A method and a system for controlling a vehicle based on user's intent

Abstract

The present invention discloses a method for controlling a vehicle (202) by a user equipment (100). The user equipment (100) is associated with the user and receives a plurality of wireless signals from the vehicle (202) when the user equipment is within a predetermined distance from the vehicle (202). Further, a received signal strength indicator (RSSI) value is measured for the plurality of the wireless signals over a time period. The RSSI values are pre-processed by filtering and normalizing the RSSI values. The preprocessed RSSI values are provide to a learning module (108) to determine an intent of a user to approach the vehicle (202). Based on the determined intent an indication is transmitted to the vehicle (202) to unlock the vehicle (202).

Description

A METHOD AND A SYSTEM FOR CONTROLLING A VEHICLE BASED ON USER'S INTENTFIELD OF INVENTION

[0001] The present disclosure generally relates to vehicles, and more particularly to controlling the operation of a vehicle.BACKGROUND

[0001] Keyless entry systems are common in the automotive industry. In such keyless entry systems, a keyfob is generally used to open the doors and trunk of a vehicle. This mechanism uses a wireless communication protocol to send signals to lock or unlock a vehicle. Further, nowadays, a smartphone may also be used to automatically lock or unlock a vehicle. However, in such systems, the vehicle is automatically locked or unlocked based on a distance of the smartphone from the vehicle. Such locking and unlocking of the vehicle may not be secure as a user of the vehicle may not intend to lock / unlock the vehicle even if the user is within a certain distance from the vehicle.SUMMARY OF THE INVENTION

[0002] The following presents a simplified summary of the subject matter in order to provide a basic understanding of some of the aspects of subject matter embodiments. This summary is not an extensive overview of the subject matter. It is not intended to identify key / critical elements of the embodiments or to delineate the scope of the subject matter. Its sole purpose is to present some concepts of the subject matter in a simplified form as a prelude to the more detailed description that is presented later.The implementation of the present disclosure provides a system and method for controlling a vehicle. The method for controlling the vehicle is performed by a user equipment associated with a user. The method for controlling a vehicle comprises receiving a plurality of wireless signals from the vehicle when the user equipment is within a predetermined distance from the vehicle. In an example, the wireless signals may be at least one of Bluetooth low energy (BLE) signals, infrared signals, or Wi-Fi signals transmitted by the vehicle. Thereafter, a relative signal strengthindicator (RSSI) value for the plurality of the wireless signals is detected over a period of time. The RSSI values are pre-processed by filtering and normalizing the RSSI values. The pre-processed and normalized values are provided to a learning module to determine an intent of a user to approach the vehicle. Based on the determined intent, an indication is transmitted to the vehicle to unlock the vehicle.The details of one or more examples are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent from the description, drawings, and claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0003] The foregoing and further objects, features, and advantages of the present subject matter will become apparent from the following description of exemplary embodiments with reference to the accompanying drawings, wherein like numerals are used to represent like elements.

[0004] It is to be noted, however, that the appended drawings illustrate only typical embodiments of the present subject matter, and are, therefore, not to be considered for limiting of its scope, for the subject matter may admit to other equally effective embodiments.

[0005] For a better understanding of the present disclosure, reference is made to the following description of an exemplary embodiment thereof, considered in conjunction with the accompanying drawings, in which:

[0006] FIG. 1 illustrates a user equipment to operate a vehicle in an example implementation of the present subject matter.

[0007] FIG. 2 illustrates a user equipment to operate a vehicle in another example implementation of the present subject matter.

[0008] FIG. 3 illustrates a method controlling a vehicle in an example implementation of the present subject matter.

[0009] FIG. 4 illustrates a method controlling a vehicle in another example implementation of the present subject matter.

[0010] Although specific features of various embodiments may be shown in some drawings and not in others, this is for convenience only. Any feature of any drawingmay be referenced and / or claimed in combination with any feature of any other drawing.DETAILED DESCRIPTION

[0011] The embodiments of the present subject matter are described in detail with reference to the accompanying drawings. However, the present subject matter is not limited to these embodiments which are only provided to explain more clearly the present subject matter to the ordinarily skilled in the art of the present disclosure. In the accompanying drawings, like reference numerals are used to indicate like components.

[0012] This invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The invention is capable of other embodiments and of being practiced or of being carried out in various ways. Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” or “having,” “containing,” “involving,” and variations thereof herein, is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.

[0013] Various aspects of the proposed system and method are described fully hereinafter with reference to the accompanying drawings. The same reference numbers in different drawings may identify the same or similar elements. The teachings disclosed may, however, be embodied in many different models with variations and should not be construed as limited to any specific structure or function presented throughout this disclosure. Rather, these aspects are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be understood that any aspect disclosed herein may be embodied by one or more elements of a claim, and also that the following detailed description does not limit the claims.

[0014] Also, all logical units described and depicted in the figures include the software and / or hardware components required for the unit to function. Further, each unit may comprise within itself one or more components which are implicitlyunderstood. These components may be operatively coupled to each other and be configured to communicate with each other to perform the function of the said unit.

[0015] Figure 1 illustrates a user equipment 100 in accordance with an example implementation of the present subject matter. The user equipment 100 may be a mobile device, a smartphone, a tablet, a computer etc.

[0016] The user equipment 100 comprises a receiver 102 to receive a plurality of wireless signals from a vehicle (not shown) when the user equipment 100 is within a predetermined distance of the vehicle. The user equipment may be associated with a user, and when the user is carrying the user equipment near the vehicle and is within a predetermined distance from the vehicle, the user equipment 100 may start receiving wireless signals transmitted by the vehicle. The wireless signals are transmitted by the vehicle continuously and the user equipment 100 continuously receives a stream of wireless signals from the vehicle. In an example, the wireless signals may be at least one of low energy Bluetooth signals (BLE) signals, infrared signals, near field communication signals, Wi-Fi signals transmitted by the vehicle. The vehicle may comprise a wireless module to generate and transmit the wireless signals.

[0017] A signal analyzer 104 of the user equipment 100 measures a received signal strength indicator (RSSI) values for the plurality of wireless signals received by the receiver 102. The received signal strength indicator (RSSI) is a measurement of the strength of the received wireless signals.

[0018] A pre-processor 106 pre-processes the RSSI values detected by the signal analyzer. The RSSI values are generally full of fluctuations and irregularities. The fluctuations and irregularities may be caused due to a multitude of reasons, such as obstructions in the path of the user equipment, humidity, quality of the wireless module of the vehicle etc. A Kalman filter is applied to the RSSI values to remove the fluctuations and irregularities in the RSSI values caused by noise. Kalman filtering the RSSI values provides a sequential, unbiased, and minimum error variance estimate of the RSSI values. After Kalman filtering the RSSI values, the rolling window average of the RSSI values is determined.

[0019] Further, normalization of the RS SI is also done to eliminate the differences in the RSSI values due to different types of user equipment being used by different users. In an example, the type and model of the user equipment used will vary from user to user. In an example, the user of the vehicle may use a user equipment of a manufacturer ‘A’ and another user of another vehicle may use a user equipment of a different manufacturer, say manufacturer ‘B’. Thus, there may be differences in the RSSI values detected by each user equipment even when such user equipments are at a similar distance from the vehicle. To remove such inconsistencies in the RSSI values, the RSSI values are normalized by the pre-processor 106. To normalize the RSSI values, a constant value, known as normalizing factor, is calculated, and is added to the RSSI values, scaling them to a generic range of values. These generic values can be used by a learning module 108, and thus, the inconsistencies in the RSSI values due to the differences in the user equipment is accounted for. The process of determining the normalization factor is described in detail with reference to Figure 2.

[0020] The pre-processed RSSI values, i.e., the filtered and normalized RSSI values are provided to the learning module 108. The learning module 108 analyzes the RSSI values to determine an intent of the user associated with the user equipment 100. In an example, the intent may be at least one of moving towards the vehicle, moving away from the vehicle, moving past the vehicle, moving around the vehicle etc. Based on the determined intent of the user, a transmitting module 110 transmits an indication to the vehicle to at least one of lock or unlock the vehicle. In an example, when it is determined that the intent of the user is to approach the vehicle, the indication for unlocking the vehicle may be sent. In another example, if the intent of the user is determined to be moving away from the vehicle, the indication may be sent to unlock the vehicle.

[0021] In the present subject matter only one type of wireless signal is used for determining the intent of the user. Thus, the user equipment 100 has to only process one type of wireless signal to determine the intent of the user, which improves the battery consumption of the user equipment 100.Training the learning module 108

[0022] Like any learning module, the learning module 108 needs to be trained for determining the intent of the user. For training the learning module 108, the user equipment 100 was carried around the vehicle in different walking styles. In an example, the learning module 108 was trained for various types of walks for approaching the vehicle, moving away from the vehicle, walking past the vehicle, walking around the vehicle etc. Further, the fluctuations of the RSSI values based on the user keeping the user equipment 100 in the user’s hand or in user’s front and back pocket was also taken into consideration for training the learning module 108. For example, when the user has kept the user equipment 100 in his front pocket, the RSSI values are shifted to account for the decrease in RSSI values due to fabric of the pockets.

[0023] To train the learning module 108, the wireless signals were captured from the vehicle for different types of walks and the RSSI values were detected from the received signals. In an example, 10 RSSI values were detected every second for the training. The RSSI values were then pre-processed and provided to the learning module 108. The learning module 108 thereafter learned to determine the intent of the user (carrying the user equipment) based on the RSSI values. To learn to determine the intent of the user, the learning module 108 may analyze the RSSI value curves. An example RSSI curve is shown below:In the above, the RSSI values are shown on the vertical axis and the time (in seconds) is shown on the horizontal axis. In the above case, a user carrying the user equipment 100 walked towards the vehicle. In this walk, the RSSI value mostly had a constant positive slope. The learning module 108 learned that this type of walk was a ‘move towards the vehicle’ type of walk, and hence determined the intent of the user as ‘moving towards the vehicle’. Further, the learning module 108 learned various iterations of this walk till the learning module 108 was correctly able to identify the intent of the user in a practical use case scenario. Similarly, the learning module 108 was trained for other types of walks, such as moving away from the vehicle, moving around the vehicle etc. Based on such training, the learning module 108 was able to correctly identify the intent of the user for various use case scenarios. In an example, a convolutional neural network (CNN) may be employed by the learning module 108 to process the training data to determine the intent of the user.

[0024] Figure 2 shows a system 200 in accordance with an example implementation of the present subject matter. The system 200 comprises a vehicle 202 and the user equipment 100. In an example, the vehicle 202 may be a two-wheeler, a four wheeler, or a multi-axle vehicle.

[0025] When a user intends to use the user equipment 100 for the first time to control the vehicle 202, the user needs to perform a set of single-time operations. One such operation is pairing of the user equipment 100 with the vehicle 202. The user equipment 100 is paired with the vehicle 202 using a wireless communication protocol, such as low energy Bluetooth (BLE) protocol, near field communication protocol etc. In an example, the user equipment 100 may need to download an application as specified by the manufacturer of the vehicle 202 to carry out the pairing operation. To pair the user equipment 100 with vehicle 202, the user may open the downloaded application on the user equipment 100 and select pairing. The pairing of the user equipment and the vehicle 202 is then done based on any of the specified wireless protocol.

[0026] After the pairing and before the user equipment 100 may be used to operate or control the vehicle 202 for the first time, calibration needs to be performed fordetermining the normalizing factor to be used for normalizing the RSSI values. For performing the calibration, the user of the user equipment 100 has to stand (along with the user equipment 100) at different specified positions from the vehicle 202 and capture the wireless signal being transmitted from the vehicle 202. In an example, the user may have to stand at four predefined positions near the vehicle 202 to capture the wireless signals transmitted by the vehicle 202. In an example, the application downloaded for pairing the vehicle 202 may specify or indicate the locations at which the user needs to stand for calibration. The strength of the wireless signals received from the vehicle 202 at different locations is determined, and thereafter the minimum value of these signals is considered as the normalizing factor for normalizing the RSSI values. To further refine the normalizing factor, the calibration may be performed at different locations, such as open parking and underground parking so that the factors, such as humidity, are also accounted for when normalizing the RSSI values. Also, normalization may be performed for scenarios, such as user carrying the user equipment 100 in user’s hand, user carrying the user equipment in the user’s front pocket, or the user carrying the user equipment 100 in user’s back pocket.

[0027] After the calibration has been done, the user can now use the user equipment 100 to operate the vehicle 202 for automatically locking and unlocking the vehicle. In an operation, consider an example when a user along with the paired user equipment 100 is walking away from the vehicle 202. The receiver 102 of the user equipment 100 receives the plurality of wireless signals from the vehicle 202 while walking away from the vehicle 202. In an example, the vehicle 202 keeps emitting the wireless signals and when the user equipment 100 is within a certain range of the vehicle 202, a communication channel is established between the user equipment.

[0028] The wireless signals are received by the user equipment 100 on the established communication channel. The signal analyzer 104, thereafter, detects the RSSI values for the received wireless signals. In an example, 10 RSSI values of wireless signals per second are considered for performing the invention in accordance with an example implementation of the present subject matter. Thecaptured RSSI values are thereafter pre-processed by the pre-processor 106. As discussed above, pre-processing involves Kalman filtering and normalizing the RSSI values. In an example, the pre-processor 106 may also determine and construct a RSSI curve based on the RSSI values.

[0029] The pre-preprocessed RSSI values are thereafter provided to the learning module 108. To determine the intent of the user, the learning module 108 may analyze the RSSI curve. An example RSSI curve for a scenario when the user is walking away is depicted below:The RSSI values of the signals are depicted on the vertical axis and the time (in seconds) is depicted on the horizontal axis.To determine the intent of the user, based on the RSSI values, a plurality of slopes and intercepts of the RSSI curve may be considered. In an example, the median slope, median intercept, max slope, and max intercept may be determined for the RSSI values. Based on the intercept and slope values of the RSSI curve, the learning module 108 may determine the intent of the user. In an example implementation, the RSSI curve and the plurality of slopes and intercepts are calculated by the preprocessor 106 and provided to the learning module 108.

[0030] The learning module 108 analyzes the slope and intercept of the RSSI curve to determine the intent of the user. For example, based on the above curve, thelearning module 108 determines that the slope of the curve is negative (RSSI values decrease with time) and thus, the slope of the graph matches with the slope for ‘moving away’ scenarios. Thus, the learning module 108 may determine that the user’s intent is moving away from the vehicle.

[0031] Further, the learning module 108 may also generate a confidence score for the determined intent. In the above example, the confidence score may be generated based on how accurately the RSSI curve depicts that the user is moving away. In an example, the learning module 108 may generate the confidence score for a predefined number of times. The frequency of calculation of the confidence score may vary based on the value of the confidence score. In an example, the confidence score may be generated once every 0.6 seconds when the confidence score is below a certain threshold. Once the confidence score reaches the certain threshold, the confidence score may be generated for every 0.2 seconds, further improving accuracy of the determined intent.

[0032] If it is determined that the confidence score of the intent is more than a threshold for a predetermined number of times, the learning module 108 may instruct the transmitting module 110 to send an indication to the vehicle 202 to operate the vehicle. In response, the transmitting module 110 may transmit the indication to the vehicle to operate the vehicle. For example, in the above example, when the determined intent of the user is ‘moving away’ from the vehicle, the transmitting module may send an indication to lock the vehicle.

[0033] In an example, the vehicle 202 may comprise a receiving module to receive the indications transmitted from the user equipment 100 and a control module to control the operation of the vehicle based on the received indication.

[0034] Consider another example when a user of the vehicle 202 moves past the vehicle 202 along with the user equipment 100. Similar to the example as discussed above, the learning module 108 may analyze the RSSI graph for the user moving past the vehicle. An example RSSI curve for such a scenario is provided below:

[0035] The RSSI values of the signals are depicted on the vertical axis and the time (in seconds) is depicted on the horizontal axis. In an example, to determine the user’s intent based on the above graph, learning module 108 determines that between point A and point B, the slope of the graph indicates increasing RSSI values with time (positive slope), i.e., the user is moving towards the vehicle. At point B, if the RSSI value is more than a predefined threshold, the learning module 108 starts calculating the confidence scores for the intent of the user moving towards the vehicle. The learning module 108 determines that the confidence score does not increase with time as the slope of the graph changes after point B and becomes negative, which means that the user has started moving away from the vehicle from point B. Accordingly, the confidence score for the user ‘moving towards the vehicle 202 decreases with time after point B. Thus, the learning module 108 determines that the user has walked past the vehicle and does not intend to unlock the vehicle. Accordingly, the learning module 108 does not send the instruction to the vehicle 202 to unlock the vehicle 202.

[0036] Similarly, in cases when the user moves around the vehicle, the learning module 108 may determine that there is no intent of the user to approach the vehicle and hence, no instruction is provided to the transmitting module 110 to send an indication for unlocking of the vehicle 202. Thus, the vehicle 202 is maintained in the locked state. An example, RSSI curve for moving around scenario (when theuser does not approach the vehicle but moves around the vehicle) is illustrated below:

[0037] The RSSI values of the signals are depicted on the vertical axis and the time (in seconds) is depicted on the horizontal axis. The learning module 108 determines the intent of the user based on analyzing the slope values of the above graph. As seen from the above graph, the moving around scenario, the slopes of RSSI values have more than one spike and have alternating positive and negative slopes. The learning module 108 determines that between point A and B in the above graph, the values of slope is negative, and the user is moving away from the vehicle. However, after point B, the slope changes and the learning module 108 now determines that the user is moving towards vehicle till point C. However, as discussed above, the learning module 108 determines a confidence score for a predetermined number of times before positively classifying the intent of the user to be ‘moving towards’ the vehicle 202. Thus, even though the RSSI values graph between point B and C shows user moving towards the vehicle, the learning module 108 does not simply classify this action has ‘moving towards’ intent. Rather, the learning module 108 calculates a plurality of confidence values to determine the intent. As seen from the graph above, the value of the slope is approximately constant between point C and D, which results in a constant confidence score between the points C and D. Thus, the learning module 108 does not classify the movement between point C to D as ‘moving towards’ since the confidence score does not increase with time. Thereafter, after point D, the RSSI graph again have negative scope (moving away)between point D to E, a positive slope (moving towards) between pint E to F, a constant slope between F to G, and a negative slope between point G to H (moving away). Thus, the leaning module 108 determines that the user is moving around the vehicle 202 and does not intend to approach the vehicle 202 to unlock it. Based on the determination that the user is moving around the vehicle 202, the learning module 108 does not send an instruction to unlock the vehicle and the vehicle is maintained in locked state.

[0038] Thus, the learning module 108 does not simply determine that the user is moving towards the vehicle and sends an instruction for unlocking the vehicle. Rather, the learning module 108 analyzes the changing slopes and determines the plurality of confidence values to correctly determine the intent of the user. The learning module 108, thereafter, sends the instruction for unlocking the vehicle only when it is positively determined that the user is moving towards the vehicle to unlock it and is not just moving past the vehicle or moving around the vehicle.

[0039] To further enhance security of the vehicle, the RSSI values are discarded in certain situations. An example of such a situation is when the user is determined to be located more than a certain distance from the vehicle 202. In such a situation, the RSSI values are discarded. For example, when the user is more than 20 feet away from the vehicle, the RSSI values are discarded, and no operation is performed on the vehicle.

[0040] Further, in some situations, the learning module 108 may not instruct to unlock the vehicle even when the confidence score of the intent is higher than a threshold value. For example, the user may be determined to be stationary based on the inputs of sensors of the user equipment 100. In an example, the user may be determined to be stationary based on inputs of the accelerometer signals of the user equipment. In such cases, the learning module 108 may not instruct the transmitting module to send an indication for unlocking the vehicle.

[0041] Thus, not unlocking the vehicle in such scenarios ensures security of the vehicle as the vehicle will not be unlocked even when the user is standing very near to the vehicle and does not intend to use the vehicle.

[0042] Figure 3 shows a method 300 for controlling a vehicle in an example implementation of the present subject matter. It should be understood that the method steps are shown as a reference only and the sequence of the method steps should not be construed as limiting. The method steps can include any additional steps in any order. Although the method 300 may be implemented in any user equipment, the example method 300 is provided in reference to the user equipment 100 for ease of understanding.

[0043] At block 302, a plurality of wireless signals are received from a vehicle when a user equipment is within a predetermined distance from the vehicle. The plurality of wireless signals are received by the user equipment, such as the user equipment 100. In an example, the user equipment may be a mobile phone, a smartphone, a tablet computer etc. The plurality of wireless signals may be at least one of low energy Bluetooth signals (BLE) signals, infrared signals, near field communication signals, Wi-Fi signals transmitted by the vehicle.

[0044] At block 304, received signal strength indicator (RSSI) values are detected for the plurality of wireless signals over a time period. The received signal strength indicator (RSSI) is a measurement of strength of the received wireless signals.

[0045] Further, at block 306, the RSSI values are pre-processed by filtering and normalizing the RSSI values. Pre-processing is done to remove fluctuations and irregularities in the RSSI values. For pre-processing the RSSI values, a Kalman filter is applied to remove the spikes in the RSSI values. After, Kalman filtering the RSSI values, the rolling window average of the RSSI values is determined. Further, normalization of the RSSI is also done to eliminate the differences in the RSSI values due to different types of user equipment being used by different users.

[0046] At block 308, the preprocessed RSSI values are provided to a learning module to determine an intent of a user to approach the vehicle. At block 310, an indication is transmitted to the vehicle to unlock the vehicle based on the determined intent.

[0047] Figure 4 shows a method 400 for controlling a vehicle in another example implementation of the present subject matter. It should be understood that the method steps are shown as a reference only and the sequence of the method stepsshould not be construed as limiting. The method steps can include any additional steps in any order. Although, the method 400 may be implemented in any user equipment, the example method 400 is provided in reference to the user equipment 100 for ease of understanding.

[0048] At block 402, a plurality of wireless signals are received from a vehicle when a user equipment is within a predetermined distance from the vehicle. In an example, the predetermined distance may be the user equipment 100 being less than 20 feet from vehicle. According to the present subject matter, the wireless signals are received by the user equipment that is associated with the user.

[0049] At block 404, a received signal strength indicator (RSSI) value for the plurality of the wireless signals is detected for a time period. In an example, 10 values of RSSI may be detected every second.

[0050] At block 406, the RSSI values are pre-processed by filtering and normalizing the RSSI values. Filtering and normalization are done to remove irregularities and fluctuations in the RSSI values.

[0051] At block 408, a RSSI curve is created based on the pre-processed RSSI values. The RSSI curves show the variations of RSSI values with respect to time.

[0052] At block 410, a plurality of slopes and a plurality of intercepts of the RSSI curve are determined. In an example, a median slope, median intercept, max slope, and max intercept may be calculated for RSSI values.

[0053] At block 412, a learning module determines an intent of a user based on the plurality of slopes and intercepts of the RSSI curve. In an example, the intent of the user may be at least one of approaching the vehicle, moving away from the vehicle, moving past the vehicle, moving around the vehicle etc.

[0054] At block 414, a confidence score of the intent is determined. The confidence score may indicate a probability of occurrence of the determined intent. In an example, the confidence score is calculated for a pre-determined number of times.

[0055] At block 416, an indication is transmitted to the vehicle (is there a specific module here?) to operate the vehicle based on the confidence score of the determined intent being more than a threshold for a predetermined number of times. In an example, when the determined intent is approaching the vehicle, the indicationis sent to unlock the vehicle. In an example, when the determined intent is moving away from the vehicle, the indication is sent to lock the vehicle. In an example, when the determined intent is moving past the vehicle, the vehicle is maintained in a locked state. In another example, if the determined intent is moving around the vehicle, the vehicle is maintained in the locked state.

[0056] Further, in an example implementation, if the user is determined to be stationary for a predetermined period of time based on inputs of one or more sensors of the user equipment, an indication may be sent to maintain the locked state of the vehicle even if the confidence score of the intent is more than the threshold. In an example, the one or more sensors may be an accelerometer, a gyroscope etc.

[0057] Similarly, in another example implementation, if it is determined that a distance of the user equipment from the vehicle is outside a defined range, an indication may be sent to the vehicle to maintain the locked state of the vehicle.

[0058] Although the present invention has been explained in relation to its preferred embodiment, it is to be understood that many other possible modifications and variations can be made without departing from the spirit and scope of the invention as herein described.

[0034] A person skilled in the art may be clearly aware that the descriptions of the embodiments provided in this application may be referred to each other. For convenience and conciseness, for example, for functions of the devices provided in the embodiments of this application and the performed steps, refer to related descriptions of the method embodiments of this application. The method embodiments and the device embodiments may also be referred to each other or combined with each other.

[0035] In the several embodiments provided in this application, the disclosed system, device, and method may be implemented in another manner. For example, some features of the method embodiments described above may be ignored or not performed. The described device embodiments are merely examples.

[0036] The term based on is not exclusive and allows for being based on additional factors not described unless the context clearly dictates otherwise. In addition,throughout the specification, the meaning of “a”, “an” and “the” include plural references. The meaning of “in” includes “in” and “on”

[0037] As used herein the terms “and” and “or” may be used interchangeably to refer to a set of items in both the conjunctive and disjunctive in order to encompass the full description of combinations and alternatives of the items. In either case, the set is to be interpreted as meaning each of the items singularly as alternatives, as well as any combination of the listed items.

[0038] The description above is merely illustrating the technical scope of the present disclosure, and various changes and modifications may be made by those skilled in the art without departing from the essential characteristics of the present disclosure. Therefore, the embodiments of the present disclosure described above may be implemented separately or in combination with each other.

[0039] The embodiments disclosed in the present disclosure are intended to illustrate rather than limit the scope of the present disclosure, and the scope of the technical spirit of the present disclosure is not limited by these embodiments. The scope of the present disclosure should be construed by the claims below, and all technical spirits within a range equivalent to claims should be construed as being included in the right scope of the present disclosure.

[0040] While only certain features have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the disclosure.

Claims

We claim:

1. A method for controlling a vehicle (202) to be performed by a user equipment (100), the user equipment (100) being associated with the user, the method comprising: receiving (302) a plurality of wireless signals from the vehicle (202) when the user equipment (100) is within a predetermined distance from the vehicle (202); detecting (304) a received signal strength indicator (RSSI) value for the plurality of the wireless signals over a time period; pre-processing (306) the RSSI values by filtering and normalizing the RSSI values; providing (308) the preprocessed RSSI values to a learning module to determine an intent of a user to approach the vehicle (202); transmitting (310) an indication to the vehicle (202) to unlock the vehicle (202) based on the determined intent.

2. The method as claimed in claim 1, wherein the method comprises: determining an intent of the user to move away from the vehicle (202); and transmitting an indication to the vehicle (202) to lock the vehicle (202) based on the determined intent.

3. The method as claimed in claim 1, wherein the method comprises: determining an intent of the user to move past the vehicle (202); and maintaining a locked state of the vehicle (202).

4. The method as claimed in claim 1 comprising: creating a RSSI curve based on the pre-processed RSSI values; and determining a plurality of slopes and a plurality of intercepts of the RSSI curve,wherein the learning module is to determine the intent of the user based on the plurality of the slopes and the plurality of intercepts.

5. The method as claimed in claim 2 comprising: calculating a confidence score of the determined intent; and transmitting the indication based on the confidence score being more than a predefined threshold.

6. The method as claimed in claim 5, wherein the calculating the confidence score comprises: calculating the confidence score for a predetermined number of times; and determining that the confidence score for the predetermined number of times is more than the predefined threshold.

7. The method as claimed in claim 1 wherein the method further comprises: determining the user to be stationary for a predetermined period of time based on inputs of one or more sensors of the user equipment (100); and transmitting the indication to the vehicle (202) to maintain a locked state of the vehicle (202).

8. The method as claimed in claim 1, wherein the method comprises: determining a distance of the user equipment (100) from the vehicle (202); and transmitting the indication to the vehicle (202) to maintain a locked state of the vehicle (202) when the distance of the user equipment (100) from the vehicle (202) is outside a defined range.

9. A user equipment (100) to operate a vehicle (202), the user equipment (100) comprising:a receiver (102) to receive a plurality of wireless signals from a vehicle (202) when the user equipment (100) is within a predetermined distance from the vehicle (202); a signal analyzer (104) to detect a received signal strength indicator (RSSI) value for the plurality of the wireless signals; a pre-processor (106) to pre-process the RSSI values, wherein to pre-process the RSSI values the pre-processor (106) is to filter and normalize the RSSI values; a learning module (108) to: determine an intent of a user to approach the vehicle (202) based on the pre-processed RSSI values; and calculate a confidence score for the determined intent; and a transmitting module (110) to transmit an indication to the vehicle (202)to unlock the vehicle (202) based on the confidence score of the intent being more than a threshold value.

10. The user equipment (100) as claimed in claim 9, wherein the pre-processor is to: create a RSSI curve based on the pre-processed RSSI values and determine a plurality of slopes and a plurality of intercepts of the RSSI curve, wherein the learning module is to determine the intent based on the plurality of slopes and plurality of intercepts.

11. The user equipment (100) as claimed in claim 9, wherein the learning module is to: calculate the confidence score of the intent for a predetermined number of times; and determine that the confidence score for the predetermined number of times is more than the predefined threshold.

12. The user equipment (100) as claimed in claim 9 wherein the pre-processor is to apply a Kalman filter to filter the RSSI values.

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