CN114529131A

CN114529131A - Risk assessment method and device, electronic equipment and storage medium

Info

Publication number: CN114529131A
Application number: CN202210001948.3A
Authority: CN
Inventors: 李博; 边俊; 胡建侃; 肖扬; 潘坚伟
Original assignee: Wuhan Lotus Cars Co Ltd
Current assignee: Wuhan Lotus Cars Co Ltd
Priority date: 2022-01-04
Filing date: 2022-01-04
Publication date: 2022-05-24

Abstract

The application relates to a risk assessment method, a risk assessment device, an electronic device and a storage medium, which comprise the steps of determining system capacity information according to state information of a vehicle driving system, determining environment safety information according to traffic environment around a vehicle, determining risk assessment information based on the system capacity information and the environment safety information, and determining a target risk assessment result according to the risk assessment information. According to the method and the device, risk assessment can be achieved, the corresponding current driving risk level is obtained according to the target risk assessment result of the current vehicle driving state, and the vehicle can execute corresponding operation for improving driving safety according to the driving risk level, so that the reliability and the safety of the driving process of the automatic driving vehicle are guaranteed.

Description

Risk assessment method and device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of automatic driving technologies, and in particular, to a risk assessment method and apparatus, an electronic device, and a storage medium.

Background

The automatic driving vehicle is a fusion product of technologies such as automobile electronics, intelligent control and internet, and the automatic driving system can replace a driver to carry out most driving operations. However, due to the complexity and real-time variability of the traffic environment facing the autonomous driving system, the capabilities of sensors, actuators, and the like of the autonomous driving vehicle are greatly affected by various environments, and therefore the autonomous driving system cannot be used for safely driving in various scenes by independently depending on the capabilities of the system.

Since the ability of an autopilot system to travel under certain conditions is dynamic and not deterministic, for example, strong backlighting or rainy and snowy weather can reduce the ability of the system to perceive the surrounding traffic environment, increasing the probability of a traffic accident. Meanwhile, the traffic conditions faced by the automatic driving system are also changed in real time, for example, traffic jam and mixed traffic flow can increase the probability of accidents, and the severity of the traffic accidents can also be improved along with the improvement of the speed of the vehicle. Therefore, it is necessary to judge the driving risk based on real-time judgment of the automatic driving performance and the traffic environment complexity.

At present, the current driving risk is evaluated only by considering the current road traffic environment, but not by considering that the capacity of the automatic driving system is fluctuant under different external environments, and the external environments can influence the control capacity of the automatic driving system on vehicles, so that the automatic driving system can possibly have the situation that safe driving cannot be realized under the same road traffic condition.

Disclosure of Invention

The embodiment of the application provides a risk assessment method, a risk assessment device, electronic equipment and a storage medium, from the perspective that an automatic driving system ensures that the safe driving capability is matched with the environmental safety information of the road traffic condition, the current driving risk is comprehensively assessed according to the automatic driving system capability, the current driving environmental safety and the state of a driver, and corresponding operation is executed according to the obtained driving risk, so that the safety of an automatic driving vehicle can be improved, and the driving risk is reduced.

The embodiment of the application provides a risk assessment method, which comprises the following steps:

determining system capacity information according to state information of a vehicle driving system;

determining environmental safety information according to the traffic environment around the vehicle;

determining risk assessment information based on the system capability information and the environmental security information;

and determining a target risk evaluation result according to the risk evaluation information.

Further, determining system capability information based on the state information of the vehicle driving system includes:

acquiring first performance information corresponding to a sensing module;

determining first state information corresponding to the sensing module based on the first performance information;

acquiring position information of a vehicle;

acquiring the information of the driving track;

acquiring second performance information corresponding to the control module;

determining second state information corresponding to the control module based on the driving track information, the position information and the second performance information;

acquiring third performance information corresponding to the execution module;

determining third state information corresponding to the execution module based on the third performance information;

and determining system capability information according to the first state information, the second state information and the third state information.

Further, determining environmental safety information according to a traffic environment surrounding the vehicle includes:

determining a target environment risk type according to the traffic environment;

acquiring a target environment risk type influence factor corresponding to the target environment risk type;

and determining environmental safety information based on the traffic environment and the target environment risk type influence factors.

Further, the method further comprises:

uploading the first performance information, the driving track information, the position information, the second performance information, the third performance information, the system capacity information, the environmental safety information, the risk assessment information and the target assessment result to the cloud.

Further, the method also includes:

determining driver state information;

updating the risk assessment information based on the driver status information;

and uploading the driver state information and the updated risk assessment information to a cloud.

Further, the method further comprises:

if the system capacity information, the environment safety information and/or the driver state information are lost, acquiring historical data of a cloud; the historical data comprises system capacity information, environmental safety information and/or driver state information under the same traffic environment;

missing system capability information, environmental safety information, and/or driver status information is determined based on historical data.

Further, after determining the target risk assessment result according to the risk assessment information, the method includes:

grading the range of the risk assessment information to obtain risk grade information;

determining a risk level corresponding to the current vehicle based on the target risk assessment result and the risk level information;

and if the risk level reaches a preset level, sending a safe driving operation instruction corresponding to the risk level to a vehicle driving system and/or sending a safe driving prompt corresponding to the risk level to a driver based on the level classification information.

Correspondingly, the embodiment of the application also provides a risk assessment device, which comprises:

the system capacity information determining module is used for determining system capacity information according to the state information of the vehicle driving system;

the environment safety information determining module is used for determining environment safety information according to the traffic environment around the vehicle;

the risk assessment information determining module is used for determining risk assessment information based on the system capacity information and the environmental safety information;

and the target risk assessment result determining module is used for determining a target risk assessment result according to the risk assessment information.

Further, a system capability information determination module to:

acquiring first performance information corresponding to a sensing module;

acquiring position information of a vehicle;

acquiring the information of the driving track;

acquiring second performance information corresponding to the control module;

acquiring third performance information corresponding to the execution module;

Further, the environment security information determination module is configured to:

Further, the apparatus further comprises:

and the data transmission module is used for uploading the first performance information, the driving track information, the position information, the second performance information, the third performance information, the system capacity information, the environmental safety information, the risk assessment information and the target assessment result to the cloud.

Further, the apparatus further comprises:

the driver state information determining module is used for determining the driver state information;

the risk assessment information updating module is used for updating the risk assessment information based on the state information of the driver;

and the data transmission module is used for uploading the driver state information and the updated risk assessment information to the cloud.

Further, the apparatus further comprises:

the historical data acquisition module is used for acquiring the historical data of the cloud if the system capacity information, the environmental safety information and/or the driver state information are lost; the historical data comprises the system capacity information, the environmental safety information and/or the driver state information under the same traffic environment;

a missing data determination module to determine missing system capability information, environmental safety information, and/or driver status information based on historical data.

Further, the apparatus further comprises:

the risk grade information determining module is used for carrying out grade division on the range of the risk evaluation information to obtain risk grade information;

the risk grade determining module is used for determining the risk grade corresponding to the current vehicle based on the target risk evaluation result and the risk grade information;

and the safe driving operation instruction sending module is used for sending a safe driving operation instruction corresponding to the risk level to the vehicle driving system and/or sending a safe driving prompt corresponding to the risk level to the driver based on the grade division information if the risk level reaches the preset level.

Accordingly, an embodiment of the present application further provides an electronic device, which includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or a set of instructions, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the risk assessment method described above.

Accordingly, embodiments of the present application also provide a computer-readable storage medium, in which at least one instruction, at least one program, a code set, or a set of instructions is stored, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by a processor to implement the risk assessment method described above.

By adopting the technical scheme, the invention has the following beneficial effects:

(1) considering that the fluctuation of the automatic driving system exists in the actual driving state, and the external environment comprises weather, illumination, position, road curvature, road gradient, adhesion coefficient and the like, which can influence the control capability of the automatic driving system on the vehicle, so that the automatic driving system can possibly have the situation that safe driving cannot be realized under the same road traffic condition, the capability of the automatic driving system and the risk existing in the current road traffic condition are analyzed in real time, the automatic driving system and the current road traffic condition are comprehensively evaluated, the real-time dynamic driving risk calculation is realized, and the authenticity and the reliability of the obtained current risk information are ensured;

(2) by additionally evaluating the state information of the driver, the expected state of the driver is evaluated, the driver can be ensured to participate in driving operation in time according to the actual driving risk condition, the dynamic change of the state of the driver is allowed, and the user experience is improved;

(3) and carrying out risk grade division according to the obtained risk evaluation result, and executing operation favorable for driving safety according to the corresponding risk grade so as to avoid environmental risk, avoid traffic accidents and improve the safety of the automatic driving system vehicle.

Drawings

In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.

FIG. 1 is a schematic diagram of an application environment provided by an embodiment of the present application;

fig. 2 is a schematic flowchart of a risk assessment method according to an embodiment of the present application;

fig. 3 is a first schematic flowchart of a risk assessment method according to an embodiment of the present application;

fig. 4 is a schematic flowchart illustrating a risk assessment method according to an embodiment of the present application;

fig. 5 is a schematic flowchart illustration three of a risk assessment method provided in the embodiment of the present application;

fig. 6 is a fourth schematic flowchart of a risk assessment method according to an embodiment of the present application;

fig. 7 is a schematic flowchart of a risk assessment method according to an embodiment of the present application;

fig. 8 is a sixth schematic flowchart of a risk assessment method according to an embodiment of the present application;

fig. 9 is a schematic structural diagram of a risk assessment apparatus according to an embodiment of the present application;

fig. 10 is a block diagram of a hardware structure of a server of a risk assessment method according to an embodiment of the present application.

Detailed Description

To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings. It should be apparent that the described embodiment is only one embodiment of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

An "embodiment" as referred to herein relates to a particular feature, structure, or characteristic that may be included in at least one implementation of the present application. In the description of the embodiments of the present application, it should be understood that the terms "upper", "lower", and the like refer to orientations or positional relationships based on those shown in the drawings, and are used for convenience in describing the present application and simplifying the description, but do not indicate or imply that the referred device/system or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus should not be construed as limiting the present application. The terms "first", "second", "third", "fourth" and "fifth" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first," "second," "third," "fourth," and "fifth" may explicitly or implicitly include one or more of the features. Moreover, the terms "first," "second," "third," "fourth," and "fifth," etc. are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in other sequences than described or illustrated herein. Furthermore, the terms "comprising," "having," and "being," as well as any variations thereof, are intended to cover non-exclusive inclusions.

Referring to fig. 1, fig. 1 is a schematic view of an application environment provided by an embodiment of the present application, where the schematic view includes a vehicle 101 and a server 102, where in an alternative implementation, the server 102 may be an on-board server disposed in the vehicle 101, and the on-board server may obtain desired data in real time so as to obtain a result of risk assessment later. In another alternative embodiment, the vehicle 101 may be provided with its own vehicle-mounted server, and the vehicle-mounted server is not the same as the server 102 shown in fig. 1, and after the vehicle-mounted server transmits the obtained data to the server 102, the server may complete the subsequent steps to finally obtain the result of risk assessment. The in-vehicle server relating to the first case and the server relating to the second case will be collectively referred to as a server hereinafter. In another alternative embodiment, the server may be an external server, such as one provided by the vehicle manufacturer.

Optionally, the server may include an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a web service, cloud communication, a middleware service, a domain name service, a security service, a CDN (Content Delivery Network), a big data and artificial intelligence platform, and the like.

Specifically, the server 102 may determine system capability information from state information of a driving system of the vehicle 101, determine environmental safety information from a traffic environment of the vehicle, determine risk assessment information based on the system capability information and the environmental safety information, and determine a target risk assessment result from the risk assessment information.

While specific embodiments of a risk assessment method of the present application are described below, the present specification provides the method steps as shown in the embodiments or flowcharts, but may include more or less steps based on routine or non-inventive efforts. The order of steps recited in the embodiments is only one of many possible orders of execution and does not represent the only order of execution, and in actual execution, the steps may be performed sequentially or in parallel as in the embodiments or methods shown in the figures (e.g., in the context of parallel processors or multi-threaded processing). Fig. 2 is a schematic flowchart of a risk assessment method provided in an embodiment of the present application, and as shown in fig. 2, the method may include:

s201: determining system capacity information according to state information of a vehicle driving system;

in an alternative embodiment, the system capability information may include general information of the vehicle's perception corresponding to cognitive system status, control system status, and executive system status. The state of the automatic driving system can be evaluated by analyzing the external natural environment conditions, the internal software and hardware running conditions and the vehicle running state. Optionally, the operating conditions of the software and the hardware in the vehicle may include failure or invalidation of the hardware equipment, failure of the software system to obtain a calculation result within a specified time, communication timeout inside the software module and between the software modules, and the like. Alternatively, the vehicle running state may include a six-degree-of-freedom speed and acceleration of the vehicle, and the like. Alternatively, the six degrees of freedom of the vehicle are expressed as the vehicle performing translation along the x-axis, translation along the y-axis, translation along the z-axis, rotation about the x-axis, rotation about the y-axis, and rotation about the z-axis.

In the embodiment of the present application, fig. 3 is a schematic flowchart of a risk assessment method provided in the embodiment of the present application, where system capability information is determined according to state information of an automatic driving system of a vehicle, and specifically as shown in fig. 3, the method includes the following steps:

s2011: acquiring first performance information corresponding to a sensing module;

in an alternative embodiment, the perception module can confirm the road traffic environment around the vehicle and establish a world model of the environment where the current vehicle is located. The sensing module needs to be assisted by various sensors when the sensing module completes duties, the sensors which are widely used at present comprise a camera, a millimeter wave radar, an ultrasonic radar, a laser radar, a combined positioning system and the like, and the sensing module can confirm the traffic environment of surrounding roads and establish a world model of the current environment where vehicles are located.

In an alternative embodiment, the factors affecting the performance of the sensing module in the current driving state may be obtained from the limitations of the sensors, for example, the current vehicle is in a tunnel condition, the current vehicle is in a rainy or snowy weather, the current ambient temperature, the backlight condition, and the like. The method comprises the steps of obtaining the current traffic environment of the vehicle through a sensing module, and obtaining first performance information which influences the performance of the sensing module at present.

S2012: determining first state information corresponding to the sensing module based on the first performance information;

in an optional implementation mode, the influence of the ability of the sensing module on part of indexes under the influence condition of each factor is evaluated in a theoretical analysis or actual test mode to obtain first state information. Optionally, the influence on the performance of the current vehicle sensing module may be obtained according to the obtained first performance information, and the first state information of the sensing module may be determined by integrating influences on some indexes of the vehicle, such as an influence on an index of a target position error, an error of a target course angle, an error of a target speed measurement, an error of a target acceleration measurement, an error of an end point position of a target enveloping polygon, an identification recall rate, an identification accuracy rate, and the like.

In an alternative embodiment, the index may be evaluated using actual testing. Specifically, for example, the vehicle may be used to simulate the situation of the vehicle in the rainy day in the test stage, including the capability of detecting indexes such as a speed measurement error of a target object for the vehicle sensing system under different rainfall conditions, obtain a mapping relationship of the external environment to the capability of the vehicle automatic driving system sensing module through test data of a large number of different external environment conditions, obtain the current first performance information obtained by the vehicle in the actual driving process, correspondingly obtain the influence on each index through the mapping relationship, and synthesize the current influence on each index to determine the first state information of the sensing module.

In another alternative embodiment, the indicators may be evaluated using theoretical analysis. Optionally, for example, when the current vehicle is in an extreme weather condition, such as a strong typhoon, and an influence situation on each index may occur when the wind power level of the typhoon or a strong rainfall condition is not obtained in the test stage, the first state information corresponding to the weather condition may be obtained according to a fitting or extension result by fitting or extending test data of other wind powers.

In an optional implementation manner, the first state information determined according to the appropriate indexes may be operations such as combination, superposition, priority ranking and the like of the performance influence of each index on the sensing module, the operations such as combination, superposition and the like may be performed with the same weight, different weights may be assigned according to the importance degree of each index, and then the operations such as combination, superposition and the like may be performed, so that a higher weight is assigned to an index that influences the automatic driving safety.

In an alternative embodiment, the first state information may be a specific score value, and may also be a table, a curve, a function, a state grade, and the like.

S2013: acquiring position information of a vehicle;

in an optional embodiment, the vehicle locates the vehicle through an on-board sensing module, and may acquire the current location information of the vehicle, and optionally, the location information may be coordinates acquired by a Global Positioning System (GPS), including three-dimensional coordinates of longitude-latitude-height, and may further include an absolute location of the vehicle and a relative location on the road.

S2014: acquiring the information of the driving track;

in an alternative embodiment, the driving path information output by the planning module of the automatic driving system in real time is obtained, and the control module is mainly responsible for controlling the vehicle to track the driving path information. The deviation of the vehicle position and trajectory may be a criterion for evaluating the control system capability.

S2015: acquiring second performance information corresponding to the control module;

in an optional implementation manner, second performance information affecting the performance of the control module is obtained, optionally, for example, the shape of the trajectory is a factor affecting the control system, the shape of the trajectory is closely related to the shape of the current road, and the ability of the control system is evaluated from the shape of the current road, and the road curvature change rate are used to evaluate the ability of the control system.

S2016: determining second state information corresponding to the control module based on the driving track information, the position information and the second performance information;

in an alternative embodiment, the second performance information is a factor that affects the output control amount of the control module to the vehicle when the control module is currently running, and the second state information corresponding to the control module at the time can be determined by using a theoretical analysis or an actual test mode through appropriate indexes, such as control deviation, control overshoot, control reaction time and the like. Optionally, the control module may output a control quantity according to the shape of the driving track, the current vehicle position, and the deviation of the track, where the control quantity includes a target acceleration, a target steering wheel angle, and the like, and obtain the control capability of the control module under the current road through the difference between the output target driving track and the actual driving track, so as to determine the second state information of the control module during the current driving.

In an optional implementation manner, the second state information determined according to the appropriate indexes may be superposition of the influence of each index on the performance of the control module, such as control deviation, control overshoot, control reaction time, and the like, the indexes influencing the capability of the control module may be combined and superposed with the same weight, different weights may be assigned according to the importance degree of each index, and then the combination and superposition operations are performed, so that a higher weight is assigned to the index influencing the higher safety of automatic driving.

In an alternative embodiment, the second state information may be a specific score value, and may also be a table, a curve, a function, a state grade, and the like.

S2017: acquiring third performance information corresponding to the execution module;

in an alternative embodiment, the execution module is mainly responsible for implementing the control quantity output by the control module, and optionally, the control quantity may include a target acceleration, a target steering wheel and the like. The third performance information corresponding to the execution module may be the execution capacity of the control amount output by the control module by the execution module. Alternatively, the factors that affect the performance of the actuator module include road adhesion coefficient, grade, since the actuator module directly addresses the road's effect on the wheels. Alternatively, the execution module sends instructions directly to the actuator, so a failure of the execution module may also affect the capability of the execution module.

S2018: determining third state information corresponding to the execution module based on the third performance information;

in an alternative embodiment, third performance information affecting the capability of the execution module is obtained, and the third state information corresponding to the capability of the module may be perceived by using a theoretical analysis or an actual test according to a suitable index, where the suitable index is an index for evaluating the capability of the execution module, such as acceleration tracking accuracy, speed tracking accuracy, front wheel steering angle speed tracking accuracy, front wheel torque tracking accuracy, and the like. The indexes influencing the performance of the execution module can be combined, superposed, prioritized and the like, the indexes influencing the capability of the control module can be combined, superposed and the like with the same weight, different weights can be distributed according to the importance degree of each index for combining, superposing and the like, and higher weight is distributed to the indexes influencing the automatic driving safety.

In an optional implementation manner, the third state information of the execution module may also be a case where the execution module fails, for example, the execution module cannot send an instruction to the actuator within a specified time, and the performance of the execution module is further affected. Optionally, a fault indication, such as a fault in the autopilot system, indicates a fault in the implement module.

In an alternative embodiment, the third state information may be a specific score value, and may also be a table, a curve, a function, a state grade, and the like.

S2019: and determining system capability information according to the first state information, the second state information and the third state information.

In an optional implementation manner, the first state information, the second state information, and the third state information are comprehensively evaluated to obtain system capability information. Optionally, a functional relationship is preset for the first state information, the second state information and the third state information, the first state information, the second state information and the third state information may be combined, superposed, prioritized and the like with the same weight, different weights may be assigned according to the importance degree of driving safety by the sensing module, the control module and the execution module, and then the combination, superposition and the like may be performed, so that a higher weight is correspondingly assigned to a module that affects the safety of automatic driving.

In an alternative embodiment, the system capability information may be a specific score value, and may also be a table, a curve, a functional relationship, a status level, and the like.

In an alternative embodiment, the state of the autopilot system further includes a prediction module state, a decision module state, a planning module state, a human-machine interaction module state, etc. of the vehicle. The states of the prediction module, the decision module, the planning module, the human-computer interaction module and the like when the current vehicle runs in real time can be determined in a manner similar to the determination of the first state information of the sensing module and the like, and the state information of the modules is added into the system capacity information for comprehensive consideration.

The function of evaluating the capability of the automatic driving system is to evaluate the self performance of the automatic driving system facing safety under the current environment and ensure the capability of vehicle driving safety. When the algorithm of the automatic driving system is completely determined, the environment affecting the automatic driving system is the environment outside the system, and the environmental safety information of the traffic environment where the vehicle is located may have an influence on the driving risk.

S203: determining environmental safety information according to the traffic environment around the vehicle;

in an alternative embodiment, the environmental safety information may indicate a potential risk of the current road traffic environment to the safe driving of the vehicle, which may pose a threat to the safe driving of the vehicle. Damage that occurs to a vehicle during travel can be described as damage due to a collision, and damage can be used to describe the severity of the collision. Alternatively, the damage includes damage to the own vehicle and passengers and articles in the own vehicle by the collision, and damage to other participating objects by the collision. The collision is not certain, the severity of the collision is uncertain, and different types of collision probabilities and calculation methods of the severity of the collision are different and may be related to the current speed of the vehicle and the mass of the vehicle.

In an optional implementation mode, the traffic environment around the vehicle is obtained when the vehicle runs, and the traffic environment comprises static and dynamic traffic participants, road obstacles, road condition risks, view blind areas and the like. The damage of colliding other participants may be caused by collisions with stationary and dynamic traffic participants, including motor vehicles, non-motor vehicles, pedestrians, animals, etc. in motion, and road obstacles, including obstacles appearing on all roads, such as roadblocks, construction areas, traffic lights, isolation zones, guardrails, road potholes, road ponding, cargo dropped by other vehicles, etc. The damage caused by collision with other participating objects can be caused by vehicles entering into a no-driving area, and the no-driving area can be an area where vehicles cannot drive, such as the areas where vehicles cannot drive around all roads, including flower beds, sidewalks with road edge stones, buildings, rivers, cliffs, mountains, areas outside viaducts causing vehicles to fall, and the like.

In an alternative embodiment, the traffic environment around the vehicle may be obtained by the sensing module. Specifically, the vehicle surroundings are monitored in real time by a detection device such as a camera, a radar, a sensor, and the like, and a high-precision map or a navigation system, so that the traffic environment around the vehicle under the current driving condition is acquired in real time. Optionally, for example, when an obstacle which may threaten the driving safety of the vehicle is detected, acquiring the traffic environment corresponding to the dynamic obstacle in real time, including the relative distance, the relative speed, the shape and the type of the obstacle and the like of the obstacle and the vehicle; optionally, for example, the current traffic risk is acquired, the navigation system obtains the traffic risk such as a section with a high accident probability or a congested section in front of the vehicle driving path according to the vehicle-mounted map, and acquires information such as a starting point position corresponding to the traffic risk, a relative distance between the traffic risk and the vehicle, and the like in real time; optionally, for example, when a blind area of a field of view, which may have a collision risk and is formed by an obstruction blocking the field of view of a driver on the driving path of the vehicle, is detected, information such as a relative distance between the obstruction and the vehicle, an obstruction size, a type, and a blind area range corresponding to the blind area of the field of view is acquired in real time.

In the embodiment of the present application, fig. 4 is a schematic flow chart diagram of a risk assessment method provided in the embodiment of the present application, and specifically shown in fig. 4:

s2031: determining a target environment risk type according to the traffic environment;

in an alternative embodiment, the target environmental risk type refers to a risk type corresponding to the detected traffic environment around the current vehicle. Optionally, according to the acquired traffic environment, distinguishing the targets with risks into different types, such as the above dynamic and static traffic participants, road barriers, view blind areas, road conditions and other risk types; the environmental risk information can be classified into a first-level risk type, a second-level risk type, a third-level risk type and the like according to the emergency state or the severity information of the environmental risk information. The multiple classification modes can be combined, overlapped, prioritized and the like, so that comprehensive and accurate assessment of risks is achieved, and potential risks faced by vehicles are reduced to the lowest. By classifying various traffic environment risks and determining the target environment risk types according to the acquired environment risk information in the current driving environment of the vehicle, the environment risks of different types are judged and processed in a targeted manner, and further the traffic environment risks are comprehensively and accurately evaluated.

S2033: acquiring a target environment risk type influence factor corresponding to the target environment risk type;

in an alternative embodiment, different types of environmental risks correspond to different environmental risk type influence factors, and the environmental risk type influence factors are related to the types of potential environmental risks, the sizes of obstacles, the speed of the vehicle, the relative distance between the vehicle and the environmental risks, and the like. Alternatively, the target environmental risk type impact factor may be the severity of the collision, and the collision risk may be defined as the product of the collision probability of the collision and the severity of the collision, i.e., the collision risk ═ collision probability × severity. The higher the risk of collision, the higher the traffic safety score, and the different types of target environmental risk types differ in collision probability and severity. The environmental risks of different target environmental risk types may be associated with the vehicle colliding in different ways at different locations, corresponding to different risk points, which may refer to pre-determined locations at which collisions with the vehicle may occur.

In an alternative embodiment, for example, if the vehicle collides with other traffic participants, the collision risk between the autonomous vehicle and the cluster of other traffic participants may be obtained by functional relationship calculation or the like according to the number of obstacles, the aggregation degree, and the feasible area of the host vehicle. Optionally, a group of traffic participants of the same type with similar positions and vector speeds is defined as other traffic participant clusters, the average relative distance of the traffic participants in the traffic participant clusters is used for describing the aggregation degree of the obstacles, and the larger the number of the obstacles, the more discrete the aggregation degree and the closer the distance to the vehicle, the larger the occupied road area and the larger the collision probability with the traffic participant clusters. The collision severity degree is related to the typical quality of the traffic participant cluster, the average cluster speed vector, the vehicle speed vector and the vehicle quality, and the greater the typical cluster quality and the vehicle quality, the greater the difference between the average cluster speed vector and the vehicle speed vector and the higher the severity degree. Alternatively, if the vehicle collides with road infrastructure and other static obstacles, the probability of collision is related only to the area occupied by the obstacle compared to the area of the passable region, the greater the area ratio, the higher the probability of collision. The severity of the collision is related to the type of obstacle and the speed of the vehicle.

In an alternative embodiment, for example, if the vehicle enters an impassable area, the collision risk may be determined by a functional calculation or the like based on the ratio of the probability of entering an impassable area of a particular type to the length of the boundary dividing the impassable area and the impassable area occupying the entire boundary length of the impassable area, or the distance of the vehicle from the boundary. The higher the boundary length ratio and the closer the vehicle distance, the higher the probability of entering the impassable area. The severity of the crash relates to the loss caused by the crash, wherein the loss of the vehicle into the impassable area is related to the type of impassable area.

S2035: and determining environmental safety information based on the traffic environment and the target environment risk type influence factors.

In an optional implementation manner, after the target environment risk type is determined, the corresponding target environment risk type influence factor is obtained according to the target environment risk type, wherein a target risk point corresponding to the target environment risk type can be determined, and the corresponding environment risk type influence factor and risk point are determined for different types of environment risks, so that different types of environment risks are processed in a targeted manner, and comprehensive and accurate assessment of the traffic environment risks is facilitated.

In an optional implementation manner, an environmental risk mathematical model may be established for the environmental risk, an environmental risk potential energy is calculated, the environmental risk size is evaluated by using the environmental risk potential energy, and the environmental safety information is determined.

S205: determining risk assessment information based on the system capability information and the environmental security information;

in an optional implementation manner, the system capacity information and the environmental safety information are comprehensively evaluated to obtain risk evaluation information, optionally, the system capacity information and the environmental safety information are combined and superimposed with the same weight, different weights can be assigned according to the importance degrees of the system capacity information and the environmental safety information, and then the combination and superimposition operations are performed, so that higher weights are assigned to indexes which have higher influence on the safety of automatic driving. The functional relationship between the two information can be preset, and the risk assessment information can be a specific score value, and can also be a table, a curve, a functional relationship, a state grade and the like which comprise system capacity information and environmental safety information.

S207: and determining a target risk evaluation result according to the risk evaluation information.

In an optional implementation manner, the target risk assessment result is determined as an output value according to risk assessment information obtained by comprehensively assessing system capacity information and environmental safety information. Optionally, when the risk assessment information establishes a functional relationship between the system capability information and the environmental safety information, for example, the risk assessment information is the system capability information-environmental safety information, that is, a function of subtracting the two fractional values, a determined output value is obtained for the current driving risk as a target risk assessment result.

In an alternative embodiment, the data of the vehicle autopilot system may interact with the cloud. Fig. 5 is a third schematic flowchart of a risk assessment method provided in an embodiment of the present application, specifically, as shown in fig. 5, the risk assessment method of the present invention further includes:

s209: uploading the first performance information, the driving track information, the position information, the second performance information, the third performance information, the system capacity information, the environmental safety information, the risk assessment information and the target assessment result to the cloud.

In an optional implementation, the data for risk assessment and the cloud are exchanged. The method comprises the steps of uploading currently-collected original data input into a risk assessment system, including first performance information, second performance information, third performance information, vehicle position information, driving track information and the like, and system capacity information, environmental safety information, risk assessment information and target risk assessment results obtained through assessment to a cloud end, wherein the original data can be used for data statistics and can also be used for reference data and standby data of other vehicles.

In an alternative embodiment, the state of the driver during the current driving may also have an influence on the risk of automatic driving. The driver state information is determined, and whether the participation degree of the driver needs to be improved or not can be judged according to the current state information of the driver.

Fig. 6 is a fourth schematic flowchart of a risk assessment method provided in the embodiment of the present application, specifically, as shown in fig. 6, the risk assessment method of the present invention further includes:

s601: determining driver state information;

in an alternative embodiment, the current driver state includes driver attention to the environment surrounding the vehicle, driver attention to the state of operation of the autonomous driving system, driver workload, and driver type.

In an alternative embodiment, driver status information is determined, and in particular, the autopilot system may be configured to monitor driver attention to the environment surrounding the vehicle and driver attention to the operating state of the autopilot system. The automatic driving system can judge the workload of a driver according to data such as the driving time of a vehicle, can distinguish the types of the driver according to the operation habits of the current driver, optionally, the operation habits comprise habits of operating an accelerator, a brake, a steering wheel and the like, and can distinguish the types of the driver according to different operation habits, such as conservative type, stable type, aggressive type and the like.

In an optional implementation manner, the current driver state may be comprehensively evaluated according to various factors affecting the driver state, and may be operations such as combination, superposition, priority ranking and the like of the various factors affecting the driver state, the operations such as combination, superposition and the like may be performed with the same weight, different weights may be assigned according to the importance degree of each index, and then the operations such as combination, superposition and the like may be performed, and a higher weight may be assigned to an index affecting the automatic driving safety, so as to obtain the driver state information.

S603: updating the risk assessment information based on the driver status information;

in an optional implementation mode, the state information of the driver is added, and the system capacity information and the environmental safety information are comprehensively evaluated to obtain updated risk evaluation information. Optionally, for example, a functional relationship between the three may be preset, or the three may be superimposed and combined with corresponding weights to obtain updated risk assessment information. The updated risk assessment information may be a specific score value or a corresponding grade, and the corresponding target risk assessment result is updated accordingly.

S605: and uploading the driver state information and the updated risk assessment information to the cloud.

In an optional implementation manner, the driver state information and the updated risk assessment information can be uploaded to a cloud, and the cloud can be used for data statistics, can be used as standby data of the vehicle, and can also be used for reference data of other vehicles. The data transmitted by the in-vehicle system to the cloud end comprise all the acquired data used for determining system capacity information, environment safety information and driver state information and calculation results, and the data transmitted by the cloud end to the in-vehicle system are historical statistical information of the data.

In an optional implementation manner, the vehicle automatic driving system may acquire data from a cloud, and fig. 7 is a schematic flow diagram of a risk assessment method provided in an embodiment of the present application, specifically as shown in fig. 7:

in an optional implementation manner, the cloud end includes the own vehicle driving record having the system capability information, the driver state information and the environmental safety information, and also includes the driving record of other vehicles, the driving record of the current driver and the driving record of other drivers having the system capability information, the driver state information and the environmental safety information.

S701: if the system capacity information, the environment safety information and/or the driver state information are lost, acquiring historical data of a cloud; the historical data comprises system capacity information, environmental safety information and/or driver state information under the same traffic environment;

s703: missing system capability information, environmental safety information, and/or driver status information is determined based on historical data.

In an optional implementation manner, when any one or any two of the environmental safety information, the driver state information and the system capacity information are missing, historical data can be used for replacing the missing data or estimating the missing data based on the historical data, wherein the historical data can be downloaded and obtained from a cloud end, and the missing data can be supplemented by using data which is the same as or similar to the current traffic environment in the historical data, for example, available data which is closer to the current time, so that risk assessment is achieved.

In an alternative embodiment, the current traffic environment may present a situation without safety risk, and the risk assessment may be performed only by the system capability information and the driver status information.

In an alternative embodiment, when any one or any two of the environmental safety information, the driver state information and the system capability information are absent, the risk assessment can be performed only by the acquired data such as the natural environmental conditions.

After the target risk assessment result is determined according to the risk assessment information, a risk level corresponding to the target risk assessment result can be determined, and the vehicle is enabled to execute corresponding operation capable of reducing driving risk according to the risk level. Fig. 8 is a flowchart illustrating a sixth method for risk assessment according to an embodiment of the present application, specifically as shown in fig. 8:

s801: grading the range of the risk assessment information to obtain risk grade information;

in an alternative embodiment, the risk level information may be a chart including a plurality of risk levels, which may be represented by numbers, letters, Chinese characters, or the like.

S803: determining a risk level corresponding to the current vehicle based on the target risk assessment result and the risk level information;

in an optional implementation manner, the current risk level corresponding to the target risk assessment result is obtained by comparing the target risk assessment result with the risk level information.

S805: and if the risk level reaches a preset level, sending a safe driving operation instruction corresponding to the risk level to a vehicle driving system and/or sending a safe driving prompt corresponding to the risk level to a driver based on the level classification information.

In an alternative embodiment, the operation corresponding to the risk level may be an operation capable of improving the reliability of the automatic driving system, an operation capable of improving the degree of participation of the driver in the driving task, and an operation capable of reducing the traffic risk faced by the system. In an optional implementation, the information of the operation instruction for improving safety of automatic driving and the reminding of the driver is uploaded to the cloud.

In an alternative embodiment, the operation that can improve the reliability of the autopilot system may be a software restart, a hardware restart, a vehicle light start, a sensor clean, a wiper start.

In an optional implementation manner, the operation capable of improving the degree of participation of the driver in the driving task may be one of steering wheel vibration, seat vibration, safety belt early warning, voice or warning sound reminding, human-computer interaction interface reminding, in-vehicle light reminding, small-amplitude lateral reciprocating motion of the vehicle, and small-amplitude acceleration and deceleration of the vehicle.

In an alternative embodiment, the operation that can reduce the traffic risk faced by the system may be entering a minimum risk state, restricting vehicle states, interacting with other traffic participants. The vehicle limiting state can be the maximum vehicle speed, the limit longitudinal acceleration, the limit lateral acceleration and the like of the vehicle, and the interaction with other traffic participants can be the switching of high and low beams, a loudspeaker, the voice outside the vehicle, a dot matrix screen outside the vehicle and the like.

In an optional implementation manner, the three operations corresponding to the risk levels may be activated at a certain priority and then executed, or may be simultaneously activated and executed. And (3) enabling the vehicle to execute corresponding operations according to different risk levels, optionally enabling the vehicle to execute operations of partially improving the reliability of the automatic driving system and partially reducing traffic risks faced by the system if the current risk level is 2. If the current risk level is 4 levels, the vehicle can execute all operations capable of improving the reliability of the automatic driving system and reducing the traffic risk faced by the system as far as possible, the safety of automatic driving is improved, and the user experience is also improved.

In an alternative embodiment, a safe driving reminder corresponding to the risk level may also be issued to the driver. Optionally, for example, the current risk level is the highest level, the driver is reminded of driving safely, the driver can be reminded of taking over the vehicle manually according to the current highest risk level, and the driver can be reminded of performing operations capable of reducing the traffic risk after taking over manually, such as reducing the vehicle speed, interacting with other traffic participants, braking urgently, and the like.

By adopting the risk assessment method provided by the embodiment of the application, the system capacity information is determined according to the state information of the vehicle driving system, the environmental safety information is determined according to the traffic environment around the vehicle, the risk assessment information is determined based on the system capacity information and the environmental safety information, the target risk assessment result is determined according to the risk assessment information, and the corresponding current driving risk grade is obtained according to the target risk assessment result, so that whether the current driving is safe or not is judged in real time, whether measures such as deceleration or avoidance need to be taken or not, or a driver is reminded to take over the vehicle in advance, so that the vehicle executes corresponding operation, the environmental risk is avoided, the occurrence of traffic accidents is avoided, and the driving safety of the automatically driven vehicle is ensured.

Fig. 9 is a schematic structural diagram of a risk assessment apparatus provided in an embodiment of the present application, and as shown in fig. 9, the apparatus may include:

a system capability information determining module 901, configured to determine system capability information according to state information of a vehicle driving system;

an environmental safety information determination module 902, configured to determine environmental safety information according to a traffic environment around a vehicle;

a risk assessment information determination module 903, configured to determine risk assessment information based on the system capability information and the environmental security information;

and a target risk assessment result determining module 904, configured to determine a target risk assessment result according to the risk assessment information.

In an optional embodiment, the system capability information determining module 901 is configured to obtain first performance information corresponding to the sensing module;

acquiring position information of a vehicle;

acquiring the information of the driving track;

acquiring second performance information corresponding to the control module;

acquiring third performance information corresponding to the execution module;

determining system capability information based on the first state information, the second state information, and the third state information

In an alternative embodiment, the environmental safety information determining module 902 is configured to determine a target environmental risk type according to a traffic environment;

acquiring a target environment risk type influence factor corresponding to a target environment risk type;

In an alternative embodiment, the risk assessment device further comprises:

the risk level information determining module is used for carrying out level division on the range of the risk assessment information to obtain risk level information;

The device and method embodiments in the embodiments of the present application are based on the same application concept.

The method provided by the embodiment of the application can be executed in a computer terminal, a server or a similar operation device. Taking an example of the server running on the server, fig. 10 is a block diagram of a hardware structure of the server of the risk assessment method provided in the embodiment of the present application. As shown in fig. 10, the server 1000 may have a relatively large difference due to different configurations or performances, and may include one or more Central Processing Units (CPUs) 1010 (the processor 1010 may include but is not limited to a Processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 1030 for storing data, and one or more storage media 1020 (e.g., one or more mass storage devices) for storing applications 1023 or data 1022. Memory 1030 and storage media 1020 may be, among other things, transient or persistent storage. The program stored in the storage medium 1020 may include one or more modules, each of which may include a series of instruction operations for a server. Still further, the central processor 1010 may be configured to communicate with the storage medium 1020 and execute a series of instruction operations in the storage medium 1020 on the server 1000. The server 1000 may also include one or more power supplies 1060, one or more wired or wireless network interfaces 1050, one or more input-output interfaces 1040, and/or one or more operating systems 1021, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, and so forth.

Input/output interface 1040 may be used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the server 1000. In one example, i/o Interface 1040 includes a Network adapter (NIC) that may be coupled to other Network devices via a base station to communicate with the internet. In one example, the input/output interface 1040 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.

It will be understood by those skilled in the art that the structure shown in fig. 10 is merely illustrative and is not intended to limit the structure of the electronic device. For example, server 1000 may also include more or fewer components than shown in FIG. 10, or have a different configuration than shown in FIG. 10.

Embodiments of the present application further provide a storage medium, which may be disposed in a server to store at least one instruction, at least one program, a set of codes, or a set of instructions related to implementing a risk assessment method in the method embodiments, where the at least one instruction, the at least one program, the set of codes, or the set of instructions is loaded and executed by the processor to implement the risk assessment method.

Alternatively, in this embodiment, the storage medium may be located in at least one network server of a plurality of network servers of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

It should be noted that: the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

It will be understood by those skilled in the art that all or part of the steps of implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

The present invention is not intended to be limited to the particular embodiments shown and described, but is to be accorded the widest scope consistent with the principles and novel features herein disclosed.

Claims

1. A method of risk assessment, comprising:

2. The risk assessment method of claim 1, wherein determining system capability information from state information of a vehicle driving system comprises:

acquiring first performance information corresponding to a sensing module;

determining first state information corresponding to the perception module based on the first performance information;

acquiring position information of the vehicle;

acquiring the information of the driving track;

acquiring second performance information corresponding to the control module;

acquiring third performance information corresponding to the execution module;

determining the system capability information according to the first state information, the second state information, and the third state information.

3. The risk assessment method according to claim 1, wherein the determining environmental safety information from the traffic environment surrounding the vehicle comprises:

and determining the environmental safety information based on the traffic environment and the target environmental risk type influence factor.

4. The risk assessment method according to claim 2, further comprising:

uploading the first performance information, the driving track information, the position information, the second performance information, the third performance information, the system capacity information, the environmental safety information, the risk assessment information and the target assessment result to a cloud.

5. The risk assessment method according to any one of claims 2-4, further comprising:

determining driver state information;

updating the risk assessment information based on the driver state information;

6. The risk assessment method of claim 5, further comprising:

if the system capacity information, the environmental safety information and/or the driver state information are missing, acquiring historical data of the cloud; the historical data comprises the system capability information, the environmental safety information and/or the driver state information under the same traffic environment;

determining the missing system capability information, the environmental safety information, and/or the driver status information based on the historical data.

7. The risk assessment method according to claim 6, wherein said determining a target risk assessment result from said risk assessment information comprises:

and if the risk level reaches a preset level, based on the level classification information, sending a safe driving operation instruction corresponding to the risk level to the vehicle driving system and/or sending a safe driving prompt corresponding to the risk level to a driver.

8. A risk assessment device, comprising:

a risk assessment information determination module for determining risk assessment information based on the system capability information and the environmental security information;

9. An electronic device comprising a memory and a processor, the electronic device comprising a processor and a memory, the memory having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, the at least one instruction, the at least one program, the set of codes, or the set of instructions, which is loaded and executed by the processor to implement the risk assessment method of any one of claims 1-7.

10. A computer readable storage medium having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by a processor to implement the risk assessment method according to any one of claims 1-7.