Non-inductive vehicle door unlocking method and system based on wearable device and vehicle

Abstract

The invention discloses a wearable device-based non-inductive vehicle door unlocking method and system and a vehicle, and the method comprises the steps: obtaining the distance between a wearable device and the vehicle, and building the communication connection between the wearable device and the vehicle when the distance between the wearable device and the vehicle is smaller than a first preset distance; when the distance between the wearable device and the vehicle is smaller than a second preset distance, acquiring a real-time hand movement track of the user; comparing the real-time hand movement track with a preset reference hand movement track data set; when the real-time hand movement track is matched with the reference hand movement track data set, the vehicle door is unlocked; when the real-time hand movement track is not matched with the reference hand movement track data set, reminding a user of unlocking failure; according to the real-time hand motion trail, the reference hand motion trail data set is corrected, a user can complete unlocking of the vehicle door through the subconscious motion of the user under the condition that other redundant motion is not carried out, and the convenience and safety of unlocking the vehicle door are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of contactless unlocking of automobiles, and in particular to a contactless door unlocking method, system and vehicle based on a wearable device. Background Art

[0002] At present, with the advancement of technology, it is becoming more and more common for users to unlock vehicles without physical keys. There are two commonly used unlocking methods without physical keys. One is to remotely unlock the vehicle through a mobile phone app, and the other is to use a digital key instead of a physical key for near-field unlocking, such as using wireless transmission technologies such as Bluetooth, NFC, and UWB to communicate with the vehicle, and then unlock the vehicle by operating the app or placing the NFC key close to the door. Some mobile phones can identify the distance between the phone and the vehicle when connected to the vehicle through Bluetooth, and unlock the vehicle when the distance between the phone and the vehicle is less than a certain threshold.

[0003] The invention patent with application number CN105809783A discloses a method and system for realizing car unlocking based on smart bracelet gestures. The method includes: a proximity sensor set on the car door lock detects in real time whether the smart bracelet is close to the car door lock. When the smart bracelet is detected to be close to the car door lock, a wireless connection is established with the car door lock; within a preset time threshold, a three-axis acceleration sensor set in the smart bracelet is used to detect whether a gesture action is made. When a gesture action is detected within the time threshold, the current motion trajectory corresponding to the gesture action is obtained; the current motion trajectory is matched with the unlocking trajectory pre-stored in the car door lock, and the car door lock is unlocked when the match is successful. The present invention obtains gestures through a smart bracelet connected to the car door lock, and determines whether to unlock based on the gesture. The car door lock can be successfully unlocked even if the user does not carry the car key, which is convenient for the user.

[0004] Among the above unlocking methods, when remote unlocking is performed through the mobile phone App, it is more dependent on the network conditions between the mobile phone and the vehicle computer, and the unlocking method is not stable enough; when unlocking through the digital key, it will add the unnecessary action of taking out the mobile phone or NFC key, and the unlocking method is not convenient enough; when using the distance threshold for judgment, there may be a situation where the vehicle is unlocked without intention but passes by the vehicle, which is not safe enough. When the unlocking trajectory is pre-stored in the comparison file 1, the unlocking trajectory may be insufficient in accuracy, which affects the unlocking accuracy. Summary of the invention

[0005] To achieve the above-mentioned purpose and other related purposes, the present invention proposes a method for unlocking a vehicle door without any sense of touch based on a wearable device, comprising:

[0006] Acquire the distance between the wearable device and the vehicle, and when the distance between the wearable device and the vehicle is less than a first preset distance, establish a communication connection between the wearable device and the vehicle;

[0007] When the distance between the wearable device and the vehicle is less than a second preset distance, obtaining the real-time hand movement trajectory of the user;

[0008] Compare the real-time hand motion trajectory with a preset benchmark hand motion trajectory dataset;

[0009] When the real-time hand motion trajectory matches the reference hand motion trajectory dataset, the door is unlocked;

[0010] When the real-time hand motion trajectory does not match the reference hand motion trajectory dataset, the user is reminded that the unlocking fails;

[0011] The baseline hand motion trajectory dataset is corrected according to the real-time hand motion trajectory.

[0012] Furthermore, the first preset distance is greater than the second preset distance.

[0013] Furthermore, the acquiring of the distance between the wearable device and the vehicle, when the distance between the wearable device and the vehicle is less than a first preset distance, before establishing a communication connection between the wearable device and the vehicle, further includes:

[0014] Create a digital key in the wearable device;

[0015] Pair and authenticate the digital key with the vehicle;

[0016] Check the validity of the digital key, and if the digital key is valid, connect the digital key to the vehicle;

[0017] The digital key that is successfully connected to the vehicle is stored.

[0018] Furthermore, the preset reference hand motion trajectory data set includes the following steps:

[0019] Obtain a preset number of three-axis angular velocity sequence data sets;

[0020] Perform data preprocessing on the three-axis angular velocity sequence data set;

[0021] The two sets of three-axis angular velocity sequence data with the highest similarity in the three-axis angular velocity sequence data set are used as the benchmark hand motion trajectory data set.

[0022] Furthermore, the benchmark hand motion trajectory dataset includes:

[0023] A combination of one or more of the trajectory of the hand and wrist when the user raises his hand, the trajectory of the hand and wrist when the user holds the door handle, and the trajectory of the hand and wrist when pulling toward the inside of the body.

[0024] Furthermore, the correcting of the reference hand motion trajectory dataset according to the real-time hand motion trajectory comprises the following steps:

[0025] The similarity between two sets of three-axis angular velocity sequence data of the benchmark hand motion trajectory dataset is used as the benchmark similarity;

[0026] When the similarity between the real-time hand motion trajectory and any three-axis angular velocity sequence data in the benchmark hand motion trajectory dataset is greater than the benchmark similarity, the benchmark similarity is updated, and the benchmark hand motion trajectory dataset is updated to two sets of three-axis angular velocity sequence data corresponding to the benchmark similarity.

[0027] Furthermore, the method further comprises:

[0028] When it is detected that the user is in the car, the hand movement trajectory of the user in the car is obtained;

[0029] When the hand motion trajectory in the vehicle matches the preset hand motion trajectory data set for starting the vehicle, obtaining the brake pedal state of the vehicle;

[0030] When the brake pedal is in the braking state, start the vehicle.

[0031] Furthermore, the method further comprises:

[0032] Collect user feedback on the matching results, establish an adaptive learning mechanism based on the feedback results, and optimize the benchmark hand motion trajectory data set.

[0033] In a second aspect, the present invention provides a system, comprising:

[0034] A detection module is used to obtain the distance between the wearable device and the vehicle;

[0035] A communication module, used to establish a communication connection between the wearable device and the vehicle when the distance between the wearable device and the vehicle is less than a first preset distance;

[0036] A motion trajectory acquisition module, used to acquire the real-time hand motion trajectory of the user when the distance between the wearable device and the vehicle is less than a second preset distance;

[0037] The data processing module is used to unlock the car door when the real-time hand motion trajectory matches the baseline hand motion trajectory dataset; when the real-time hand motion trajectory does not match the baseline hand motion trajectory dataset, it reminds the user that the unlocking fails and corrects the baseline hand motion trajectory dataset according to the real-time hand motion trajectory.

[0038] In a third aspect, the present invention provides a vehicle, comprising:

[0039] An electronic device, used to implement the steps of a method for senseless door unlocking based on a wearable device;

[0040] A processor, wherein the processor runs a program, and when the program runs, data outputted from the electronic device executes steps of a method for senseless door unlocking based on a wearable device;

[0041] The storage medium is used to store a program, and when the program is running, it executes the steps of the method for unlocking the vehicle door without feeling based on the wearable device for the data output from the electronic device.

[0042] By adopting the above technical solution, when the user enters the first preset distance range from the vehicle, a communication connection is established between the wearable device and the vehicle. When the distance is less than the second preset distance, the real-time hand motion trajectory is obtained, and the real-time hand motion trajectory is compared with the reference hand motion trajectory data set. When the two match, the door is unlocked. If the two do not match, the user is reminded that the unlocking failed, and the reference hand motion trajectory data set is corrected according to the real-time hand motion trajectory. The door can be unlocked without using a physical key, which is more convenient. The action trajectory needs to be identified when unlocking, which improves safety. At the same time, the added action trajectory is a subconscious action that the user must perform when unlocking the door. There is no need to add unnecessary actions to unlock the door, which further increases the convenience of unlocking the door. At the same time, the reference hand motion trajectory data set is continuously adjusted according to the real-time hand motion trajectory, thereby increasing the accuracy of unlocking. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0044] Figure 1 This is a flow chart of a method for unlocking a car door without any sense of touch based on a wearable device;

[0045] Figure 2 A schematic diagram of the steps of creating a digital key in an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of the steps of constructing a reference hand motion trajectory dataset in an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of the steps of unlocking a vehicle door in an embodiment of the present invention;

[0048] Figure 5This is a system block diagram of a wearable device-based contactless door unlocking system. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0050] Reference Figure 1 The embodiment of the present invention provides a method for unlocking a vehicle door without any sense of touch based on a wearable device, comprising the following steps:

[0051] S1: Create a digital key.

[0052] Reference Figure 2 Specifically, S1 includes the following steps:

[0053] Create a digital key in the wearable device;

[0054] Pair and authenticate the digital key with the vehicle;

[0055] Check the validity of the digital key, and if the digital key is valid, connect the digital key to the vehicle;

[0056] The digital key that is successfully connected to the vehicle is stored.

[0057] In the above technical solution, the wearable device is a smart wearable device, including smart bracelets, smart watches, smart rings and other devices. When a digital key needs to be created for the first use, the user first opens the APP in the smart wearable device, activates the Bluetooth digital key in the smart wearable device, and then uploads the instruction to activate the digital key to the cloud, which creates the digital key and stores and manages it.

[0058] After the digital key is created, the digital key is paired and authenticated with the Bluetooth module of the vehicle's car-side. In this embodiment, when the Bluetooth digital key of the wearable device is turned on, the wearable device scans the car-side within the Bluetooth search range. When the car-side is scanned, the wearable device initiates a Bluetooth connection request to the car-side. The user confirms the Bluetooth connection request through the car-side, thereby completing the pairing between the digital key and the car-side. After the pairing is completed, the digital key and the car-side are authenticated, including verifying the identities of both parties, exchanging keys, and encrypting communications. The Bluetooth pairing and authentication methods between the digital key and the car-side are prior arts familiar to those skilled in the art, and will not be repeated here. After the pairing is completed, the APP of the wearable device stores the digital key and completes the creation of the digital key, which is convenient for connecting to the car-side through the digital key.

[0059] When you need to unlock the door, refer to Figure 4 , including the following steps:

[0060] S2: Obtain the distance between the wearable device and the vehicle, and when the distance between the wearable device and the vehicle is less than a first preset distance, establish a communication connection between the wearable device and the vehicle.

[0061] In the above technical solution, when a user approaches a vehicle with a wearable device that has completed digital key pairing, the ibeacon broadcast initiated by the vehicle terminal is first used to wake up the APP of the wearable device. iBeacon is a technology based on Bluetooth Low Energy (BLE) launched by Apple in 2013, mainly used for near-field communication and positioning services. iBeacon technology allows small wireless devices (called "Beacon") to broadcast a specific ID, as well as other optional information, such as UUID (Universally Unique Identifier), major and minor values. When an iBeacon-enabled device (such as an iPhone, iPad or other BLE-enabled device) enters the signal range of the Beacon, the application on the device can detect these signals and take corresponding actions based on the received information. The Bluetooth module on the vehicle terminal sends ibeacon broadcasts at regular intervals. When the wearable device senses that it has entered the ibeacon range, that is, within the first preset distance, the APP recognizes the ibeacon broadcast signal, and the digital key automatically completes the Bluetooth connection with the vehicle terminal. The first preset distance is set by technical personnel in this field according to actual conditions. In this embodiment, the first preset distance is preferably 10 meters. When the user brings the wearable device within 10 meters of the vehicle, the wearable device is connected to the Bluetooth module on the vehicle side through the digital key.

[0062] S3: When the distance between the wearable device and the vehicle is less than a second preset distance, the real-time hand movement trajectory of the user is obtained.

[0063] Specifically, the second preset distance in this embodiment is set by a person skilled in the art, and the second preset distance is less than the first preset distance. The second preset distance in this embodiment is preferably 1 meter. When the wearable device recognizes that the user is outside the car and the distance from the car is within 1 meter, the real-time hand motion trajectory of the user is obtained. In this embodiment, the gyroscope in the wearable device is used to obtain the three-axis angular velocity sequence data to complete the collection of the real-time hand motion trajectory of the user.

[0064] S4: The cloud platform compares the real-time hand motion trajectory with the preset benchmark hand motion trajectory dataset:

[0065] When the real-time hand motion trajectory matches the reference hand motion trajectory dataset, the APP sends an unlock command to the vehicle-side Bluetooth module, and the vehicle-side Bluetooth module unlocks the door.

[0066] When the real-time hand motion trajectory does not match the reference hand motion trajectory dataset, the user is reminded that the unlocking fails.

[0067] S5: Correcting the baseline hand motion trajectory dataset according to the real-time hand motion trajectory:

[0068] The similarity between two sets of three-axis angular velocity sequence data of the benchmark hand motion trajectory dataset is used as the benchmark similarity;

[0069] When the similarity between the real-time hand motion trajectory and any three-axis angular velocity sequence data in the benchmark hand motion trajectory dataset is greater than the benchmark similarity, the benchmark similarity is updated, and the benchmark hand motion trajectory dataset is updated to two sets of three-axis angular velocity sequence data corresponding to the benchmark similarity.

[0070] Specifically, the baseline hand motion trajectory in this embodiment includes at least one or more combinations of: the trajectory of the hand and wrist when the user raises his hand, the trajectory of the hand and wrist when the user holds the door handle, and the trajectory of the hand and wrist pulling toward the inside of the body.

[0071] In the above technical solution, the reference hand motion trajectories selected in this embodiment are all actions that the user must perform when unlocking the car door. The user does not need to perform extra actions to unlock the door during the unlocking process, thereby improving the convenience of unlocking.

[0072] Reference Figure 3 Specifically, in this embodiment, constructing a benchmark hand motion trajectory dataset includes the following steps:

[0073] Obtain a preset number of three-axis angular velocity sequence data sets;

[0074] Perform data preprocessing on the three-axis angular velocity sequence data set;

[0075] The two sets of three-axis angular velocity sequence data with the highest similarity in the three-axis angular velocity sequence data set are used as the benchmark hand motion trajectory data set.

[0076] Among them, when preprocessing the three-axis angular velocity sequence data set, the data in the three-axis angular velocity sequence data set is first intercepted for validity. In this embodiment, when intercepting the three-axis angular velocity sequence data, it starts from the point where the data change difference in the three-axis angular velocity sequence data is greater than the preset threshold, and ends at the point where the data change difference in the three-axis angular velocity sequence data is less than the preset threshold, and the data between the two points is used as the data required to construct the benchmark hand motion trajectory data set. After that, the intercepted data is smoothed and filtered to reduce the interference value in the data. The preset threshold is set by those skilled in the art and will not be repeated here.

[0077] This embodiment preferably uses the Kalman filter algorithm to filter the data, and the main formula is as follows:

[0078] Predicted State Estimates:

[0079] Forecast error covariance:

[0080] Optimal Kalman gain:

[0081] Update the state estimate:

[0082] Update state estimation error covariance: P k|k =(IK k H k ) k|k-1

[0083] Among them, Kalman filtering can predict and update data. represents the predicted value of state x at time k based on all available information up to time k-1; and At time k, combined with the newly arrived observation data z k After that, the updated estimated value of state x. The number on the right of the subscript “|” here represents the information cutoff time point used, and the number on the left represents the time point of the estimated state. k is the state transition matrix, B k is the control input matrix, u k is the control input, Q k is the covariance matrix of the system process noise, H kis the observation matrix, R k is the covariance matrix of the observation noise, I is the identity matrix, z k is the observation vector, i.e. the observation value in the three-axis angular velocity sequence data. At each moment, the algorithm updates the current state estimate and error covariance matrix based on the state estimate at the previous moment and the observation at the current moment, thereby processing the state estimation problem of the dynamic system in real time.

[0084] After filtering the data, a dynamic time warping algorithm is used to construct a baseline hand motion trajectory data set. The APP collects multiple sets of user actions. This embodiment preferably selects 5 sets of actions for collection and construction. Assuming that the length of the previous template X data sequence is N, and the length of the new template Y time data is M, initialize a (N+1)*(M+1) cumulative distance matrix D to store the cumulative distance during the calculation process. Set the boundary condition D(0,0)=0, and the formula for calculating D(i,j) is:

[0085] D(i,j)=d(X i ,Y j )+min(D(i-1,j),D(i,j-1),D(i-1,j-1))

[0086] Among them, 1≤i≤N, 1≤j≤M, d(X i ,Y j ) is the distance between the i-th point in X and the j-th point in Y (such as Euclidean distance or Manhattan distance), thereby obtaining the DTW distance between the two sequences, and then evaluating the similarity between the two data. Finally, two sets of three-axis angular velocity sequence data with the largest similarity, i.e., the smallest DTW (Dmin), are selected from the collected data as the benchmark hand motion trajectory data set, and the benchmark hand motion trajectory data set is stored in the APP and cloud platform. The recognition standard Dst is calculated according to a certain weight, and the formula is:

[0087]

[0088] n is the total number of acquisitions, D n Dst is the DTW distance calculated after the nth acquisition and filtering. k is the new recognition standard. When the DTW distance D between the real-time hand motion trajectory and the benchmark hand motion trajectory dataset n If it is less than Dst set in the template data, the match is successful and the unlock is performed. Otherwise, the match fails and the user is reminded of the unlock failure. In this embodiment, a voice reminder is used to remind the user of the unlock failure. n If it is less than Dmin, update the baseline hand motion trajectory dataset and Dmin, and set D nThe corresponding two sets of three-axis angular velocity sequence data are used as the benchmark hand motion trajectory data set. n As Dmin, completing the correction of the baseline hand motion trajectory dataset can continuously improve the accuracy of unlocking.

[0089] Furthermore, the method in this embodiment also includes:

[0090] When it is detected that the user is outside the vehicle and the vehicle is not locked, the user's hand movement trajectory outside the vehicle is obtained;

[0091] When the hand motion trajectory outside the vehicle matches the preset locking hand motion trajectory data set, the vehicle door is locked.

[0092] In the above technical solution, the locking hand motion trajectory when unlocking can adopt gestures such as making a fist, which can automatically lock the car, thereby improving the convenience of use. The method of matching the hand motion trajectory outside the car with the locking hand motion trajectory data set is the same as above and will not be repeated here.

[0093] Furthermore, the method in this embodiment also includes:

[0094] When it is detected that the user is in the car, the hand movement trajectory of the user in the car is obtained;

[0095] When the hand motion trajectory in the vehicle matches the preset hand motion trajectory data set for starting the vehicle, obtaining the brake pedal state of the vehicle;

[0096] When the brake pedal is in the braking state, start the vehicle.

[0097] When the user is in the car, the user's hand motion trajectory in the car can be obtained. The action of the hand motion trajectory in the car can be selected from actions such as clenching a fist or turning the steering wheel, and the user does not need to make extra actions to start the vehicle, which further improves convenience. The method of matching the hand motion trajectory in the car with the hand motion trajectory dataset for starting the vehicle is the same as above and will not be repeated here.

[0098] Furthermore, in a preferred embodiment, multiple devices are used to collaboratively collect and verify user operation data, and cross-device data synchronization is used. For example, when the data of a ring and a watch are synchronized, the difference of the real-time hand movement trajectory at each collection moment on the ring and the watch when unlocking the door is calculated by acquiring the historical data of the ring and the watch, and the data of the two are mutually converted through the difference, thereby increasing the diversity of the data on the watch and the ring, which is conducive to further improving the unlocking accuracy.

[0099] Furthermore, in a preferred embodiment, user feedback on the matching results is collected, an adaptive learning mechanism is established based on the feedback results, the baseline hand motion trajectory data set is optimized, and the unlocking accuracy is further adjusted and optimized, thereby improving the user experience, which will not be repeated here.

[0100] Reference Figure 5 , an embodiment of the present invention further provides a senseless door unlocking system based on a wearable device, comprising:

[0101] A detection module is used to obtain the distance between the wearable device and the vehicle;

[0102] A communication module, used to establish a communication connection between the wearable device and the vehicle when the distance between the wearable device and the vehicle is less than a first preset distance;

[0103] A motion trajectory acquisition module, used to acquire the real-time hand motion trajectory of the user when the distance between the wearable device and the vehicle is less than a second preset distance;

[0104] The data processing module is used to unlock the car door when the real-time hand motion trajectory matches the baseline hand motion trajectory dataset; when the real-time hand motion trajectory does not match the baseline hand motion trajectory dataset, it reminds the user that the unlocking fails and corrects the baseline hand motion trajectory dataset according to the real-time hand motion trajectory.

[0105] An embodiment of the present invention further provides a vehicle, comprising:

[0106] An electronic device, used to implement the steps of a method for senseless door unlocking based on a wearable device;

[0107] A processor, wherein the processor runs a program, and when the program runs, data outputted from the electronic device executes steps of a method for senseless door unlocking based on a wearable device;

[0108] The storage medium is used to store a program, and when the program is running, it executes the steps of the method for unlocking the vehicle door without feeling based on the wearable device for the data output from the electronic device.

[0109] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0110] The electronic device includes a hardware layer, an operating system layer running on the hardware layer, and an application layer running on the operating system. The hardware layer includes hardware such as a central processing unit (CPU), a memory management unit (MMU), and a memory. The operating system can be any one or more computer operating systems that implement electronic device control through a process, such as a Linux operating system, a Unix operating system, an Android operating system, an iOS operating system, or a Windows operating system. In addition, in an embodiment of the present invention, the electronic device can be a handheld device such as a smart phone or a tablet computer, or can be an electronic device such as a desktop computer or a portable computer, which is not particularly limited in the embodiment of the present invention.

[0111] The execution subject of the electronic device control in the embodiment of the present invention may be an electronic device, or a functional module in the electronic device that can call and execute a program. The electronic device may obtain the firmware corresponding to the storage medium. The firmware corresponding to the storage medium is provided by the supplier. The firmware corresponding to different storage media may be the same or different, which is not limited here. After the electronic device obtains the firmware corresponding to the storage medium, the firmware corresponding to the storage medium may be written into the storage medium, specifically, the firmware corresponding to the storage medium may be burned into the storage medium. The process of burning the firmware into the storage medium may be implemented using existing technology, which will not be described in detail in the embodiment of the present invention.

[0112] The electronic device may also obtain a reset command corresponding to the storage medium. The reset command corresponding to the storage medium is provided by the supplier. The reset commands corresponding to different storage media may be the same or different, and are not limited here.

[0113] At this time, the storage medium of the electronic device is a storage medium in which the corresponding firmware is written, and the electronic device can respond to the reset command corresponding to the storage medium in the storage medium in which the corresponding firmware is written, so that the electronic device resets the storage medium in which the corresponding firmware is written according to the reset command corresponding to the storage medium. The process of resetting the storage medium according to the reset command can be implemented by the existing technology and will not be described in detail in the embodiments of the present invention.

[0114] For the convenience of description, the above devices are described in terms of functions and are divided into various units and modules. Of course, when implementing the present application, the functions of each unit and module can be implemented in the same or multiple software and / or hardware.

[0115] Those skilled in the art will appreciate that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined.

[0116] For the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0117] It can be known from the description of the above implementation modes that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute the methods described in the various implementation modes of the present application or certain parts of the implementation modes.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for unlocking car doors without any sense of touch based on wearable devices. It is characterized in that include: Acquire the distance between the wearable device and the vehicle, and when the distance between the wearable device and the vehicle is less than a first preset distance, establish a communication connection between the wearable device and the vehicle; When the distance between the wearable device and the vehicle is less than a second preset distance, obtaining the real-time hand movement trajectory of the user; Compare the real-time hand motion trajectory with a preset benchmark hand motion trajectory dataset; When the real-time hand motion trajectory matches the reference hand motion trajectory dataset, the door is unlocked; When the real-time hand motion trajectory does not match the reference hand motion trajectory dataset, the user is reminded that the unlocking fails; The baseline hand motion trajectory dataset is corrected according to the real-time hand motion trajectory.

2. The method according to claim 1, It is characterized in that The first preset distance is greater than the second preset distance.

3. The method according to claim 1, It is characterized in that The obtaining of the distance between the wearable device and the vehicle, when the distance between the wearable device and the vehicle is less than a first preset distance, before establishing a communication connection between the wearable device and the vehicle, further includes: Create a digital key in the wearable device; Pair and authenticate the digital key with the vehicle; Check the validity of the digital key, and if the digital key is valid, connect the digital key to the vehicle; The digital key that is successfully connected to the vehicle is stored.

4. The method according to claim 1, It is characterized in that The preset reference hand motion trajectory data set comprises the following steps: Obtain a preset number of three-axis angular velocity sequence data sets; Perform data preprocessing on the three-axis angular velocity sequence data set; The two sets of three-axis angular velocity sequence data with the highest similarity in the three-axis angular velocity sequence data set are used as the benchmark hand motion trajectory data set.

5. The method according to claim 1, It is characterized in that The benchmark hand motion trajectory dataset includes: A combination of one or more of the trajectory of the hand and wrist when the user raises his hand, the trajectory of the hand and wrist when the user holds the door handle, and the trajectory of the hand and wrist when pulling toward the inside of the body.

6. The method according to claim 4, It is characterized in that The correcting of the reference hand motion trajectory data set according to the real-time hand motion trajectory comprises the following steps: The similarity between two sets of three-axis angular velocity sequence data of the benchmark hand motion trajectory dataset is used as the benchmark similarity; When the similarity between the real-time hand motion trajectory and any three-axis angular velocity sequence data in the benchmark hand motion trajectory dataset is greater than the benchmark similarity, the benchmark similarity is updated, and the benchmark hand motion trajectory dataset is updated to two sets of three-axis angular velocity sequence data corresponding to the benchmark similarity.

7. The method according to claim 1, It is characterized in that The method further comprises: When it is detected that the user is in the car, the hand movement trajectory of the user in the car is obtained; When the hand motion trajectory in the vehicle matches the preset hand motion trajectory data set for starting the vehicle, obtaining the brake pedal state of the vehicle; When the brake pedal is in the braking state, start the vehicle.

8. The method according to claim 1, It is characterized in that The method further comprises: Collect user feedback on the matching results, establish an adaptive learning mechanism based on the feedback results, and optimize the benchmark hand motion trajectory data set.

9. A non-sensing door unlocking system based on wearable devices. It is characterized in that include: Detection module, used to obtain the distance between the wearable device and the vehicle; A communication module, used to establish a communication connection between the wearable device and the vehicle when the distance between the wearable device and the vehicle is less than a first preset distance; A motion trajectory acquisition module, used to acquire the real-time hand motion trajectory of the user when the distance between the wearable device and the vehicle is less than a second preset distance; The data processing module is used to unlock the car door when the real-time hand motion trajectory matches the baseline hand motion trajectory dataset; when the real-time hand motion trajectory does not match the baseline hand motion trajectory dataset, it reminds the user that the unlocking fails and corrects the baseline hand motion trajectory dataset according to the real-time hand motion trajectory.

10. A vehicle, It is characterized in that include: An electronic device for implementing the steps of the method according to any one of claims 1 to 8; A processor, wherein the processor runs a program, and when the program runs, the data output from the electronic device executes the steps of the method according to any one of claims 1 to 8; A storage medium for storing a program, wherein when the program is run, the program executes the steps of the method according to any one of claims 1 to 8 for data output from an electronic device.

Images