Vehicle control method, device, vehicle, storage medium and program product

Abstract

The present disclosure relates to a vehicle control method and device, a vehicle, a storage medium and a program product, and relates to the technical field of vehicle control. The method comprises: obtaining a risk confidence, the risk confidence being used to represent the reliability of a first collision risk determined by an auxiliary braking system of a vehicle. In the case where the risk confidence is less than a first preset confidence threshold, a braking intention of a user is obtained. A triggering strategy of the auxiliary braking system is determined according to the first collision risk and the braking intention. In this way, when it is determined that the reliability of the collision risk determined by the auxiliary braking system is low, the triggering strategy of the auxiliary braking system of the vehicle is determined in combination with the collision risk determined by the auxiliary braking system and the braking intention of the user, which can avoid the risk misjudgment of the auxiliary braking system and improve the accuracy of the auxiliary braking.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle technology, and more particularly to a vehicle control method, device, vehicle, storage medium, and program product. Background Technology

[0002] Active safety is a fundamental function in driver assistance systems and a crucial guarantee for the safe and reliable operation of advanced intelligent driver assistance systems. In recent years, the applicable operating scenarios for active safety functions have been greatly expanded. It is necessary to continuously improve the effective triggering performance of the system while controlling the probability of false triggering, in order to avoid unnecessary automatic braking behaviors affecting the normal driving experience of passengers. Summary of the Invention

[0003] To overcome the problems existing in the related technologies, this disclosure provides a vehicle control method, device, vehicle, storage medium, and program product.

[0004] According to a first aspect of the present disclosure, a vehicle control method is provided, the method comprising: Obtain the risk confidence level; the risk confidence level is used to characterize the credibility of the first collision risk determined by the vehicle's auxiliary braking system; If the risk confidence level is less than a first preset confidence threshold, the user's braking intention is obtained; The triggering strategy of the auxiliary braking system is determined based on the first collision risk and the braking intention.

[0005] By adopting the above implementation method, when the reliability of the collision risk determined by the auxiliary braking system is low, the triggering of the vehicle's auxiliary braking system is controlled by combining the collision risk determined by the auxiliary braking system and the user's braking intention. This can avoid the auxiliary braking system from misjudging the risk and improve the accuracy of auxiliary braking.

[0006] In some possible implementations, the method further includes: If the risk confidence level is greater than or equal to the first preset confidence threshold, the triggering strategy of the auxiliary braking system is determined according to the risk level of the first collision risk.

[0007] By adopting the above implementation method, when the risk confidence level is high, the triggering strategy of the auxiliary braking system is determined based on the collision risk determined by the auxiliary braking system, which can quickly and accurately realize the vehicle's auxiliary braking.

[0008] In some possible implementations, obtaining the risk confidence level includes: The risk confidence level is determined based on the perceived information of obstacles around the vehicle.

[0009] By adopting the above implementation method, since obstacles are the main factors causing collision risks, the risk confidence level of collision risks can be obtained accurately and effectively based on the perception information of obstacles.

[0010] In some possible implementations, determining the risk confidence level based on perceived information about obstacles around the vehicle includes: If the perceived information meets a first preset condition, the risk confidence level is determined to be high confidence; the first preset condition includes at least one of the following: The detection duration of the obstacle is greater than a first preset duration threshold; The confidence level for identifying the obstacle is greater than a second preset confidence threshold; The detection devices that detect the obstacles include various types; The rate of change of the obstacle's moving speed is less than a first preset rate of change threshold.

[0011] Using the above implementation method, based on the obstacle detection time, identification confidence, detection device type and motion state, the risk confidence can be judged comprehensively and accurately from multiple dimensions.

[0012] In some possible implementations, determining the risk confidence level based on perceived information about obstacles around the vehicle includes: If the perceived information and the environmental information surrounding the vehicle meet a second preset condition, the risk confidence level is determined to be low; the second preset condition includes at least one of the following: The detection time of the obstacle is less than the second preset time threshold; The confidence level for identifying the obstacle is less than a third preset confidence threshold; The detection device that detects the obstacle includes one type; The rate of change of the obstacle's moving speed is greater than a second preset rate of change threshold; The environmental information indicates that the environment around the vehicle is a weakly perceived environment.

[0013] Using the above implementation method, based on the obstacle detection time, identification confidence, detection device type and motion state, the risk confidence can be judged comprehensively and accurately from multiple dimensions.

[0014] In some possible implementations, determining the risk confidence level based on perceived information about obstacles around the vehicle includes: When the vehicle simultaneously meets the first preset condition and the second preset condition, the risk confidence level is determined based on the trigger sensitivity of the auxiliary braking system.

[0015] By adopting the above implementation method, when the vehicle simultaneously meets both high confidence and low confidence conditions, the risk confidence is further determined based on the trigger sensitivity of the auxiliary braking system, so that the risk confidence can be flexibly adapted to the user's driving style.

[0016] In some possible implementations, the perceived information includes multiple components; determining the risk confidence level based on the perceived information of obstacles around the vehicle includes: Determine the confidence index value corresponding to each of the perceived information; The risk confidence level is obtained by weighted summation of multiple confidence index values.

[0017] By adopting the above implementation method, based on the confidence index values ​​corresponding to multiple perception information, the risk confidence level can be determined comprehensively and accurately from multiple dimensions, thereby improving the accuracy of the risk confidence level.

[0018] In some possible implementations, obtaining the user's braking intent includes: Obtain the brake pedal opening of the vehicle during multiple preset time periods; The braking intention is determined based on the multiple brake pedal openings.

[0019] Using the above implementation method, the driver's braking intention can be determined in real time, quickly and accurately based on the brake pedal opening of the vehicle in multiple preset time periods, thereby obtaining the user's assessment result of the collision risk.

[0020] In some possible implementations, determining the braking intention based on the plurality of brake pedal openings includes: When multiple brake pedal openings are within the same preset opening range, or when a specified brake pedal opening is greater than a preset pedal opening threshold, the braking intention is determined based on the specified brake pedal opening, wherein the time period corresponding to the specified brake pedal opening is closest to the current time; or... If multiple brake pedal openings are located in different preset opening ranges, and the specified brake pedal opening is less than or equal to the preset pedal opening threshold, the braking intention is determined to be unclear.

[0021] By adopting the above implementation method, when the brake pedal opening tends to be stable, the user's braking intention is judged based on the brake pedal opening in the preset time period closest to the current time, so as to avoid misjudgment of braking intention and ensure the effectiveness and accuracy of braking intention.

[0022] In some possible implementations, determining the triggering strategy of the auxiliary braking system based on the first collision risk and the braking intention includes: If the risk level of the first collision risk is greater than or equal to a first preset level threshold, the triggering strategy is determined based on the risk level of the second collision risk represented by the braking intention and the second preset level threshold; or... If the risk level of the first collision risk is less than the first preset level threshold, the triggering strategy is determined based on the risk level of the second collision risk and the third preset level threshold.

[0023] By adopting the above implementation method, the user's assessment of the collision risk level can be accurately obtained through the user's braking intention. For different levels of first collision risk, the triggering of the auxiliary braking system is controlled based on the corresponding preset level threshold, which can effectively avoid the problem of false triggering of the auxiliary braking system while ensuring timely braking.

[0024] According to a second aspect of the present disclosure, a vehicle control device is provided, the device comprising: The first acquisition module is configured to acquire a risk confidence level; the risk confidence level is used to characterize the credibility of the first collision risk determined by the vehicle's auxiliary braking system. The second acquisition module is configured to acquire the user's braking intention when the risk confidence level is less than a first preset confidence threshold. The control module is configured to determine the triggering strategy of the auxiliary braking system based on the first collision risk and the braking intention.

[0025] In some possible implementations, the control module is further configured to: If the risk confidence level is greater than or equal to the first preset confidence threshold, the triggering strategy of the auxiliary braking system is determined according to the risk level of the first collision risk.

[0026] In some possible implementations, the first acquisition module is configured as follows: The risk confidence level is determined based on the perceived information of obstacles around the vehicle.

[0027] In some possible implementations, the second acquisition module is configured as follows: Obtain the brake pedal opening of the vehicle during multiple preset time periods; The braking intention is determined based on the multiple brake pedal openings.

[0028] According to a third aspect of the present disclosure, a vehicle is provided, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to execute processor-executable instructions in the memory to implement the steps of the method described in the first aspect of the present disclosure.

[0029] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described in the first aspect of the present disclosure.

[0030] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in the first aspect of the present disclosure.

[0031] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: This embodiment first obtains a risk confidence level, which characterizes the credibility of the first collision risk determined by the vehicle's auxiliary braking system. If the risk confidence level is less than a first preset confidence threshold, the user's braking intention is obtained, and the triggering strategy of the auxiliary braking system is determined based on this intention. Thus, when the credibility of the collision risk determined by the auxiliary braking system is low, combining the collision risk determined by the auxiliary braking system with the user's braking intention controls the triggering of the vehicle's auxiliary braking system, avoiding misjudgments of risk by the auxiliary braking system and improving the accuracy of auxiliary braking.

[0032] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0033] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0034] Figure 1 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment.

[0035] Figure 2 This is a schematic diagram illustrating a type of collision risk according to an exemplary embodiment.

[0036] Figure 3 This is a schematic diagram illustrating the correspondence between braking intent and collision risk level according to an exemplary embodiment.

[0037] Figure 4 This is a schematic diagram illustrating a control method for an auxiliary braking system according to an exemplary embodiment.

[0038] Figure 5This is a block diagram illustrating a vehicle control device according to an exemplary embodiment.

[0039] Figure 6 This is a block diagram illustrating a vehicle according to an exemplary embodiment. Detailed Implementation

[0040] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0041] It should be noted that all actions involving the acquisition of signals, information, or data in this disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with authorization from the owner of the relevant device.

[0042] Furthermore, the intelligent driving described in this disclosure can also be called assisted driving. All intelligent driving-related technologies in this disclosure are designed in accordance with the relevant laws and regulations of the country where the vehicle is located. For example, intelligent driving requires the driver's hands not to be taken off the steering wheel.

[0043] Specifically, with the development of sensing technology and the improvement of chip capabilities, intelligent driving provides people with more and more rich driving functions, gradually realizing different levels of driving experience. The Society of Automotive Engineers (SAE) provides a driving automation classification standard, including driving levels L0 to L5, where L0 is no automation, and the human driver has full control of the car. During driving, the driver can receive warnings or assistance from the driving system, such as automatic emergency braking (AEB), blind spot monitoring (BSM), or lane departure warning (LDW). Level 1 is driver assistance, where driving operations are jointly performed by the human driver and the driving system. The driving system provides driving support based on the driving environment, such as steering wheel inputs or acceleration / deceleration. Other driving operations are performed by the human driver, such as adaptive cruise control (ACC) or lane keep assistance / support (LKA / LKS). Level 2 is partial automation, providing driving support based on the driving environment, including steering wheel inputs and acceleration / deceleration. Other driving actions are performed by the human driver, such as a combination of adaptive cruise control (ACC) and lane keep assist. Level 0 to Level 2 are driving systems that primarily support the driver, who still needs to monitor the driving process and perform actions such as steering, braking, or acceleration as needed for safety. Levels 0 to 2 primarily provide support to the driver, who still needs to supervise the driving process and perform actions such as steering, braking, or acceleration as needed. Levels 3 to 5 can replace the driver in performing all driving operations. At Level 3, the driver must be prepared to take over driving. Levels 4 and 5 allow for partial and full driving under certain conditions, with the driver having the option to take over. Level 4 is highly automated, where the driving system can perform all driving operations, and the driver may not need to respond to requests from the system, for example, under suitable road and environmental conditions (such as closed parks, highways, city roads, or fixed routes). Level 5 is fully automated, where the driving system can autonomously perform all driving operations under various road and environmental conditions that a human driver can handle.

[0044] Vehicles can roam based on high-precision maps, including changing lanes, changing driving direction, passing intersections, and changing some navigation strategies during navigation driving based on high-precision maps. It can also include intelligent driving processes performed by vehicles based on high-precision map information and their understanding of the surrounding road structure when no specific destination is set.

[0045] It should be understood that there is some semantic overlap among the aforementioned concepts (passing an intersection, changing lanes, changing local navigation strategies, and changing driving direction). For example, there is some semantic overlap between "changing lanes" and "changing driving direction" because in some cases, such as when a vehicle changes from a straight lane to a right-turn lane, not only does the driving lane change, but the driving direction also changes. This disclosure lists these concepts for the purpose of clarity and completeness.

[0046] In related technologies, driver behavior characteristics are learned and integrated into the AEB control strategy. For example, for drivers with an aggressive driving style, the AEB braking intervention timing is appropriately delayed, while for drivers with a cautious driving style, the braking trigger point is set in advance, thus adapting to the driving habits of different groups. However, the above technical solutions do not consider the collision risk assessed by the user in real time, and cannot combine the collision risk assessed by the AEB system and the collision risk assessed by the driver to achieve precise control of the AEB system.

[0047] In view of this, embodiments of this disclosure provide a vehicle control method, apparatus, vehicle, storage medium, and program product. The method first obtains a risk confidence level, whereby the risk confidence level characterizes the credibility of a first collision risk determined by the vehicle's auxiliary braking system. If the risk confidence level is less than a first preset confidence threshold, the user's braking intention is obtained, and a triggering strategy for the auxiliary braking system is determined based on the braking intention. Thus, when the credibility of the collision risk determined by the auxiliary braking system is determined to be low, the triggering of the vehicle's auxiliary braking system is controlled by combining the collision risk determined by the auxiliary braking system and the user's braking intention. This avoids misjudgment of risk by the auxiliary braking system and improves the accuracy of auxiliary braking.

[0048] Figure 1 This is a flowchart illustrating a vehicle control method according to an exemplary embodiment, such as... Figure 1 As shown, the method may include the following steps.

[0049] In step S101, the risk confidence level is obtained.

[0050] For example, the auxiliary braking system in this embodiment may be an Automatic Emergency Braking (AEB) system or other types of vehicle auxiliary braking control systems. The auxiliary braking system is used to assess the collision risk of the vehicle and perform auxiliary braking control on the vehicle based on the collision risk to improve driving safety. The first collision risk determined by the auxiliary braking system can be represented by different levels, such as high risk, medium risk, and low risk. Since the first collision risk determined by the auxiliary braking system may have some error, in order to control the auxiliary braking system more accurately and effectively, the error can be reduced by obtaining the risk confidence level of the collision risk.

[0051] Risk confidence level can be used to characterize the credibility of the first collision risk determined by the vehicle's auxiliary braking system. Risk confidence level can be represented by different levels, such as high confidence, medium confidence, and low confidence, or first-level confidence, second-level confidence, ..., N-level confidence. Risk confidence level can also be represented by different numerical values, such as values ​​in the range [0,1], where a larger value indicates higher confidence and a smaller value indicates lower confidence.

[0052] In step S102, if the risk confidence level is less than the first preset confidence threshold, the user's braking intention is obtained.

[0053] In some embodiments, when the risk confidence level is greater than or equal to a first preset confidence threshold, the triggering strategy of the auxiliary braking system can be determined based on the risk level of the first collision risk determined by the auxiliary braking system.

[0054] For example, when risk confidence is represented by different levels, the first preset confidence threshold can be a pre-defined confidence level. For instance, risk confidence levels include high, medium, and low confidence. The first preset confidence threshold could be medium confidence. If the risk confidence is less than medium confidence, i.e., the risk confidence is low confidence, it indicates that the reliability of the first collision risk determined by the auxiliary braking system is low. In this case, the user's braking intention can be further obtained to control the auxiliary braking function of the auxiliary braking system in conjunction with the braking intention. If the risk confidence is greater than or equal to medium confidence, i.e., the risk confidence is high or medium confidence, it indicates that the reliability of the first collision risk determined by the auxiliary braking system is high. The auxiliary braking system can be controlled based on the first collision risk level determined by the auxiliary braking system.

[0055] In step S103, the triggering strategy of the auxiliary braking system is determined based on the first collision risk and braking intention.

[0056] For example, a user's braking intention can be used to characterize the vehicle's second-collision risk, which can be represented by different levels, such as high risk, medium risk, and low risk. For instance, if the user's braking operation is light or nonexistent, the risk level of the second-collision risk can be considered low; if the user's braking operation is moderate, the risk level of the second-collision risk can be considered medium; and if the user's braking operation is heavy, the risk level of the second-collision risk can be considered high.

[0057] In some embodiments, when the risk level of the first collision risk is greater than or equal to a first preset level threshold, the triggering strategy of the auxiliary braking system is determined based on the second collision risk and the second preset level threshold representing the braking intention; when the risk level of the first collision risk is less than the first preset level threshold, the triggering strategy of the auxiliary braking system is determined based on the risk level of the second collision risk and a third preset level threshold. The second preset level threshold and the third preset level threshold are set independently and are not related to each other. The second preset level threshold and the third preset level threshold can be different or the same; for example, the second preset level threshold can be less than the third preset level threshold.

[0058] For example, if the risk level of the first collision risk is greater than or equal to a first preset risk level threshold, and the risk level of the second collision risk is greater than or equal to a second preset risk level threshold, the auxiliary braking system can be triggered; if the risk level of the second collision risk is less than the second preset risk level threshold, the auxiliary braking system can not be triggered. Conversely, if the risk level of the first collision risk is less than the first preset risk level threshold, and the risk level of the second collision risk is greater than or equal to a third preset risk level threshold, the auxiliary braking system can be triggered; if the risk level of the second collision risk is less than the third preset risk level threshold, the auxiliary braking system can not be triggered.

[0059] For example, the risk levels of the first collision risk include high risk and low risk, and the risk levels of the second collision risk include high risk, medium risk, and low risk. The first preset risk level threshold is high risk, the second preset risk level threshold is medium risk, and the third preset risk level threshold is low risk. If the risk level of the first collision risk is high, and the risk level of the second collision risk is also high or medium risk, the auxiliary braking system can be triggered; if the risk level of the second collision risk is low risk, the auxiliary braking system may not be triggered. Conversely, if the risk level of the first collision risk is low, and the risk level of the second collision risk is high risk, the auxiliary braking system can be triggered; if the risk level of the second collision risk is medium or low risk, the auxiliary braking system may not be triggered.

[0060] In other embodiments, if the risk level of the second collision risk represented by the braking intention is greater than or equal to a fourth preset threshold, it indicates that the user judges the vehicle's collision risk level to be high, and therefore the auxiliary braking system can be triggered. If the risk level of the second collision risk represented by the braking intention is less than the fourth preset threshold, or the braking intention is unclear, it indicates that the user judges the vehicle's collision risk level to be low, and therefore the auxiliary braking system may not be triggered. Triggering the auxiliary braking system can be understood as the auxiliary braking system being activated, assisting the user in performing driving avoidance maneuvers by outputting hazard warning information and actively applying braking force.

[0061] For example, the risk level of the second collision risk represented by the braking intention can include high risk, medium risk and low risk. The fourth preset level threshold can be medium risk. If the risk level of the second collision risk is greater than or equal to medium risk, that is, when the risk level of the second collision risk is high risk or medium risk, the auxiliary braking system can be triggered. If the risk level of the second collision risk is less than medium risk, that is, when the risk level of the second collision risk is low risk, the auxiliary braking system can not be triggered.

[0062] In this way, the user's braking intention can accurately determine the collision risk level judged by the user, and the triggering of the auxiliary braking system can be controlled based on the collision risk level and the preset risk level threshold, which can effectively avoid the problem of false triggering of the auxiliary braking system.

[0063] By employing the above implementation method, based on risk confidence level and a preset confidence threshold, when the risk confidence level is low, the vehicle's auxiliary braking system is controlled in conjunction with the user's braking intention. This avoids misjudging risks by the auxiliary braking system and improves the accuracy of auxiliary braking. When the risk confidence level is high, the triggering strategy of the auxiliary braking system is determined in conjunction with the first collision risk identified by the auxiliary braking system, enabling rapid and accurate implementation of vehicle auxiliary braking.

[0064] In some embodiments, one implementation of step S101 may be: determining the risk confidence level based on the perception information of obstacles around the vehicle.

[0065] The obstacle perception information can be understood as obstacle-related information collected, identified, and analyzed by the vehicle through onboard perception equipment. This obstacle perception information may include at least one of the following: obstacle detection time, obstacle recognition confidence level, type of detection device used to detect the obstacle, and rate of change of the obstacle's moving speed.

[0066] By adopting the above implementation method, since obstacles are the main factors causing collision risks, the risk confidence level of collision risks can be obtained accurately and effectively based on the perception information of obstacles.

[0067] For example, if the perceived information about the obstacle meets a first preset condition, the risk confidence level can be determined to be high. The first preset condition may include at least one of the following conditions, or at least two of the following conditions.

[0068] Condition 1: The detection time of the obstacle is greater than the first preset time threshold, wherein the first preset time threshold can be 1 second; Condition 2: The confidence level for obstacle recognition is greater than the second preset confidence threshold, where the second preset confidence threshold can be 0.8 (maximum confidence level is 1). Condition 3: The detection devices that detect obstacles include various types, such as obstacles being detected simultaneously by a camera and millimeter-wave radar, or simultaneously by a camera and lidar. Condition 4: The rate of change of the obstacle's moving speed is less than the first preset rate of change threshold, wherein the first preset rate of change threshold can be 30%.

[0069] For example, if the perceived information about the obstacle and the environmental information around the vehicle meet the second preset condition, the risk confidence level can be determined to be low. The second preset condition may include at least one of the following conditions, or at least two of the following conditions.

[0070] Condition 1: The detection time of the obstacle is less than the second preset time threshold, where the second preset time threshold can be 1 second; Condition 2: The confidence level for obstacle recognition is less than the third preset confidence threshold, where the third preset confidence threshold can be 0.4 (maximum confidence level is 1). Condition 3: The detection device that detects the obstacle includes one type, for example, the obstacle is only detected by the camera, but not by millimeter-wave radar or lidar; Condition 4: The rate of change of the obstacle's moving speed is less than the second preset rate of change threshold, where the second preset rate of change threshold can be 50%; Condition 5: Environmental information characterizes the environment around the vehicle as a weakly perceived environment, such as when the vehicle is in a parking garage, tunnel, or during rain or snow.

[0071] In this way, based on the obstacle detection time, the obstacle identification confidence level, the type of obstacle detection device, and the obstacle's motion state, the risk confidence level can be judged comprehensively and accurately from multiple dimensions.

[0072] In other embodiments, when the vehicle simultaneously meets the first preset condition and the second preset condition, the risk confidence level can be determined based on the trigger sensitivity of the auxiliary braking system.

[0073] For example, if the vehicle simultaneously meets both the first and second preset conditions, the risk confidence level cannot be directly determined. Instead, the trigger sensitivity of the auxiliary braking system can be further obtained. This trigger sensitivity can be dynamically set based on the user's driving style, or it can be user-defined; this disclosure does not specifically limit this. The risk confidence level can then be determined based on the trigger sensitivity, allowing it to flexibly adapt to the user's driving style. The trigger sensitivity and risk confidence level can be positively correlated; that is, higher trigger sensitivity leads to higher risk confidence level, and lower trigger sensitivity leads to lower risk confidence level.

[0074] For example, if the trigger sensitivity is greater than or equal to a preset sensitivity threshold, the risk confidence level can be determined as high confidence; if the trigger sensitivity is less than the preset sensitivity threshold, the risk confidence level can be determined as high confidence.

[0075] In other embodiments, if the vehicle does not meet either the first preset condition or the second preset condition, the triggering strategy of the auxiliary braking system can be determined based on the collision risk determined by the auxiliary braking system.

[0076] In other embodiments, a third preset condition can be pre-set to determine the risk confidence level. If the perceived information of the obstacle meets the third preset condition, the risk confidence level can be determined to be high; if the perceived information of the obstacle does not meet the third preset condition, the risk confidence level can be determined to be low.

[0077] For example, the third preset condition may include at least one of the following conditions, or at least two of the following conditions: Condition 1: The duration of detecting an obstacle with a collision risk is greater than a third preset duration threshold; Condition 2: The confidence level of the detected obstacle information is greater than a fourth preset confidence threshold; Condition 3: The detection device that detected the obstacle includes multiple types; Condition 4: The moving speed of the obstacle is less than a third preset speed threshold; Condition 5: The environmental information characterizes the environment around the vehicle as not a weakly perceived environment.

[0078] In other embodiments, the perceived information may include multiple values. First, a confidence index value corresponding to each perceived information can be determined. Then, the multiple confidence index values ​​are weighted and summed to obtain the risk confidence level. The confidence index value can take values ​​in the range of [0,1].

[0079] For example, the perceived information may include: obstacle detection duration, obstacle recognition confidence, type of device used to detect the obstacle, and obstacle movement speed. The obstacle detection duration can be positively correlated with the confidence index value; that is, the longer the obstacle detection duration, the higher the corresponding confidence index value. The obstacle recognition confidence can be directly obtained from the neural network model. The type of device used to detect the obstacle can be negatively correlated with the confidence index; that is, the more types of devices used to detect the obstacle, the higher the corresponding confidence index value. The obstacle movement speed can be negatively correlated with the confidence index; that is, the faster the obstacle moves, the lower the corresponding confidence index value.

[0080] In this way, the confidence index value obtained by quantifying multiple perceptual information can comprehensively and accurately determine the risk confidence level from multiple dimensions, thereby improving the accuracy of the risk confidence level.

[0081] Figure 2 This is a schematic diagram illustrating a collision risk type determined by an auxiliary braking system according to an exemplary embodiment, such as... Figure 2 As shown, when the auxiliary braking system determines that there is a collision risk, if the risk confidence level is high, then the collision risk is determined to be high risk and can be confirmed; if the risk confidence level is low, then the collision risk is determined to be high risk and uncertain. When the auxiliary braking system determines that there is no collision risk, if the risk confidence level is high, then the collision risk is determined to be low risk and can be confirmed; if the risk confidence level is low, then the collision risk is determined to be low risk and uncertain.

[0082] In some possible implementations, step S102 can be implemented as follows: obtaining the brake pedal opening of the vehicle within multiple preset time periods; and determining the braking intention based on the multiple brake pedal openings.

[0083] For example, when multiple brake pedal openings fall within the same preset opening range, braking intent can be determined based on a specified brake pedal opening. When a specified brake pedal opening exceeds a preset pedal opening threshold, braking intent can also be determined based on the specified brake pedal opening, where the time period corresponding to the specified brake pedal opening is closest to the current time. When multiple brake pedal openings fall within different preset opening ranges, and the specified brake pedal opening is less than or equal to the preset pedal opening threshold, the braking intent is unclear. In cases of unclear braking intent, the auxiliary braking system can be controlled based on the first collision risk determined by the auxiliary braking system. In this way, the driver's braking intent can be determined in real-time, quickly, and accurately based on the vehicle's brake pedal openings over multiple preset time periods, thereby obtaining the user's assessment of collision risk.

[0084] For example, multiple preset time periods may include a first preset time period and a second preset time period, wherein the first preset time period is closer to the current time than the second preset time period. The average brake pedal opening during the first preset time period can be the first brake pedal opening, and the average brake pedal opening during the second preset time period can be the second brake pedal opening. If the first brake pedal opening and the second brake pedal opening are within the same preset opening range, or if the first brake pedal opening is greater than a preset pedal opening threshold, the braking intention can be determined based on the first brake pedal opening. If the first brake pedal opening and the second brake pedal opening are within different preset opening ranges, and the first brake pedal opening is less than or equal to the preset pedal opening threshold, the braking intention can be determined to be unclear.

[0085] For example, at the current moment, the brake pedal opening at historical moments is averaged over time using a sliding window method. The sliding window length can be selected as 1 second. The window segment preceding the current moment is defined as N-1, and the window segment preceding the k-th moment is defined as Nk. The window corresponding to the first preset time period can be N-1, and the window corresponding to the second preset time period can be N-2. The preset opening range can include [0, 25%), [25%, 50%), and [50%, 100%), and the preset pedal opening threshold can be 50%.

[0086] Figure 3 This is a diagram illustrating the correspondence between braking intent and the risk of a second collision, such as... Figure 3 As shown, if the braking is light or non-existent in window N-1 (i.e., the average brake pedal opening is <25%), while the average brake pedal opening in window N-2 is >25%, it can be determined that the vehicle has no braking or light braking in the short term. The brake pedal openings corresponding to windows N-1 and N-2 are located in different preset opening ranges, which means that the user's braking behavior has changed significantly. Therefore, it can be determined that the braking intention is unclear.

[0087] If both N-1 and N-2 windows show light or no braking, i.e., the average brake pedal opening is <25%, it can be determined that the vehicle has been without braking or with light braking for a long time. The brake pedal openings corresponding to N-1 and N-2 windows are located in the same preset opening range, i.e. the user has not been applying significant braking. Therefore, the risk level of the second collision risk represented by the braking intention can be determined to be low risk.

[0088] If the braking level in window N-1 is moderate (i.e., the average brake pedal opening ranges from 25% to 50%), while the average brake pedal opening in window N-2 is less than 25% or greater than 50%, it can be determined that the vehicle is braking in the short term. The brake pedal openings corresponding to windows N-1 and N-2 are located in different preset opening ranges, indicating that the user's braking behavior has changed significantly. Therefore, it can be determined that the braking intention is unclear.

[0089] If both N-1 and N-2 windows contain moderate braking (i.e., the average brake pedal opening range is [25%, 50%)), then it can be determined that the vehicle is under long-term moderate braking. The brake pedal openings corresponding to the N-1 and N-2 windows are located in the same preset opening range, indicating that the user is continuously decelerating. Therefore, the risk level of the second collision risk represented by the braking intention can be determined to be medium risk.

[0090] If the braking is heavy within the N-1 window, i.e. the average brake pedal opening is greater than 50%, it can be determined that the vehicle has been under heavy braking for a long time and the driver has made a heavy pressing action. Therefore, the risk level of the second collision risk represented by the braking intention can be determined to be high risk.

[0091] By adopting the above implementation method, when the brake pedal opening tends to be stable, the user's braking intention is determined based on the brake pedal opening in the preset time period closest to the current time. When the brake pedal opening is unstable, the braking intention is determined to be unclear, thus avoiding misjudgment of the braking intention and ensuring the effectiveness and accuracy of the braking intention.

[0092] Figure 4 This is a schematic diagram illustrating a control method for an auxiliary braking system according to an exemplary embodiment, such as... Figure 4 As shown, if the collision risk type determined by the auxiliary braking system is high risk and can be determined, the auxiliary braking system can be directly triggered; if the collision risk type determined by the auxiliary braking system is high risk and uncertain, it can be determined whether to trigger the auxiliary braking system based on the second collision risk represented by the braking intention. If the risk level of the second collision risk is medium risk or high risk, the auxiliary braking system can be triggered; if the risk level of the second collision risk is low risk, the auxiliary braking system can not be triggered.

[0093] If the collision risk type determined by the auxiliary braking system is low and certain, the auxiliary braking system may not be triggered. If the collision risk type determined by the auxiliary braking system is low and uncertain, the decision to trigger the auxiliary braking system may be based on the second collision risk represented by the braking intent. If the risk level of the second collision risk is high, the auxiliary braking system may be triggered. If the risk level of the second collision risk is medium or low, the auxiliary braking system may not be triggered.

[0094] In summary, the embodiments of this disclosure, based on risk confidence and a preset confidence threshold, determine the triggering strategy of the vehicle's auxiliary braking system when the confidence of the collision risk determined by the auxiliary braking system is low. This strategy combines the collision risk determined by the auxiliary braking system with the user's braking intention, thereby avoiding misjudgment of risk by the auxiliary braking system and improving the accuracy of auxiliary braking.

[0095] Figure 5This is a block diagram illustrating a vehicle control device according to an exemplary embodiment, such as... Figure 5 As shown, the device 200 may include the following modules.

[0096] The first acquisition module 201 is configured to acquire a risk confidence level; the risk confidence level is used to characterize the credibility of the first collision risk determined by the vehicle's auxiliary braking system. The second acquisition module 202 is configured to acquire the user's braking intention when the risk confidence level is less than a first preset confidence threshold. The control module 203 is configured to determine the triggering strategy of the auxiliary braking system based on the first collision risk and the braking intention.

[0097] In some embodiments, the control module 203 is further configured to: determine the triggering strategy of the auxiliary braking system based on the risk level of the first collision risk when the risk confidence level is greater than or equal to the first preset confidence threshold.

[0098] In other embodiments, the first acquisition module 201 is configured to determine the risk confidence level based on the perception information of obstacles around the vehicle.

[0099] In some other embodiments, the first acquisition module 201 is configured to: determine the risk confidence level as high confidence level when the perceived information meets a first preset condition.

[0100] The first preset condition includes at least one of the following: the detection duration of the obstacle is greater than a first preset duration threshold; the recognition confidence of the obstacle is greater than a second preset confidence threshold; the detection device that detects the obstacle includes multiple types; and the rate of change of the obstacle's movement speed is less than a first preset rate of change threshold.

[0101] In other embodiments, the first acquisition module 201 is configured to: If the perceived information and the environmental information around the vehicle meet the second preset condition, the risk confidence level is determined to be low.

[0102] The second preset condition includes at least one of the following: the detection time of the obstacle is less than a second preset time threshold; the recognition confidence of the obstacle is less than a third preset confidence threshold; the detection device that detects the obstacle includes one type; the rate of change of the obstacle's moving speed is greater than a second preset rate of change threshold; and the environmental information indicates that the environment around the vehicle is a weakly perceived environment.

[0103] In other embodiments, the first acquisition module 201 is configured to determine the risk confidence level based on the trigger sensitivity of the auxiliary braking system when the vehicle simultaneously meets the first preset condition and the second preset condition.

[0104] In other embodiments, the perceived information includes multiple components; the first acquisition module 201 is configured to: determine the confidence index value corresponding to each of the perceived information components; and perform a weighted summation of the multiple confidence index values ​​to obtain the risk confidence level.

[0105] In other embodiments, the second acquisition module 202 is configured to: acquire the brake pedal opening of the vehicle during multiple preset time periods; and determine the braking intention based on the multiple brake pedal openings.

[0106] In other embodiments, the first acquisition module 201 is configured to: determine the braking intention based on the specified brake pedal opening when multiple brake pedal openings are located in the same preset opening range, or when the specified brake pedal opening is greater than a preset pedal opening threshold, wherein the time period corresponding to the specified brake pedal opening is closest to the current time; or, determine that the braking intention is unclear when multiple brake pedal openings are located in different preset opening ranges, and the specified brake pedal opening is less than or equal to the preset pedal opening threshold.

[0107] In other embodiments, the control module 203 is configured to: determine the triggering strategy based on the risk level of the second collision risk and the risk level threshold of the second preset risk when the risk level of the first collision risk is greater than or equal to the first preset level threshold; or, determine the triggering strategy based on the risk level of the second collision risk and the third preset level threshold when the risk level of the first collision risk is less than the first preset level threshold.

[0108] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0109] In summary, the embodiments of this disclosure, based on risk confidence and a preset confidence threshold, determine the triggering strategy of the vehicle's auxiliary braking system when the confidence of the collision risk determined by the auxiliary braking system is low. This strategy combines the collision risk determined by the auxiliary braking system with the user's braking intention, thereby avoiding misjudgment of risk by the auxiliary braking system and improving the accuracy of auxiliary braking.

[0110] This disclosure also provides a non-transitory computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the steps of the vehicle control method provided in this disclosure.

[0111] Figure 6 This is a block diagram illustrating a vehicle 600 according to an exemplary embodiment. For example, vehicle 600 may be a hybrid vehicle, a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other types of vehicle. Vehicle 600 may have driver assistance functions.

[0112] Reference Figure 6 The vehicle 600 may include various subsystems, such as an infotainment system 610, a perception system 620, a decision control system 630, a drive system 640, and a computing platform 650. The vehicle 600 may also include more or fewer subsystems, and each subsystem may include multiple components. Furthermore, each subsystem and each component of the vehicle 600 can be interconnected via wired or wireless means.

[0113] In some embodiments, the infotainment system 610 may include a communication system, an entertainment system, and a navigation system, etc.

[0114] The perception system 620 may include several sensors for sensing information about the environment surrounding the vehicle 600. For example, the perception system 620 may include a global positioning system (which may be GPS, BeiDou, or other positioning systems), an inertial measurement unit (IMU), lidar, millimeter-wave radar, ultrasonic radar, and a camera device.

[0115] The decision control system 630 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0116] The drive system 640 may include components that provide powered motion to the vehicle 600. In one embodiment, the drive system 640 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of internal combustion engines, electric motors, and compressed air engines. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0117] Some or all of the functions of vehicle 600 are controlled by computing platform 650. Computing platform 650 may include at least one processor 651 and memory 652, processor 651 can execute instructions 653 stored in memory 652.

[0118] Processor 651 can be any conventional processor, such as a commercially available CPU. Processors may also include graphics processing units (GPUs), field-programmable gate arrays (FPGAs), systems-on-chips (SoCs), application-specific integrated circuits (ASICs), or combinations thereof.

[0119] The memory 652 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0120] In addition to instruction 653, memory 652 can also store data, such as road maps, route information, vehicle position, direction, speed, and other data. The data stored in memory 652 can be used by computing platform 650.

[0121] In this embodiment of the disclosure, processor 651 may execute instructions 653 to complete all or part of the steps of the vehicle control method described above.

[0122] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the vehicle control method described above when executed by the programmable device.

[0123] Those skilled in the art will also understand that the various illustrative logical blocks and steps listed in the embodiments of this disclosure can be implemented by electronic hardware, computer software, or a combination of both. Whether such functionality is implemented in hardware or software depends on the specific application and the overall system design requirements. Those skilled in the art can implement the described functionality using various methods for each specific application, but such implementation should not be construed as exceeding the scope of protection of the embodiments of this disclosure.

[0124] Furthermore, the term “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as advantageous compared to other aspects or designs. Rather, the use of the term “exemplary” is intended to present the concept in a concrete manner. As used herein, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise specified or clear from the context, “X applies A or B” is intended to mean any of the natural inclusive arrangements. That is, “X applies A or B” satisfies any of the foregoing instances if X applies A; X applies B; or both X applies A and B. Additionally, unless otherwise specified or clear from the context to refer to the singular form, the articles “a” and “an” as used in this application and the appended claims are generally understood to mean “one or more.”

[0125] Similarly, although this disclosure has been shown and described with respect to one or more implementations, equivalent variations and modifications will occur to those skilled in the art upon reading and understanding this specification and the accompanying drawings. This disclosure includes all such modifications and variations and is limited only by the scope of the claims. In particular, with respect to the various functions performed by the components described above (e.g., elements, resources, etc.), unless otherwise indicated, the terminology used to describe such components is intended to correspond to any component (functionally equivalent) that performs the specific function of the described component, even if structurally not equivalent to the disclosed structure. Furthermore, although specific features of this disclosure may have been disclosed with respect to only one of several implementations, such features may be combined with one or more other features of other implementations, as may be desired and advantageous to any given or particular application. Moreover, with regard to the terms “comprising,” “owning,” “having,” “having,” or variations thereof as used in the detailed description or claims, such terms are intended to be inclusive in a manner similar to the term “including.”

[0126] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

[0127] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

[0128] It should be understood that, unless otherwise specifically indicated, features of various embodiments of this disclosure described herein can be combined with each other. As used herein, the term “and / or” includes any one of the relevant listed items and any combination of any two or more; similarly, “at least one of…” includes any one of the relevant listed items and any combination of any two or more.

[0129] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In this description, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A vehicle control method characterized by, The method includes: Obtain the risk confidence level; the risk confidence level is used to characterize the credibility of the first collision risk determined by the vehicle's auxiliary braking system; If the risk confidence level is less than a first preset confidence threshold, the user's braking intention is obtained; The triggering strategy of the auxiliary braking system is determined based on the first collision risk and the braking intention.

2. The method according to claim 1, characterized in that, The method further includes: If the risk confidence level is greater than or equal to the first preset confidence threshold, the triggering strategy of the auxiliary braking system is determined according to the risk level of the first collision risk.

3. The method according to claim 1, characterized in that, The acquisition of risk confidence includes: The risk confidence level is determined based on the perceived information of obstacles around the vehicle.

4. The method according to claim 3, characterized in that, Determining the risk confidence level based on perceived information about obstacles around the vehicle includes: If the perceived information meets a first preset condition, the risk confidence level is determined to be high confidence; the first preset condition includes at least one of the following: The detection duration of the obstacle is greater than a first preset duration threshold; The confidence level for identifying the obstacle is greater than a second preset confidence threshold; The detection devices that detect the obstacles include various types; The rate of change of the obstacle's moving speed is less than a first preset rate of change threshold.

5. The method according to claim 4, characterized in that, Determining the risk confidence level based on perceived information about obstacles around the vehicle includes: If the perceived information and the environmental information surrounding the vehicle meet a second preset condition, the risk confidence level is determined to be low; the second preset condition includes at least one of the following: The detection time of the obstacle is less than the second preset time threshold; The confidence level for identifying the obstacle is less than a third preset confidence threshold; The detection device that detects the obstacle includes one type; The rate of change of the obstacle's moving speed is greater than a second preset rate of change threshold; The environmental information indicates that the environment around the vehicle is a weakly perceived environment.

6. The method according to claim 5, characterized in that, Determining the risk confidence level based on perceived information about obstacles around the vehicle includes: When the vehicle simultaneously meets the first preset condition and the second preset condition, the risk confidence level is determined based on the trigger sensitivity of the auxiliary braking system.

7. The method according to claim 3, characterized in that, The perceived information includes multiple components; determining the risk confidence level based on the perceived information of obstacles around the vehicle includes: Determine the confidence index value corresponding to each of the perceived information; The risk confidence level is obtained by weighted summation of multiple confidence index values.

8. The method according to claim 1, characterized in that, The acquisition of the user's braking intent includes: Obtain the brake pedal opening of the vehicle during multiple preset time periods; The braking intention is determined based on the multiple brake pedal openings.

9. The method according to claim 8, characterized in that, Determining the braking intention based on the plurality of brake pedal openings includes: When multiple brake pedal openings are within the same preset opening range, or when a specified brake pedal opening is greater than a preset pedal opening threshold, the braking intention is determined based on the specified brake pedal opening, wherein the time period corresponding to the specified brake pedal opening is closest to the current time; or... If multiple brake pedal openings are located in different preset opening ranges, and the specified brake pedal opening is less than or equal to the preset pedal opening threshold, the braking intention is determined to be unclear.

10. The method according to any one of claims 1-9, characterized in that, The step of determining the triggering strategy of the auxiliary braking system based on the first collision risk and the braking intention includes: If the risk level of the first collision risk is greater than or equal to a first preset level threshold, the triggering strategy is determined based on the risk level of the second collision risk represented by the braking intention and the second preset level threshold; or... If the risk level of the first collision risk is less than the first preset level threshold, the triggering strategy is determined based on the risk level of the second collision risk represented by the braking intention and the third preset level threshold.

11. A vehicle control device, characterized in that, The device includes: The first acquisition module is configured to acquire a risk confidence level; the risk confidence level is used to characterize the credibility of the first collision risk determined by the vehicle's auxiliary braking system. The second acquisition module is configured to acquire the user's braking intention when the risk confidence level is less than a first preset confidence threshold. The control module is configured to determine the triggering strategy of the auxiliary braking system based on the first collision risk and the braking intention.

12. The apparatus according to claim 11, characterized in that, The control module is also configured to: If the risk confidence level is greater than or equal to the first preset confidence threshold, the triggering strategy of the auxiliary braking system is determined according to the risk level of the first collision risk.

13. The apparatus according to claim 11, characterized in that, The first acquisition module is configured as follows: The risk confidence level is determined based on the perceived information of obstacles around the vehicle.

14. The apparatus according to claim 11, characterized in that, The second acquisition module is configured as follows: Obtain the brake pedal opening of the vehicle during multiple preset time periods; The braking intention is determined based on the multiple brake pedal openings.

15. A vehicle, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to execute processor-executable instructions in the memory to implement the steps of the method according to any one of claims 1-10.

16. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program performs the steps of the method described in any one of claims 1-10.

17. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the steps of the method according to any one of claims 1-10.

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