajectory position relationship between the vehicle and other vehicles;

[0012] Step 4: If the trajectories intersect, an intersection point is generated and the location of the intersection point in the current coordinate system is calculated. If the trajectories are parallel, first determine whether a collision is possible. If a collision is possible, calculate the location of the rear-end collision point;

[0013] Step 5: Use the artificial potential field method to construct the risk domains of lane boundaries, static obstacles, dynamic vehicles, estimated trajectory intersections, and collision points in the surrounding environment;

[0014] Step 6: Considering the dangerous conditions of the ego vehicle potentially skidding and rolling over when avoiding risks, a curvature constraint is imposed on the collision avoidance path.

[0015] Step 7: Based on the principle of low driving risk and curvature constraint, plan the optimal virtual force and path in the previous time period;

[0016] Step 8: The electronic control unit tracks the local path planned in step 7;

[0017] Step 9: Repeat the above steps until the vehicle reaches a safe position, achieving active safety collision avoidance.

[0018] Furthermore, the vehicle driving parameters and surrounding environment information in step 1 are acquired via GPS and vehicle body sensors. The vehicle's driving parameters include its position, speed, acceleration, and current yaw angle. Environmental information includes the position, speed, acceleration, and current yaw angle of other vehicles, lane boundary information, and static obstacle information.

[0019] Furthermore, the trajectories of the vehicle and the other vehicle described in step 2 are expressed as follows:

[0020]

[0021] Among them, (x self ,y self ) is the vehicle position coordinate, (x veh ,y veh ) is the position coordinate of the other car. θ self is the current yaw angle information of the vehicle, P veh is the distance between the current vehicle and the origin of the coordinate system, θ veh It is the current yaw angle information of the vehicle.

[0022] Furthermore, the method for determining the trajectory position relationship between the vehicle and other vehicles in step 3 is as follows:

[0023]

[0024] Furthermore, if the trajectories are determined to intersect in step 4, an intersection point will be generated. By combining the trajectory equations of the self-vehicle and the other vehicle in a fixed coordinate system, the specific location of the intersection point (x0, y0) can be solved. The solution equation is as follows:

[0025]

[0026] Furthermore, if the trajectories are determined to be parallel in step 4, then we first predict whether the two vehicles are likely to collide. The specific method is as follows:

[0027]

[0028] Among them, d self_veh is the distance between the vehicle and other vehicles. Let D safeis the safe distance between the vehicle and other vehicles, when d self_veh ≥D safe When d self_veh <D safe When , there is a possibility of collision. We can judge whether a collision will occur by determining the characteristics of the solution of the equation. The equation is as follows:

[0029] (v veh0 t+a veh t 2 / 2)-(v self0 +a self t 2 / 2)-L=0

[0030] Among them, v self0 , v veh0 where x1 and y1 are the initial speeds of the ego and other vehicles, respectively. L is the axle length of a typical car, ranging from 2.4 to 2.8 meters. If the equation has no solution, the two vehicles will not collide, and the vehicles will travel safely. If the equation has a solution, the specific location of the collision point (x1, y1) must be calculated. The calculation method is as follows:

[0031] (v veh0 t ac +a veh t ac 2 / 2)-(v self0 +a self t ac 2 / 2)-L=0

[0032]

[0033] Among them, a self , a veh are the accelerations of the vehicle and the other vehicle, t ac is the time when the collision occurs, which is obtained by solving the above quadratic equation.

[0034] Furthermore, different types of risk domains are constructed in step 5, as follows:

[0035] (1) Lane boundary risk area:

[0036]

[0037] Wherein, w is the lane width, which is set as 3.75m according to national standards. lane is the lane risk factor, U lane is the lane boundary risk domain.

[0038] (2) Static obstacle risk area:

[0039]

[0040] Among them, (x st ,y st ) is the position coordinate of the static obstacle in the fixed coordinate system, k st is the static risk coefficient, with a reference value range of 0.1 to 10. ζ is a smaller coefficient to prevent the denominator from being 0.

[0041] (3) Dynamic obstacle risk area:

[0042]

[0043] Among them, v obs is the longitudinal velocity of the car ahead, v rel is the absolute value of the longitudinal relative velocity between the vehicle and the vehicle in front, k mov is the dynamic risk coefficient, with a reference value range of 0.05 to 0.5, σ mov It is the risk range coefficient, and the reference value range is 1 to 6.

[0044] (4) Predicting the risk domain of intersection or collision points:

[0045]

[0046] Among them, (x risk ,y risk ) is the predicted intersection or collision point location, k risk is the risk point coefficient, the reference value is 0.3, σ risk It is the risk point impact coefficient, and the reference value range is 1.2 to 6.

[0047] Furthermore, step 6 considers the dangerous conditions of the ego vehicle potentially skidding and rolling over when avoiding risks, and imposes real-time constraints on the continuously changing steering curvature during the ego vehicle's lane change. The specific process is as follows:

[0048]

[0049] Among them, v ch , v cf are the critical speeds when the vehicle turns and skids or rolls over, μ is the road adhesion coefficient, R is the turning radius, g is the acceleration of gravity, and H is the height of the vehicle's center of mass from the ground. max , we can get the critical collision avoidance path curvature κ at the current moment max , calculated as follows:

[0050] v max =min{v ch ,v cf}

[0051]

[0052] Assuming that the current time is t, the virtual force F generated in the small time period [t, t+△t] is t Expressed as:

[0053]

[0054] Among them, k c is the virtual coefficient for collision avoidance, with a reference value range of 1 to 30, κ i To generate different path curvatures, F max is the critical road curvature κ max The corresponding maximum virtual force.

[0055] Furthermore, in step 7, according to the principle of low driving risk and curvature constraint, the virtual force FL corresponding to the local path with the minimum total risk is selected. t For the optimal virtual force within this time period, a real-time optimal local path is planned.

[0056] Furthermore, in step 8, the electronic control unit performs trajectory tracking according to the path planned in step 7 to achieve active and safe collision avoidance.

[0057] The present invention also provides an on-vehicle electronic device, which can execute the above-mentioned active collision avoidance method when in operation.

[0058] Beneficial effects of the present invention:

[0059] (1) The multivariate risk fusion potential field model proposed in the present invention can take into account static obstacles, dynamic obstacles, lane boundary constraints and predicted risk points around the road surface, establish a suitable multivariate risk potential field model, and fully consider the safety hazards during vehicle driving.

[0060] (2) The present invention takes into account the dangerous conditions in which the vehicle may skid or roll over when avoiding risks, and imposes a maximum curvature constraint on the collision avoidance path, which can effectively avoid the occurrence of road traffic accidents.

[0061] (3) The present invention plans a real-time collision avoidance path based on the principle of low driving risk and curvature constraint, which can avoid different types of risk sources on the road. At the same time, it can avoid dangerous working conditions and improve driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a flow chart of the method of the present invention

[0063] Figure 2 This is a schematic diagram of predicted intersection risk

[0064] Figure 3 This is a schematic diagram of the predicted collision point risk

[0065] Figure 4 Schematic diagram of the surrounding environment risk area DETAILED DESCRIPTION

[0066] The present invention proposes an active collision avoidance method based on a multivariate risk fusion potential field, comprising the following steps: 1: obtaining information about the vehicle and its surroundings; 2: predicting and estimating the driving trajectories of the vehicle and other vehicles in the surroundings; 3: determining the positional relationship between the trajectories of the vehicle and other vehicles; and 4: if the trajectories intersect, an intersection point is generated, and the position of the intersection point in a fixed coordinate system is calculated. If the trajectories are parallel, first determine whether there is a possibility of collision. If there is a possibility of collision, calculate the position of the rear-end collision point in the fixed coordinate system; 5: Use the artificial potential field method to construct the risk domain of lane boundaries, static obstacles, dynamic vehicles, estimated trajectory intersections and collision points in the surrounding environment respectively; 6: Considering the dangerous conditions of skidding and rollover that may occur when the vehicle avoids risks, perform real-time curvature constraints on the collision avoidance path, and plan the virtual force range within a small time period; 7: According to the principle of low driving risk, determine the optimal virtual force in the current time period, and plan the real-time optimal local path; 8: Based on the optimal virtual force planned in real time in different time periods, a continuous collision avoidance trajectory can be obtained, and the collision avoidance is controlled by the electronic control unit; 9: Repeat the above steps until the vehicle reaches a safe position.

[0067] The present invention will be further described below with reference to the accompanying drawings.

[0068] The present invention provides an active collision avoidance method based on multi-risk fusion potential field, such as Figure 1 As shown, it includes the following steps:

[0069] Step 1: Obtain the vehicle's driving parameters and surrounding environment information.

[0070] The vehicle's driving parameters and surrounding environment information are acquired through GPS and vehicle body sensors. These parameters include its position, speed, acceleration, and current yaw angle. Environmental information includes the position, speed, acceleration, and current yaw angle of other vehicles, lane boundaries, and static obstacle information.

[0071] Step 2: Predict the trajectories of the ego vehicle and other vehicles.

[0072] The driving characteristics of the vehicle are: I self (t)={v self ,a self ,θ self}, where I self(t) represents the vehicle information at time t, v self , a self ,θ self are the vehicle's velocity information, vehicle's acceleration information, and current yaw angle information, respectively. Assuming the initial position of the vehicle's center of mass as the coordinate origin, a fixed coordinate system is established. Assuming all vehicles on the road are moving at a uniformly accelerated speed, the vehicle's trajectory equation is as follows:

[0073] y self =x self tanθ self

[0074] Where (x self ,y self ) represents the vehicle position coordinate information at time t, θ self is the current yaw angle information of the vehicle.

[0075] The predicted trajectory of other vehicles is characterized by: veh (t) = {P veh ,v veh ,a veh ,θ veh}, where I veh (t) represents the information of other vehicles at time t, P veh is the distance between the vehicle and the origin of the coordinate system at the current moment, v veh , a veh ,θ veh are the other car's speed information, acceleration information, and current yaw angle information. In the coordinate system, the other car's trajectory equation is as follows:

[0076] y veh =tanθ veh (x veh -P veh cosθ veh )+P veh sinθ veh

[0077] Among them, (x veh ,y veh ) is the position coordinate of his car, P veh is the distance between the current vehicle and the origin of the coordinate system, θ veh It is the current yaw angle information of the vehicle.

[0078] Step 3: Predict whether there will be a danger point between the vehicle and other vehicles. If so, calculate the specific location of the point.

[0079] Determine the trajectory position relationship between the vehicle and other vehicles.

[0080]

[0081] Step 4: Predict the possible trajectory intersection points and rear-end collision points in the current coordinate system based on the position relationship.

[0082] If the trajectories are determined to intersect, an intersection will be generated. By combining the trajectory equations of the self-vehicle and the other vehicle in a fixed coordinate system, the specific location of the intersection (x0, y0) can be solved. The predicted risk intersection is as follows: Figure 2 As shown, the equation to be solved is as follows:

[0083]

[0084] If the trajectories are determined to be parallel, then first predict whether the two vehicles are likely to collide. The specific method is as follows:

[0085]

[0086] Among them, d self_veh is the distance between the vehicle and other vehicles. Let D safe is the safe distance between the vehicle and other vehicles, when d self_veh ≥D safe When d self_veh <D safe When , there is a possibility of collision. We can judge whether a collision will occur by determining the characteristics of the solution of the equation. The equation is as follows:

[0087] (v veh0 t+a veh t 2 / 2)-(v self0 +a self t 2 / 2)-L=0

[0088] Among them, v self0 , v veh0 are the initial speeds of the self-vehicle and the other vehicle, respectively. L is the axle length of a common car, which is 2.4 to 2.8 meters. When the equation has no solution, the two vehicles will not have a collision point and the vehicles will drive safely. When the equation has a solution, the specific location of the collision point (x1, y1) needs to be calculated. The predicted risk collision point is as follows: Figure 3 As shown, the calculation method is as follows:

[0089] (v veh0 t ac +a veh t ac 2 / 2)-(v self0 +a self t ac 2 / 2)-L=0

[0090]

[0091] Among them, a self , a veh are the accelerations of the vehicle and the other vehicle, t ac is the time when the collision occurs, which is obtained by solving the above quadratic equation.

[0092] Step 5: Construct risk domain based on environmental information.

[0093] (1) Lane boundary risk area:

[0094]

[0095] Wherein, w is the lane width, which is set as 3.75m according to national standards. lane is the lane risk factor, U lane is the lane boundary risk domain.

[0096] (2) Static obstacle risk domain U st :

[0097]

[0098] Among them, (x st ,y st ) is the position coordinate of the static obstacle in the fixed coordinate system, k st is the static risk coefficient, with a reference value range of 0.1 to 10. ζ is a smaller coefficient to prevent the denominator from being 0.

[0099] (3) Dynamic obstacle risk domain U mov :

[0100]

[0101] Among them, v obs is the longitudinal velocity of the car ahead, v rel is the absolute value of the longitudinal relative velocity between the vehicle and the vehicle in front, k mov is the dynamic risk coefficient, with a reference value range of 0.05 to 0.5, σ mov It is the risk range coefficient, and the reference value range is 1 to 6.

[0102] (4) Predict the intersection or collision risk domain U risk :

[0103]

[0104] Among them, (x risk ,y risk ) is the position coordinate of the predicted intersection or collision point, krisk is the risk point coefficient, the reference value is 0.3, σ risk It is the risk point impact coefficient, and the reference value range is 1.2 to 6.

[0105] The specific construction of environmental risk domain is as follows: Figure 4 shown.

[0106] Step 6: Consider dangerous road conditions and make real-time curvature constraints for collision avoidance path planning.

[0107] The specific process is as follows:

[0108]

[0109] Among them, v ch , v cf are the critical speeds when the vehicle turns and skids or rolls over, μ is the road adhesion coefficient, R is the turning radius, g is the acceleration of gravity, and H is the height of the vehicle's center of mass from the ground. max , we can get the critical collision avoidance path curvature κ at the current moment max , calculated as follows:

[0110] v max =min{v ch ,v cf}

[0111]

[0112] Assuming the current time is t, the virtual force F that can be generated in the small time period [t, t+△t] is t It can be expressed as:

[0113]

[0114] Among them, k c is the virtual coefficient for collision avoidance, with a reference value range of 1 to 30, κ i To generate different path curvatures, F max is the critical road curvature κ max The corresponding maximum virtual force.

[0115] Step 7: According to the principle of low driving risk and curvature constraint, select the virtual force FL corresponding to the local path with the minimum total risk t For the optimal virtual force within this time period, the real-time optimal local path is planned.

[0116] The total risk of the vehicle at time t is:

[0117] U t =U lane +U st +Umov +U risk

[0118] According to the virtual force F t ∈(0,F max ], we can predict the local collision avoidance paths generated by different virtual forces within a small time period [t, t+△t]. These local collision avoidance paths L k The total risk UL k It can be expressed as:

[0119]

[0120] Among them U k It represents the risk of the vehicle under the action of the kth virtual force at a certain moment.

[0121] Select the virtual force FL corresponding to the local path with the minimum total risk t is the optimal virtual force within this time period, so that the optimal local collision avoidance path can be planned.

[0122] The local optimal virtual force changes continuously. Since the constructed risk potential field is continuously changing, the change in the risk potential field within the small time interval [t, t+Δt] is very small. Consequently, the change in the optimal virtual force is also very small. This allows for a continuous virtual force throughout the entire collision avoidance process, resulting in an ideal, smooth trajectory that avoids different types of risk sources along the lane. Furthermore, in inclement weather such as ice and snow, this trajectory prevents the vehicle from skidding and rolling during cornering. This method can effectively improve vehicle safety.

[0123] Step 8: The electronic control unit tracks the path planned in step 7 to achieve active and safe collision avoidance.

[0124] Step 9: Repeat the above steps until the vehicle reaches a safe location.

[0125] During actual operation, the above method can be executed in an electronic device.

[0126] The multivariate risk potential field model proposed in the present invention can predict dangerous intersections or collision points that may occur in the future and include them in the category of risk sources, while also taking into account other different types of risk sources on the road; considering the driving safety risks brought about by skidding and rollover phenomena, the curvature constraints are analyzed and calculated, and based on these two points, the real-time optimal collision avoidance path is planned, effectively realizing active collision avoidance and improving the driving safety of the vehicle.

[0127] The series of detailed descriptions listed above are only specific descriptions of feasible implementation methods of the present invention. They are not intended to limit the scope of protection of the present invention. Any equivalent methods or changes that do not deviate from the technology of the present invention should be included in the scope of protection of the present invention.

Claims

1. An active collision avoidance method based on multivariate risk fusion potential field, characterized in that: The steps include: S1: Obtain information about the vehicle's own driving parameters and surrounding environment; S2: Predict the trajectory of the vehicle and other vehicles in the surrounding environment; S3: Determine the trajectory position relationship between the vehicle and other vehicles; S4: If the trajectories intersect, an intersection point is generated and the position of the intersection point in the current coordinate system is calculated; if the trajectories are parallel, a collision is first determined. If a collision is possible, the position of the rear-end collision point is calculated; S5: Use the artificial potential field method to construct the risk domains of lane boundaries, static obstacles, dynamic obstacles, estimated trajectory intersections, and collision points in the surrounding environment; The risk domain of lane boundaries, static obstacles, dynamic obstacles, estimated trajectory intersections, and collision points in the surrounding environment is constructed in S5, which is specifically implemented as follows: (1) Lane boundary risk area: Where w is the lane width, k lane is the lane risk factor, U lane is the lane boundary risk domain; (2) Static obstacle risk area: Among them, (x st ,y st ) is the position coordinate of the static obstacle in the fixed coordinate system, (x self ,y self ) represents the vehicle position coordinate information at time t, v self represents the vehicle speed, U st represents the static obstacle risk domain, k st is the static risk coefficient, with a reference value range of 0.1 to 10, and ζ is a smaller coefficient to prevent the denominator from being 0; (3) Dynamic obstacle risk area: Among them, (x veh ,y veh ) is the position coordinate of his car, U mov represents the dynamic obstacle risk domain, v obs is the longitudinal velocity of the car ahead, v rel is the absolute value of the longitudinal relative velocity between the vehicle and the vehicle in front, k mov is the dynamic risk coefficient, with a reference value range of 0.05 to 0.5, σ mov is the risk range coefficient, with a reference value range of 1 to 6; (4) Predicting the risk domain of intersection or collision points: Among them, U risk Represents the predicted intersection or collision point risk domain, (x risk ,y risk ) is the predicted intersection or collision point location, k risk is the risk point coefficient, the reference value is 0.3, σ risk is the risk point impact coefficient, with a reference value range of 1.2 to 6; S6: Apply curvature constraints to the collision avoidance path; The curvature constraint of step S6 is as follows: Where L is the axle length of the car, ranging from 2.4 to 2.8 m, and v ch , v cf are the critical speeds when the vehicle skids and rolls over when turning, μ is the road adhesion coefficient, R is the turning radius, g is the acceleration of gravity, H is the height of the vehicle's center of mass from the ground, and the critical speed v is the critical speed when the vehicle skids and rolls over, μ is the road adhesion coefficient, R is the turning radius, g is the acceleration of gravity, H is the height of the vehicle's center of mass from the ground, and the critical speed v is the critical speed when the vehicle skids and roll max , we can get the critical collision avoidance path curvature κ at the current moment max , calculated as follows: v max =min{v ch ,v cf } Assuming that the current time is t, the virtual force F generated in the small time period [t, t+△t] is t Expressed as: Among them, △t represents the time period, k c is the virtual coefficient for collision avoidance, with a reference value range of 1 to 30, κ i To generate different path curvatures, F max is the critical road curvature κ max The corresponding maximum virtual force; S7: Based on the principle of low driving risk and curvature constraint, the optimal virtual force and path in the previous time period are planned; The method for selecting the optimal virtual force in S7 is as follows: The total risk of the vehicle at time t is: IN t =U lane +U st +U mov +U risk According to the virtual force F t ∈(0,F max ], we can predict the local collision avoidance paths generated by different virtual forces within a small time period [t, t+△t]. These local collision avoidance paths L k The total risk UL k It can be expressed as: Among them U k represents the risk of the vehicle under the kth virtual force at a certain moment, and △t represents the time period; Select the virtual force FL corresponding to the local path with the minimum total risk t is the optimal virtual force within this time period, so that the optimal local collision avoidance path can be planned; S8: Tracking according to the path planned in step 7; S9: Repeat the above steps until the vehicle reaches a safe position, achieving active and safe collision avoidance.

2. The active collision avoidance method based on multivariate risk fusion potential field according to claim 1, characterized in that: The information of the vehicle's own driving parameters and the surrounding environment in S1 includes: the vehicle's position information, vehicle speed information, vehicle acceleration information and vehicle's current yaw angle information; the environmental information includes: other vehicle's position information, vehicle speed information, vehicle acceleration information, other vehicle's current yaw angle information, lane boundary information and static obstacle information.

3. The active collision avoidance method based on multivariate risk fusion potential field according to claim 1, characterized in that: In S2, the driving trajectories of the vehicle and the other vehicle are expressed as follows: Among them, (x self ,y self ) is the vehicle position coordinate, (x veh ,y veh ) is the position coordinate of his car, θ self is the current yaw angle information of the vehicle, P veh is the distance between the current vehicle and the origin of the coordinate system, θ veh It is the current yaw angle information of the vehicle.

4. The active collision avoidance method based on multivariate risk fusion potential field according to claim 1, characterized in that: In S3, the method for determining the trajectory position relationship between the vehicle and other vehicles is as follows: Among them, θ veh is the current yaw angle information of the other vehicle, θ self is the current yaw angle information of the vehicle.

5. The active collision avoidance method based on multivariate risk fusion potential field according to claim 1, characterized in that: In S4, if the trajectories are determined to intersect, the specific location of the intersection (x0, y0) can be solved by jointly solving the driving trajectory equations of the self-vehicle and the other vehicle in the fixed coordinate system. The solution equation is as follows: P veh is the distance between the current vehicle and the origin of the coordinate system, θ veh is the current yaw angle information of the other vehicle, θ self is the current yaw angle information of the vehicle.

6. The active collision avoidance method based on multivariate risk fusion potential field according to claim 1, characterized in that: In S4, if the trajectories are determined to be parallel, then first predict whether the two vehicles are likely to collide. The specific prediction method is as follows: Among them, P veh is the distance between the current vehicle and the origin of the coordinate system, θ veh is the current yaw angle information of the other vehicle, d self_veh is the distance between the vehicle and other vehicles, let D safe is the safe distance between the vehicle and other vehicles, when d self_veh ≥D safe When d self_veh <D safe When , there is a possibility of collision. We can judge whether a collision will occur by determining the characteristics of the solution of the equation. The equation is as follows: (v veh0 t+a veh t 2 / 2)-(v self0 +a self t 2 / 2)-L=0 Among them, v self0 , v veh0 are the initial speeds of the ego vehicle and the other vehicle, t represents the current time t, and L is the axle length of a common car, ranging from 2.4 to 2.8 m. If the equation has no solution, the two vehicles will not collide and will travel safely. If the equation has a solution, the specific location of the collision point (x1, y1) must be calculated. The calculation method is as follows: (v veh0 t ac +a veh t ac 2 / 2)-(v self0 +a self t ac 2 / 2)-L=0 Among them, a self , a veh are the accelerations of the vehicle and the other vehicle, t ac is the time when the collision occurs, which is obtained by solving the above quadratic equation.

7. An in-vehicle electronic device, characterized in that: The vehicle-mounted electronic device can execute the active collision avoidance method described in any one of claims 1 to 6.

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