CN115700204A - Confidence determination method and device of automatic driving strategy - Google Patents
Confidence determination method and device of automatic driving strategy Download PDFInfo
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Abstract
The confidence coefficient determining method and device for the automatic driving strategy can determine a plurality of confidence coefficients according to the acquired external environment information and vehicle state information, sum the confidence coefficients, and finally obtain the confidence coefficient capable of representing the confidence level of the target automatic driving strategy by combining the confidence coefficients of the driving mode, so that the safety of the used target automatic driving strategy is quantized through the confidence coefficients, the safety of the automatic driving strategy can be more accurately and effectively quantitatively evaluated, and the safety and the flexibility of automatic driving can be simultaneously met.
Description
Technical Field
The present application relates to the field of automatic driving, and in particular, to a confidence determination method and apparatus for an automatic driving strategy.
Background
With the continuous development of automotive technology and electronic technology, more and more autonomous vehicles with an autonomous driving function gradually enter the market. The vehicle of the type not only allows a driver to operate the vehicle to run, but also can actively control the vehicle to run by an automatic driving module arranged in the vehicle under the condition that the driver does not need to operate, so that the driving mode of the vehicle is enriched, and the vehicle is more intelligent. The vehicle can acquire road condition information of a road ahead of a driving direction of the vehicle through sensors such as a radar and a camera arranged on the vehicle in an automatic driving mode, and finally determine an automatic driving strategy for controlling automatic driving of the vehicle by combining navigation map data and the like.
In the prior art, even though an automatic driving vehicle can calculate an automatic driving strategy for subsequent driving more quickly and accurately through a machine learning model, the driving strategy cannot be decided under some emergency situations, and a driver is still required to take over the driving work of the vehicle to ensure the safety of the vehicle and personnel. Therefore, how to measure the safety of the automatic driving strategy so as to prompt the driver in time or switch to a manual driving mode in time when the safety is low, so as to improve the flexibility and safety of automatic driving, which is a technical problem to be solved urgently in the field.
Disclosure of Invention
The application provides a confidence determining method and a confidence determining device for an automatic driving strategy, which are used for measuring the safety of the automatic driving strategy so as to improve the flexibility and the safety of automatic driving.
The application provides a confidence determination method for an automatic driving strategy in a first aspect, which comprises the following steps: acquiring external environment information and vehicle state information when a vehicle runs according to a target automatic driving strategy in an automatic driving mode; determining a first confidence coefficient according to the external environment information; the first confidence coefficient is used for representing the influence degree of external environment information on the automatic driving strategy; determining a second confidence level according to the vehicle state information; the second confidence coefficient is used for representing the influence degree of the vehicle state information on the automatic driving strategy; determining a third confidence level according to the external environment information and the vehicle state information; the third confidence coefficient is used for representing the influence degree of the driving state of the vehicle on the automatic driving strategy; determining a fourth confidence coefficient according to the first confidence coefficient, the second confidence coefficient and the third confidence coefficient; wherein the fourth confidence level is used for representing the credibility of the automatic driving strategy; and determining the reliability level of the automatic driving strategy according to the fourth confidence level and the fifth confidence level of the vehicle driving mode.
In an embodiment of the first aspect of the present application, the level of confidence of the autonomous driving maneuver comprises: a first level indicating that the vehicle cannot continue to travel in the autonomous driving maneuver; a second level for indicating that the vehicle is able to continue driving in the autonomous driving maneuver and for indicating a driver of the vehicle to assist in participating in an autonomous driving process of the vehicle; and a third level for indicating that the vehicle can continue to travel in the autonomous driving maneuver.
In an embodiment of the first aspect of the present application, determining the first confidence level according to the external environment information includes: a first confidence level is determined based on the presence of the obstacle target in the external environment.
In an embodiment of the first aspect of the present application, determining the second confidence level according to the vehicle state information includes: and determining a second confidence coefficient according to the current position, the current posture and the current motion parameters of the vehicle.
In an embodiment of the first aspect of the present application, determining the third confidence level according to the external environment information and the vehicle state information includes: and determining a third confidence level according to the number, the coverage area and the collection quality of the sensors, the map and the position used by the target automatic driving strategy and other vehicles passing by in front of the driving path.
In an embodiment of the first aspect of the present application, determining the fourth confidence level according to the first confidence level, the second confidence level, and the third confidence level includes: and performing weighting decision on the first confidence coefficient, the second confidence coefficient and the third confidence coefficient to obtain a fourth confidence coefficient.
In an embodiment of the first aspect of the present application, determining the confidence level of the automatic driving strategy based on the fourth confidence level and the fifth confidence level of the driving mode of the vehicle comprises: and adding the fourth confidence coefficient and the fifth confidence coefficient to obtain the confidence level of the automatic driving strategy.
In an embodiment of the first aspect of the present application, after determining the confidence level of the automatic driving strategy, the method further includes: prompting the reliability level of the automatic driving strategy to a driver of the vehicle in a multi-mode prompting mode; wherein the multimodal comprises: one or more of sight, hearing, touch and smell.
In an embodiment of the first aspect of the present application, the method further includes: when detecting driver fatigue, vehicle failure, automated driving strategy abnormal exit and/or completed automated driving, a prompt is sent to the driver.
A second aspect of the present application provides a confidence determination apparatus for an automated driving strategy, which may be used to execute the confidence determination method for the automated driving strategy as provided in the first aspect of the present application, and the apparatus includes: the information acquisition module is used for acquiring external environment information and vehicle state information when the vehicle runs according to a target automatic driving strategy in an automatic driving mode; the first confidence coefficient determining module is used for determining a first confidence coefficient according to the external environment information; sending the external environment information to a third confidence coefficient determining module and sending the first confidence coefficient to a fourth confidence coefficient determining module; the first confidence coefficient is used for representing the influence degree of external environment information on the automatic driving strategy; the second confidence coefficient determining module is used for determining a second confidence coefficient according to the vehicle state information; the vehicle state information is sent to a third confidence coefficient determining module, and the second confidence coefficient is sent to a fourth confidence coefficient determining module; the second confidence coefficient is used for representing the influence degree of the vehicle state information on the automatic driving strategy; the third confidence coefficient determining module is used for determining a third confidence coefficient according to the external environment information and the vehicle state information and sending the third confidence coefficient to the fourth confidence coefficient determining module; the third confidence coefficient is used for representing the influence degree of the driving state of the vehicle on the automatic driving strategy; the fourth confidence coefficient determining module is used for determining a fourth confidence coefficient according to the first confidence coefficient, the second confidence coefficient and the third confidence coefficient; wherein the fourth confidence level is used for representing the credibility of the automatic driving strategy; and the fifth confidence coefficient determining module is used for determining the confidence level of the automatic driving strategy according to the fourth confidence coefficient and the fifth confidence coefficient of the vehicle running mode.
In an embodiment of the second aspect of the present application, the level of confidence of the automatic driving strategy comprises: a first level for indicating that the vehicle cannot continue to travel with the autonomous driving maneuver; a second level for indicating that the vehicle is able to continue driving in the autonomous driving maneuver and for indicating a driver of the vehicle to assist in participating in an autonomous driving process of the vehicle; and a third level for indicating that the vehicle can continue to travel in the autonomous driving maneuver.
In an embodiment of the second aspect of the present application, the first confidence level determining module is specifically configured to determine the first confidence level according to an obstacle target existing in the external environment.
In an embodiment of the second aspect of the present application, the second confidence determining module is specifically configured to determine the second confidence according to the current position, posture and motion parameters of the vehicle.
In an embodiment of the second aspect of the present application, the third confidence level determining module is specifically configured to determine the third confidence level according to the number of sensors, the coverage area, the acquisition quality, the map and the location used by the target automatic driving strategy, and other vehicles passing in front of the driving path.
In an embodiment of the second aspect of the present application, the fourth confidence level determining module is specifically configured to perform a weighting decision on the first confidence level, the second confidence level, and the third confidence level to obtain a fourth confidence level.
In an embodiment of the second aspect of the present application, the fifth confidence level determining module is specifically configured to sum the fourth confidence level and the fifth confidence level to obtain a confidence level of the automatic driving strategy.
In an embodiment of the second aspect of the present application, the apparatus further includes: an interaction module; the interaction module is specifically used for prompting the reliability level of the automatic driving strategy to a driver of the vehicle in a multi-mode prompting mode; wherein the multimodal comprises: one or more of sight, hearing, touch and smell.
In an embodiment of the second aspect of the present application, the interaction module is further configured to send a prompt to the driver when the driver fatigue is detected, the vehicle has a fault, the automatic driving strategy is abnormally exited, and/or automatic driving is completed.
In summary, the confidence determining method and device for the automatic driving strategy provided by the application can determine a plurality of confidence levels according to the acquired external environment information and the vehicle state information, sum the confidence levels, and combine the confidence levels of the driving mode to finally obtain the confidence level which can be used for representing the confidence level of the target automatic driving strategy, so that the safety of the used target automatic driving strategy is quantified through the confidence levels. The obtained confidence is triggered from multiple dimensions and is subjected to addition quantification, so that the safety of the automatic driving strategy can be quantitatively evaluated more accurately and effectively, and the safety of an automatic driving system is improved. When the confidence degree representation safety is low, the driver can be prompted in time or switched to a manual driving mode in time to ensure the safety of vehicles and personnel, when the safety is high, the attention of the driver to the automatic driving process is reduced to a greater extent, the user experience is improved, and meanwhile the safety and the flexibility of automatic driving are met.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a schematic diagram of an application scenario of the present application;
FIG. 2 is a schematic flow chart diagram illustrating an embodiment of a confidence determination method for an automatic driving maneuver according to the present disclosure;
FIG. 3 is a schematic structural diagram of a confidence determination device for an automatic driving strategy according to the present application;
fig. 4 is a schematic structural diagram of an interaction module according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the 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.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the drawings described above, if any, 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 may be interchanged under appropriate circumstances such that the embodiments of the application described herein may be implemented, for example, in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Before formally describing the embodiments of the present application, the following description will be made with reference to the accompanying drawings. Fig. 1 is a schematic diagram of an application scenario of the present application, where a vehicle in the scenario shown in fig. 1 has an automatic driving function, which may also be referred to as an Adaptive Cruise Control (ACC) mode, an automatic driving mode, and the like, in the automatic driving mode, an automatic driving system arranged in the vehicle may detect a road condition of a road ahead of a driving direction through sensors such as a radar and a camera, and meanwhile, the automatic driving system may obtain or store a high-precision map in advance, so as to determine an automatic driving policy in time according to information provided by the high-precision map of the road condition information of the road ahead, adjust driving parameters of the vehicle according to the automatic driving policy, and implement automatic driving of the vehicle without driver intervention.
For example, in the example shown in fig. 1, when the vehicle travels forward, a radar sensor provided in the vehicle continuously emits a radar signal in the direction indicated by reference numeral (1) in the drawing. When a pedestrian appears in front of the vehicle in the driving direction, the radar receives a radar signal reflected by the pedestrian in the direction of the reference number (2) in the figure. And (3) calculating the distance between the vehicle and the pedestrian by the automatic driving system according to the received radar signal reflected by the pedestrian, determining a lane which can be lane-changed on the right side of the current lane according to a high-precision map, and controlling the vehicle to change the lane to the right lane when the sensor determines that no vehicle exists in the right lane, so as to prevent the vehicle from colliding with the pedestrian while keeping the normal running of the vehicle.
In some embodiments, in an autonomous driving system of a vehicle, an autonomous driving maneuver is generated by a machine learning model, information acquired by a sensor and map data are input into the machine learning model, and the autonomous driving maneuver is output. However, even though the autonomous vehicle can calculate the autonomous driving maneuver for subsequent driving more quickly and accurately through the machine learning model, the driving maneuver cannot be decided in some emergency situations, and the driver is still required to take over the driving work of the vehicle to ensure the safety of the vehicle and the personnel.
Therefore, the present application provides a confidence level determining method and apparatus for an automatic driving strategy, which can be applied to a vehicle shown in fig. 1, and can determine a confidence level to quantify the safety of the automatic driving strategy when the vehicle is in an automatic driving mode and the automatic driving system drives according to the determined automatic driving strategy. Then, when the safety level of the automatic driving strategy can be determined by the automatic driving system, the safety of a vehicle and personnel can be ensured by prompting a driver in time or switching to a manual driving mode in time when the safety is low; when the safety is higher, the attention of a driver to the automatic driving process is reduced to a greater extent, the user experience is improved, and meanwhile, the safety and the flexibility of automatic driving are met.
The technical means of the present application will be described in detail with specific examples. These several specific embodiments may be combined with each other below, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 2 is a schematic flowchart of an embodiment of a confidence determination method for an automatic driving strategy provided in the present application, where the method shown in fig. 2 may be applied to the scenario shown in fig. 1 and executed by an automatic driving system in a vehicle, and specifically, the method includes:
s101: the method comprises the steps of obtaining external environment information and vehicle state information when a vehicle runs according to a target automatic driving strategy in an automatic driving mode.
Specifically, the present embodiment may indicate the safety of the automatic driving policy by determining the confidence of the target automatic driving policy, so that the automatic driving system may obtain real-time external environment information and vehicle state information of the vehicle when controlling the automatic driving of the vehicle by using the target automatic driving policy, thereby evaluating the automatic driving policy currently being used; alternatively, the autonomous driving system may evaluate an autonomous driving maneuver to be used before using the autonomous driving maneuver after determining the autonomous driving maneuver for the vehicle to travel forward.
In some embodiments, a sensor provided on the vehicle may be used to collect external environment information of an environment where the vehicle is located, for example, a camera, an infrared sensor, a radar, and other types of sensors are provided on the vehicle, and all the sensors may be used together to obtain the external environment information. The obtained external environment information includes lanes, curbs, other vehicles and pedestrians, buildings, obstacles and the like.
In some embodiments, sensors disposed within the vehicle may be used to collect vehicle status information, such as speedometers, thermometers, levels, and the like. The obtained vehicle state information includes: direction of travel, speed, level state of the vehicle, tire pressure, engine temperature, etc.
Subsequently, after obtaining the external environment information and the vehicle state information, the automatic driving system determines a first confidence degree according to the external environment information, a second confidence degree according to the vehicle state information, and a third confidence degree according to the external environment information and the vehicle state information, respectively, through S102-S104. The following describes steps of S102-S104, respectively, and the execution sequence of S102-S104 is not limited, or S102-S104 may also be executed simultaneously.
S102: determining a first confidence coefficient according to the external environment information; the first confidence coefficient is used for representing the influence degree of the external environment information on the target automatic driving strategy.
In particular, the first confidence level may be used to characterize how much external environmental information affects autonomous driving when the vehicle is traveling using the target autonomous driving maneuver. For example, when the vehicle is traveling forward under control of the target autonomous driving maneuver, an obstacle ahead of the vehicle traveling direction may be considered to have a greater degree of influence on normal traveling of the vehicle, a pedestrian behind the vehicle traveling direction may be considered to have a smaller influence on normal traveling of the vehicle, and so on. Or the size, the position, the motion trail, the motion parameters and the like of the obstacle can influence the target automatic driving strategy to different degrees, influence under different conditions is divided in advance and stored in the system in a mapping relation mode, and after the external environment information is obtained, the corresponding first confidence coefficient can be determined according to the external environment information.
In some embodiments, the first confidence level may be divided into at least three confidence levels, a first level: for indicating that external environmental information will greatly affect autonomous driving of the vehicle, e.g., a sudden obstacle is detected right in front of the target driving strategy, the resulting first confidence level corresponding to a first level; a second stage: a first confidence level corresponding to a first level is obtained indicating that there are factors in the external environmental information that affect the automatic driving of the vehicle, such as the detection of a slowly moving object directly in front of the target driving strategy; a third level for indicating that the external environmental information will not affect the automatic driving of the vehicle, the resulting first degree of confidence corresponding to the third level.
In some embodiments, fig. 3 is a schematic structural diagram of a confidence level determining apparatus for an automatic driving strategy provided by the present application, where the apparatus shown in fig. 3 may be used to execute the method shown in fig. 2, and then in the apparatus, a first confidence level determining module (also referred to as a cognitive prediction module, etc.) may be used to acquire data of a sensor, and after sensing the data, obtain a first confidence level by predicting an influence of an obstacle on a current automatic driving strategy, send the obtained first confidence level to a subsequent fourth confidence level determining module for subsequent calculation, and at the same time, the first confidence level determining module further sends external environment information acquired by the sensor to a third confidence level determining module for subsequent calculation.
S103: determining a second confidence level according to the vehicle state information; and the second confidence coefficient is used for representing the influence degree of the vehicle state information on the target automatic driving strategy.
In particular, the second confidence level may be used to characterize how much the vehicle state information affects autonomous driving when the vehicle is traveling using the target autonomous driving maneuver. For example, the degree of influence on the autonomous driving maneuver is large when the speed of the vehicle is too fast, the influence on the autonomous driving maneuver is small when the vehicle speed is slow, and the like. Or the position, the remaining oil amount, the movement direction, the horizontal degree and other attitude information of the vehicle, the speed, the turning angle and other movement parameters and the like can generate different degrees of influence on the target automatic driving strategy, the influence under different conditions is divided in advance and stored in the system in the form of a mapping relation, and after the vehicle state information is obtained, the corresponding second confidence coefficient can be determined according to the vehicle state information.
In some embodiments, the second confidence level may also be divided into a first rank: for indicating that vehicle state information will greatly affect the automatic driving of the vehicle, a second level: a third level for indicating that there are factors in the vehicle state information that affect vehicle autodrive: for indicating that the vehicle status information will not affect the automatic driving of the vehicle.
In some embodiments, the second confidence level determining module (also referred to as Localization & Map module, etc.) in the apparatus shown in fig. 3 may be configured to determine the second confidence level according to the information such as the current position of the vehicle, etc., according to the sensor such as the locator, etc., and send the second confidence level to the fourth confidence level determining module for subsequent calculation, and at the same time, the second confidence level determining module also sends the vehicle state information collected by the second confidence level determining module to the third confidence level determining module for subsequent calculation.
S104: determining a third confidence level according to the external environment information and the vehicle state information; wherein the third confidence level is used for representing the influence degree of the driving state of the vehicle on the target automatic driving strategy.
Specifically, the third confidence level is used for representing the degree of influence of the driving state of the vehicle on the target automatic driving strategy, and is determined according to the external environment information and the vehicle state information, and is used for measuring the safety of the region which is about to pass in front. For example, when the number of sensors in the front area where the vehicle travels according to the target automatic driving strategy is large, the coverage area in the acquired front area is wide, the blind area is small, and the quality of acquisition is good, the degree of influence on the target automatic driving strategy is low, and conversely, the degree of influence is high. When the lane information in the area where the vehicle passes through can be provided through a high-precision map and can also be sensed through a sensor, the source of the acquired information is more, and when the information from multiple sources is consistent, the safety of automatic driving can be ensured, and the influence degree on the target automatic driving strategy is lower at the moment. For another example, when a sensor such as a locator determines that another vehicle passes through the area where the vehicle passes through, the influence degree is low; the higher the accuracy of the map used to specify the target autopilot strategy, the lower the degree of influence.
In some embodiments, the third confidence level may also be divided into a first level: for indicating that the driving state of the vehicle will greatly affect the automatic driving of the vehicle, the second level: a factor for indicating that there is an influence on the automatic driving of the vehicle in the running state of the vehicle and a third level: for indicating that the running state of the vehicle will not affect the automatic driving of the vehicle.
In some embodiments, a third confidence determining module (also referred to as a world model module, etc.) in the apparatus shown in fig. 3 may be configured to determine a third confidence, where the third confidence determining module performs Map fusion together according to a Data Driven real-time Map (Data drive Map, abbreviated as DDMap) obtained by fusing external environment information provided by the first confidence determining module, map information in front of a driving direction obtained by performing Map fusion on a high-precision Map provided by the second determination module, and vehicle state information provided by the second confidence determining module to obtain the third confidence, and sends the calculated third confidence to the fourth confidence determining module for subsequent calculation. In some embodiments, the third confidence level determining module may store different map information, vehicle state information, and confidence levels in the system in the form of a mapping relationship, and after obtaining the map information and the vehicle state information, the third confidence level determining module may determine a corresponding third confidence level according to the mapping relationship.
S105: determining a fourth confidence coefficient according to the first confidence coefficient, the second confidence coefficient and the third confidence coefficient; wherein the fourth confidence level is used to characterize the confidence level of the target autopilot strategy.
Specifically, after the first confidence level, the second confidence level, and the third confidence level are calculated through S102-S104, the three confidence levels are further subjected to weighted decision processing in the embodiment of the present application, so as to jointly obtain a fourth confidence level including the fusion of the three confidence levels. In a specific implementation manner, the fourth confidence may be determined through multi-decision arbitration, policy arbitration, and the like, or different weights may be assigned to each confidence, and a level corresponding to the fourth confidence may be determined through weighted summation, and for example, a weight 0.25 may be assigned to the first confidence, a weight 0.25 may be assigned to the second confidence, a weight 0.5 may be assigned to the third confidence, and the like. It can be understood that, since the fourth confidence coefficient comprehensively considers the first confidence coefficient, the second confidence coefficient and the third confidence coefficient, the fourth confidence coefficient can be determined jointly from multiple aspects such as the vehicle state, the external environment and the driving state more comprehensively, so that the fourth confidence coefficient has higher accuracy and reliability.
In some embodiments, as shown in fig. 3, after receiving the first confidence level sent by the first confidence level determining module, the second confidence level sent by the second confidence level determining module, and the third confidence level sent by the third confidence level determining module, respectively, a fourth confidence level is obtained together and output to a subsequent fifth confidence level determining module.
S106: and determining the reliability level of the target automatic driving strategy according to the fourth confidence level and the fifth confidence level of the vehicle driving mode.
Specifically, the fifth confidence may be a decision made by the automatic driving system in the overall control, may be information added by the system, and may also be divided into three levels, for example, when the vehicle is about to drive out of a designed operation area (ODD), the influence degree on the current automatic driving strategy is large, and the fifth confidence may correspond to the first level: for indicating that the driving mode of the vehicle will greatly affect the automatic driving of the vehicle; when the vehicle has a preset distance to exit the ODD, the fifth confidence may correspond to the second level: for indicating that a driving mode of the vehicle is about to affect vehicle autopilot; when the ODD is not driven within the preset distance of the vehicle, the fifth confidence level corresponds to a third level: for indicating that the driving state of the vehicle will not affect the current automatic driving of the vehicle.
Then in S106, a final sixth confidence may be determined by performing a weighted summation of the fourth confidence and the fifth confidence determined in S105, and for example, a weight of 0.5 may be assigned to the fifth confidence, a weight of 0.5 may be assigned to the fourth confidence, and the sum may be added. The sixth confidence coefficient can be used for representing the confidence level of the target automatic driving strategy, and can also be divided into three levels, wherein when the first level of the sixth confidence coefficient is used for indicating that the vehicle uses the target driving strategy to carry out automatic driving, the safety is low, and the vehicle needs to exit the automatic driving mode and be manually driven by a driver; the second level is used for indicating that a certain risk exists when the vehicle uses a target driving strategy to carry out automatic driving, the automatic driving mode can be kept, but a driver needs to assist in participating in the automatic driving process, or the driver judges at any time and switches the vehicle from the automatic driving mode to a manual driving mode; and the third level is used for indicating that the safety is higher when the vehicle is automatically driven by using the target driving strategy, and the driver can completely depend on automatic driving and can reduce the attention of the driver to the vehicle. In some embodiments, the level of Confidence used to represent the target autonomous driving maneuver may also be referred to as Confidence Awareness (CA) or the like.
In summary, through S101 to S106 in the embodiment of the present application, the automatic driving system may determine multiple confidence levels according to the acquired external environment information and the vehicle state information, sum the multiple confidence levels, and finally obtain a confidence level that may be used to represent a confidence level of the target automatic driving policy by combining the confidence levels of the driving modes, so as to quantify the security of the target automatic driving policy used through the confidence levels. The obtained confidence is triggered from multiple dimensions and is subjected to addition quantification, so that the safety of the automatic driving strategy can be quantitatively evaluated more accurately and effectively, and the safety of an automatic driving system is improved. When the confidence degree representation safety is low, the driver can be prompted in time or switched to a manual driving mode in time to ensure the safety of vehicles and personnel, when the safety is high, the attention of the driver to the automatic driving process is reduced to a greater extent, the user experience is improved, and meanwhile the safety and the flexibility of automatic driving are met.
In some embodiments, after the automatic driving system determines different reliability levels of the target automatic driving strategy through the method, the current reliability level is prompted to the driver through the interaction module in a multi-mode reminding mode and the like, so that the driver performs corresponding operation according to the current reliability level. Wherein the multi-modality comprises: one or more of sight, hearing, touch and smell.
Fig. 4 is a schematic structural diagram of an embodiment of an interaction module provided by the present application, where the interaction module may provide an icon composed of three transverse lines as shown in fig. 4 on a human-computer interaction interface arranged in a vehicle, and light all of the three transverse lines when determining that the confidence level of the automatic driving strategy is a third level, at this time, a driver may determine that a current confidence level is the third level according to the icon, and the driver does not need to provide too much attention on driving, which may improve the driving experience of the driver. And when the credibility level of the automatic driving strategy is determined to be the second level, the next two horizontal lines in the three horizontal lines are lightened, at the moment, the driver can determine that the current credibility level is the second level according to the icon, at the moment, although the automatic driving mode is not exited, the driver still needs to pay attention to the driving situation in real time, and auxiliary driving is carried out or the driving is taken over when needed. When the credibility level of the automatic driving strategy is determined to be the first level, the lowest transverse line of the three transverse lines is lightened, at the moment, the driver can determine that the current credibility level is the first level according to the icon, at the moment, manual driving is required to be carried out by the driver, the automatic driving system can directly exit from the automatic driving mode and prompt the driver to carry out the manual driving, or the automatic driving can exit from the automatic driving mode according to the operation of the driver after prompting.
In some embodiments, the interaction module provided herein may also prompt the driver using other means, and these different means may be performed separately or together. For example, when the level of confidence of the automatic driving strategy is determined to be the third level, the voice prompts of a mild attitude sentence such as "don't worry, i look at the worship" and the like to prompt the driver not to be too intense for the current automatic driving, so that the tension of the driver can be relieved. And when the credibility level of the automatic driving strategy is determined to be the second level, reminding the driver to take over the automobile at any time through the sentences of prompting preparation and the like by voice. When the credibility level of the automatic driving strategy is determined to be the first level, the driver is prompted to take over the vehicle directly for manual driving through statements such as 'please drive' and the like of voice prompt.
In some embodiments, the interaction module may prompt the driver in different ways when the vehicle is in different states, for example, in the evening or when it is detected that the brightness of the vehicle in the tunnel is low, the interaction module may prompt the driver in a way of lighting an icon as shown in fig. 4; and in the daytime or when the vehicle is detected to be under the sunny day and the light is strong, the driver can be prompted in a sound, vibration or other mode at the time. The prompt modes corresponding to different states can be stored in advance through a mapping relation mode, and the corresponding prompt modes are determined when prompt is needed, so that the experience of a driver can be improved, the prompt efficiency of the interaction module is further improved, the probability of the driver receiving the prompt is improved, and the difficulty of receiving the prompt is reduced.
In some embodiments, the interactive module provided by the application can also photograph the driver through equipment such as a camera in a manual driving mode, and play prompt information to make the driver concentrate on driving under the conditions of distraction, fatigue and the like of the driver detected in an image recognition mode, or directly switch to an automatic driving mode to replace the driver to drive, so as to ensure the safety of vehicles and personnel to a greater extent.
In some embodiments, the interaction module provided by the application can also play prompt information to prompt a driver of a current vehicle fault after determining that a component of the vehicle has a fault through sensors such as a temperature sensor and a tire pressure sensor arranged in the vehicle; or directly switching to an automatic driving mode to avoid danger. For example, when a tire burst of the vehicle is detected, the automatic driving mode is switched to and the vehicle is decelerated to the roadside in time, so that the safety of the vehicle and the automatic driving system is improved.
In some embodiments, when the vehicle is in the automatic driving mode, if a certain function is not completed, the interaction module is required to prompt the driver in real time to inform the current state. For example, when the vehicle is changing lanes, the speed of coming vehicles behind is suddenly detected to be increased, then the vehicle returns to the original lane, at the moment, the interaction module prompts the driver through voice playing 'coming vehicles behind and trying a bar later', the emotion of the driver can be stabilized, and potential safety hazards brought by the driver when problems are checked during driving are prevented.
In some embodiments, when the vehicle is in the automatic driving mode and the automatic driving system abnormally exits, the reason can be actively described for the driver, for example, the driver is prompted to manually drive through voice playing "no map, i exit from automatic driving, drive by your own and be safer", so that the prompt of the problem is more humanized and the driver can more easily accept.
In some embodiments, after the automatic driving system normally exits, the driver can be prompted to evaluate and feed back the automatic driving through voice playing in the form of 'i have progress today and see my performance score bar' and the like, so that after the manufacturer receives the feedback, the related functions and algorithms of the automatic driving can be adjusted in time, subsequent continuous use is facilitated, and the experience of the driver is improved.
In the foregoing embodiments, the confidence level determining method of the automatic driving strategy provided by the embodiment of the present application is described, and in order to implement each function in the method provided by the embodiment of the present application, the confidence level determining device of the automatic driving strategy as the execution subject may include a hardware structure and/or a software module, and each function is implemented in the form of a hardware structure, a software module, or a hardware structure plus a software module. Such as the modules in fig. 3, and whether any of the above-described functions are performed by hardware structures, software modules, or hardware structures plus software modules, depends upon the particular application and design constraints imposed on the technical solution.
It should be noted that, in the confidence determination device of the automatic driving maneuver described above in this embodiment, the division of each component and each module is only a division of a logical function, and may be wholly or partially integrated into a physical entity or may be physically separated in actual implementation. For example, the first confidence level determining module, the second confidence level determining module, the third confidence level determining module, the fourth confidence level determining module, the fifth confidence level determining module, etc. may be separate entities or any multiple of them may be integrated on one entity, and these modules may all be implemented in the form of software invoked by the processing element; or can be implemented in the form of hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. The processing element may be a separate processing element, or may be integrated into a chip of the apparatus, or may be stored in the memory of the apparatus in the form of program code, and a processing element of the apparatus may call and execute the functions of the above determination module. The other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The master control component described herein may be an integrated circuit having signal processing capabilities. In the implementation process, each step of the above method or each module above may be completed by an integrated logic circuit of hardware in the main control assembly or an instruction in the form of software.
For example, the above components/modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).
In the above embodiments, all or part of the method steps performed by the confidence determination device of the automatic driving strategy may be implemented by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), among others.
The present application further provides an electronic device, comprising: a processor and a memory; wherein the memory has stored therein a computer program which, when executed by the processor, is operable to perform a confidence determination method of an autopilot strategy as in any of the preceding embodiments of the present application.
The present application further provides a computer readable storage medium having stored thereon a computer program, which when executed, is operable to perform a confidence determination method of an autonomous driving maneuver as in any of the previous embodiments of the present application.
The embodiment of the application further provides a chip for running the instructions, and the chip is used for executing the confidence level determination method of any one of the automatic driving strategies.
Embodiments of the present application further provide a program product, which includes a computer program, where the computer program is stored in a storage medium, and the computer program can be read from the storage medium by at least one processor, and when the computer program is executed by the at least one processor, the method for determining confidence of an automatic driving strategy according to any one of the foregoing embodiments of the present application can be implemented.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A confidence determination method for an autonomous driving maneuver, comprising:
acquiring external environment information and vehicle state information when a vehicle runs according to a target automatic driving strategy in an automatic driving mode;
determining a first confidence coefficient according to the external environment information; wherein the first confidence level is used for representing the influence degree of the external environment information on the automatic driving strategy;
determining a second confidence level according to the vehicle state information; wherein the second confidence level is used to characterize a degree of influence of the vehicle state information on the autonomous driving maneuver;
determining a third confidence level according to the external environment information and the vehicle state information; wherein the third confidence level is used to characterize a degree of influence of a driving state of the vehicle on the autonomous driving maneuver;
determining a fourth confidence degree according to the first confidence degree, the second confidence degree and the third confidence degree; wherein the fourth confidence level is used to characterize a degree of confidence in the autonomous driving maneuver;
determining a confidence level for the autonomous driving maneuver based on the fourth confidence level and a fifth confidence level for the vehicle driving mode.
2. The method of claim 1, wherein the level of confidence of the autonomous driving maneuver comprises:
a first level indicating that the vehicle is unable to continue traveling in the autonomous driving maneuver;
a second level for indicating that the vehicle is able to continue driving in the autonomous driving maneuver and for indicating a driver of the vehicle to assist in engaging in an autonomous driving process of the vehicle;
a third level indicating that the vehicle can continue to travel with the autonomous driving maneuver.
3. The method of claim 1 or 2, wherein said determining a first confidence level based on said external environment information comprises:
determining the first confidence level according to an obstacle target present in the external environment.
4. The method of claim 1 or 2, wherein said determining a second confidence level from the vehicle state information comprises:
and determining the second confidence coefficient according to the current position, the current posture and the current motion parameters of the vehicle.
5. The method of claim 1 or 2, wherein said determining a third confidence level from the external environmental information and the vehicle state information comprises:
and determining the third confidence level according to the number, the coverage area and the acquisition quality of the sensors, a map and a position used by the target automatic driving strategy and other vehicles passing by in front of the driving path.
6. The method of claim 1 or 2, wherein said determining a fourth confidence level based on the first confidence level, the second confidence level, and the third confidence level comprises:
and performing weighting decision on the first confidence coefficient, the second confidence coefficient and the third confidence coefficient to obtain a fourth confidence coefficient.
7. The method of claim 1 or 2, wherein determining a confidence level for the autonomous driving maneuver based on the fourth confidence level and a fifth confidence level for the vehicle travel mode comprises:
and adding the fourth confidence coefficient and the fifth confidence coefficient to obtain the confidence level of the automatic driving strategy.
8. The method of any of claims 1-7, wherein the determining a level of confidence in the autonomous driving maneuver further comprises:
prompting the reliability level of the automatic driving strategy to a driver of the vehicle in a multi-mode prompting mode; wherein the multi-modality comprises: one or more of visual, auditory, tactile, and olfactory.
9. The method of claim 8, further comprising:
and when detecting that the driver is tired, the vehicle breaks down, the automatic driving strategy is abnormally quitted and/or automatic driving is completed, sending a prompt to the driver.
10. A confidence determination device for an automated driving maneuver, comprising:
the information acquisition module is used for acquiring external environment information and vehicle state information when the vehicle runs according to a target automatic driving strategy in an automatic driving mode;
the first confidence coefficient determining module is used for determining a first confidence coefficient according to the external environment information; sending the external environment information to a third confidence coefficient determining module and sending the first confidence coefficient to a fourth confidence coefficient determining module; wherein the first confidence level is used for representing the influence degree of the external environment information on the automatic driving strategy;
the second confidence coefficient determining module is used for determining a second confidence coefficient according to the vehicle state information; the vehicle state information is sent to a third confidence coefficient determining module, and the second confidence coefficient is sent to a fourth confidence coefficient determining module; wherein the second confidence level is used to characterize a degree of influence of the vehicle state information on the autonomous driving maneuver;
the third confidence coefficient determining module is used for determining a third confidence coefficient according to the external environment information and the vehicle state information and sending the third confidence coefficient to the fourth confidence coefficient determining module; wherein the third confidence level is used to characterize a degree of influence of a driving state of the vehicle on the autonomous driving maneuver;
a fourth confidence coefficient determining module, configured to determine a fourth confidence coefficient according to the first confidence coefficient, the second confidence coefficient, and the third confidence coefficient; wherein the fourth confidence level is used to characterize the trustworthiness of the autonomous driving maneuver;
and the fifth confidence coefficient determining module is used for determining the confidence level of the automatic driving strategy according to the fourth confidence coefficient and the fifth confidence coefficient of the vehicle running mode.
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| CN202110796378.7A CN115700204A (en) | 2021-07-14 | 2021-07-14 | Confidence determination method and device of automatic driving strategy |
| PCT/CN2022/083508 WO2023284333A1 (en) | 2021-07-14 | 2022-03-28 | Method and apparatus for determining confidence of automatic driving strategy |
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| CN116176607B (en) * | 2023-04-27 | 2023-08-29 | 南京芯驰半导体科技有限公司 | Driving method, driving device, electronic device, and storage medium |
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| US9989963B2 (en) * | 2016-02-25 | 2018-06-05 | Ford Global Technologies, Llc | Autonomous confidence control |
| CN109358612B (en) * | 2018-08-29 | 2022-08-09 | 上海商汤智能科技有限公司 | Intelligent driving control method and device, vehicle, electronic equipment and storage medium |
| US10467487B1 (en) * | 2018-12-11 | 2019-11-05 | Chongqing Jinkang New Energy Automobile Co., Ltd. | Fusion-based traffic light recognition for autonomous driving |
| CN109901574B (en) * | 2019-01-28 | 2021-08-13 | 华为技术有限公司 | Automatic driving method and device |
| WO2021189385A1 (en) * | 2020-03-26 | 2021-09-30 | 华为技术有限公司 | Target detection method and apparatus |
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| CN116394981A (en) * | 2023-06-07 | 2023-07-07 | 北京集度科技有限公司 | Vehicle control method, automatic driving prompt method and related device |
| CN116394981B (en) * | 2023-06-07 | 2023-09-01 | 北京集度科技有限公司 | Vehicle control method, automatic driving prompting method and related devices |
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