Vehicle door unlocking method and device and vehicle

Abstract

The invention discloses a vehicle door unlocking method and device and a vehicle, and belongs to the technical field of vehicle safety control. The method comprises the steps that vehicle state data and environment sensing data of the vehicle are obtained in real time; the self-vehicle state data comprises motion state data, real vehicle body posture data and operation intention data, and the environment perception data comprises surrounding environment data and road data; determining a collision risk level and an instability risk level of the vehicle based on the vehicle state data and the environment perception data; determining a driving risk level of the vehicle based on the collision risk level and the instability risk level; and under the condition that the driving risk level judges that the accident is inevitable, a vehicle door emergency unlocking instruction is generated before the accident occurs. According to the scheme, active vehicle door unlocking before vehicle accidents can be effectively achieved.

Description

Technical Field

[0001] This application relates to the field of vehicle safety control technology, specifically to a door unlocking method, device, and vehicle. Background Technology

[0002] During vehicle operation, sudden accidents such as collisions, rollovers, and plunging into rivers often cause severe damage to the vehicle's structure and electronic systems, greatly hindering occupant escape and external rescue. In related technologies, vehicle door unlocking largely relies on a trigger mechanism after a collision, either automatically unlocking after a collision sensor detects a collision signal or requiring manual unlocking by the occupants. However, after an accident, vehicles are highly susceptible to body deformation, electrical system failure, and electronic control system malfunctions. These problems can momentarily prevent the transmission or execution of unlocking commands, rendering the doors unable to open normally. Especially with the widespread use of concealed door handles in new energy vehicles, if an accident causes a power outage, these handles often fail to pop out automatically, further complicating door opening and severely delaying crucial rescue time.

[0003] Currently, while some vehicles are equipped with mechanical emergency unlocking devices—such as those requiring a mechanical key to be pulled out or a cover to be pried open from the outside, or hidden mechanical handles from the inside—the use of these devices relies on occupants learning how to operate them beforehand. Most users do not proactively consult the owner's manual to understand the emergency unlocking procedure after purchasing their vehicle, and may be unable to quickly activate the device due to panic or unfamiliarity with the operation after an accident. Furthermore, existing unlocking solutions are all reactive measures after an accident, unable to anticipate potential risks and prepare for unlocking in advance. In the event of an extreme accident leading to complete electronic system failure or mechanical jamming, the door will be permanently locked, posing a serious threat to the lives of the occupants.

[0004] It is evident that most vehicle door unlocking solutions based on accidents only trigger door unlocking after an accident occurs, resulting in the inability to unlock the doors in time before an accident. Summary of the Invention

[0005] A method, apparatus, and vehicle for unlocking vehicle doors are provided, aiming to solve the problem that vehicle door unlocking schemes based on accidents cannot unlock the doors in time before an accident occurs.

[0006] Firstly, a method for unlocking car doors is provided, including the following steps: The system acquires real-time vehicle status data and environmental perception data; the vehicle status data includes motion status data, actual vehicle posture data, and operation intention data, and the environmental perception data includes surrounding environment data and road data. Based on the vehicle status data and the environmental perception data, the collision risk level and instability risk level of the vehicle are determined. Based on the collision risk level and the instability risk level, the driving risk level of the vehicle is determined; If the driving risk level determines that an accident is unavoidable, an emergency door unlocking command is generated. This emergency door unlocking command is used to unlock the door before the accident occurs.

[0007] In some embodiments, determining the collision risk level and instability risk level of the vehicle based on the vehicle state data and the environmental perception data includes: The surrounding environment data and the road data are fused to determine the effective obstacle targets and the relative motion relationship between each effective obstacle target and the vehicle. Based on the vehicle status data, predict the motion trajectories of the vehicle and each of the effective obstacle targets; Based on the motion trajectories of the vehicle and each of the effective obstacle targets, determine the intersection probability and collision time between the motion trajectories of the vehicle and each of the effective obstacle targets; The collision risk level is determined based on the intersection probability and the collision time.

[0008] In some embodiments, predicting the motion trajectories of the vehicle and each of the effective obstacle targets based on the vehicle state data includes: Based on the motion state data in the vehicle state data, the initial motion trajectories of the vehicle and each of the effective obstacle targets are predicted respectively. The initial trajectory is corrected based on the road data to predict the trajectory of the vehicle and each of the effective obstacle targets.

[0009] In some embodiments, determining the collision risk level and instability risk level of the vehicle based on the vehicle status data and the environmental perception data includes: Determine the ideal vehicle posture data corresponding to the operation intention data; The actual vehicle body posture data is compared with the ideal vehicle body posture data to determine the deviation of the vehicle body posture data; Based on the vehicle posture data deviation and current vehicle speed, predict the vehicle instability time; The instability risk level is determined based on the vehicle's instability time.

[0010] In some embodiments, determining the instability risk level based on the vehicle instability time includes: If the collision time is greater than the first preset instability time threshold, it is determined to be a low instability risk level; If the collision time is less than the first preset instability time threshold and greater than the second preset instability time threshold, it is determined to be a medium instability risk level. If the collision time is less than the second preset instability time threshold, it is determined to be a high instability risk level.

[0011] In some embodiments, determining the collision risk level based on the intersection probability and the collision time includes: If the collision time is greater than a first preset collision time threshold, it is determined to be a low collision risk level; If the intersection probability determines that the trajectory of the vehicle and at least one of the effective obstacle targets intersects, and the collision time is less than the first preset collision time threshold and greater than the second preset collision time threshold, the collision risk level is determined to be medium. If the intersection probability determines that the trajectory of the vehicle and at least one of the effective obstacle targets intersects, and the collision time is less than the second preset collision time threshold, the collision risk level is determined to be high.

[0012] In some embodiments, determining the vehicle's driving risk level based on the collision risk level and the instability risk level includes: If both the instability risk level and the collision risk level are low risk levels, it is determined to be a low driving risk level; If at least one of the instability risk level and the collision risk level is a medium risk level, it is determined to be a medium driving risk level; If at least one of the instability risk level and the collision risk level is a high risk level, it is determined to be a high driving risk level. Under the condition of a high driving risk level, the driving risk level is determined to indicate that an accident is unavoidable.

[0013] In some embodiments, after generating the emergency door unlocking command, the method further includes: Trigger an audible and visual alarm; The audible and visual alarm methods include at least one of alarm sound, dashboard warning display, and head-up display warning.

[0014] Secondly, a door unlocking device is also provided, including: The data acquisition unit is used to acquire vehicle status data and environmental perception data in real time; the vehicle status data includes motion status data, actual vehicle posture data and operation intention data, and the environmental perception data includes surrounding environment data and road data. The intermediate risk level determination unit is used to determine the collision risk level and instability risk level of the vehicle based on the vehicle status data and the environmental perception data. A driving risk level determination unit is used to determine the driving risk level of the vehicle based on the collision risk level and the instability risk level. An emergency unlocking unit is used to generate an emergency door unlocking command when the driving risk level determines that an accident is unavoidable. The emergency door unlocking command is used to unlock the door before the accident occurs.

[0015] Thirdly, a vehicle is also provided, comprising: The chassis module is used to acquire the vehicle's self-state data, which includes motion state data, actual vehicle posture data, and operation intention data. The intelligent driving module is used to acquire the vehicle's environmental perception data, and based on the vehicle status data and the environmental perception data, determine the vehicle's collision risk level and instability risk level; based on the collision risk level and the instability risk level, determine the vehicle's driving risk level; the environmental perception data includes surrounding environment data and road data; The vehicle body control module is used to generate an emergency door unlocking command when the driving risk level determines that an accident is unavoidable. The emergency door unlocking command is used to unlock the door before the accident occurs. The door lock module is used to execute an emergency door unlocking action based on the emergency door unlocking command; The alarm module is used to generate an audible and visual alarm activation command that matches the driving risk level based on the emergency door unlocking command, thereby triggering the alarm at the corresponding level.

[0016] Fourthly, a computer-readable storage medium is also provided, on which a computer program is stored, which is loaded by a processor to perform the steps in any of the above-described door unlocking methods.

[0017] Beneficial effects: This application acquires comprehensive vehicle status data and environmental perception data through multi-source sensors. Based on these two types of data, it accurately determines the collision risk level and instability risk level, and then combines the two risk levels to determine the vehicle driving risk level. Finally, it triggers emergency door unlocking when the high driving risk level determines that an accident is unavoidable, which can effectively realize active door unlocking before a vehicle accident occurs. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic flowchart of a door unlocking method provided by an exemplary embodiment of this disclosure; Figure 2 This is a schematic diagram of vehicle functional modules provided in an exemplary embodiment of this disclosure; Figure 3 This is a schematic diagram of the functional modules of the door unlocking device provided in an exemplary embodiment of this disclosure. Detailed Implementation

[0020] 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, and 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.

[0021] In the description of this application, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application. 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 indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.

[0022] "A and / or B" includes the following three combinations: A only, B only, and a combination of A and B.

[0023] The use of "applies to" or "configured to" in this application implies open and inclusive language, which does not exclude the applicability to or configuration to devices performing additional tasks or steps. Additionally, the use of "based on" implies openness and inclusivity, because processes, steps, calculations, or other actions "based on" one or more of the stated conditions or values ​​may in practice be based on additional conditions or values ​​beyond those stated.

[0024] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0025] On the one hand, this embodiment provides a door unlocking method applicable to autonomous vehicles, semi-autonomous vehicles, and non-autonomous vehicles, etc. This application does not specifically limit the type of vehicle, such as... Figure 1 As shown, it includes the following steps: Step 100: Acquire real-time vehicle status data and environmental perception data; vehicle status data includes motion status data, actual vehicle posture data, and operation intention data, while environmental perception data includes surrounding environment data and road data.

[0026] Specifically, after the vehicle is started, the multi-source sensors on the vehicle are activated simultaneously. The multi-source sensors include vehicle status sensors and environmental perception sensors. The vehicle status sensors include wheel speed sensors, steering wheel angle sensors, yaw angle sensors, brake pressure sensors, and inertial measurement units (IMUs). These sensors are connected to the vehicle chassis module or are installed on the vehicle chassis module. The environmental perception sensors include at least millimeter-wave radar, lidar, and cameras. These sensors are connected to the vehicle intelligent driving module or are installed on the vehicle intelligent driving module.

[0027] Simultaneously, the chassis module communicates with the intelligent driving module, acquiring real-time vehicle data such as yaw rate, lateral acceleration, vehicle speed calculated from wheel speed, and steering angle through the chassis module. This data is used to calculate the yaw rate and lateral acceleration values ​​the vehicle should exhibit under the driver's intent, thereby obtaining vehicle attitude data and stability status. The detected status information is then sent to the intelligent driving domain controller. Figure 2 As shown.

[0028] These sensors acquire real-time vehicle status data and environmental perception data. The vehicle status sensors include at least one of wheel speed sensors, steering wheel angle sensors, yaw angle sensors, brake pressure sensors, and inertial measurement units. The environmental perception sensors include at least one of millimeter-wave radar, lidar, and cameras. All data collected by the sensors are transmitted to the subsequent processing unit in real time, providing a data basis for determining vehicle driving risks.

[0029] The vehicle status data includes: wheel speed information collected by wheel speed sensors to assess actual vehicle speed and confirm whether the vehicle is locked or slipping; pitch and roll angle data collected by inertial measurement unit; yaw rate information collected by yaw angle sensor to characterize the vehicle's rotational speed around the vertical axis and assess the vehicle's actual motion state; steering wheel angle sensor to collect steering angle and speed information and other operational intent data to assess the vehicle's upcoming direction; and brake force information and throttle control signals collected by brake pressure sensor to characterize the driver's desired braking intensity. Environmental perception data includes data on the relative motion between the vehicle and various obstacles detected by millimeter-wave radar, including longitudinal distance, longitudinal relative speed, and azimuth angle; high-precision 3D environmental modeling provided by lidar to capture obstacle outlines and details; and real-time road information collected by camera to provide identification and classification of various obstacles, including road data such as road width and curvature, lane line positions, and surrounding environmental data such as the type and lateral position of obstacles such as vehicles and pedestrians.

[0030] Furthermore, to improve the effectiveness and accuracy of the collected data, this embodiment preprocesses the vehicle status data and environmental perception data separately. The preprocessing methods include at least one of data cleaning, timestamp synchronization, heterogeneous data format unification, and missing data completion. Specifically, data cleaning removes invalid and erroneous data caused by sensor interference or malfunctions, ensuring the authenticity and validity of the data used in subsequent calculations from the source. Timestamp synchronization unifies the time base of the two types of data, eliminating time differences in multi-sensor acquisition and transmission, and ensuring spatiotemporal consistency of the data. Heterogeneous data format unification converts the two types of heterogeneous sensor data into a unified format and dimension, achieving data compatibility and providing a unified input standard for multi-source data fusion calculations. Missing data completion compensates for data loss caused by momentary sensor malfunctions or occlusions, ensuring the continuity and integrity of data acquisition and preventing distortion in subsequent calculations due to data interruptions.

[0031] Step 200: Based on the vehicle status data and environmental perception data, determine the vehicle's collision risk level and instability risk level.

[0032] Specifically, the collision risk level is determined by integrating surrounding environment data and road data from environmental perception data to identify and classify obstacle targets, as well as confirm the relative motion relationship between each obstacle target and the vehicle. Combined with motion state data from the vehicle status data, the trajectory of the vehicle and surrounding targets is predicted and corrected by road data. Then, based on the trajectory intersection probability and collision time, multiple levels of collision risk are divided according to preset standards.

[0033] It should be noted that the collision risk in this application embodiment refers to the risk of direct contact with obstacles caused by any sudden accident that may cause a partial collision of the vehicle body, such as a collision, rollover, or falling into a river.

[0034] To determine the level of instability risk, the ideal vehicle posture data corresponding to the driver's operating intention is obtained by integrating the motion state, posture state and operation intention data in the vehicle status data. The deviation between the ideal vehicle posture data and the actual vehicle posture data is compared. Based on the magnitude of the deviation and the current vehicle speed, the instability time is predicted, and multiple levels of instability risk are divided according to preset standards.

[0035] Step 300: Determine the vehicle's driving risk level based on the collision risk level and instability risk level.

[0036] Specifically, the determined collision risk level and instability risk level are comprehensively analyzed according to preset comprehensive judgment rules to determine the vehicle's driving risk level. For example, if both the collision risk level and the instability risk level are low, the driving risk level is determined to be low; if either the collision risk level or the instability risk level is medium and the other is low, the driving risk level is determined to be medium; if either the collision risk level or the instability risk level is high, regardless of whether the other risk level is low or medium, the driving risk level is determined to be high. It is also clear that only when the driving risk level is high is the prerequisite for determining that the accident is unavoidable.

[0037] Step 400: If the driving risk level determines that an accident is unavoidable, generate an emergency door unlocking command. The emergency door unlocking command is used to unlock the door before the accident occurs.

[0038] Specifically, the system continuously monitors the driving risk level. If the driving risk level is determined to be high, and multi-source data fusion calculations further confirm that an accident is unavoidable, an emergency door unlocking command is immediately sent to the body control module, which is connected to the intelligent driving module. This emergency door unlocking command is used to unlock the door before the accident occurs. Upon receiving the command, the body control module quickly forwards a control signal to the door lock module, driving the door lock actuator to perform the emergency door unlocking action, thereby releasing the mechanical locking state of the door before the accident occurs. Figure 2 As shown.

[0039] In particular, when the vehicle is equipped with a concealed door handle, the door handle pop-out mechanism is simultaneously activated when the door is unlocked, so that the door handle pops out to an operable state, ensuring that in the event of an accident, the occupants inside the vehicle can quickly open the door to escape, and external rescuers can also directly open the door to carry out rescue.

[0040] In this embodiment, comprehensive vehicle status data and environmental perception data are acquired through multi-source sensors. Based on these two types of data, accurate judgments are made on collision risk level and instability risk level, respectively. Then, the vehicle driving risk level is determined by combining the two risk levels. Finally, when the high driving risk level is determined to be unavoidable, emergency door unlocking is triggered. This can effectively realize active door unlocking before a vehicle accident occurs. It can effectively avoid the door unlocking failure problem caused by vehicle body deformation, power or control system failure that is easily caused by accidents. It ensures that the door is always unlockable before an accident occurs, which can not only buy golden escape time for the occupants, but also facilitate the rapid rescue by external rescuers. This greatly improves the effectiveness of the vehicle's passive safety protection and maximizes the protection of the lives of the drivers and passengers.

[0041] In some embodiments, step 200, based on vehicle state data and environmental perception data, determines the vehicle's collision risk level and instability risk level, including: Step 210: Integrate the surrounding environment data and road data to determine the effective obstacle targets and the relative motion relationship between each effective obstacle target and the vehicle.

[0042] Specifically, based on road environment constraints such as the vehicle's lane boundary range and road width restrictions, the system integrates road data, surrounding environment data, relative motion data, and dynamic trajectories of obstacle targets to accurately identify effective obstacle targets around the vehicle, exclude non-collision risk targets such as road bumps and guardrails, and clarify the type of each effective obstacle target, such as vehicles, pedestrians, and non-motorized vehicles. At the same time, it determines the relative motion relationship between each effective obstacle target and the vehicle, including the relative motion direction, distance change trend, and orientation distribution.

[0043] Step 220: Based on the vehicle's state data, predict the motion trajectories of the vehicle and each effective obstacle target.

[0044] Specifically, based on preprocessed vehicle state data, motion state data such as vehicle speed, acceleration, and yaw rate are extracted, along with operational intent data such as steering wheel angle and speed, and braking force information. Through a preset dynamic model, the vehicle's trajectory over a future period under the current operating state is calculated, ensuring that the trajectory conforms to the vehicle's own dynamic characteristics and the driver's operational intent. Simultaneously, by combining the obstacle type, dynamic trajectory, and relative motion data corresponding to each effective obstacle target, and referring to road environment constraints, the motion patterns of the effective obstacle targets are analyzed, and the motion trajectories of each effective obstacle target over the same future period are predicted, ensuring that the trajectory prediction reflects the actual motion trend of each target, such as whether pedestrians continue to cross the lane or whether the vehicle in front will slow down.

[0045] Step 230: Based on the motion trajectories of the vehicle and each effective obstacle target, determine the intersection probability and collision time between the motion trajectories of the vehicle and each effective obstacle target.

[0046] Specifically, the predicted vehicle trajectory and the trajectories of each effective obstacle are superimposed onto a road model that incorporates road curvature, lane lines, and other road geometric features. Through trajectory matching analysis, it is determined whether there is a possibility of overlap between the vehicle's trajectory and the trajectory of each effective obstacle, and then the intersection probability of the two trajectories is calculated. The higher the intersection probability, the greater the possibility of a collision. At the same time, based on the relative distance and relative speed between the vehicle and each effective obstacle, combined with the direction of the predicted trajectory, the time from the current moment to the possible trajectory intersection if the vehicle and the effective obstacle both move along the current predicted trajectory is calculated, i.e., the collision time. The length of the collision time directly reflects the urgency of the collision risk.

[0047] The trajectory matching analysis between the vehicle and each effective obstacle target involves superimposing the vehicle's trajectory, corrected by road data, and the trajectory of each effective obstacle target onto a unified road model that integrates road geometry features and environmental constraints. By comparing and analyzing the trends of their driving paths and position changes over a future period, the analysis determines whether there are overlapping areas between the vehicle's and each effective obstacle target's trajectories, clarifies the specific location and time of the trajectory intersection, and further analyzes the continuity of the intersection by combining the speeds of both, thereby determining the possibility of a collision between the vehicle and each effective obstacle target.

[0048] Step 240: Determine the collision risk level based on the intersection probability and collision time.

[0049] Specifically, three intersection probability thresholds and collision time thresholds are preset for three collision risk levels. The collision risk level is determined by combining the magnitude of the intersection probability and the comparison result between the collision time and the preset threshold.

[0050] For example, by setting a first preset collision time threshold and a second preset collision time threshold, with the second preset collision time threshold being less than the first preset collision time threshold, the collision risk level can be divided into three levels.

[0051] If the collision time is greater than the first preset collision time threshold, it means that when the vehicle and each effective obstacle target are traveling on the current predicted trajectory, the time interval between collisions is long enough, or the probability of trajectory intersection is extremely low. The driver has enough time to avoid risks through braking, steering and other operations. Therefore, the collision risk level is determined to be a low collision risk level.

[0052] If the intersection probability can determine that the trajectory of the vehicle intersects with that of at least one effective obstacle, the collision time is further compared with the first preset collision time threshold and the second preset collision time threshold. If the collision time is less than the first preset collision time threshold and greater than the second preset collision time threshold, it indicates that the collision may occur in a short period of time, but there is still a certain window for driver intervention. At this time, the collision risk level is determined to be a medium collision risk level, and the driver is reminded to take evasive action immediately.

[0053] If the intersection probability determines that the trajectory of the vehicle and at least one effective obstacle target inevitably intersects, meaning there is no possibility of avoidance, the collision time is further compared with the second preset collision time threshold. If the collision time is less than the second preset collision time threshold, it indicates that a collision is imminent and driver intervention is unavoidable. At this point, the collision risk level is determined to be a high collision risk level, which serves as the condition for triggering emergency unlocking.

[0054] It should be noted that this embodiment divides the collision risk level into three levels by setting two preset collision time thresholds. As other preferred embodiments, one or more preset collision time thresholds can be set to divide the collision risk level into two or more levels. This embodiment does not impose specific limitations on this.

[0055] In this embodiment, effective obstacle target identification and relative motion relationship confirmation are completed by fusing surrounding environmental data and road data. Then, the vehicle's state data is combined to predict the trajectory of the vehicle and each target, which is corrected by road data. Subsequently, the intersection probability and collision time are calculated through trajectory matching analysis. Finally, the collision risk level is accurately determined according to a preset threshold. This can effectively avoid the limitations of single sensor data and trajectory prediction deviations caused by the lack of road constraints. It can also quantify the collision risk through the dual dimensions of intersection probability and collision time, thereby greatly improving the accuracy, timeliness and rationality of collision risk determination. In this way, it can effectively avoid the occurrence of subsequent operational errors caused by misjudgment or omission of collision risk.

[0056] In some embodiments, step 220, based on the vehicle state data, predicts the motion trajectories of the vehicle and each effective obstacle target, including: Step 221: Based on the motion state data in the vehicle state data, predict the initial motion trajectory of the vehicle and each effective obstacle target respectively.

[0057] Specifically, motion state data is extracted from the vehicle's state data, including vehicle speed calculated by wheel speed sensors, lateral and longitudinal acceleration, and yaw rate collected by yaw angle sensors. Combined with the vehicle's own dynamic characteristics, and through a preset trajectory prediction logic, the initial motion trajectory of the vehicle in the future over a period of time under the current motion state is calculated. This trajectory directly reflects the path trend of the vehicle continuing to travel at the current speed and posture. At the same time, referring to the types of effective obstacle targets and their relative motion data, and combining the general motion patterns of different types of targets, such as vehicles mostly traveling along lanes and pedestrians moving at slower speeds with potentially flexible trajectories, the initial motion trajectory of each effective obstacle target under the condition of no road constraints is predicted to ensure that the initial trajectory can match the current motion state of the target.

[0058] Step 222: Correct the initial trajectory based on road data and predict the trajectory of the vehicle and each effective obstacle target.

[0059] Specifically, by combining road geometry features and environmental constraints from road data, the initial trajectories of vehicles and various effective obstacles are corrected to predict their actual movement. For example, for the initial vehicle trajectory, the curvature is adjusted based on road curvature, and the lateral range of the trajectory is limited according to lane line positions, ensuring that the corrected vehicle trajectory conforms to the physical constraints of the current road and fits the actual drivable path. For the initial trajectories of each effective obstacle, adjustments are made based on road width, lane line distribution, and the road position of the effective obstacle (e.g., whether pedestrians are on sidewalks or vehicles are in lanes). For example, vehicle trajectory is restricted from crossing lane lines, and pedestrian trajectory is constrained from exceeding road boundaries, ultimately resulting in more accurate vehicle and obstacle trajectories that better reflect actual road conditions.

[0060] In this embodiment, the initial motion trajectories of the vehicle and each environmental target are initially predicted based on the motion state data in the vehicle state data, combined with the vehicle dynamics characteristics and the motion law of environmental targets. Then, the initial trajectory is specifically corrected according to the geometric features and environmental constraints in the road data, so that the vehicle trajectory conforms to the actual drivable path and the environmental target trajectory conforms to the physical boundary of the road, thereby greatly improving the accuracy and conformity of the prediction of the motion trajectory of the vehicle and each environmental target.

[0061] In some embodiments, step 200, based on vehicle state data and environmental perception data, determines the vehicle's collision risk level and instability risk level, including: Step 250: Obtain the vehicle's actual body posture data and determine the ideal body posture data corresponding to the operation intention data.

[0062] Specifically, real vehicle posture data is extracted from the vehicle status data. This real vehicle posture data includes yaw rate collected by the yaw angle sensor, lateral acceleration calculated from relevant data, pitch angle and roll angle collected by the inertial measurement unit, etc., which directly reflect the current actual dynamic state of the vehicle. At the same time, based on the operation intention data in the vehicle status data (such as steering wheel angle and speed, braking force information collected by the brake pressure sensor, throttle control signal, etc.), combined with vehicle dynamics characteristics and preset algorithms, the ideal vehicle posture data that the vehicle should present under the driver's operation intention is calculated. This ideal vehicle posture data is the dynamic standard of the vehicle when it is driving normally as expected by the driver.

[0063] Step 260: Compare the actual vehicle body posture data with the ideal vehicle body posture data to determine the deviation of the vehicle body posture data.

[0064] Specifically, the acquired real vehicle posture data is compared one by one with the ideal vehicle posture data. For each key data such as yaw rate, lateral acceleration, pitch angle, and roll angle, the numerical difference between the two is calculated. Then, by comprehensively integrating the differences of these individual data, a vehicle posture data deviation that can comprehensively reflect the degree of deviation between the actual dynamics of the vehicle and the expected dynamics is formed. This vehicle posture data deviation reflects the degree of fit between the current driving state of the vehicle and the driver's operating intention.

[0065] Step 270: Based on the deviation of vehicle posture data and the current vehicle speed, predict the time of vehicle instability.

[0066] Specifically, based on the deviation of the vehicle body posture data, the degree of impact on vehicle driving stability is determined. The larger the deviation, the further the vehicle deviates from the normal driving state. At the same time, the current actual speed of the vehicle is taken into account. The higher the speed, the more prominent the risk of instability caused by the deviation tends to be. Based on the deviation of the vehicle body posture data and the actual speed, the estimated time until the vehicle continues to drive at the current deviation state and speed until an instability accident such as rollover or skidding occurs is calculated through the preset risk prediction logic.

[0067] The preset risk prediction logic refers to the judgment logic established based on the preprocessed vehicle status data and environmental perception data collected by multiple sensors, targeting two types of risks: collision and instability. For collision risk prediction, the vehicle's trajectory is superimposed onto a unified road model for trajectory matching analysis. The collision risk level is determined by combining the trajectory intersection probability and collision time with a preset threshold. For instability risk prediction, the deviation value is obtained by comparing the ideal vehicle dynamic parameters corresponding to the driver's intention with the actual vehicle dynamic parameters. This deviation is then combined with the current vehicle speed to predict the instability time and determine the instability risk level according to a preset threshold.

[0068] Step 280: Determine the instability risk level based on the vehicle instability time.

[0069] Specifically, by setting a first preset instability time threshold and a second preset instability time threshold, with the second preset instability time threshold being less than the first preset instability time threshold, the instability risk level is divided into three levels based on the comparison between the vehicle instability time and these two preset instability time thresholds.

[0070] If the vehicle instability time exceeds the first preset instability time threshold, it indicates that the current vehicle body posture data deviation is small. If the vehicle continues to drive according to the current driving state, there will be no instability accidents such as rollover or skidding in the short term. The driver has sufficient time to correct the vehicle state by adjusting the operation. Therefore, it is judged as a low instability risk level.

[0071] If the vehicle instability time is less than the first preset instability time threshold but greater than the second preset instability time threshold, it indicates that the current vehicle body posture data deviation has reached a certain level, and an instability accident may occur if the vehicle continues to drive in the current state. However, there is still a certain time window at this time, and the driver can avoid the risk by taking active intervention operations such as emergency braking and adjusting the steering wheel. Therefore, it is judged as a medium instability risk level, and the possibility of an instability accident is clearly indicated.

[0072] If the vehicle instability time is less than the second preset instability time threshold, it means that the current vehicle body posture data deviation is already very significant. If the vehicle continues to drive in the current state, an instability accident will occur in a very short time. Any intervention by the driver will hardly change the result, and an instability accident is inevitable. Therefore, it is judged as a high instability risk level.

[0073] It should be noted that this embodiment divides the instability risk level into three levels by setting two preset instability time thresholds. In other preferred embodiments, one or more preset instability time thresholds can be set to divide the instability risk level into two or more levels. This embodiment does not impose any specific restrictions on this.

[0074] In this embodiment, by acquiring the vehicle's actual dynamic parameters, deriving the ideal dynamic parameters corresponding to the driver's operating intention, and then determining the deviation of the vehicle's dynamic parameters through parameter comparison, the vehicle's instability time is predicted in combination with the current vehicle speed, and the instability risk level is determined accordingly. This achieves accurate capture of the deviation between the vehicle's actual driving state and the driver's operating intention. Furthermore, by considering the instability impact caused by the deviation in combination with vehicle speed, the accuracy and scientific nature of the instability risk determination can be greatly improved, thereby effectively identifying potential instability hazards such as vehicle skidding and rollover.

[0075] In some embodiments, determining the vehicle's driving risk level in step 300 based on the collision risk level and the instability risk level includes: Step 310: If both the instability risk level and the collision risk level are low risk levels, the vehicle is classified as having a low driving risk level.

[0076] Specifically, if both the collision risk level and the instability risk level are low, it means that the vehicle currently has neither a collision hazard nor an instability risk, and its driving state is stable and safe. Therefore, the vehicle's driving risk level is determined to be low.

[0077] Step 320: If at least one of the instability risk level and collision risk level is a medium risk level, the vehicle is determined to be at a medium driving risk level.

[0078] Specifically, if either the collision risk level or the instability risk level is a medium risk level and the other is a low risk level, or if both risk levels are medium risk levels, then the vehicle has a potential collision or instability hazard and the driver needs to be alerted to its driving status. Therefore, the vehicle's driving risk level is determined to be a medium driving risk level.

[0079] Step 330: If at least one of the instability risk level and collision risk level is a high risk level, it is determined to be a high driving risk level. Under the condition of a high driving risk level, the driving risk level is determined to be an unavoidable accident.

[0080] Specifically, if there is at least one high-risk level in the collision risk level and the instability risk level, including one high-risk level and the other low or medium-risk level, or both high-risk levels, it indicates that the vehicle is facing an imminent threat of collision or instability, and the accident is unavoidable. Therefore, the vehicle's driving risk level is determined to be a high driving risk level, and it is clear that under this level, the accident corresponding to the driving risk is unavoidable.

[0081] In this embodiment, a comprehensive assessment of vehicle driving risks is achieved through a dual-risk-level integrated judgment method, which avoids the one-sidedness of a single risk judgment and can accurately distinguish different risk states of the vehicle, thereby helping to ensure the timeliness and accuracy of subsequent emergency operations.

[0082] In some embodiments, after generating the emergency door unlocking command, step 400 further includes step 500, which specifically includes: Trigger an audible and visual alarm; The methods of sound and light alarm include at least one of the following: alarm sound, dashboard warning sign display, and head-up display warning.

[0083] Specifically, while triggering the emergency unlocking of the vehicle door, a signal is simultaneously sent to the alarm module (such as the intelligent driving module) that is connected to the intelligent driving module. Figure 2 As shown, the system sends an emergency door unlocking command. The alarm module generates an audible and visual alarm activation command that matches the current driving risk level based on the emergency door unlocking command, triggering the corresponding level of audible and visual alarm to ensure that the unlocking action and alarm reminder are executed in tandem.

[0084] It is easy to understand that when the driving risk level determines that the accident is avoidable, such as when the driving risk level is determined to be medium or other situations where a collision is likely to occur, the intelligent driving module will send an audible and visual alarm activation command to trigger the corresponding level of audible and visual alarm to remind the driver to pay attention to driving safety.

[0085] Upon receiving a command, the alarm module immediately activates a preset audible and visual alarm mechanism. The alarm method can be selected from at least one of the following: audible alarm, dashboard warning display, or head-up display (HUD) warning. The audible alarm emits a clear and alert sound through the vehicle's audio system, continuously reminding occupants that an emergency is imminent. The dashboard warning display illuminates preset emergency warning icons, such as red exclamation marks or collision warning icons, on the instrument panel to visually convey risk information. The HUD warning projects the emergency warning content onto the head-up display area of ​​the windshield, allowing the driver to quickly perceive the warning without looking down, ensuring that all occupants are promptly aware of the danger and are prepared for escape or rescue.

[0086] On the other hand, this embodiment provides a door unlocking device, such as... Figure 3 As shown, it includes: The data acquisition unit 301 is used to acquire the vehicle's self-state data and environmental perception data in real time; the self-state data includes motion state data, actual vehicle posture data and operation intention data, and the environmental perception data includes surrounding environment data and road data. The intermediate risk level determination unit 302 is used to determine the collision risk level and instability risk level of the vehicle based on the vehicle status data and environmental perception data. The driving risk level determination unit 303 is used to determine the driving risk level of the vehicle based on the collision risk level and the instability risk level. The emergency unlocking unit 304 is used to generate an emergency door unlocking command when the driving risk level determines that an accident is unavoidable. The emergency door unlocking command is used to unlock the door before the accident occurs.

[0087] On the other hand, this embodiment provides a vehicle, including: The chassis module is used to acquire the vehicle's self-state data, which includes motion state data, actual vehicle posture data, and operation intention data. The intelligent driving module is used to acquire environmental perception data of the vehicle, and based on the vehicle status data and environmental perception data, to determine the vehicle's collision risk level and instability risk level; based on the collision risk level and instability risk level, it determines the vehicle's driving risk level; the environmental perception data includes surrounding environment data and road data; The vehicle body control module is used to generate an emergency door unlocking command when the driving risk level determines that an accident is unavoidable. The emergency door unlocking command is used to unlock the door before the accident occurs. The door lock module is used to execute emergency door unlocking actions based on the emergency door unlocking command; The alarm module is used to generate an audible and visual alarm activation command that matches the driving risk level based on the emergency door unlocking command, triggering the alarm of the corresponding level.

[0088] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to perform the steps of any of the methods in the above embodiments.

[0089] In the embodiments of this application, the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0090] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0091] The above provides a detailed description of a door unlocking method, device, and vehicle provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for unlocking a car door, characterized in that, Includes the following steps: The system acquires real-time vehicle status data and environmental perception data; the vehicle status data includes motion status data, actual vehicle posture data, and operation intention data, and the environmental perception data includes surrounding environment data and road data. Based on the vehicle status data and the environmental perception data, the collision risk level and instability risk level of the vehicle are determined. Based on the collision risk level and the instability risk level, the driving risk level of the vehicle is determined; If the driving risk level determines that an accident is unavoidable, an emergency door unlocking command is generated. This emergency door unlocking command is used to unlock the door before the accident occurs.

2. The door unlocking method according to claim 1, characterized in that, The process of determining the collision risk level and instability risk level of the vehicle based on the vehicle status data and the environmental perception data includes: The surrounding environment data and the road data are fused to determine the effective obstacle targets and the relative motion relationship between each effective obstacle target and the vehicle. Based on the vehicle status data, predict the motion trajectories of the vehicle and each of the effective obstacle targets; Based on the motion trajectories of the vehicle and each of the effective obstacle targets, determine the intersection probability and collision time between the motion trajectories of the vehicle and each of the effective obstacle targets; The collision risk level is determined based on the intersection probability and the collision time.

3. The door unlocking method according to claim 2, characterized in that, The step of predicting the motion trajectories of the vehicle and each of the effective obstacle targets based on the vehicle state data includes: Based on the motion state data in the vehicle state data, the initial motion trajectories of the vehicle and each of the effective obstacle targets are predicted respectively. The initial trajectory is corrected based on the road data to predict the trajectory of the vehicle and each of the effective obstacle targets.

4. The door unlocking method according to claim 2, characterized in that, Based on the vehicle status data and the environmental perception data, the collision risk level and instability risk level of the vehicle are determined, including: Determine the ideal vehicle posture data corresponding to the operation intention data; The actual vehicle body posture data is compared with the ideal vehicle body posture data to determine the deviation of the vehicle body posture data; Based on the vehicle posture data deviation and current vehicle speed, predict the vehicle instability time; The instability risk level is determined based on the vehicle's instability time.

5. The door unlocking method according to claim 4, characterized in that, The determination of the instability risk level based on the vehicle instability time includes: If the collision time is greater than the first preset instability time threshold, it is determined to be a low instability risk level; If the collision time is less than the first preset instability time threshold and greater than the second preset instability time threshold, it is determined to be a medium instability risk level. If the collision time is less than the second preset instability time threshold, it is determined to be a high instability risk level.

6. The door unlocking method according to claim 5, characterized in that, Determining the collision risk level based on the intersection probability and the collision time includes: If the collision time is greater than a first preset collision time threshold, it is determined to be a low collision risk level; If the intersection probability determines that the trajectory of the vehicle and at least one of the effective obstacle targets intersects, and the collision time is less than the first preset collision time threshold and greater than the second preset collision time threshold, the collision risk level is determined to be medium. If the intersection probability determines that the trajectory of the vehicle and at least one of the effective obstacle targets intersects, and the collision time is less than the second preset collision time threshold, the collision risk level is determined to be high.

7. The door unlocking method according to claim 6, characterized in that, Determining the vehicle's driving risk level based on the collision risk level and the instability risk level includes: If both the instability risk level and the collision risk level are low risk levels, it is determined to be a low driving risk level; If at least one of the instability risk level and the collision risk level is a medium risk level, it is determined to be a medium driving risk level; If at least one of the instability risk level and the collision risk level is a high risk level, it is determined to be a high driving risk level. Under the condition of a high driving risk level, the driving risk level is determined to indicate that an accident is unavoidable.

8. The vehicle door unlocking method according to claim 1, characterized in that, After generating the emergency door unlocking command, the method further includes: Trigger an audible and visual alarm; The audible and visual alarm methods include at least one of alarm sound, dashboard warning display, and head-up display warning.

9. A vehicle door unlocking device, characterized in that, include: The data acquisition unit is used to acquire vehicle status data and environmental perception data in real time; the vehicle status data includes motion status data, actual vehicle posture data and operation intention data, and the environmental perception data includes surrounding environment data and road data. The intermediate risk level determination unit is used to determine the collision risk level and instability risk level of the vehicle based on the vehicle status data and the environmental perception data. A driving risk level determination unit is used to determine the driving risk level of the vehicle based on the collision risk level and the instability risk level. An emergency unlocking unit is used to generate an emergency door unlocking command when the driving risk level determines that an accident is unavoidable. The emergency door unlocking command is used to unlock the door before the accident occurs.

10. A vehicle, characterized in that, include: The chassis module is used to acquire the vehicle's self-state data, which includes motion state data, actual vehicle posture data, and operation intention data. The intelligent driving module is used to acquire the vehicle's environmental perception data, and based on the vehicle status data and the environmental perception data, determine the vehicle's collision risk level and instability risk level. Based on the collision risk level and the instability risk level, the driving risk level of the vehicle is determined; The environmental perception data includes surrounding environment data and road data; The vehicle body control module is used to generate an emergency door unlocking command when the driving risk level determines that an accident is unavoidable. The emergency door unlocking command is used to unlock the door before the accident occurs. The door lock module is used to execute an emergency door unlocking action based on the emergency door unlocking command; The alarm module is used to generate an audible and visual alarm activation command that matches the driving risk level based on the emergency door unlocking command, thereby triggering the alarm at the corresponding level.

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