Quantitative evaluation method and device for controllability of potential driving hazards

Abstract

This application provides a quantitative evaluation method and device for the controllability of potential driving hazards, which obtains the operating data of the self-vehicle and other entities in the current driving scenario; based on the operating data of the self-vehicle, combined with the self-vehicle's kinematic model, the time function of the control output under the influence of influencing factors, and the motion constraints of the self-vehicle, determines the self-vehicle's reachable domain within a preset control cycle from the current moment; the self-vehicle's reachable domain refers to the spatial range to which the self-vehicle can move according to its own motion capabilities in the current driving scenario; based on the operating data of other entities, the self-vehicle's interactive reachable domain within the preset control cycle is determined from the self-vehicle's reachable domain; the interactive reachable domain refers to the spatial range to which the self-vehicle can actually move when considering the motion trajectory of other entities. Subsequently, an objective and quantitative controllability index is defined by the self-vehicle reachable domain and the interactive reachable domain to evaluate the vehicle's ability to avoid potential hazards in a specific driving scenario, thereby achieving an accurate evaluation of the self-vehicle's controllability.

Description

Technical Field

[0001] The present application relates to the field of vehicle safety technology, and in particular to a quantitative evaluation method and device for the controllability of potential driving hazards. Background Art

[0002] The rapid development of autonomous driving technology has shown tremendous potential in improving traffic efficiency, reducing traffic accidents, and enhancing the driving experience. However, the safety of autonomous vehicles (AVs) remains a core concern within the industry and beyond. A series of safety issues associated with AVs are defined as Safety of the Intended Functionality (SOTIF), which requires AVs to avoid unreasonable risks due to performance limitations, even in the absence of faults.

[0003] In actual driving road environments, vehicles may encounter various unexpected situations, including emergencies caused by the external environment and unforeseen behaviors that occur during the AVs' own decision-making process. Whether potential hazards are generated actively or passively, they pose a challenge to the safety of autonomous driving systems. In particular, when the behaviors and actions performed by the autonomous driving system involve interactions with the surrounding environment and other traffic participants, the level of its controllability directly determines whether the system can effectively avoid risks and ensure driving safety. Controllability refers to the ability of the vehicle to avoid hazards when faced with them. It requires the system to be able to identify potential threats in a timely manner and to have sufficient flexibility to adjust strategies to meet the needs of different situations.

[0004] However, current research on the controllability of potential hazards in autonomous vehicle behavior lacks a robust methodology to quantitatively assess the degree of controllability. This makes it difficult for developers to objectively evaluate the safety of their designs and complicates the development of relevant standards and regulations for regulators. Furthermore, during the SOTIF analysis phase, test scenarios must be quantitatively constructed based on identified hazards, which presents the challenge of quantifying the controllability of potential hazards during driving. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide a quantitative assessment method and device for the controllability of potential hazards during driving, so as to solve the problem in the prior art that the controllability of potential hazards during driving is difficult to reasonably quantify and assess.

[0006] The present application provides a quantitative evaluation method for the controllability of potential driving hazards, the method comprising:

[0007] Obtain the vehicle's operating data and other entities' operating data in the current driving scenario;

[0008] Determine the reachable domain of the ego vehicle within a preset control period starting from the current moment based on the ego vehicle's operating data, in combination with the ego vehicle's kinematic model, the time function of the control output under the influence of influencing factors, and the ego vehicle's motion constraints; wherein the reachable domain of the ego vehicle refers to the spatial range to which the ego vehicle can move based on its own motion capabilities in the current driving scenario; the influencing factors include the vehicle's control execution delay and / or the inhibition of the control output by the road environment; and the time function of the control output is used to represent the change of the control output over time.

[0009] Determining, based on the operating data of the other entities, an interactively reachable domain of the ego vehicle within the preset control period from the ego vehicle's reachable domain; wherein the interactively reachable domain refers to the spatial range to which the ego vehicle can actually move when considering the motion trajectory of the other entities;

[0010] An index value of a controllability index of the ego vehicle at the current moment is determined according to the ego vehicle reachable domain and the interactive reachable domain.

[0011] Furthermore, based on the operating data of the ego vehicle, combined with the ego vehicle's kinematic model, the time function of the control output under the influence of influencing factors, and the ego vehicle's motion constraints, the reachable range of the ego vehicle within a preset control period from the current moment is determined, including:

[0012] constructing an actual kinematic model of the ego vehicle based on the kinematic model of the ego vehicle, the time function of the control output, and the motion constraints of the ego vehicle;

[0013] Substituting the operating data of the ego vehicle at the current moment into the actual kinematic model, and calculating backward the preset control period to determine the spatial position of the ego vehicle within the preset control period;

[0014] The reachable area of ​​the ego vehicle is determined according to the spatial position of the ego vehicle within the preset control period.

[0015] Furthermore, when the influencing factor includes a control execution delay of the vehicle, the method further includes determining a time function of the control output under the influence of the influencing factor in the following manner:

[0016] Determine the driving mode of the ego vehicle;

[0017] When the driving mode is manual driving, the delay time is determined according to the human perception delay time and the human action delay time;

[0018] When the driving mode is automatic driving, determining the delay time according to the operation time of the automatic driving system;

[0019] The control output is corrected according to the delay time to obtain a time function of the control output.

[0020] Furthermore, when the influencing factor includes the inhibition of the control output by the environment, the method further includes determining a time function of the control output under the influence of the influencing factor by:

[0021] Obtain environmental data in the current driving scenario;

[0022] Determining a suppression coefficient based on the environmental data; wherein the suppression coefficient is used to characterize the suppression effect from the expected control output of the vehicle to the actual control effect of the vehicle due to the road environment;

[0023] The control output is corrected according to the suppression coefficient to obtain a time function of the control output.

[0024] Furthermore, the motion constraint of the ego vehicle includes at least one of the following: a maximum constraint of the control output, an anti-rollover constraint, and a road constraint;

[0025] For the maximum constraint of the control output, the control output is greater than or equal to the maximum deceleration of the vehicle in the current driving scenario, and less than or equal to the maximum acceleration of the vehicle;

[0026] For the anti-rollover constraint, the lateral acceleration of the vehicle is less than or equal to the lateral acceleration threshold of the vehicle in the current driving scenario;

[0027] Regarding the road constraint, if there is a physical obstruction at the road boundary in the current driving scenario, the vehicle position must not exceed the road boundary.

[0028] Furthermore, determining the interactive reachable domain of the vehicle within the preset control period from the reachable domain of the vehicle according to the operation data of the other entity includes:

[0029] Predicting the operation trajectory of the other entities within the preset control period based on the operation data of the other entities;

[0030] The interactively reachable domain is obtained by removing the spatial intersection of the running trajectory and the reachable domain of the own vehicle from the reachable domain of the own vehicle.

[0031] Furthermore, determining an index value of a controllability index of the ego vehicle at the current moment based on the ego vehicle reachable domain and the interactive reachable domain includes:

[0032] Performing rasterization and discretization processing on the lanes in the current driving scenario;

[0033] Determine a first grid corresponding to the vehicle's reachable domain and a second grid corresponding to the interactive reachable domain;

[0034] The ratio of the number of the second grids to the number of the first grids is determined as the index value of the controllability index of the vehicle at the current moment; or, the ratio of the area of ​​the second grid to the area of ​​the first grid is determined as the index value of the controllability index of the vehicle at the current moment.

[0035] Furthermore, the preset control period includes a plurality of control time nodes; and determining an index value of a controllability index of the vehicle at the current moment according to the vehicle reachable domain and the interactive reachable domain includes:

[0036] For each control time node, determine the index value of the controllability index corresponding to the control time node according to the vehicle-reachable subdomain and the interactively reachable subdomain corresponding to the control time node;

[0037] The minimum index value among the index values ​​of the controllability index corresponding to each control time node is determined as the index value of the controllability index of the vehicle at the current moment.

[0038] The present application also provides a device for quantitatively evaluating the controllability of potential driving hazards, the device comprising:

[0039] The acquisition module is used to obtain the operating data of the vehicle and other entities in the current driving scenario;

[0040] A first determination module is configured to determine a reachable domain of the ego vehicle within a preset control period starting at a current moment based on the ego vehicle's operating data, a kinematic model of the ego vehicle, a time function of a control output under influencing factors, and the ego vehicle's motion constraints; wherein the reachable domain of the ego vehicle refers to a spatial range to which the ego vehicle can move based on its own motion capabilities in the current driving scenario; the influencing factors include a control execution delay of the vehicle and / or environmental suppression of the control output; and the time function of the control output is used to represent a change in the control output over time.

[0041] a second determining module, configured to determine, from the ego vehicle's reachable domain, an interactive reachable domain of the ego vehicle within the preset control period based on the operating data of the other entity; wherein the interactive reachable domain refers to a spatial range to which the ego vehicle can actually move when considering the motion trajectory of the other entity;

[0042] The third determining module is configured to determine an index value of a controllability index of the ego vehicle at the current moment according to the ego vehicle reachable domain and the interactive reachable domain.

[0043] An embodiment of the present application also provides an electronic device, comprising: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps of the quantitative assessment method for the controllability of potential driving hazards as described above are performed.

[0044] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned method for quantitatively evaluating the controllability of potential driving hazards are executed.

[0045] The embodiments of the present application provide a quantitative evaluation method and device for the controllability of potential driving hazards. By defining an objective and quantitative controllability index based on the vehicle's reachable domain and the interactive reachable domain, the method evaluates the vehicle's ability to avoid potential hazards in specific driving scenarios, thereby achieving an accurate assessment of the vehicle's controllability. The method considers the vehicle's kinematic characteristics and introduces motion constraints to comprehensively reflect the vehicle's behavioral capabilities. Furthermore, the method combines the vehicle's actual physical limitations when performing hazard avoidance maneuvers, introduces the vehicle's control execution delay and / or the road environment's suppression of control output to determine the time function of the control output, and simulates the vehicle's actual control process during hazard avoidance maneuvers, ensuring the reasonable accuracy of the controllability index.

[0046] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 A flowchart of a quantitative evaluation method for controllability of potential driving hazards provided by an embodiment of the present application is shown;

[0049] Figure 2 A schematic diagram of the structure of a quantitative evaluation system for controllability of potential driving hazards provided by an embodiment of the present application is shown;

[0050] Figure 3 A schematic diagram of the structure of a device for quantitatively evaluating the controllability of potential driving hazards provided by an embodiment of the present application is shown;

[0051] Figure 4 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, each other embodiment obtained by those skilled in the art without making creative work falls within the scope of protection of the present application.

[0053] Research has found that existing vehicle safety assessment methods often use a series of standards and rules set by experts to divide vehicle safety into different levels, attempting to provide a reference standard for the safety of autonomous driving systems. However, this hierarchical assessment method based on expert experience has certain issues of non-interpretability and non-repeatability in actual application. When applied to controllability research, the knowledge background and personal preferences of experts in different fields may lead to significant differences in their understanding of the controllability of vehicle behavior in the same scenario, resulting in a lack of consistency in the final assessment results. Furthermore, because this method is inherently qualitative rather than quantitative, the explanation of controllability is insufficient in terms of rationality, making it difficult to provide design guidance for developers.

[0054] Therefore, current research on the controllability of potential hazards in autonomous vehicle behavior lacks a robust methodology to quantitatively assess the degree of such controllability. This makes it difficult for developers to objectively evaluate the safety of their designs and complicates the development of relevant standards and regulations by regulators. Furthermore, during the SOTIF analysis phase, test scenarios must be quantitatively constructed based on identified hazards, which presents the challenge of quantifying the controllability of potential hazards during driving.

[0055] Based on this, the embodiment of the present application provides a quantitative evaluation method for the controllability of potential driving hazards, so as to evaluate the vehicle's ability to avoid potential hazards in specific driving scenarios through an objective and quantitative controllability index, thereby achieving an accurate evaluation of the vehicle's controllability.

[0056] See also Figure 1 , Figure 1This is a flow chart of a quantitative evaluation method for the controllability of potential driving hazards provided by an embodiment of the present application. Figure 1 As shown in , the evaluation method provided in the embodiment of the present application includes:

[0057] S101: Acquire the operating data of the vehicle and other entities in the current driving scenario.

[0058] In this step, the vehicle's operating data may include its location coordinates and speed, while the operating data of other entities may include the location coordinates and speed information of surrounding vehicles. In practice, data collection can be performed using onboard sensor systems and V2X communication technology. Sensor systems may include radar, lidar, cameras, and other technologies. To ensure accuracy and timeliness, sensors must possess high-precision positioning capabilities and rapid response characteristics. V2X communication should support low-latency information exchange to achieve real-time data updates.

[0059] S102: Determine a reachable region of the ego vehicle within a preset control period from the current moment based on the ego vehicle's operating data, in combination with the ego vehicle's kinematic model, a time function of the control output under influencing factors, and the ego vehicle's motion constraints.

[0060] The control period T can be set based on the computing power of the computing device equipped with the algorithm, but at least T must be greater than the duration of the potential hazardous event. The vehicle's reachable domain refers to the spatial range to which the vehicle can move based on its own movement capabilities in the current driving scenario. More specifically, the boundary of the vehicle's reachable domain can be determined based on the maximum maneuver performed by the vehicle in its maximum avoidance capability, that is, the edge to which it can move under maximum lateral control, steering control, and combined lateral and longitudinal control. This boundary can represent the upper limit of the vehicle's maneuvers and avoidance capabilities in the current driving scenario, and thus the spatial extent of the vehicle's reachable domain can be determined based on this boundary.

[0061] The influencing factors include the control execution delay of the vehicle and / or the inhibition of the control output by the road environment; the time function of the control output is used to characterize the change of the control output over time.

[0062] Here, control execution delay refers to the necessary delay before the driver makes the expected response when avoiding risks in potential hazards and hazardous events. It should be noted that the driver here can be a human driver or an autonomous driving system. The inhibition of the control output by the road environment refers to the inhibition of the vehicle's action output by the road environment. Generally speaking, the control output of the vehicle mainly includes the longitudinal speed change output, that is, the acceleration or deceleration of the vehicle, denoted as a t , and the lateral velocity change output of the vehicle, that is, the turning rate of the vehicle, recorded as ωt . t is the time.

[0063] S103 : Determine, based on the operation data of the other entities, an interactively reachable domain of the own vehicle within the preset control period from the own vehicle's reachable domain.

[0064] The interactive reachable domain refers to the spatial range within which the ego vehicle can actually move, taking into account the motion trajectories of other entities. It should be noted that the driving environment includes not only the ego vehicle but also other moving entities, such as other vehicles (including motor vehicles and non-motor vehicles) and pedestrians. The positions of other entities may also change within the preset control cycle, forming motion trajectories and occupying the range of the ego vehicle's reachable domain.

[0065] Therefore, in this step, the motion trajectory can be predicted through the operating data of other entities, and then the spatial range occupied by the motion trajectory can be removed from the reachable domain of the ego vehicle to obtain the spatial range to which the ego vehicle can actually move, that is, the interactive reachable domain.

[0066] Furthermore, the driving environment also includes fixed entities, such as road facilities and obstacles. When calculating the vehicle's reachable area and the interactive reachable area, the space occupied by these fixed entities can be removed to achieve more accurate calculation results.

[0067] S104: Determine an index value of a controllability index of the ego vehicle at the current moment according to the ego vehicle reachable domain and the interactive reachable domain.

[0068] In this step, the controllability index is defined as the ratio between the spatial extent of the interaction reachable domain and the spatial extent of the ego vehicle reachable domain. This ratio represents the ego vehicle's ability to reach a safe area without colliding with other traffic participants under all possible maneuvers, i.e., its controllability.

[0069] In this way, the quantitative evaluation method for the controllability of potential driving hazards provided in the embodiment of the present application defines an objective quantitative controllability index through the vehicle's reachable domain and the interactive reachable domain to evaluate the vehicle's ability to avoid potential hazards in specific driving scenarios, thereby achieving an accurate evaluation of the vehicle's controllability.

[0070] In a possible implementation, step S102 may include:

[0071] An actual kinematic model of the ego vehicle is constructed based on the kinematic model of the ego vehicle, the time function of the control output, and the motion constraints of the ego vehicle. The operating data of the ego vehicle at the current moment is substituted into the actual kinematic model, and the preset control period is calculated backward to determine the spatial position of the ego vehicle within the preset control period. The reachable range of the ego vehicle is determined based on the spatial position of the ego vehicle within the preset control period.

[0072] Here, the kinematic model of the vehicle is used to describe the vehicle's kinematic behavior, that is, the changes in the vehicle's operating parameters over time. For example, the vehicle's kinematic behavior can be described using a Markov model, differential equations, or a stochastic process (such as a Poisson process). If the vehicle's motion process is modeled as a Markov process, the vehicle motion control model can be described by the following equation:

[0073] x t+1 =x t +v t cos(θ t )Δt,

[0074] y t+1 =y t +v t sin(θ t )Δt,

[0075] v t+1 =v t +a t Δt,

[0076] θ t+1 =θ t +ω t Δt,

[0077] Among them, x t ,y t is the position of the vehicle at time t; v t is the speed of the vehicle; θ t is the heading angle of the vehicle (relative to the direction in front; a t is the acceleration of the vehicle; ω t is the angular velocity (turning rate) of the vehicle; Δt is the time step, which represents a time interval in discrete time.

[0078] It's worth noting that during actual evasive maneuvers, the driver begins to control the vehicle's lateral and longitudinal directions. Because the driver cannot instantly complete control of the vehicle, the vehicle's acceleration and steering do not reach their maximum values ​​immediately but change gradually. Therefore, a control time function is required to simulate the driver's actual control operations. To address this issue, in the embodiments of the present application, the time function of the control output under the influence of influencing factors can be determined in the following manner.

[0079] Method 1: When the influencing factor includes the control execution delay of the vehicle, first determine the driving mode of the vehicle.

[0080] When the driving mode is manual driving, the delay time is determined based on the human perception delay time and the human action delay time. The human perception delay time refers to the delay time for the human body to perceive potential hazards and hazardous events, and the human action delay time refers to the delay time for the human body to react after perception, and can be determined based on human experience data.

[0081] When the driving mode is autonomous driving, the delay time is determined based on the autonomous driving system's computing time. The autonomous driving system's computing time includes the time it takes for the autonomous driving system to receive potential hazard and hazard event signals, the time it takes to calculate control signals based on these signals, and the time it takes to output the control signals. This time can be determined based on the autonomous driving system's computing power.

[0082] Afterwards, the control output is corrected according to the delay time to obtain a time function of the control output.

[0083] Here, the delay time can be substituted into the time function of the control response to achieve the correction of the control output. The time function can be in the form of Sigmoid function, linear, exponential and polynomial functions. For example, assuming that the maximum value of the control output acceleration and steering increases with time according to the Sigmoid time function curve, the time function of the control output can be expressed as:

[0084]

[0085] Among them, a max and ω max are the maximum values ​​of acceleration and angular velocity respectively; t0 is the starting point of the response time after delay, that is, the delay time; k controls the steepness of the curve.

[0086] In addition, it was noted that during the actual control process, when performing risk avoidance operations, the control output could not fully produce the corresponding actual control effect due to the influence of the road environment; for example, the deceleration effect expected by the driver was -4m / s. 2 , that is, the expected control output acceleration is -4m / s 2 However, due to the slippery road surface, the actual deceleration effect of the vehicle is -3.5m / s 2 , at this time the road environment has an inhibitory effect on the control output. Considering this problem, in the embodiment of the present application, the time function of the control output under the influence of the influencing factors can be determined in the following way.

[0087] Method 2: When the influencing factor includes the environment inhibiting the control output, first obtain environmental data for the current driving scenario. Environmental data may include current weather, road conditions, etc., and can also be obtained using the vehicle's sensor system and V2X communication technology.

[0088] Afterwards, a suppression coefficient is determined based on the environmental data; wherein the suppression coefficient is used to characterize the suppression effect from the expected control output of the vehicle to the actual control effect of the vehicle due to the road environment.

[0089] Finally, the control output is corrected according to the suppression coefficient to obtain a time function of the control output.

[0090] For example, the time function of the control output can be expressed as: a(t)=a′ max =λa max ω(t)=ω′ max =λω max , λ is the friction coefficient, and its value range is 0<λ≤1. On dry roads, λ≈1; on wet or icy roads, λ drops to 0.3 or lower.

[0091] Furthermore, considering the above two points, in the third approach, when the influencing factors include the control execution delay of the vehicle and the suppression of the control output by the environment, the time function of the control output can be expressed as:

[0092]

[0093] On the other hand, the motion constraint of the ego vehicle includes at least one of the following: a maximum constraint of the control output, an anti-rollover constraint, and a road constraint;

[0094] For the maximum constraint of the control output, the control output is greater than or equal to the maximum deceleration of the vehicle in the current driving scenario, and less than or equal to the maximum acceleration of the vehicle; the formula is expressed as:

[0095] a′ min ≤a′ t ≤a′ max

[0096] -ω′ max ≤ω′ t ≤ω′ max

[0097] where a′ max is the maximum acceleration of the vehicle, a′ min is the maximum deceleration (negative acceleration during sudden braking); ω′ max is the maximum angular velocity that the vehicle can achieve. This constraint takes into account the road friction coefficient λ that suppresses the output.

[0098] The anti-rollover constraint is to ensure that the vehicle itself does not have the risk of rolling over when performing the combined lateral and longitudinal extreme maneuvers to avoid risks. Therefore, the lateral acceleration of the vehicle is less than or equal to the lateral acceleration threshold of the vehicle in the current driving scenario. The critical lateral acceleration threshold can be defined based on the vehicle's center of gravity height h and wheelbase b, and the formula is expressed as where g is the acceleration due to gravity, a′ lat is the vehicle's lateral acceleration taking output suppression into account.

[0099] The lateral acceleration a′ lat Through a′ lat =v t ·ω t Calculation shows that if the lateral acceleration exceeds this critical value, the vehicle may roll over, so a rollover constraint is set:

[0100] Regarding the road constraint, if there is a physical obstruction at the road boundary in the current driving scenario, the vehicle position must not exceed the road boundary.

[0101] Road constraints are considered in two cases: a hard constraint when there are physical obstructions at the road boundary; and a soft constraint when there are no physical obstructions. A soft constraint allows the vehicle to choose to exit the road area when performing emergency risk avoidance. In this case, the reachable region is not considered during the controllability calculation.

[0102] Here, by obtaining environmental data, it is possible to determine whether there is a physical obstruction at the road boundary in the current driving scene; if so, the lateral boundary of the road is set to [y min ,y max ], the road constraint means that the position of the vehicle needs to satisfy y min ≤y t ≤y max .

[0103] Optionally, constraints can be adjusted dynamically based on the vehicle state, or more complex physical models can be used to predict and constrain vehicle behavior.

[0104] In one example, the actual kinematic model of the constructed ego vehicle can be expressed as:

[0105]

[0106] The reachable domain is the set of all road areas a vehicle can reach within its constraints. Specifically, the reachable domain is based on the vehicle's motion model. Within the constraints, it calculates all possible vehicle positions for each frame in the next T time steps. Since the vehicle's position is continuous over time, the reachable domain of a vehicle diffuses over time and has a continuous boundary.

[0107] In a possible implementation, step S103 may include:

[0108] The operation trajectory of the other entity within the preset control period is predicted based on the operation data of the other entity; and the spatial intersection of the operation trajectory and the self-vehicle reachable domain is removed from the self-vehicle reachable domain to obtain the interactive reachable domain.

[0109] In this step, the positions of other entities within a preset future control period can be predicted based on the prediction model and operational data, forming a motion trajectory. The prediction model can be consistent with the motion model of the ego vehicle described above. The prediction method infers the future trajectory based on the current position and velocity of the entity, resulting in a valid time-series trajectory and future position.

[0110] Afterwards, the spatial intersection of the running trajectory and the ego vehicle reachable domain is removed from the ego vehicle reachable domain to obtain the interactive reachable domain.

[0111] Here, in order to facilitate calculation, an embodiment of the present application provides a road rasterization method.

[0112] Specifically, the road is gridded, all reachable areas within the next T frames are discretized into grids, and each grid point is calculated to determine whether it is within the constraints. The reachable domain is divided into n×m grids, each with a size of Δx and Δy. For each grid point (x, y), if there is a path from the initial state that satisfies all constraints to reach that point, then the point belongs to the ego vehicle's reachable domain. All possible grid points are traversed, and those that meet the above conditions are marked as part of the ego vehicle's reachable domain:

[0113]

[0114] The Grid Space is the gridded road, and the Reachable Set describes a set that contains all grid points (x, y) that can be reached by the vehicle under given constraints. Specifically, the reachable domain of the vehicle is composed of those points that can be reached by a certain acceleration a′. t and angular velocity ω tThe set of all (x, y) points that can be reached from the initial state in the next T frames and comply with all given constraints. In other words, if there is a path that allows the vehicle to move from the current state to a certain position (x, y) without violating any constraints in the process, then this position (x, y) is included in the reachable domain of the vehicle.

[0115] At the same time, the reachable domains of all other entities in the scene are represented using the same grid representation method. Based on the future positions of other entities in the scene, after aligning the predicted time frame, the grid cells in the reachable domain of the ego vehicle are marked as occupied. In a frame within the future T timeframe, if a grid cell in the reachable domain of the ego vehicle is occupied by another entity, the occupied grid cell is marked as unreachable.

[0116] Accordingly, step S104 may include:

[0117] The lanes in the current driving scenario are subjected to grid discretization processing; and a first grid corresponding to the ego-vehicle reachable domain and a second grid corresponding to the interactive reachable domain are determined.

[0118] If a uniformly sized grid is used for road division, the ratio of the number of grid cells in the second grid to the number of grid cells in the first grid can be used to determine the controllability index of the vehicle at the current moment. Alternatively, grid cells of different sizes or an adaptive grid division method can be used, in which case the ratio of the area of ​​the second grid to the area of ​​the first grid can be used to determine the controllability index of the vehicle at the current moment. Furthermore, the use of non-uniform grids or other spatial discretization techniques, such as quadtree or octree structures, can also be considered to improve efficiency or accuracy.

[0119] It should be noted that when real-time performance is not a concern, non-rasterized methods can also be used to determine the vehicle's reachable area, such as using geometric methods (such as convex hulls) or trajectory optimization methods in continuous space. Although more complex, such methods can provide higher accuracy and flexibility in some cases.

[0120] Furthermore, the preset control period T includes multiple control time nodes t (prediction frames); then step S104 may further include:

[0121] For each control time node, the index value of the controllability index corresponding to the control time node is determined based on the vehicle's reachable subdomain and interactively reachable subdomain corresponding to the control time node; the minimum index value among the index values ​​of the controllability index corresponding to each control time node is determined as the index value of the vehicle's controllability index at the current moment.

[0122] Assume that the occupied area of ​​the other car in the grid of time frame t is The controllability of the current prediction frame is defined as the proportion of the unoccupied reachable domain:

[0123]

[0124] in, It means that when t=T, the subdomain that the vehicle can reach under the prediction frame is is the number of grid cells in the subdomain that the vehicle can reach under the prediction frame. is the number of grids occupied by other cars in the prediction frame; It is the number of grid cells in the subdomain that is not occupied by other vehicles in the prediction frame, that is, the number of grid cells in the interactively reachable subdomain.

[0125] Similarly, the ratio between the interactive reachable area and the ego vehicle reachable area in each prediction frame represents the quantitative result of the ego vehicle's ability to reach a safe area without colliding with other traffic participants under all possible operations at that point in time, that is, its controllability.

[0126] Furthermore, the controllability of the current frame t0 is calculated based on the reachable domain of the next T frames. For each frame t, the proportion of the reachable domain that is not occupied is calculated, and the minimum value among all frames is taken as the controllability of the current frame:

[0127]

[0128] The embodiment of the present application provides a quantitative evaluation method for the controllability of potential driving hazards. On the one hand, it proposes a method for calculating the vehicle's reachable domain that comprehensively considers execution delay, output suppression, control output maximum constraint, road constraint, and anti-rollover constraint. By introducing a kinematic model and a control time function to simulate the actual control process of the driver or the automatic driving system in the risk avoidance operation, the controllability evaluation is closer to the actual situation. In addition, by considering the influence of the road friction coefficient, the accuracy of the evaluation under different road conditions is ensured. This method overcomes the limitations of the traditional method of relying solely on expert experience for qualitative grading, and provides an objective and quantifiable evaluation standard.

[0129] On the other hand, a gridding approach is used to discretize all reachable areas within the next T frames into grids and calculate whether each grid point is within the constraints. This approach simplifies the calculation of the controllability index and improves computational efficiency. By comparing the reachable area of ​​the ego vehicle with the predicted positions of other vehicles, the occupied grids are marked and the controllability index is calculated for each frame, thus achieving an accurate assessment of the ego vehicle's controllability.

[0130] See also Figure 2 , Figure 2This is a schematic diagram of the structure of a quantitative evaluation system for the controllability of potential driving hazards provided by an embodiment of the present application. The evaluation system can be run on a vehicle controller or a cloud platform. Figure 2 As shown in , the evaluation system provided by the embodiment of the present application includes: a data reading system 201, a controllability calculation system 202 and an output and recording module 203.

[0131] The data reading system 201 is responsible for collecting all necessary input information and providing basic data for subsequent calculations. The system consists of three submodules: road information acquisition module, other vehicle information acquisition module and own vehicle information acquisition module.

[0132] The road information acquisition module uses on-board sensors (e.g., cameras, lidar) and V2X communication technology to obtain road parameters of the current driving environment, including lane width, curvature radius, road surface coverage, and road boundary type. High-precision map data can also be used to supplement or correct the information collected by the sensors. The other vehicle information acquisition module uses V2V (vehicle-to-vehicle) communication technology to receive the position coordinates, speed, and other relevant information of other nearby vehicles. In the absence of V2V communication, on-board radar, lidar, and visual sensors can be used to monitor surrounding traffic conditions. The vehicle information acquisition module directly reads key parameters such as position coordinates, speed, acceleration, and heading angle from the vehicle's own sensor system. These sensors typically include, but are not limited to, GPS positioning systems, inertial measurement units (IMUs), and wheel speed sensors. Data exchange between these submodules can be carried out via the CAN bus or Ethernet.

[0133] The controllability calculation system 202 performs real-time vehicle controllability calculation based on the data collected by the data reading system 201, including three parts: vehicle reachable area calculation module, vehicle position prediction and vehicle controllability calculation.

[0134] The vehicle reachable domain calculation module calculates the vehicle's reachable domain for the next T time steps based on the vehicle motion control model, taking into account execution delay, output suppression, maximum control output constraints, road boundary constraints, and rollover prevention constraints. The vehicle position prediction module predicts the future trajectory of the ego vehicle and the future positions of other vehicles in the scene. This step relies on the vehicle motion model and infers future trajectories based on the current state. The ego vehicle controllability calculation module compares the ego vehicle's reachable domain with the predicted positions of other vehicles, marking the occupied grids using a rasterization method. Based on this, the controllability index is calculated for each frame. Finally, the minimum value is selected as the ego vehicle's controllability at the current moment. In this process, the grid size must balance computational efficiency and accuracy and can generally be flexibly adjusted based on the actual application scenario.

[0135] The output and recording module 203 is responsible for outputting the calculated real-time controllability results of the vehicle to the driver or the autonomous driving system and storing this information for subsequent analysis. This output can be presented through intuitive dashboard displays, audio prompts, or more detailed visualizations via the HMI interface. Furthermore, to facilitate data analysis and accident investigations, all controllability information is recorded, with storage available on the vehicle's hard drive or in a cloud service.

[0136] See also Figure 3 , Figure 3 This is a schematic diagram of a device for quantitatively evaluating the controllability of potential driving hazards provided by an embodiment of the present application. Figure 3 As shown in , the quantitative evaluation device 300 includes:

[0137] An acquisition module 310 is used to acquire the operating data of the vehicle and other entities in the current driving scenario;

[0138] A first determination module 320 is configured to determine a reachable domain of the ego vehicle within a preset control period starting at a current moment based on the ego vehicle's operating data, a kinematic model of the ego vehicle, a time function of a control output under influencing factors, and the ego vehicle's motion constraints. The reachable domain of the ego vehicle refers to a spatial range to which the ego vehicle can move based on its own motion capabilities in the current driving scenario. Influencing factors include a control execution delay of the vehicle and / or environmental suppression of the control output. The time function of the control output represents how the control output changes over time.

[0139] A second determining module 330 is configured to determine, from the ego vehicle's reachable domain, an interactive reachable domain of the ego vehicle within the preset control period based on the operating data of the other entity; wherein the interactive reachable domain refers to a spatial range to which the ego vehicle can actually move when considering the motion trajectory of the other entity;

[0140] The third determining module 340 is configured to determine an index value of a controllability index of the ego vehicle at the current moment according to the ego vehicle reachable domain and the interactive reachable domain.

[0141] Furthermore, when the first determining module 320 is configured to determine the reachable range of the ego vehicle within a preset control period from the current moment based on the operating data of the ego vehicle, in combination with the ego vehicle's kinematic model, the time function of the control output under the influence of influencing factors, and the ego vehicle's motion constraints, the first determining module 320 is configured to:

[0142] constructing an actual kinematic model of the ego vehicle based on the kinematic model of the ego vehicle, the time function of the control output, and the motion constraints of the ego vehicle;

[0143] Substituting the operating data of the ego vehicle at the current moment into the actual kinematic model, and calculating backward the preset control period to determine the spatial position of the ego vehicle within the preset control period;

[0144] The reachable area of ​​the ego vehicle is determined according to the spatial position of the ego vehicle within the preset control period.

[0145] Furthermore, when the influencing factor includes the control execution delay of the vehicle, the quantitative evaluation device 340 includes: a function determination module; the function determination module is used to determine the time function of the control output under the influence of the influencing factor in the following manner:

[0146] Determine the driving mode of the ego vehicle;

[0147] When the driving mode is manual driving, the delay time is determined according to the human perception delay time and the human action delay time;

[0148] When the driving mode is automatic driving, determining the delay time according to the operation time of the automatic driving system;

[0149] The control output is corrected according to the delay time to obtain a time function of the control output.

[0150] Furthermore, when the influencing factors include the inhibition of the control output by the environment, the function determination module is further configured to determine the time function of the control output under the influence of the influencing factors in the following manner:

[0151] Obtain environmental data in the current driving scenario;

[0152] Determining a suppression coefficient based on the environmental data; wherein the suppression coefficient is used to characterize the suppression effect from the expected control output of the vehicle to the actual control effect of the vehicle due to the road environment;

[0153] The control output is corrected according to the suppression coefficient to obtain a time function of the control output.

[0154] Furthermore, the motion constraint of the ego vehicle includes at least one of the following: a maximum constraint of the control output, an anti-rollover constraint, and a road constraint;

[0155] For the maximum constraint of the control output, the control output is greater than or equal to the maximum deceleration of the vehicle in the current driving scenario, and less than or equal to the maximum acceleration of the vehicle;

[0156] For the anti-rollover constraint, the lateral acceleration of the vehicle is less than or equal to the lateral acceleration threshold of the vehicle in the current driving scenario;

[0157] Regarding the road constraint, if there is a physical obstruction at the road boundary in the current driving scenario, the vehicle position must not exceed the road boundary.

[0158] Furthermore, when the second determining module 330 is used to determine the interactive reachable domain of the own vehicle within the preset control period from the own vehicle reachable domain based on the operation data of the other entity, the second determining module 330 is used to:

[0159] Predicting the operation trajectory of the other entities within the preset control period based on the operation data of the other entities;

[0160] The interactively reachable domain is obtained by removing the spatial intersection of the running trajectory and the reachable domain of the own vehicle from the reachable domain of the own vehicle.

[0161] Furthermore, when the third determination module 340 is used to determine the index value of the controllability index of the ego vehicle at the current moment based on the ego vehicle reachable domain and the interactive reachable domain, the third determination module is used to:

[0162] Performing rasterization and discretization processing on the lanes in the current driving scenario;

[0163] Determine a first grid corresponding to the vehicle's reachable domain and a second grid corresponding to the interactive reachable domain;

[0164] The ratio of the number of the second grids to the number of the first grids is determined as the index value of the controllability index of the vehicle at the current moment; or, the ratio of the area of ​​the second grid to the area of ​​the first grid is determined as the index value of the controllability index of the vehicle at the current moment.

[0165] Furthermore, the preset control period includes a plurality of control time nodes; when the third determination module 340 is used to determine the index value of the controllability index of the ego vehicle at the current moment based on the ego vehicle reachable domain and the interactive reachable domain, the third determination module is used to:

[0166] For each control time node, determine the index value of the controllability index corresponding to the control time node according to the vehicle-reachable subdomain and the interactively reachable subdomain corresponding to the control time node;

[0167] The minimum index value among the index values ​​of the controllability index corresponding to each control time node is determined as the index value of the controllability index of the vehicle at the current moment.

[0168] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 4As shown in FIG, the electronic device 400 includes a processor 410 , a memory 420 and a bus 430 .

[0169] The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device 400 is running, the processor 410 communicates with the memory 420 via the bus 430. When the machine-readable instructions are executed by the processor 410, the above-mentioned Figure 1 The steps of the method for quantitatively evaluating the controllability of potential driving hazards in the method embodiment shown are specifically implemented in accordance with the method embodiment and will not be described in detail here.

[0170] The embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 1 The steps of the method for quantitatively evaluating the controllability of potential driving hazards in the method embodiment shown are specifically implemented in accordance with the method embodiment and will not be described in detail here.

[0171] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0172] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. There may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed may be through some communication interface, indirect coupling or communication connection of devices or units, which may be electrical, mechanical or other forms.

[0173] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0174] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0175] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0176] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The scope of protection of the present application is not limited thereto. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-mentioned embodiments within the technical scope disclosed in the present application, or perform equivalent replacements for some of the technical features thereof. These modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A quantitative evaluation method for the controllability of potential driving hazards, characterized by: The method comprises: Obtain the vehicle's operating data and other entities' operating data in the current driving scenario; Based on the vehicle's operating data, combined with the vehicle's kinematic model, the time function of the control output under the influence of influencing factors, and the vehicle's motion constraints, the vehicle's reachable domain within a preset control period from the current moment is determined; wherein the vehicle's reachable domain refers to the spatial range to which the vehicle can move based on its own motion capabilities in the current driving scenario; the influencing factors include the vehicle's control execution delay and / or the inhibition of the control output by the road environment; the time function of the control output is used to characterize the change of the control output over time; the boundary of the vehicle's reachable domain is determined based on the edge to which the vehicle can move under the maximum maneuver performed under the maximum avoidance capability, and is used to represent the upper limit of the vehicle's maneuverability and avoidance capabilities in the current driving scenario; Determining, based on the operating data of the other entities, an interactively reachable domain of the ego vehicle within the preset control period from the ego vehicle's reachable domain; wherein the interactively reachable domain refers to the spatial range to which the ego vehicle can actually move when considering the motion trajectory of the other entities; Determining an index value of a controllability index of the ego vehicle at the current moment according to the ego vehicle reachable domain and the interactive reachable domain; The preset control cycle includes a plurality of control time nodes; determining an index value of a controllability index of the ego vehicle at the current moment according to the ego vehicle reachable domain and the interactive reachable domain, including: For each control time node, determine the index value of the controllability index corresponding to the control time node according to the vehicle-reachable subdomain and the interactively reachable subdomain corresponding to the control time node; The minimum index value among the index values ​​of the controllability index corresponding to each control time node is determined as the index value of the controllability index of the vehicle at the current moment.

2. The method according to claim 1, characterized in that Determining a reachable region of the ego vehicle within a preset control period from a current moment based on the ego vehicle's operating data, in combination with the ego vehicle's kinematic model, a time function of the control output under the influence of influencing factors, and the ego vehicle's motion constraints, includes: constructing an actual kinematic model of the ego vehicle based on the kinematic model of the ego vehicle, the time function of the control output, and the motion constraints of the ego vehicle; Substituting the operating data of the ego vehicle at the current moment into the actual kinematic model, and calculating backward the preset control period to determine the spatial position of the ego vehicle within the preset control period; The reachable area of ​​the ego vehicle is determined according to the spatial position of the ego vehicle within the preset control period.

3. The method according to claim 1, characterized in that When the influencing factor includes a control execution delay of the vehicle, the method further includes determining a time function of the control output under the influence of the influencing factor by: Determine the driving mode of the ego vehicle; When the driving mode is manual driving, the delay time is determined according to the human perception delay time and the human action delay time; When the driving mode is automatic driving, determining the delay time according to the operation time of the automatic driving system; The control output is corrected according to the delay time to obtain a time function of the control output.

4. The method according to claim 1 or 3, characterized in that When the influencing factor includes the inhibition of the control output by the environment, the method further includes determining a time function of the control output under the influence of the influencing factor by: Obtain environmental data in the current driving scenario; Determining a suppression coefficient based on the environmental data; wherein the suppression coefficient is used to characterize the suppression effect from the expected control output of the vehicle to the actual control effect of the vehicle due to the road environment; The control output is corrected according to the suppression coefficient to obtain a time function of the control output.

5. The method according to claim 1, wherein The motion constraint of the vehicle includes at least one of the following: a maximum constraint of the control output, an anti-rollover constraint, and a road constraint; For the maximum constraint of the control output, the control output is greater than or equal to the maximum deceleration of the vehicle in the current driving scenario, and less than or equal to the maximum acceleration of the vehicle; For the anti-rollover constraint, the lateral acceleration of the vehicle is less than or equal to the lateral acceleration threshold of the vehicle in the current driving scenario; Regarding the road constraint, if there is a physical obstruction at the road boundary in the current driving scenario, the vehicle position must not exceed the road boundary.

6. The method according to claim 1, characterized in that Determining, based on the operation data of the other entities, an interactively reachable domain of the own vehicle within the preset control period from the own vehicle reachable domain, including: Predicting the operation trajectory of the other entities within the preset control period based on the operation data of the other entities; The interactively reachable domain is obtained by removing the spatial intersection of the running trajectory and the reachable domain of the own vehicle from the reachable domain of the own vehicle.

7. The method according to claim 1, characterized in that Determining an index value of a controllability index of the ego vehicle at the current moment according to the ego vehicle reachable domain and the interactive reachable domain includes: Performing rasterization and discretization processing on the lanes in the current driving scenario; Determine a first grid corresponding to the vehicle's reachable domain and a second grid corresponding to the interactive reachable domain; The ratio of the number of the second grids to the number of the first grids is determined as the index value of the controllability index of the vehicle at the current moment; or, the ratio of the area of ​​the second grid to the area of ​​the first grid is determined as the index value of the controllability index of the vehicle at the current moment.

8. A quantitative evaluation device for the controllability of potential driving hazards, characterized by: The device comprises: The acquisition module is used to obtain the operating data of the vehicle and other entities in the current driving scenario; A first determination module is configured to determine a reachable domain of the ego vehicle within a preset control period starting at a current moment based on the ego vehicle's operating data, a kinematic model of the ego vehicle, a time function of a control output under influencing factors, and the ego vehicle's motion constraints; wherein the reachable domain of the ego vehicle refers to a spatial range to which the ego vehicle can move based on its own motion capabilities in the current driving scenario; the influencing factors include a control execution delay of the vehicle and / or environmental suppression of the control output; and the time function of the control output is used to represent a change in the control output over time. a second determining module, configured to determine, from the ego vehicle's reachable domain, an interactive reachable domain of the ego vehicle within the preset control period based on the operating data of the other entity; wherein the interactive reachable domain refers to a spatial range to which the ego vehicle can actually move when considering the motion trajectory of the other entity; a third determining module, configured to determine an index value of a controllability index of the ego vehicle at the current moment according to the ego vehicle reachable domain and the interactive reachable domain; The preset control cycle includes a plurality of control time nodes; when the third determination module is used to determine the index value of the controllability index of the ego vehicle at the current moment based on the ego vehicle reachable domain and the interactive reachable domain, the third determination module is used to: For each control time node, determine the index value of the controllability index corresponding to the control time node according to the vehicle-reachable subdomain and the interactively reachable subdomain corresponding to the control time node; The minimum index value among the index values ​​of the controllability index corresponding to each control time node is determined as the index value of the controllability index of the vehicle at the current moment.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate through the bus. When the processor is running, the machine-readable instructions execute the steps of the method for quantitatively evaluating the controllability of potential driving hazards as described in any one of claims 1 to 7.

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