Automatic driving vehicle lane changing risk assessment method considering heterogeneity of surrounding vehicles

Abstract

The invention is suitable for the technical field of automatic driving safety, and provides an automatic driving vehicle lane changing risk assessment method considering the heterogeneity of surrounding vehicles, which comprises the following steps that: an automatic driving vehicle acquires own and surrounding environment information in real time through a sensing system; calculating a threat degree for each surrounding vehicle by introducing heterogeneity of the vehicle in consideration of the reaction time and the maximum braking deceleration; dividing a road space into grids, respectively constructing risk energy fields for surrounding vehicles and lane lines, and superposing to obtain a comprehensive risk energy field of a road area; and calculating the risk force borne by the automatic driving vehicle in the comprehensive risk energy field of the road area by combining the motion state of the automatic driving vehicle, comparing the risk force with a set risk grade threshold value, and evaluating the risk in the lane changing process. According to the method, the problems that an existing risk assessment method cannot deal with a complex road environment and the characteristics and motion uncertainty of surrounding vehicles are not comprehensively considered are solved, and lane changing risk dynamic early warning under the condition of considering the characteristics of the surrounding vehicles and automatic driving vehicles is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of autonomous driving safety, and in particular to a lane changing risk assessment method for an autonomous driving vehicle taking into account the heterogeneity of surrounding vehicles. Background Art

[0002] The core system of an autonomous vehicle can be divided into modules such as environmental perception, risk analysis, decision-making, control and execution. The risk analysis module plays a bridging role. It assesses risks based on environmental perception results and motion predictions of dynamic objects, and uses the assessment results as input to the decision-making module. Autonomous vehicles have two main driving behaviors during driving: following and lane changing. Compared with following, lane changing is more complex and dangerous. Its risk assessment and prediction are crucial to ensuring the driving safety of autonomous vehicles.

[0003] Existing risk assessment methods are mainly divided into methods based on time and kinematic indicators, statistics, potential fields, and abnormal behavior detection. The time indicator-based method evaluates driving risks from a time perspective, including headway time (THW), time to collision (TTC), and time to reaction (TTR). The kinematic indicator-based method uses kinematic parameters to evaluate driving risks, such as the minimum distance and acceleration required for collision avoidance. This type of method performs well in longitudinal collision scenarios, but has limitations when dealing with lateral collision risks. The statistical method uses machine learning based on data to quantify and evaluate risks. Although it can model uncertainty, the calculation is complex and the real-time performance is low. At the same time, the model training process requires a large amount of high-quality data. The potential field-based method uses field theory to integrate the risks of different factors in the traffic environment to the vehicle, but usually ignores the impact of the vehicle's own characteristics and motion uncertainty. The adaptability in complex scenarios such as vehicle lane changes still needs to be improved; the abnormal behavior detection-based method can evaluate non-collision-related driving risks, such as driver fatigue, running red lights, and other unexpected behaviors, and quantify risks based on the degree of deviation from normal behavior, but its detection and risk quantification are difficult.

[0004] Based on the above analysis, although the existing risk assessment methods can quantify the risks to a certain extent, they are still insufficient in dealing with complex road environments and considering the heterogeneity of surrounding vehicles and motion uncertainty, and further research and improvement are urgently needed. Therefore, a lane change risk assessment method for autonomous driving vehicles considering the heterogeneity of surrounding vehicles is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a lane change risk assessment method for an autonomous driving vehicle taking into account the heterogeneity of surrounding vehicles, aiming to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: The lane-changing risk assessment method for an autonomous driving vehicle considering the heterogeneity of surrounding vehicles includes the following steps: Step S1, considering vehicle heterogeneity to evaluate the threat level of surrounding vehicles: the autonomous driving vehicle collects information about itself and its surrounding environment in real time through a perception system, and calculates the threat level of each surrounding vehicle by introducing vehicle heterogeneity by considering reaction time and maximum braking deceleration; Step S2, constructing a comprehensive risk energy field of the road area based on the threat degree of surrounding vehicles: dividing the road space into grids, constructing risk energy fields for surrounding vehicles and lane lines respectively, and obtaining a comprehensive risk energy field of the road area after superposition; Step S3, using the comprehensive risk energy field of the road area to evaluate the lane changing risk of the autonomous driving vehicle: the autonomous driving vehicle calculates the risk force it is subjected to in the comprehensive risk energy field of the road area based on its own motion state, and compares the risk force it is subjected to with the set risk level threshold to evaluate the risk during the lane changing process.

[0007] Furthermore, the specific process of the step of considering vehicle heterogeneity to assess the threat level of surrounding vehicles is as follows: Step S11: The autonomous driving vehicle intending to change lanes or in the process of changing lanes uses the perception system to obtain information about the environment and surrounding vehicles, and obtains the trajectory and motion status of surrounding vehicles based on the prediction algorithm; specifically, it includes: obtaining the length of surrounding vehicles l box ,width w box ; Get the first type of surrounding vehicles to determine the reaction time, and get the second type of surrounding vehicles to determine the maximum braking deceleration; Get the surrounding vehicles T + t Moment-by-moment predicted probability of intent , and the probability of intention p k The corresponding motion status information includes T + t The coordinates of the trajectory point at time ( x k , y k ),speed v k and steering angle ; Obtain the road surface material to determine the road adhesion coefficient; Obtain the lane line position and type; Obtain the position coordinates of the autonomous driving vehicle itself ( x T , y T ),speed v T and the front wheel steering angle ; The first type includes CAV (connected and autonomous vehicles), AV (autonomous driving vehicles) and HDV (human-driven vehicles); the second type includes light vehicles and heavy vehicles; Step S12: Calculate the threat level of each surrounding vehicle : Formula 1: ; Formula 2: ; Where: Indicates surrounding vehicles i degree of threat; Indicates surrounding vehicles i Reaction time of CAV, AV and HD vehicles is the default value, ; Indicates surrounding vehicles i Maximum braking deceleration for light and heavy vehicles is the default value, ; k t Indicates the reaction time adjustment coefficient of surrounding vehicles; c It represents the road adhesion coefficient; X s Represents a 0-1 variable, used to determine whether the vehicle is speeding. X s =1; k t0 , k ts All represent undetermined coefficients.

[0008] Furthermore, the specific process of constructing the surrounding vehicle risk energy field based on the threat level of the surrounding vehicles is as follows: Step 21: Divide the road space in the road area D into The grid is as follows: Formula 3: ; Formula 4: ; Formula 5: ; Formula 6: ; Where: L D Indicates the length of the road area D along the lane line; s var Indicates the longitudinal visual range of the vehicle; W D It represents the width of the road area perpendicular to the lane line; n represents the number of lanes contained in the road area D; llane Indicates lane width; a 0 indicates the road grid unit length; b 0 indicates the road grid unit width; f Indicates that the road area along the lane line is divided into sections of length a The number of grid cells that are 0; Indicates that the road area perpendicular to the lane line is divided into two areas with a width of b The number of grid cells that are 0; Step 22: Calculate the risk radiation energy of surrounding vehicles to other grids and construct the risk energy field of surrounding vehicles. The formula is as follows: ; Where: represents the longitudinal risk modification factor; represents the horizontal risk correction factor; Both are undetermined coefficients, representing the influence of distance on longitudinal and transverse risks respectively; Both are undetermined coefficients, representing the influence of speed on longitudinal and transverse risks respectively; Indicates the length of surrounding vehicles; Indicates the width of surrounding vehicles; Represents the grid coordinates of the predicted trajectory point at time T+1 under the k-th behavior intention of the surrounding vehicles; Represents the original coordinates of the grid points; It indicates the corresponding deflection value after the vehicle steering angle turns to the original coordinates of the grid point; represents the speed of surrounding vehicles with the kth behavior intention; Indicates the steering angle when the vehicle turns; Indicates the angle between the vehicle speed direction and the positive direction of the x-axis; Indicates surrounding vehicles i The risk energy value generated by the grid where the predicted trajectory point is located to other grids under the k-th type of behavior intention; Indicates surrounding vehicles i degree of threat; Indicates surrounding vehicles i The strength of the risk energy field generated; Indicates surrounding vehicles i The probabilities of different driving intentions are k={1(turn left), 2(go straight), 3(turn right)}.

[0009] Furthermore, a lane risk energy field is constructed based on the threat level of the surrounding vehicles, as follows: Formula 12: ; Where: Indicates lane lines jThe strength of the risk energy field generated at other grids; Indicates the different risk radiation coefficients generated by different lane line types; represents the undetermined coefficient; Represents the y-axis coordinates of other grids; Indicates the y-axis coordinate of the grid where the lane line is located; l lane Indicates lane width.

[0010] Furthermore, the comprehensive risk energy field of the road area at time t is established, as follows: Formula 13: ; Where: Indicates surrounding vehicles i The strength of the risk energy field generated at other grids; Indicates lane lines j The intensity of the risk energy field generated at other grids; m represents the number of surrounding vehicles in the road area; s represents the number of lane lines in the road area; Indicates the comprehensive risk energy field strength generated by surrounding vehicles and lane lines.

[0011] Further, the autonomous driving vehicle is calculated The risk from surrounding vehicles at all times , as follows: Formula 14: ; Where: Indicates surrounding vehicles in the same lane as the autonomous vehicle The strength of the risk energy field generated at the location of the autonomous vehicle; Indicates surrounding vehicles that are in different lanes than the autonomous vehicle The strength of the risk energy field generated at the location of the autonomous vehicle; Indicates the number of surrounding vehicles in the same lane as the autonomous vehicle; Indicates the number of surrounding vehicles that are in different lanes from the autonomous vehicle; It represents the intensity of the risk energy field generated by the surrounding vehicles in the same lane as the autonomous vehicle at the location of the autonomous vehicle; It represents the intensity of the risk energy field generated by surrounding vehicles in different lanes from the autonomous vehicle at the location of the autonomous vehicle; represents the speed of the autonomous vehicle; are undetermined coefficients, representing the sensitivity of the longitudinal motion state of the autonomous driving vehicle to the risk of surrounding vehicles in the same lane and the sensitivity of the lateral motion state of the autonomous driving vehicle to the risk of surrounding vehicles in different lanes; Represents the steering angle of the autonomous vehicle.

[0012] Further, the autonomous driving vehicle is calculated The risk forces from lane lines at all times are as follows: Formula 15: ; Where: Indicates lane lines j The strength of the risk energy field generated at the location of the autonomous vehicle; Indicates the number of lane lines in the road area; is an undetermined coefficient, which indicates the sensitivity of the lateral motion state of the autonomous driving vehicle to the lane line risk; represents the speed of the autonomous vehicle; Represents the steering angle of the autonomous vehicle.

[0013] Further, the risk level of the autonomous driving vehicle during lane changing is determined based on the risk factor. Risk Level , as follows: Formula 16: ; Formula 17: ; Where: It represents the risk force in the risk energy field during the lane changing process of the autonomous driving vehicle; Both are preset parameters, representing the minimum and maximum risk thresholds respectively.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. Consider vehicle heterogeneity: The present invention analyzes the physical properties and motion characteristics of different types of vehicles, while taking into account the uncertainty caused by changes in the behavior of surrounding vehicles. The risk energy field generated by vehicles with different characteristics and intention probabilities is heterogeneous. Through this refined analysis, the present invention can more accurately assess the potential threat posed by surrounding vehicles to autonomous vehicles, thereby significantly improving the safety of autonomous vehicles during lane changes.

[0015] 2. Construction of dynamic risk energy field: The present invention constructs a risk energy field in real time based on the motion prediction results of surrounding vehicles to predict future risk changes and provide a more comprehensive risk assessment for autonomous driving vehicles.

[0016] 3. Easy to implement and expand: The implementation of the present invention does not need to rely on a large amount of training data or complex machine learning models. Its rule-based approach makes the implementation relatively simple and can be adjusted and expanded according to different road conditions or traffic regulations.

[0017] In summary, based on the concept of risk energy field, the present invention proposes a lane changing risk assessment method for autonomous driving vehicles that takes into account the heterogeneity of surrounding vehicles. This method quantifies the impact of surrounding vehicles with different characteristics on the vehicle, and provides a theoretical basis for accurately assessing the risks in the process of autonomous driving lane changing. It helps to solve the problem that existing risk assessment technologies do not adequately consider the heterogeneity and motion uncertainty of surrounding vehicles under autonomous driving vehicle lane changing conditions, making it difficult to ensure lane changing safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 The figure is a flow chart of the method of the present invention.

[0019] Figure 2 Schematic diagram of the lane changing scenario for an autonomous driving vehicle.

[0020] Figure 3 Schematic diagram of the risk energy field of heavy HDV (FV).

[0021] Figure 4 Schematic diagram of the risk energy field of a lightweight CAV (RV).

[0022] Figure 5 Schematic diagram of the risk energy field generated by lane lines.

[0023] Figure 6 Schematic diagram of the comprehensive risk energy field changes in the road area during the lane changing process of an autonomous driving vehicle; (a) is a schematic diagram of the comprehensive risk field at the initial moment of the lane changing, (b) is a schematic diagram of the comprehensive risk field during the lane changing process, and (c) is a schematic diagram of the comprehensive risk field at the end of the lane changing.

[0024] Figure 7 Schematic diagram of the change in risk force level during the lane changing process of an autonomous driving vehicle. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0026] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0027] like Figure 1 As shown, a method for assessing lane change risk of an autonomous driving vehicle considering the heterogeneity of surrounding vehicles provided by an embodiment of the present invention includes the following steps: Step S1, considering vehicle heterogeneity to evaluate the threat level of surrounding vehicles: the autonomous driving vehicle collects information about itself and its surroundings in real time through the perception system, and calculates the threat level of each surrounding vehicle by introducing vehicle heterogeneity by considering reaction time and maximum braking deceleration. The details are as follows: (1) An autonomous vehicle that intends to change lanes or is in the process of changing lanes uses a perception system to obtain information about the environment and surrounding vehicles, and obtains the trajectory and motion status of surrounding vehicles based on a prediction algorithm; specifically, obtaining the length of surrounding vehicles l box ,width w box ; Get the first type of surrounding vehicles (CAV / AV / HDV) to determine the reaction time, and the second type of surrounding vehicles (light vehicle / heavy vehicle) to determine the maximum braking deceleration; Get the surrounding vehicles T + t The predicted probability of intention at each moment, , and the probability of intention p k Corresponding motion status information (including T + t The coordinates of the trajectory point at time ( x k , y k ),speed v k and steering angle ); obtain the road surface material to determine the road adhesion coefficient; obtain the lane line position and type; obtain the position coordinates of the autonomous driving vehicle itself ( x T , y T ),speed v T and the front wheel steering angle ; (2) Calculate the threat level of each surrounding vehicle ; Formula 1: ; Formula 2: ; Where: Indicates surrounding vehicles i degree of threat; Indicates surrounding vehicles i Reaction time of CAV, AV and HD vehicles is the default value, ; Indicates surrounding vehicles i Maximum braking deceleration for light and heavy vehicles is the default value, ; k t Indicates the reaction time adjustment coefficient of surrounding vehicles; c It represents the road adhesion coefficient; X s Represents a 0-1 variable, used to determine whether the vehicle is speeding. X s =1; k t0 , k ts All represent undetermined coefficients.

[0028] Step S2: construct a comprehensive risk energy field of the road area based on the threat level of surrounding vehicles: divide the road space into grids, construct risk energy fields for surrounding vehicles and lane lines respectively, and obtain a comprehensive risk energy field of the road area after superposition. The details are as follows: (1) In the road area D, the road space is divided into The grid is as follows: Formula 3: ; Formula 4: ; Formula 5: ; Formula 6: ; Where: L D Indicates the length of the road area D along the lane line; s var Indicates the longitudinal visual range of the vehicle; W D Indicates the width of the road area perpendicular to the lane line; n indicates the number of lanes in the road area D, n=3, n1 is defined as the target lane, n2 is the current lane, and n3 is the adjacent lane on the other side; l lane Indicates lane width; a 0 indicates the road grid unit length; b 0 indicates the road grid unit width; f Indicates that the road area along the lane line is divided into sections of length a The number of grid cells that are 0; Indicates that the road area perpendicular to the lane line is divided into two areas with a width of b The number of grid cells is 0.

[0029] (2) Calculate the risk radiation energy of surrounding vehicles to other grids and construct the risk energy field of surrounding vehicles; Calculate vertical and horizontal risk modification factors: ; Count surrounding vehicles i In the Risk radiation energy under class behavior intention: Formula 10: ; Count surrounding vehicles i All behavioral intentions The risk energy field below: Formula 11: ; In formula 7~11: represents the longitudinal risk modification factor; represents the horizontal risk correction factor; Both are undetermined coefficients, representing the influence of distance on longitudinal and transverse risks respectively; Both are undetermined coefficients, representing the influence of speed on longitudinal and transverse risks respectively; Indicates the length of surrounding vehicles; Indicates the width of surrounding vehicles; Represents the grid coordinates of the predicted trajectory point at time T+1 under the k-th behavior intention of the surrounding vehicles; Represents the original coordinates of the grid points; It indicates the corresponding deflection value after the vehicle steering angle turns to the original coordinates of the grid point; represents the speed of surrounding vehicles with the kth behavior intention; Indicates the steering angle when the vehicle turns; (the counterclockwise direction of the steering angle is defined as the positive direction); Indicates the angle between the vehicle speed direction and the positive direction of the x-axis; Indicates surrounding vehicles i The risk energy value generated by the grid where the predicted trajectory point is located to other grids under the k-th type of behavior intention; Indicates surrounding vehicles i degree of threat; Indicates surrounding vehicles i The strength of the risk energy field generated; Represents the different driving intention probabilities of surrounding vehicles, k = {1 (turn left), 2 (go straight), 3 (turn right)}.

[0030] (3) Construct the lane line risk energy field, based on a Gaussian-like model to describe the lane line risk energy, the type is The risk energy field generated by the lane line at other grids is as follows: Formula 12: ; Where: Indicates lane lines j The strength of the risk energy field generated at other grids; Indicates the different risk radiation coefficients generated by different lane line types, assuming that the white dotted line A1=1 and the white solid line A2=2; represents the undetermined coefficient; Represents the y-axis coordinates of other grids; Indicates the y-axis coordinate of the grid where the lane line is located; l lane Indicates lane width.

[0031] (4) Establish a comprehensive risk energy field for road areas. Take each grid as the basic research unit. By studying the concept of risk energy generated by different risk factors on each grid, the risk distribution of road areas within the visible range of vehicles is described. Thus, a comprehensive risk energy field for road areas at time t is constructed to reflect the spatial distribution of comprehensive risks in the traffic environment. The calculation method of the comprehensive risk energy field for road areas is as follows: Formula 13: ; Where: Indicates the risk energy field of surrounding vehicles; Indicates lane lines j The intensity of the risk energy field generated at other grids; m represents the number of surrounding vehicles in the road area; s represents the number of lane lines in the road area; Indicates the comprehensive risk energy field strength generated by surrounding vehicles and lane lines.

[0032] Step S3: Use the comprehensive risk energy field of the road area to evaluate the lane change risk of the autonomous driving vehicle: The autonomous driving vehicle calculates the risk force it is subjected to in the comprehensive risk energy field of the road area based on its own motion state, and compares the risk force it is subjected to with the set risk level threshold to evaluate the risk during the lane change process. The details are as follows: (1) Computing autonomous vehicles The risk force from surrounding vehicles at all times represents the interaction risk between the autonomous driving vehicle and surrounding vehicles, as follows: Formula 14: ; Where: Indicates surrounding vehicles in the same lane as the autonomous vehicle The strength of the risk energy field generated at the location of the autonomous vehicle; Indicates surrounding vehicles that are in different lanes than the autonomous vehicle The strength of the risk energy field generated at the location of the autonomous vehicle; Indicates the number of surrounding vehicles in the same lane as the autonomous vehicle; Indicates the number of surrounding vehicles that are in different lanes from the autonomous vehicle; It represents the intensity of the risk energy field generated by the surrounding vehicles in the same lane as the autonomous vehicle at the location of the autonomous vehicle; It represents the intensity of the risk energy field generated by surrounding vehicles in different lanes from the autonomous vehicle at the location of the autonomous vehicle; represents the speed of the autonomous vehicle; are undetermined coefficients, representing the sensitivity of the longitudinal motion state of the autonomous driving vehicle to the risk of surrounding vehicles in the same lane and the sensitivity of the lateral motion state of the autonomous driving vehicle to the risk of surrounding vehicles in different lanes; Represents the steering angle of the autonomous vehicle.

[0033] (2) Computing autonomous vehicles The risk forces from lane lines at all times are as follows: Formula 15: ; Where: Indicates lane lines j The strength of the risk energy field generated at the location of the autonomous vehicle; Indicates the number of lane lines in the road area; is an undetermined coefficient, which indicates the sensitivity of the lateral motion state of the autonomous driving vehicle to the lane line risk; represents the speed of the autonomous vehicle; Represents the steering angle of the autonomous vehicle.

[0034] (3) Determine the risk level of the autonomous vehicle during lane change based on risk factor Risk Level , as follows: Formula 16: ; Formula 17: ; Where: It represents the risk force in the risk energy field during the lane changing process of the autonomous driving vehicle; Both are preset parameters, representing the minimum and maximum risk thresholds respectively.

[0035] Example 1: The method of the present invention is applied to the following scenarios: On a three-lane asphalt road (the middle lane line is a dotted line, lane change is allowed), the weather is clear, and an autonomous vehicle (Ego vehicle) intends to change from the middle lane 2 to the right lane 3 to avoid the vehicle in front that is performing a deceleration operation and choose a safer driving route. The front vehicle (FV) in the current lane of the ego vehicle is a heavy HDV that is decelerating, and the rear vehicle (RV) in the right target lane is a light CAV that is traveling at a faster speed. The scene is shown as follows: Figure 2 The vehicle information is shown in Table 1: Table 1 Vehicle information around the autonomous driving vehicle

[0036] (1) The autonomous vehicle obtains information about the environment and surrounding vehicles based on the perception system, and predicts the future intention probability of the truck in front and the car in the target lane and the corresponding position at time T+t through the prediction algorithm. The intention probability of the truck at a certain moment is [0.6, 0.3, 0.1], and the intention probability of the car is [0, 1, 0]. The threat level of surrounding vehicle 1 and surrounding vehicle 2 is calculated respectively. The reaction time and maximum braking deceleration of different vehicle types are shown in Tables 3 and 4. The adjustment coefficient The calibration is carried out through simulation experiments, and the adhesion coefficient is obtained according to Table 2 based on the road surface material.

[0037] Table 2 Adhesion coefficients of different road materials

[0038] Table 3 Reaction time of different first type vehicles

[0039] Table 4 Maximum braking deceleration of different second type vehicles

[0040] (2) Divide the road space into The risk radiation energy of surrounding vehicles 1 and 2 to other grids is calculated to construct the vehicle risk energy field at time T+1, respectively. Figure 3 and Figure 4 As shown; calculate the risk radiation energy of the lane line to other grids and construct the lane line risk energy field, as shown Figure 5 As shown; then establish the comprehensive risk energy field of the road area.

[0041] (3) Calculate the risk force from surrounding vehicles and lane lines at each moment during the lane change duration t of the autonomous driving vehicle, characterize the risk of the autonomous driving lane change process and its interaction, and obtain the corresponding comprehensive risk level. The comprehensive risk energy field of the road area at different moments during the lane change process is as follows: Figure 6 As shown in Figure 2, the risk level of the autonomous driving lane change process changes as follows: Figure 7 shown.

[0042] The above are only preferred embodiments of the present invention. It should be pointed out that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention. These should also be regarded as the protection scope of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. A lane-changing risk assessment method for an autonomous driving vehicle considering the heterogeneity of surrounding vehicles, characterized in that: The following steps are involved: Step S1, considering vehicle heterogeneity to evaluate the threat level of surrounding vehicles: the autonomous driving vehicle collects information about itself and its surrounding environment in real time through a perception system, and calculates the threat level of each surrounding vehicle by introducing vehicle heterogeneity by considering reaction time and maximum braking deceleration; Step S2, constructing a comprehensive risk energy field of the road area based on the threat degree of surrounding vehicles: dividing the road space into grids, constructing risk energy fields for surrounding vehicles and lane lines respectively, and obtaining a comprehensive risk energy field of the road area after superposition; Step S3, using the comprehensive risk energy field of the road area to evaluate the lane changing risk of the autonomous driving vehicle: the autonomous driving vehicle calculates the risk force it is subjected to in the comprehensive risk energy field of the road area based on its own motion state, and compares the risk force it is subjected to with the set risk level threshold to evaluate the risk during the lane changing process.

2. The method for assessing lane change risk of an autonomous driving vehicle considering the heterogeneity of surrounding vehicles according to claim 1, characterized in that: The specific process of the step of considering vehicle heterogeneity to assess the threat level of surrounding vehicles is as follows: Step S11: The autonomous driving vehicle intending to change lanes or in the process of changing lanes uses the perception system to obtain information about the environment and surrounding vehicles, and predict the trajectory and motion state of surrounding vehicles; specifically, it includes: obtaining the length of surrounding vehicles l box ,width w box ; Get the first type of surrounding vehicles to determine the reaction time, and get the second type of surrounding vehicles to determine the maximum braking deceleration; Get the surrounding vehicles T + t Moment-by-moment predicted probability of intent , and the probability of intention p k The corresponding motion status information includes T + t The coordinates of the trajectory point at time ( x k , y k ),speed v k and steering angle ; Obtain the road surface material to determine the road adhesion coefficient; Obtain the lane line position and type; Obtain the position coordinates of the autonomous driving vehicle itself ( x T , y T ),speed v T and the front wheel steering angle ; The first type includes CAVs, AVs, and HDVs; the second type includes light vehicles and heavy vehicles; Step S12: Calculate the threat level of each surrounding vehicle : Formula 1: ; Formula 2: ; Where: Indicates surrounding vehicles i degree of threat; Indicates surrounding vehicles i Reaction time of CAV, AV and HD vehicles is the default value, ; Indicates surrounding vehicles i Maximum braking deceleration for light and heavy vehicles is the default value, ; k t Indicates the reaction time adjustment coefficient of surrounding vehicles; c It represents the road adhesion coefficient; X s Represents a 0-1 variable, used to determine whether the vehicle is speeding. X s =1; k t0 , k ts All represent undetermined coefficients.

3. The method for assessing lane change risk of an autonomous driving vehicle considering the heterogeneity of surrounding vehicles according to claim 2, characterized in that: The specific process of constructing the surrounding vehicle risk energy field based on the threat level of the surrounding vehicles is as follows: Step 21: Divide the road space in the road area D into The grid is as follows: Formula 3: ; Formula 4: ; Formula 5: ; Formula 6: ; Where: L D Indicates the length of the road area D along the lane line; s var Indicates the longitudinal visual range of the vehicle; W D It represents the width of the road area perpendicular to the lane line; n represents the number of lanes contained in the road area D; l lane Indicates lane width; a 0 indicates the road grid unit length; b 0 indicates the road grid unit width; f Indicates that the road area along the lane line is divided into sections of length a The number of grid cells that are 0; Indicates that the road area perpendicular to the lane line is divided into two areas with a width of b The number of grid cells that are 0; Step 22: Calculate the risk energy of surrounding vehicles to other grids and construct the risk energy field of surrounding vehicles. The formula is as follows: ； Where: represents the longitudinal risk modification factor; represents the horizontal risk correction factor; Both are undetermined coefficients, representing the influence of distance on longitudinal and transverse risks respectively; Both are undetermined coefficients, representing the influence of speed on longitudinal and transverse risks respectively; Indicates the length of surrounding vehicles; Indicates the width of surrounding vehicles; Represents the grid coordinates of the predicted trajectory point at time T+1 under the k-th behavior intention of the surrounding vehicles; Represents the original coordinates of the grid points; It indicates the corresponding deflection value after the vehicle steering angle turns to the original coordinates of the grid point; represents the speed of surrounding vehicles with the kth behavior intention; Indicates the steering angle when the vehicle turns; Indicates the angle between the vehicle speed direction and the positive direction of the x-axis; Indicates surrounding vehicles i The risk energy value generated by the grid where the predicted trajectory point is located to other grids under the k-th type of behavior intention; Indicates surrounding vehicles i degree of threat; Indicates surrounding vehicles i The strength of the risk energy field generated; Indicates surrounding vehicles i The probabilities of different driving intentions are k={1(turn left), 2(go straight), 3(turn right)}.

4. The method for assessing lane change risk of an autonomous driving vehicle considering the heterogeneity of surrounding vehicles according to claim 3, characterized in that: The lane line risk energy field is constructed based on the threat level of the surrounding vehicles, as follows: Formula 12: ; Where: Indicates lane lines j The strength of the risk energy field generated at other grids; Indicates the different risk radiation coefficients generated by different lane line types; represents the undetermined coefficient; Represents the y-axis coordinates of other grids; Indicates the y-axis coordinate of the grid where the lane line is located; l lane Indicates lane width.

5. The method for assessing lane change risk of an autonomous driving vehicle considering the heterogeneity of surrounding vehicles according to claim 4, characterized in that: The comprehensive risk energy field of the road area at time t is established as follows: Formula 13: ; Where: Indicates surrounding vehicles i The strength of the risk energy field generated at other grids; Indicates lane lines j The intensity of the risk energy field generated at other grids; m represents the number of surrounding vehicles in the road area; s represents the number of lane lines in the road area; Indicates the comprehensive risk energy field strength generated by surrounding vehicles and lane lines.

6. The method for assessing lane change risk of an autonomous driving vehicle considering the heterogeneity of surrounding vehicles according to claim 4, characterized in that: Calculating the autonomous vehicle The risk from surrounding vehicles at all times , as follows: Formula 14: ; Where: Indicates surrounding vehicles in the same lane as the autonomous vehicle The strength of the risk energy field generated at the location of the autonomous vehicle; Indicates surrounding vehicles that are in different lanes than the autonomous vehicle The strength of the risk energy field generated at the location of the autonomous vehicle; Indicates the number of surrounding vehicles in the same lane as the autonomous vehicle; Indicates the number of surrounding vehicles that are in different lanes from the autonomous vehicle; represents the speed of the autonomous vehicle; are undetermined coefficients, representing the sensitivity of the longitudinal motion state of the autonomous driving vehicle to the risk of surrounding vehicles in the same lane and the sensitivity of the lateral motion state of the autonomous driving vehicle to the risk of surrounding vehicles in different lanes; Represents the steering angle of the autonomous vehicle.

7. The method for assessing lane change risk of an autonomous driving vehicle considering the heterogeneity of surrounding vehicles according to claim 6, characterized in that: Calculating the autonomous vehicle The risk forces from lane lines at all times are as follows: Formula 15: ; Where: Indicates lane lines j The strength of the risk energy field generated at the location of the autonomous vehicle; Indicates the number of lane lines in the road area; is an undetermined coefficient, which indicates the sensitivity of the lateral motion state of the autonomous driving vehicle to the lane line risk; represents the speed of the autonomous vehicle; Represents the steering angle of the autonomous vehicle.

8. The method for assessing lane change risk of an autonomous driving vehicle considering the heterogeneity of surrounding vehicles according to claim 7, characterized in that: Determining the risk level of the autonomous driving vehicle during lane changing based on risk force Risk Level , as follows: Formula 16: ; Formula 17: ; Where: It represents the risk force in the risk energy field during the lane changing process of the autonomous driving vehicle; Both are preset parameters, representing the minimum and maximum risk thresholds respectively.

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