Vehicle braking strategy adjustment method and device, vehicle and storage medium

Abstract

The application belongs to the technical field of automobile safety and auxiliary driving, and particularly relates to a vehicle braking strategy adjustment method and device, a vehicle and a storage medium. In view of the problem that the existing fixed braking trigger threshold is prone to risk judgment distortion, thereby causing vehicle braking false triggering and braking missed triggering, the application collects environment data, vehicle state data and driver state data, respectively determines a traffic target intention set and a driver intention, and determines an intention game type corresponding to human-vehicle interaction in combination with the two types of intentions. The application dynamically adjusts a braking trigger threshold set of a vehicle braking strategy according to different intention game types, and realizes vehicle braking by using the adjusted braking strategy. The application can adapt to dynamic traffic scenes, avoid false triggering and missed triggering defects caused by the fixed threshold, and balance vehicle driving safety and driving smoothness.

Description

Technical Field

[0001] This application relates to the field of automotive safety and driver assistance technology, and in particular to a vehicle braking strategy adjustment method, device, vehicle, and storage medium. Background Technology

[0002] With the continuous development of driver assistance systems, vehicle braking has become one of the core functions of these systems, and it is implemented according to a vehicle braking strategy. Currently, the industry generally uses a fixed braking trigger threshold as the basis for triggering the vehicle braking strategy; specifically, when the detected value is not greater than the fixed braking trigger threshold, the vehicle brakes; when the detected value is greater than the fixed braking trigger threshold, the vehicle does not brake.

[0003] However, using a fixed braking trigger threshold as the basis for vehicle braking strategy triggering can easily lead to false braking or missed braking. Specifically: in some scenarios, although the threshold is not higher than the fixed braking trigger threshold, the actual collision risk of the vehicle is low, which can easily cause false braking; in other scenarios, the threshold is higher than the fixed braking trigger threshold, but the actual collision risk of the vehicle is high, which can easily cause missed braking. Summary of the Invention

[0004] To address the technical problem of false or missed braking triggers caused by the fixed braking trigger threshold in existing vehicle braking strategies, this application provides a vehicle braking strategy adjustment method, apparatus, vehicle, and storage medium. The specific technical solution is as follows: Firstly, this application provides a method for adjusting a vehicle braking strategy, including: Collect environmental data, vehicle status data, and driver status data; Based on the environmental data, determine the traffic target intent set; The driver's intention is determined based on the vehicle status data and the driver status data; The type of intention game is determined based on the traffic target intention set and the driver intention; Based on the stated intention game type, the braking trigger threshold set in the vehicle braking strategy is adjusted, and the vehicle braking strategy after the braking trigger threshold set adjustment is used to achieve vehicle braking.

[0005] In a further optional implementation, determining the traffic target intent set based on the environmental data includes: The traffic target intent prediction model is invoked, which is used to predict the behavioral intent of traffic targets around the vehicle based on environmental data. Based on the traffic target intent prediction model, a set of traffic target intents corresponding to the environmental data is determined.

[0006] A further optional implementation of the process for obtaining the traffic target intent prediction model includes: A traffic target intent prediction model is obtained by training a preset neural network model using training data that includes environmental data and intent sets corresponding to the environmental data. The preset neural network model includes an input layer, a hidden layer, and an output layer in sequence. The training process uses a cross-entropy loss function, and training stops when the loss function converges.

[0007] In a further optional implementation, determining the driver's intention based on the vehicle state data and the driver state data includes: The driver intent recognition rule is invoked, which is used to identify the driver's intent based on vehicle status data and driver status data. Based on the driver intent recognition rules, the driver intent corresponding to the vehicle state data and the driver state data is determined.

[0008] A further optional implementation includes determining the intention game type based on the traffic target intention set and the driver intention, including: The intention game evaluation rule is invoked, which is used to identify the type of intention game based on the target intention and the driver intention; Select the traffic target intent with the highest score from the set of traffic target intents; Based on the intent game evaluation rules, the intent game type corresponding to the traffic target intent with the highest score and the driver intent is determined.

[0009] A further optional implementation includes determining the intention game type corresponding to the traffic target intention with the highest score and the driver intention based on the intention game evaluation rule, including: If the traffic objective intent with the highest score is the intention to yield, and the driver's intent is a perceived intent, then the intent game type is determined to be the yielding game type. If the traffic objective intent with the highest score is an intrusion intent, and the driver's intent is a perceived intent; or, if the traffic objective intent with the highest score is a yielding intent, and the driver's intent is an unperceived intent; then the intent game type is determined to be a weakly competitive game type. If the traffic target intent with the highest score is an intrusion intent, and the driver's intent is an unnoticed intent, then the intent game type is determined to be a highly competitive game type.

[0010] In a further optional implementation, the braking trigger threshold set includes a braking time trigger threshold and a braking distance trigger threshold; The step of adjusting the braking trigger threshold set in the vehicle braking strategy according to the intention game type includes: If the intended game type is a courtesy game type, the braking time trigger threshold and the braking distance trigger threshold are adjusted to a first specified multiple of their original values, respectively. If the intended game type is a weakly competitive game type, the braking time trigger threshold and the braking distance trigger threshold are adjusted to a second specified multiple of their original values, respectively. If the intended game type is a highly competitive game type, the braking time trigger threshold and the braking distance trigger threshold are adjusted to a third specified multiple of their original values.

[0011] A further optional implementation, after adjusting the brake trigger threshold set in the vehicle braking strategy, before implementing vehicle braking using the adjusted vehicle braking strategy, further includes: If the intended game type is a weakly competitive game type, then the braking strength is set to the first braking strength; If the intended game type is a highly competitive game type, then the braking intensity is set to the second braking intensity.

[0012] A further optional implementation, after adjusting the brake trigger threshold set in the vehicle braking strategy, before implementing vehicle braking using the adjusted vehicle braking strategy, further includes: Based on the environmental data, determine the set of traffic target trajectories; Based on the traffic target trajectory set, determine the braking trigger dataset; The brake trigger dataset and the adjusted brake trigger threshold set are compared to determine if the brake trigger conditions are met, thereby enabling vehicle braking.

[0013] Secondly, this application provides a vehicle braking strategy adjustment device, the device comprising: The data acquisition module is used to collect environmental data, vehicle status data, and driver status data. The traffic target intent determination module is used to determine a set of traffic target intents based on the environmental data. The driver intent determination module is used to determine the driver intent based on the vehicle status data and the driver status data; The intention game type determination module is used to determine the intention game type based on the traffic target intention set and the driver's intention; The adjustment module is used to adjust the braking trigger threshold set in the vehicle braking strategy according to the intention game type, and to implement vehicle braking using the vehicle braking strategy after the braking trigger threshold set is adjusted.

[0014] Thirdly, this application provides a vehicle including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor, when executing a program stored in memory, implements the vehicle braking strategy adjustment method described in any of the first aspects.

[0015] Fourthly, this application provides a computer-readable storage medium storing a program for a vehicle braking strategy adjustment method, wherein when the program for the vehicle braking strategy adjustment method is executed by a processor, it implements the steps of the vehicle braking strategy adjustment method described in any of the first aspects.

[0016] The beneficial effects of this application are: This application, by collecting environmental data and further determining the intent set of traffic targets, can predict the future behavior of surrounding traffic participants. Simultaneously, by combining driver and vehicle status data, it identifies the driver's own operational intent. For example, when there is no conflict between the intents of the two parties, the intent game type can be determined as a yielding game or a weakly competitive game. In this case, the braking trigger threshold can be reasonably increased to avoid braking triggered due to a static threshold, thereby effectively reducing the false braking rate and improving driving smoothness and user acceptance.

[0017] In this application, the type of intention game can be accurately determined based on the traffic target intention set and the driver's intention. Based on this, the braking trigger threshold set in the braking strategy is dynamically adjusted, for example, by lowering the safe distance threshold or the collision time threshold. This allows for early or proactive vehicle braking before the collision risk reaches the traditional fixed threshold value, or when the driver fails to take timely and effective braking action. This adjustment solves the problem of missed triggering caused by the fixed threshold being too "sluggish," and improves the active safety protection capability under extreme conditions.

[0018] This application no longer relies on a fixed braking trigger threshold in isolation as the triggering basis. Instead, it dynamically matches the most appropriate trigger sensitivity and intervention timing by comprehensively considering the predicted behavior of surrounding targets and the driver's intentions. This design allows the braking trigger threshold to intelligently change according to the game state of the scenario: in low-risk scenarios with harmonious intentions, the threshold moves towards "no triggering" to avoid false alarms; in high-risk scenarios with conflicting intentions, the threshold moves towards "early triggering" to prevent missed braking. As a result, the decision logic of the vehicle braking system is closer to the judgment mode of an experienced driver, ensuring both safety and comfort and traffic efficiency, thereby comprehensively optimizing the adaptability and robustness of the vehicle braking strategy. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart of a vehicle braking strategy adjustment method provided in this application embodiment; Figure 2 A flowchart illustrating a method for determining a set of traffic target intentions based on environmental data, provided in an embodiment of this application. Figure 3 A flowchart illustrating the construction and training method of the traffic target intent prediction model provided in this application embodiment; Figure 4 A flowchart illustrating a method for determining a driver's intent based on vehicle status data and driver status data, provided in this application embodiment; Figure 5 A flowchart illustrating a method for determining the type of intention game based on a set of traffic target intentions and driver intentions, provided in an embodiment of this application. Figure 6 A flowchart illustrating the detailed method for hierarchical determination of intentional game types provided in this application embodiment; Figure 7 A flowchart illustrating a method for adjusting the braking trigger threshold set based on intention game type, as provided in this application embodiment; Figure 8 A flowchart illustrating the method for configuring braking intensity and delayed braking strategy based on intention game type provided in this application embodiment; Figure 9 A flowchart illustrating the method for braking threshold comparison and verification and vehicle braking execution provided in this application embodiment; Figure 10 A structural example diagram of a vehicle braking strategy adjustment device provided in this application embodiment; Figure 11 This is a structural diagram of a vehicle provided in an embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] like Figure 1 The diagram shown is a schematic representation of the implementation process of a vehicle braking strategy adjustment method provided in this application embodiment. This method is applied to a vehicle and may specifically include the following steps: Step S101: Collect environmental data, vehicle status data, and driver status data.

[0024] In this embodiment, environmental data, vehicle status data, and driver status data are collected. Through simultaneous collection of multi-dimensional data, the external traffic environment, vehicle operating conditions, and the driver's real-time status are comprehensively perceived, providing comprehensive and effective data support for subsequent identification of traffic target intentions, driver intentions, and determination of intention game types. The environmental data refers to the environmental data surrounding the vehicle.

[0025] In this embodiment, the environmental data includes, for example, surrounding traffic target location data, surrounding traffic target relative speed data, obstacle distance data, and road condition perception data; the vehicle status data includes, for example, vehicle speed data, vehicle steering angle data, throttle opening data, and vehicle longitudinal attitude data; and the driver status data includes, for example, driver's line of sight direction data, driver's head posture data, driver's hand operation data, and driver's facial status data.

[0026] Step S102: Determine the traffic target intent set based on the environmental data.

[0027] In this embodiment, a traffic target intent set is determined based on the environmental data. This allows for precise analysis of the intents of traffic targets surrounding vehicles, such as their behavioral tendencies, distinguishing between yielding and intrusion intentions, and providing a basis for subsequent determination of the intent game type in human-vehicle interaction. Specifically, the traffic target intent set summarizes the intent sets corresponding to traffic targets around vehicles, carrying information on their behavioral tendencies and supporting the identification of intent game types.

[0028] Step S103: Determine the driver's intention based on the vehicle status data and the driver status data.

[0029] In this embodiment, the driver's intention is determined based on the vehicle state data and the driver state data. This allows for accurate identification of the driver's intention, complementing the traffic target intention set obtained earlier, and providing another basis for subsequent comprehensive determination of the intention game type in human-vehicle interaction.

[0030] Step S104: Determine the intention game type based on the traffic target intention set and the driver intention.

[0031] In this embodiment, the intention game type is determined based on the traffic target intention set and the driver intention. By integrating the intentions of both the traffic target and the driver, the human-vehicle interaction scenario is refined and different types of game types are distinguished, providing a classification basis for subsequent differentiated adjustment of the braking trigger threshold set.

[0032] Step S105: According to the intention game type, adjust the braking trigger threshold set in the vehicle braking strategy, and use the vehicle braking strategy after adjusting the braking trigger threshold set to achieve vehicle braking.

[0033] In this embodiment, the braking trigger threshold set in the vehicle braking strategy is adjusted according to the intention game type, and the vehicle braking strategy after the braking trigger threshold set is adjusted is used to achieve vehicle braking.

[0034] This embodiment, by collecting environmental data and further determining the intent set of traffic targets, can predict the future behavior of surrounding traffic participants. Simultaneously, it identifies the driver's own operational intent by combining driver and vehicle status data. For example, when there is no conflict between the intents of the two parties, the intent game type can be determined as a yielding game or a weakly competitive game. In this case, the braking trigger threshold can be reasonably increased to avoid braking triggered due to a static threshold, thereby effectively reducing the false braking rate and improving driving smoothness and user acceptance.

[0035] In this embodiment, the type of intent game can be accurately determined based on the traffic target intent set and the driver's intent. Based on this, the braking trigger threshold set in the braking strategy is dynamically adjusted, for example, by lowering the safe distance threshold or collision time threshold. This allows for early or proactive vehicle braking before the collision risk reaches the traditional fixed threshold value, or when the driver fails to take timely and effective braking action. This adjustment solves the problem of missed triggering caused by overly "sluggish" fixed thresholds, improving active safety protection capabilities under extreme conditions.

[0036] This embodiment no longer relies on a fixed braking trigger threshold in isolation as the triggering basis. Instead, it dynamically matches the most appropriate trigger sensitivity and intervention timing by comprehensively considering the predicted behavior of surrounding targets and the driver's intentions. This design allows the braking trigger threshold to intelligently change according to the game state of the scenario: in low-risk scenarios with harmonious intentions, the threshold moves towards "no triggering" to avoid false interference; in high-risk scenarios with conflicting intentions, the threshold moves towards "early triggering" to prevent missed braking. As a result, the decision logic of the vehicle braking system is closer to the judgment mode of an experienced driver, ensuring both safety and comfort and traffic efficiency, thereby comprehensively optimizing the adaptability and robustness of the vehicle braking strategy.

[0037] In yet another embodiment of this application, as Figure 2 As shown, the steps for determining the traffic target intent set based on the environmental data are as follows: Step S201: Invoke the traffic target intention prediction model, which is used to predict the behavioral intentions of traffic targets around the vehicle based on environmental data.

[0038] In this embodiment, a traffic target intent prediction model is invoked. This model is used to predict the behavioral intent of traffic targets around vehicles based on environmental data. This traffic target intent prediction model can automatically and accurately identify and output a set of traffic target intents, ensuring reliable identification results and providing accurate data support for subsequent analysis of human-vehicle interaction relationships.

[0039] Step S202: Based on the traffic target intent prediction model, determine the traffic target intent set corresponding to the environmental data.

[0040] In this embodiment, based on the traffic target intention prediction model, a set of traffic target intentions corresponding to the environmental data is determined. Environmental data processing is completed using the traffic target intention prediction model, outputting a complete set of traffic target intentions, providing complete and effective basic data for subsequent determination of the intention game type based on driver intentions.

[0041] In yet another embodiment of this application, as Figure 3 As shown, the process of obtaining the traffic target intent prediction model is as follows: Step S301: Construct training data that includes environmental data and intent sets corresponding to the environmental data.

[0042] In this embodiment, training data is constructed, including environmental data and a set of intentions corresponding to the environmental data. By building complete paired training samples, the model training effect can be effectively improved, enabling the trained traffic target intention prediction model to accurately output the traffic target intention set, providing a reliable basis for subsequent intention game type determination.

[0043] In this embodiment, for example, the intent set includes at least courtesy intent, intrusion intent, and the probability corresponding to courtesy intent and the probability corresponding to intrusion intent.

[0044] In this embodiment, the correspondence between specific traffic target behaviors and target intentions is exemplarily shown in Table 1:

[0045] Table 1 In this embodiment, for example, the target intent corresponding to the specific behavior of the traffic target is determined based on the correspondence between the specific behavior of the traffic target and the target intent. The specific behavior of the traffic target is: {P(stop)=0.7, P(walk along the roadside)=0.25, P(accelerate across)=0.05}, then the set of target intents corresponding to the specific behavior is {P(yield intention)=0.95, P(intendency to intrude)=0.05}.

[0046] Step S302: Train the preset neural network model using training data to obtain a traffic target intent prediction model.

[0047] In this embodiment, the preset neural network model sequentially includes an input layer, a hidden layer, and an output layer. The model training process uses a cross-entropy loss function, and training stops when the loss function converges. Furthermore, the preset neural network model is an open-source neural network model, such as a multilayer perceptron (MLP) or a convolutional neural network (CNN).

[0048] In yet another embodiment of this application, as Figure 4 As shown, the step of determining the driver's intention based on the vehicle status data and the driver status data is as follows: Step S401: Invoke the driver intent recognition rule, which is used to identify the driver's intent based on vehicle status data and driver status data.

[0049] In this embodiment, a driver intent recognition rule is invoked. This rule is used to identify the driver's intent based on vehicle status data and driver status data. The driver intent recognition rule can comprehensively analyze the vehicle status data and driver status data to accurately output the driver's intent. This, combined with the previously obtained traffic target intent set, provides a complete basis for determining the intent game type in subsequent steps.

[0050] In this embodiment, the driver intent recognition rules are exemplarily shown in Table 2:

[0051] Table 2 Step S402: Based on the driver intent recognition rule, determine the driver intent corresponding to the vehicle state data and the driver state data.

[0052] In this embodiment, the driver's intent corresponding to the vehicle state data and the driver state data is determined based on the driver intent recognition rules. The vehicle state data and the driver state data are parsed using the driver intent recognition rules to output accurate driver intents, which can be used in conjunction with the acquired traffic target intent set to provide a complete analytical basis for subsequent comprehensive determination of intent game types.

[0053] In yet another embodiment of this application, as Figure 5 As shown, the steps for determining the intention game type based on the traffic target intention set and the driver intention are as follows: Step S501: Invoke the intention game evaluation rule, which is used to identify the intention game type based on the target intention and the driver intention.

[0054] In this embodiment, an intention game evaluation rule is invoked. This rule is used to identify the type of intention game based on the target intention and the driver's intention. The intention game evaluation rule comprehensively analyzes the traffic target intention and the driver's intention, and outputs the intention game type that matches the current traffic scenario. The intention game type can provide a classification and judgment basis for adjusting the braking trigger threshold set.

[0055] In this embodiment, the intention game evaluation rules are exemplarily shown in Table 3:

[0056] Table 3 Step S502: Select the traffic target intent with the highest score from the set of traffic target intents.

[0057] In this embodiment, the traffic target intention with the highest score is selected from the set of traffic target intentions. The traffic target intention with the highest score represents the dominant behavioral tendency of the traffic target. The traffic target intention with the highest score can be used in conjunction with the driver's intention to provide a basis for judging the type of intention game.

[0058] Step S503: Based on the intent game evaluation rules, determine the intent game type corresponding to the traffic target intent with the highest score and the driver intent.

[0059] In this embodiment, based on the intent game evaluation rules, the intent game type corresponding to the highest-scoring traffic target intent and the driver intent is determined. The highest-scoring traffic target intent and the driver intent are input into the intent game evaluation rules for comprehensive analysis, generating an intent game type that matches the current traffic conditions. The intent game type provides a clear classification basis for adjusting the braking trigger threshold set.

[0060] In yet another embodiment of this application, as Figure 6 As shown, the steps for determining the intention game type corresponding to the traffic target intention with the highest score and the driver intention based on the intention game evaluation rule are as follows: Step S601: If the traffic objective intention with the highest score is the intention to yield, and the driver's intention is the perceived intention, then the intention game type is determined to be the yielding game type.

[0061] In this embodiment, if the traffic objective intent with the highest score is a yielding intent, and the driver's intent is a perceived intent, then the intent game type is determined to be a yielding game type. The yielding game type corresponds to low-risk human-vehicle interaction scenarios, and the yielding game type can provide a clear basis for the parameter configuration of the braking trigger threshold set.

[0062] Step S602: If the traffic target intention with the highest score is an intrusion intention, and the driver's intention is a perceived intention; or, if the traffic target intention with the highest score is a yielding intention, and the driver's intention is an unperceived intention; then the intention game type is determined to be a weakly competitive game type.

[0063] In this embodiment, if the traffic target intent with the highest score is an intrusion intent, and the driver's intent is a perceived intent; or, if the traffic target intent with the highest score is a yielding intent, and the driver's intent is an unperceived intent; then the intent game type is determined to be a weakly competitive game type. Both types of intent combinations are uniformly classified as weakly competitive game types. Weakly competitive game types correspond to medium-risk human-vehicle interaction scenarios, and provide a classification basis for the parameter configuration of the braking trigger threshold set.

[0064] Step S603: If the traffic target intent with the highest score is an intrusion intent, and the driver intent is an unnoticed intent, then the intent game type is determined to be a strongly competitive game type.

[0065] In this embodiment, if the traffic target intent with the highest score is an intrusive intent, and the driver's intent is an unnoticed intent, then the intent game type is determined to be a highly competitive game type. The combination of intrusive intent and unnoticed intent corresponds to high-risk human-vehicle interaction scenarios, and the highly competitive game type can provide a clear classification basis for the parameter configuration of the braking trigger threshold set.

[0066] In this embodiment, a critical condition judgment rule is added by way of example: when the probability of judging the traffic target intention corresponding to the intrusion intention with the highest score is 0.5, and the driver intention corresponds to the unnoticed intention, the intention game type is defined as a conflict-free game type.

[0067] In this embodiment, steps S601 to S603 are parallel execution steps, and there is no restriction on the order of execution.

[0068] In another embodiment of this application, the braking trigger threshold set includes a braking time trigger threshold and a braking distance trigger threshold, such as... Figure 7 As shown, the step of adjusting the braking trigger threshold set in the vehicle braking strategy according to the intention game type is as follows: Step S701: If the intended game type is a courtesy game type, adjust the braking time trigger threshold and braking distance trigger threshold to a first specified multiple of their original values.

[0069] In this embodiment, if the intention game type is a courtesy game type, the braking time trigger threshold and the braking distance trigger threshold are adjusted to a first specified multiple of their original values. The courtesy game type corresponds to a low-risk human-vehicle interaction scenario. The braking time trigger threshold and the braking distance trigger threshold are changed according to the first specified multiple. The values ​​after the parameter change constitute a new set of braking trigger thresholds, which are adapted to low-risk traffic conditions.

[0070] In this embodiment, for example, if the intention game type is determined to be a courtesy game type, no adjustment operation is performed on the braking time trigger threshold and the braking distance trigger threshold, and only a warning prompt is sent to the driver; for example, a voice warning is used as the warning type.

[0071] In this embodiment, for example, the range of the first specified multiple is 1.1 to 1.8, and the first specified multiple is specifically 1.5.

[0072] Step S702: If the intended game type is a weakly competitive game type, adjust the braking time trigger threshold and the braking distance trigger threshold to a second specified multiple of their original values.

[0073] In this embodiment, if the intention game type is a weakly competitive game type, the braking time trigger threshold and the braking distance trigger threshold are adjusted to a second specified multiple of their original values. The weakly competitive game type corresponds to a medium-risk human-vehicle interaction scenario. The braking time trigger threshold and the braking distance trigger threshold are changed according to the second specified multiple. The values ​​after the parameter changes together constitute the braking trigger threshold set, which is adapted to medium-risk traffic conditions.

[0074] In this embodiment, for example, the range of the second specified multiple is 0.75 to 0.95, and the second specified multiple is specifically 0.9.

[0075] Step S703: If the intended game type is a highly competitive game type, adjust the braking time trigger threshold and the braking distance trigger threshold to a third specified multiple of their original values.

[0076] In this embodiment, if the intention game type is a highly competitive game type, the braking time trigger threshold and the braking distance trigger threshold are adjusted to a third specified multiple of their original values. The highly competitive game type corresponds to high-risk human-vehicle interaction scenarios. The braking time trigger threshold and the braking distance trigger threshold are changed according to the third specified multiple, and the changed values ​​together constitute the braking trigger threshold set, adapting to high-risk traffic conditions.

[0077] In this embodiment, for example, the range of the third specified multiple is 0.38 to 0.74, and the third specified multiple is specifically 0.5.

[0078] In this embodiment, for example, if the intended game type is determined to be a conflict-free game type, the original parameters of the braking time trigger threshold and the braking distance trigger threshold are kept unchanged, and no dynamic threshold adjustment operation is performed.

[0079] In this embodiment, steps S701 to S703 are parallel execution steps, and there is no restriction on the order of execution.

[0080] In yet another embodiment of this application, as Figure 8 As shown, after adjusting the brake trigger threshold set in the vehicle braking strategy, the following steps are performed before the vehicle braking is implemented using the adjusted brake trigger threshold set: Step S801: If the intended game type is a weakly competitive game type, then the braking intensity is set to the first braking intensity.

[0081] In this embodiment, if the intention game type is a weakly competitive game type, the braking intensity is set to a first braking intensity. The weakly competitive game type corresponds to a medium-risk human-vehicle interaction scenario, and the first braking intensity matches the protection requirements of the medium-risk scenario. The first braking intensity, together with the braking trigger threshold set, completes the parameter configuration for vehicle braking.

[0082] In this embodiment, for example, the range of the first braking intensity is 2.0 m / s² to 4.0 m / s², and the first braking intensity is specifically 3.0 m / s².

[0083] In this embodiment, exemplarily, if the intentional game type is a weakly competitive game type, a delay time window is set (ranging from 0.2 seconds to 0.5 seconds, specifically 0.3 seconds). During this delay window, if a traffic target is cutting into the lane where the vehicle is located, gentle braking is applied with a third braking intensity (ranging from 1.0 m / s² to 2.0 m / s², specifically 1.5 m / s²). If the traffic target completes its cut-in within the delay window, and the real-time distance between the vehicle and the traffic target is not less than a safe distance (ranging from 2.0 meters to 5.0 meters, specifically 3.0 meters in this embodiment), then braking is stopped.

[0084] Step S802: If the intended game type is a highly competitive game type, then the braking intensity is set to the second braking intensity.

[0085] In this embodiment, if the intention game type is a highly competitive game type, then the braking intensity is set to the second braking intensity. The highly competitive game type corresponds to a high-risk human-vehicle interaction scenario, and the second braking intensity matches the safety protection requirements of the high-risk scenario. The second braking intensity, together with the braking trigger threshold set, completes the parameter configuration of vehicle braking.

[0086] In this embodiment, for example, the range of the second braking intensity is 6.0 m / s² to 10.0 m / s², and the second braking intensity is specifically 8.0 m / s².

[0087] In this embodiment, steps S801 to S802 are parallel execution steps, and there is no restriction on the order of execution.

[0088] In yet another embodiment of this application, as Figure 9 As shown, after adjusting the brake trigger threshold set in the vehicle braking strategy, the following steps are performed before the vehicle braking is implemented using the adjusted brake trigger threshold set: Step S901: Determine the traffic target trajectory set based on the environmental data.

[0089] In this embodiment, a traffic target trajectory set is determined based on the environmental data. The environmental data carries the position and movement information of traffic targets around the vehicle, and the traffic target trajectory set comprehensively summarizes the continuous movement state of the traffic targets. The traffic target trajectory set provides basic motion feature support for the generation of the traffic target intent set.

[0090] In this embodiment, exemplarily, the specific process of determining the traffic target trajectory set based on the environmental data is as follows: Using the collected environmental data as input, the environmental data includes surrounding traffic target location data, surrounding traffic target relative speed data, obstacle distance data, and road condition perception data. The continuous environmental data is analyzed and temporally correlated to extract the position and motion state information of each traffic target around the vehicle at different times. Motion path fitting is performed on the discrete data, and the data is integrated to form the traffic target trajectory set. The traffic target trajectory set completely records the continuous motion state of each traffic target, providing a basic motion feature basis for subsequent traffic target intent set generation and braking trigger dataset construction.

[0091] Step S902: Determine the braking trigger dataset based on the traffic target trajectory set.

[0092] In this embodiment, a braking trigger dataset is determined based on the traffic target trajectory set. The traffic target trajectory set records the complete motion state of the traffic target, while the braking trigger dataset integrates the basic parameters required for braking determination. The braking trigger dataset provides raw data support for configuring the braking trigger threshold set.

[0093] In this embodiment, the specific process of determining the braking trigger dataset based on the traffic target trajectory set is as follows: The traffic target trajectory set records the continuous movement paths, real-time positions, and movement states of various traffic targets around the vehicle. The relative distance between the vehicle and surrounding traffic targets is extracted from the traffic target trajectory set as the measured braking distance parameter; simultaneously, the relative speed is extracted and the collision time is calculated in combination with the relative distance as the measured braking time parameter. The extracted and calculated measured braking distance and measured braking time parameters are uniformly standardized and summarized to finally form the braking trigger dataset. This braking trigger dataset provides a measured data source for subsequent comparison of the measured data with the adjusted braking trigger threshold set (including braking distance trigger threshold and braking time trigger threshold) to verify the braking trigger conditions.

[0094] Step S903: Compare the brake trigger dataset with the adjusted brake trigger threshold set to determine if the brake trigger condition is met and to achieve vehicle braking.

[0095] In this embodiment, the brake trigger dataset and the adjusted brake trigger threshold set are compared to determine if the brake trigger condition is met, thus enabling vehicle braking. The brake trigger dataset and the adjusted brake trigger threshold set are numerically compared, and the brake trigger condition serves as the criterion for vehicle braking. The entire determination process ensures that the vehicle braking action is executed in an orderly manner according to preset rules.

[0096] In this embodiment, for example, the braking trigger dataset includes measured braking distance parameters (i.e., the real-time relative distance between the vehicle and key traffic targets ahead or in the vicinity) and measured braking time parameters (i.e., the real-time collision time calculated based on the relative distance and relative speed). The adjusted braking trigger threshold set includes braking distance trigger thresholds and braking time trigger thresholds dynamically corrected according to the intention game type.

[0097] The comparison process is specifically divided into two parallel branches: First step: Compare the measured braking distance parameters with the braking distance trigger threshold. If the measured braking distance parameters of the current vehicle are less than or equal to the braking distance trigger threshold, the distance condition is deemed to be met; otherwise, if the relationship is not met, the distance condition is deemed not met.

[0098] The second approach involves comparing the measured braking time parameter with the braking time trigger threshold. If the measured braking time parameter of the current vehicle is less than or equal to the braking time trigger threshold, the time condition is deemed met; otherwise, it is not.

[0099] To ensure the reliability and safety of braking triggering, the system employs a logical AND operation: the braking triggering condition is only determined to be met when both the distance and time conditions are met simultaneously—that is, the measured braking distance parameter ≤ the braking distance triggering threshold and the measured braking time parameter ≤ the braking time triggering threshold. If either condition is not met, the system is deemed not to meet the braking triggering condition and continues monitoring.

[0100] The aforementioned dual-threshold joint comparison mechanism avoids misjudgments of the specified threshold under special operating conditions, thereby accurately and reliably triggering vehicle braking within the threshold framework adjusted by intentional game theory. Once the braking triggering condition is determined to be met, a braking command is immediately output to achieve vehicle braking.

[0101] A specific example of this embodiment is shown below: During vehicle operation, real-time data collection of environmental, vehicle, and driver status data is completed simultaneously. The collected environmental data includes real-time location data of lateral traffic targets, speed data of lateral traffic targets relative to the vehicle, real-time distance data between traffic targets and the vehicle, and road condition perception data for straight, unobstructed roads. The collected vehicle status data shows stable vehicle speed, no steering angle deviation, constant throttle opening, and no fluctuation in vehicle longitudinal attitude. The collected driver status data shows that the driver's gaze remains focused on the area directly in front of the vehicle, without any deviation towards lateral traffic targets, no head movements for sideways observation, and no arbitrary pre-judgment operations such as steering wheel correction, throttle adjustment, or touch of the brake pedal.

[0102] After completing multi-dimensional data collection, the continuous environmental data is analyzed and temporally correlated to extract the position and motion state information of lateral traffic targets at continuous moments. Discrete perception data is then fitted with motion paths to generate a traffic target trajectory set. This generated trajectory set comprehensively records the continuous motion state of lateral traffic targets as they move towards the vehicle's lane and exhibit a lane-cutting tendency. It accurately reflects the real-time motion trend of traffic targets, providing a complete foundation of motion feature data for subsequent traffic target intent recognition and braking trigger dataset calculation.

[0103] The trained traffic target intent prediction model is invoked, and the collected environmental data is input into the model for computation. The traffic target intent prediction model intelligently judges the trajectory characteristics of continuously entering traffic targets and outputs the corresponding traffic target intent set. In this intent set, the probability score corresponding to intrusion intent is the highest, and the probability score corresponding to yielding intent is the lowest. Based on this, the dominant behavioral intent of the current traffic target is determined to be intrusion intent.

[0104] Based on the acquired traffic target intent set, preset driver intent recognition rules are invoked to conduct a comprehensive quantitative verification of the real-time collected vehicle status data and driver status data. Data verification revealed that the driver's gaze was focused on a lateral traffic target for less than 0.5 seconds, the brake pedal opening was 0%, the sensor did not detect any movement of the foot towards the brake pedal, the steering wheel angular velocity was 0° / second, and the accelerator pedal opening did not decrease. All the conditions for determining perceived intent according to the driver intent recognition rules were not met, ultimately determining the current driver intent as an unperceived intent.

[0105] The intent game evaluation rules are further invoked to filter the intrusive intent with the highest score from the traffic target intent set, and a matching judgment is made in combination with the identified driver-unaware intent. According to the preset intent game evaluation rules, when the traffic target's dominant intent is an intrusive intent and the driver's intent is an unaware intent, the intent game type corresponding to the current human-vehicle interaction scenario can be determined to be a highly competitive game type. This game type accurately corresponds to the high-risk human-vehicle interaction situation in this example.

[0106] After determining the type of highly competitive game, a dynamic adjustment strategy for the corresponding braking trigger threshold set is implemented. The initially preset braking time trigger threshold and braking distance trigger threshold are uniformly adjusted to a third specified multiple of their original values. In this embodiment, the third specified multiple is specifically 0.5. The adjusted braking trigger threshold set narrows the criteria for vehicle braking, adapting to actual working conditions with high-risk collisions. This effectively improves the timeliness of vehicle braking intervention and compensates for the lag in response of traditional fixed thresholds. Simultaneously, a dedicated braking output parameter is matched for the highly competitive game type, setting the vehicle braking intensity to a second braking intensity. In this embodiment, the second braking intensity is specifically 8.0 m / s². This high-level braking intensity matches the safety protection requirements of high-risk scenarios, quickly achieving vehicle deceleration and hazard avoidance, and reducing collision risk.

[0107] After configuring the braking trigger threshold set and braking intensity parameters, the basic braking parameters are extracted based on the generated traffic target trajectory set. The real-time relative distance between the vehicle and lateral traffic targets is extracted and used as the measured braking distance parameter. Simultaneously, the real-time relative speed between the vehicle and traffic targets is extracted. The real-time collision time is calculated by combining the relative distance and relative speed, and this result is used as the measured braking time parameter. The acquired measured braking distance and braking time parameters are then uniformly standardized and summarized to generate a braking trigger dataset adapted to the current operating conditions.

[0108] The constructed braking trigger dataset and the adjusted braking trigger threshold set are compared and verified in parallel across two dimensions, using a logical AND operation as the overall judgment rule. During the distance dimension verification, if the measured braking distance parameter is less than or equal to the adjusted braking distance trigger threshold, the distance judgment condition is met. During the time dimension verification, if the measured braking time parameter is less than or equal to the adjusted braking time trigger threshold, the time judgment condition is met. When both judgment conditions are met simultaneously, the current operating condition is determined to meet the preset braking trigger condition. A braking control command is immediately output at a second braking intensity of 8.0 m / s² to complete the vehicle's active braking action, achieving active safety protection in high-risk scenarios.

[0109] The high-risk scenario illustrated in this example is a typical situation where traditional fixed-threshold braking strategies are prone to missed braking triggers. This application's technical solution accurately identifies the intrusion intent of traffic targets and the driver's unaware intent, precisely determines the type of strong competitive game, proactively narrows the braking trigger threshold, and matches a high level of braking intensity. Compared to traditional fixed-threshold braking methods, this solution can trigger braking earlier in the initial stage of collision risk formation, completely solving the problem of missed braking triggers caused by the sluggishness and delayed response of fixed-threshold parameters, significantly improving vehicle driving safety in high-risk human-vehicle interaction scenarios. This solution dynamically adapts braking parameters based on the scenario's intent game relationship, adapting to traffic conditions of different risk levels, effectively balancing vehicle smoothness and road efficiency, and achieving intelligent, scenario-based adaptive control of the vehicle braking strategy.

[0110] When the vehicle is in a medium-risk human-vehicle interaction situation, if a lateral traffic target shows a tendency to intrude into the lane and the target's dominant intention is intrusion, while the driver has completed lateral road condition observation and the driver's intention is determined to be a perceived intention, the current intention game type can be determined to be a weakly competitive game type. Correspondingly, the braking trigger threshold is adjusted to a second specified multiple of 0.9 times the original value, a first braking intensity of 3.0 m / s² is configured, and a 0.3-second delay time window is initiated. Within the delay time window, if the traffic target slowly performs a lane-entry maneuver, a gentle braking operation is performed with a third braking intensity of 1.5 m / s²; if the traffic target completes the lane-entry maneuver after the delay window ends, and the real-time distance between the vehicle and the traffic target is not less than a preset safe distance of 3.0 meters, the braking triggering action automatically stops. This effectively avoids potential driving risks while maximizing vehicle driving smoothness and avoiding unnecessary braking intervention.

[0111] When a vehicle is in a low-risk human-vehicle interaction situation, surrounding traffic targets actively perform avoidance actions towards the vehicle, and the dominant intention of these targets is to yield. Simultaneously, the driver promptly perceives the surrounding traffic conditions, and the driver's intention is determined to be perceived. Therefore, the current intention game type can be identified as a yielding game. Correspondingly, the braking trigger threshold is adjusted to a first specified multiple of 1.5 times the original value, appropriately relaxing the braking trigger judgment conditions. This effectively eliminates ineffective and mis-braking problems in conventional yielding scenarios, improving vehicle driving smoothness and overall road traffic efficiency.

[0112] In another embodiment of this application, a vehicle braking strategy adjustment device is also provided, such as... Figure 10 As shown, the device includes: a data acquisition module 1001, a traffic target intent determination module 1002, a driver intent determination module 1003, an intent game type determination module 1004, and an adjustment module 1005.

[0113] The data acquisition module 1001 is used to collect environmental data, vehicle status data, and driver status data.

[0114] The traffic target intent determination module 1002 is used to determine a traffic target intent set based on the environmental data.

[0115] The driver intent determination module 1003 is used to determine the driver intent based on the vehicle status data and the driver status data.

[0116] The intention game type determination module 1004 is used to determine the intention game type based on the traffic target intention set and the driver's intention.

[0117] The adjustment module 1005 is used to adjust the braking trigger threshold set in the vehicle braking strategy according to the intention game type, and to implement vehicle braking using the vehicle braking strategy after the braking trigger threshold set is adjusted.

[0118] In another embodiment of this application, a vehicle is also provided, characterized in that it includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. Memory, used to store computer programs; When the processor executes the program stored in the memory, it implements the vehicle braking strategy adjustment method described in any of the foregoing method embodiments.

[0119] The vehicle provided in this application embodiment uses a processor to collect environmental data, vehicle state data, and driver state data by executing a program stored in memory. Based on the environmental data, it determines a set of traffic target intentions; based on the vehicle state data and driver state data, it determines the driver's intention; based on the set of traffic target intentions and the driver's intention, it determines the intention game type; and based on the intention game type, it adjusts the braking trigger threshold set in the vehicle braking strategy, and uses the adjusted braking strategy to achieve vehicle braking. This application, by collecting environmental data and further determining the set of traffic target intentions, can predict the future behavior of surrounding traffic participants. Simultaneously, it identifies the driver's own operational intention by combining driver state data and vehicle state data. For example, when there is no conflict between the intentions of both parties, the intention game type can be determined as a yielding game or a weakly competitive game. In this case, the braking trigger threshold can be reasonably increased to avoid braking triggered due to a static threshold, thereby effectively reducing the false braking rate and improving driving smoothness and user acceptance.

[0120] In this application, the type of intent game can be accurately determined based on the traffic target intent set and the driver's intent. Based on this, the braking trigger threshold set in the braking strategy is dynamically adjusted, for example, by lowering the safe distance threshold or collision time threshold. This allows for early or proactive vehicle braking before the collision risk reaches the traditional fixed threshold value, or when the driver fails to take timely and effective braking action. This adjustment solves the problem of missed triggering caused by overly "sluggish" fixed thresholds, improving active safety protection capabilities under extreme conditions. This application no longer relies on a single fixed braking trigger threshold as the triggering basis in isolation, but rather dynamically matches the most appropriate trigger sensitivity and intervention timing by comprehensively considering the behavioral predictions of surrounding targets and the driver's intent. This design allows the braking trigger threshold to intelligently change according to the scenario's game state: in low-risk scenarios with harmonious intent, the threshold moves towards "no triggering" to avoid false interference; in high-risk scenarios with conflicting intent, the threshold moves towards "early triggering" to prevent missed protection. Therefore, the decision logic of the vehicle braking system is closer to the judgment patterns of experienced drivers, ensuring both safety and comfort and traffic efficiency, thus comprehensively optimizing the adaptability and robustness of the vehicle braking strategy.

[0121] The communication bus 1104 mentioned in the above vehicle can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 1104 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 11 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0122] Communication interface 1102 is used for communication between the aforementioned vehicle and other devices.

[0123] The memory 1103 may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0124] The processor 1101 mentioned above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0125] In another embodiment of this application, a computer-readable storage medium is provided, on which a program for a vehicle braking strategy adjustment method is stored. When the program for the vehicle braking strategy adjustment method is executed by a processor, it implements the steps of the vehicle braking strategy adjustment method described in any of the foregoing method embodiments.

[0126] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0127] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for adjusting a vehicle braking strategy, characterized in that, include: Collect environmental data, vehicle status data, and driver status data; Based on the environmental data, determine the traffic target intent set; The driver's intention is determined based on the vehicle status data and the driver status data; The type of intention game is determined based on the traffic target intention set and the driver intention; Based on the stated intention game type, the braking trigger threshold set in the vehicle braking strategy is adjusted, and the vehicle braking strategy after the braking trigger threshold set adjustment is used to achieve vehicle braking.

2. The method according to claim 1, characterized in that, The step of determining the traffic target intent set based on the environmental data includes: The traffic target intent prediction model is invoked, which is used to predict the behavioral intent of traffic targets around the vehicle based on environmental data. Based on the traffic target intent prediction model, a set of traffic target intents corresponding to the environmental data is determined.

3. The method according to claim 2, characterized in that, The process of obtaining a traffic target intent prediction model includes: A traffic target intent prediction model is obtained by training a preset neural network model using training data that includes environmental data and intent sets corresponding to the environmental data. The preset neural network model includes an input layer, a hidden layer, and an output layer in sequence. The training process uses a cross-entropy loss function, and training stops when the loss function converges.

4. The method according to claim 1, characterized in that, Determining the driver's intention based on the vehicle status data and the driver status data includes: The driver intent recognition rule is invoked, which is used to identify the driver's intent based on vehicle status data and driver status data. Based on the driver intent recognition rules, the driver intent corresponding to the vehicle state data and the driver state data is determined.

5. The method according to claim 1, characterized in that, The step of determining the intention game type based on the traffic target intention set and the driver intention includes: The intention game evaluation rule is invoked, which is used to identify the type of intention game based on the target intention and the driver intention; Select the traffic target intent with the highest score from the set of traffic target intents; Based on the intent game evaluation rules, the intent game type corresponding to the traffic target intent with the highest score and the driver intent is determined.

6. The method according to claim 5, characterized in that, The determination of the intention game type corresponding to the traffic target intention with the highest score and the driver intention based on the intention game evaluation rule includes: If the traffic objective intent with the highest score is the intention to yield, and the driver's intent is a perceived intent, then the intent game type is determined to be the yielding game type. If the traffic objective intent with the highest score is an intrusion intent, and the driver's intent is a perceived intent; or, if the traffic objective intent with the highest score is a yielding intent, and the driver's intent is an unperceived intent; then the intent game type is determined to be a weakly competitive game type. If the traffic target intent with the highest score is an intrusion intent, and the driver's intent is an unnoticed intent, then the intent game type is determined to be a highly competitive game type.

7. The method according to claim 6, characterized in that, The braking trigger threshold set includes braking time trigger threshold and braking distance trigger threshold; The step of adjusting the braking trigger threshold set in the vehicle braking strategy according to the intention game type includes: If the intended game type is a courtesy game type, the braking time trigger threshold and the braking distance trigger threshold are adjusted to a first specified multiple of their original values, respectively. If the intended game type is a weakly competitive game type, the braking time trigger threshold and the braking distance trigger threshold are adjusted to a second specified multiple of their original values, respectively. If the intended game type is a highly competitive game type, the braking time trigger threshold and the braking distance trigger threshold are adjusted to a third specified multiple of their original values.

8. The method according to any one of claims 1-7, characterized in that, After adjusting the brake trigger threshold set in the vehicle braking strategy, before implementing vehicle braking using the adjusted vehicle braking strategy, the following steps are also included: If the intended game type is a weakly competitive game type, then the braking strength is set to the first braking strength; If the intended game type is a highly competitive game type, then the braking intensity is set to the second braking intensity.

9. The method according to any one of claims 1-7, characterized in that, After adjusting the brake trigger threshold set in the vehicle braking strategy, before implementing vehicle braking using the adjusted vehicle braking strategy, the following steps are also included: Based on the environmental data, determine the set of traffic target trajectories; Based on the traffic target trajectory set, determine the braking trigger dataset; The brake trigger dataset and the adjusted brake trigger threshold set are compared to determine if the brake trigger conditions are met, thereby enabling vehicle braking.

10. A vehicle braking strategy adjustment device, characterized in that, The device includes: The data acquisition module is used to collect environmental data, vehicle status data, and driver status data. The traffic target intent determination module is used to determine a set of traffic target intents based on the environmental data. The driver intent determination module is used to determine the driver intent based on the vehicle status data and the driver status data; The intention game type determination module is used to determine the intention game type based on the traffic target intention set and the driver's intention; The adjustment module is used to adjust the braking trigger threshold set in the vehicle braking strategy according to the intention game type, and to implement vehicle braking using the vehicle braking strategy after the braking trigger threshold set is adjusted.

11. A vehicle, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in a memory, it implements the vehicle braking strategy adjustment method according to any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program for a vehicle braking strategy adjustment method, which, when executed by a processor, implements the steps of the vehicle braking strategy adjustment method according to any one of claims 1-9.

Images