Blind spot mitigation for vehicle passive entry systems

Abstract

This disclosure provides "blind spot mitigation for a passive entry system for a vehicle." Methods and apparatus for blind spot mitigation of a passive entry system for a vehicle are disclosed. An example vehicle includes an antenna module for measuring the signal strength broadcast from a mobile device. The example vehicle also includes a wireless module for generating a first and a second predicted value when the mobile device is in a blind spot, and enabling passive entry when the first predicted value matches the second predicted value and indicates that the mobile device is in a passive entry zone, and a sensor detects a user. The example vehicle also includes a body control module for unlocking the doors when passive entry is enabled.

Description

Technical Field

[0001] This disclosure generally relates to a passive entry and passive start system for a vehicle, and more specifically, to blind spot mitigation for a passive entry system for a vehicle. Background Technology

[0002] Vehicles are increasingly being manufactured with Passive Entry and Passive Start (PEPS) systems. In a PEPS system, the vehicle controls various functions based on the position of the driver's key fob or mobile device (e.g., when the vehicle includes a "Phone as a Key (PaaK)" system) relative to the vehicle. For example, when the mobile device is three meters away from the vehicle, the vehicle may enter, illuminating the exterior lights and adjusting the cabin to a welcome mode preferred by the driver, and when at two meters, the vehicle may activate the passive entry system based on triggering conditions from the user, such as sensing a hand touching a door handle or a foot kicking under a guardrail. Summary of the Invention

[0003] The appended claims define this application. This disclosure summarizes various aspects of the embodiments and is not intended to limit the claims. Other implementations of the technology described herein are contemplated and are intended to be within the scope of this application, as will be apparent to those skilled in the art upon review of the following drawings and details.

[0004] An example embodiment of blind spot mitigation for a passive entry system for a vehicle is disclosed. One example vehicle includes an antenna module for determining a signal strength measurement broadcast from a mobile device. The example vehicle also includes a wireless module for generating a first and a second predicted value when the mobile device is in a blind spot. When the first predicted value matches the second predicted value and indicates that the mobile device is in a passive entry zone, and a sensor detects a user, the wireless module also enables passive entry. The example vehicle further includes a body control module for unlocking the doors when passive entry is enabled.

[0005] One example method includes: using a vehicle's antenna module to determine a signal strength measurement from a broadcast from a mobile device. The example method further includes: when the mobile device enters a blind spot near the vehicle, using a wireless module including a processor and memory to generate multiple predicted values. The example method includes: when the multiple predicted values ​​match and indicate that the mobile device is in a passive entry zone, using the wireless module to enable passive entry. Additionally, the example method includes: when passive entry is enabled and sensors detect a user touching a door handle, using a body control module to unlock the door. Attached Figure Description

[0006] To better understand the invention, reference can be made to the embodiments shown in the following figures. Components in the figures are not necessarily drawn to scale, and related elements may be omitted, or in some cases the scale may have been enlarged to emphasize and clearly illustrate the novel features described herein. Furthermore, as is known in the art, system components may be arranged differently. Additionally, in the figures, the same reference numerals denote corresponding parts throughout several views.

[0007] Figure 1 The vehicle is shown operating in accordance with the teachings of this disclosure.

[0008] Figure 2 yes Figure 1 A block diagram of the electronic components of a vehicle.

[0009] Figure 3 It can be made by Figure 2 The flowchart illustrates a method implemented by electronic components to mitigate passive entry blind spots. Detailed Implementation

[0010] Although the invention may be implemented in various forms, some exemplary and non-limiting embodiments are shown in the accompanying drawings and described below. It should be understood that this disclosure should be regarded as an example of the invention and is not intended to limit the invention to the specific embodiments described.

[0011] Passive Entry and Passive Start (PEPS) systems help operators utilize various vehicle features while keeping the key within proximity to the vehicle. These features include a welcome mode, passive entry, and passive start. At a first distance from the vehicle (e.g., three meters, etc.), the PEPS system activates the welcome mode, which illuminates the vehicle's exterior lights and / or changes the settings of the interior systems (e.g., seat position, steering wheel position, radio presets, etc.) to the operator's preferences associated with the mobile device and / or key fob. At a second distance (e.g., two meters, etc.), the PEPS system prepares one or more doors to open. As used herein, "ready" means sending a signal to the door control unit to authorize unlocking the door in response to the detection of a hand on the door handle. When the door is ready, the PEPS system (e.g., via the door control unit) unlocks the door in response to detecting (e.g., via a touch sensor, infrared sensor, or camera, etc.) an operator's hand approaching the door when the authorized mobile device and / or key fob is also in the passive entry zone. When the authorized mobile device and / or key fob is inside the vehicle, the PEPS system enables passive start. If a mobile device and / or key fob is detected inside the vehicle, the anti-theft device will be disabled and the push-button ignition switch will be activated.

[0012] The PEPS system tracks the location of authorized mobile devices (e.g., smartphones, smartwatches, tablets, keychains, etc.) and activates these functions based on the mobile device's position relative to the vehicle. To locate the mobile device, the vehicle includes an antenna module and a wireless communication module. The antenna module provides wireless communication coverage for the area around the vehicle to communicate with the mobile device. The antenna module implements Personal Area Network (PAN) protocols (e.g., ...). Low power (BLE) (etc.) or Wireless Local Area Network (WLAN) protocols (including IEEE 802.11a / b / g / n / ac / p or other protocols). When an antenna module implements BLE, it is sometimes referred to as a "BLE antenna module (BLEAM)" and the wireless communication module is sometimes referred to as a "BLE module (BLEM)". The antenna module measures the signal strength values ​​of signals received from a mobile device, such as Received Signal Strength Indication (RSSI) and / or Received Strength (RX) values. RSSI and RX values ​​measure the open-path signal strength of radio frequency signals received by the antenna module from the mobile device. RSSI is measured as a percentage of signal strength, and its values ​​(e.g., 0 to 100, 0 to 137, etc.) are defined by the manufacturer of the hardware used to implement the antenna module. Generally, a higher RSSI means that the mobile device is closer to the corresponding antenna module. RX values ​​are measured in decibels per milliwatt (dBm). For example, the RX value might be -60 dBm when the mobile device is one meter (3.28 feet) away, and -66 dBm when the mobile device is two meters (6.56 feet) away. The wireless communication module uses the RSSI / RX value to determine the radial distance from the mobile device to a specific antenna module, where the RSSI value decreases with increasing distance. In some examples, the wireless communication module uses RSSI / RX values ​​from multiple antenna modules to perform triangulation or trilateration of the mobile device's position relative to the vehicle.

[0013] Due to the layout of the antenna module and / or the interaction between the antenna module and the antenna of the mobile device, the coverage provided by the antenna module can result in blind spots in certain areas around the vehicle. As used herein, a "blind spot" is an area near the vehicle that, within the theoretical range of the antenna module but due to the vehicle's geometry, environmental factors, the coverage area of ​​the antenna module, and / or the antenna geometry of the mobile device, the antenna module cannot accurately measure the signal strength of the mobile device and / or the signal from the mobile device cannot be received by the antenna module. For example, the aforementioned factors can cause the signal from the mobile device to be reflected, absorbed, and / or distorted, making it impossible for the wireless communication module to determine which antenna module the mobile device is closest to based on signal strength measurements from the antenna modules. That is, in such examples, the wireless communication module cannot rely on signal strength measurements to determine the location of the mobile device. Generally, the blind spot is within one meter of the vehicle's skin. In some examples, the blind spot covers the area around the handles of the rear doors and trunk or liftgate. Because the antenna module cannot accurately measure signal strength relative to the distance to the mobile device in the blind spot, the wireless communication module cannot track the location of the mobile device within the blind spot. Adding more antenna modules to provide coverage in blind spots increases the complexity of the vehicle's hardware and wiring, where additional mounting locations for such extra antenna modules may not be available due to packaging limitations.

[0014] As described below, the wireless communication module tracks the movement of a mobile device, and when the mobile device enters a blind zone (e.g., the mobile device appears to be moving further away after previously approaching the vehicle, or the wireless communication module cannot determine which of the antenna modules the mobile device is closest to, etc.), the wireless communication module estimates the current position of the mobile device based on the mobile device's past position and / or past signal strength values ​​measured by the antenna modules. When the wireless communication module determines that the mobile device is within the blind zone, it generates two or more predictions (sometimes referred to as "predicted values") of the mobile device's position. In some examples, the wireless communication module uses a variety of different techniques to generate predictions of the mobile device's position. For example, the wireless communication module may use one or more of the following: linear quadratic equations, Bayesian networks, Kalman filters, perceptrons, double exponential smoothing algorithms, and / or Markov chains. When multiple predicted values ​​match, the wireless communication module presents this information to the body control module (BCM). In some examples, the multiple predicted values ​​match when the predicted values ​​indicate that the mobile device is in a passive entry zone. In some such examples, the multiple predicted values ​​match when the predicted value indicates that the mobile device is within the passive entry zone and within a threshold distance of the door handle (e.g., 0.33 meters, etc.). Alternatively, in some examples, to reduce processing, the wireless communication module uses a single predicted value to determine when a mobile device in one of the blind spots is in the passive entry zone.

[0015] In response to an instruction received from the wireless communication module and the sensing of an operator (e.g., a hand touching a door handle or a foot kicking under a guardrail), the body control module enables the unlocking of (multiple) doors. In some examples, the body control module enables door unlocking to last for a threshold time period (e.g., three seconds, five seconds, ten seconds, etc.). In some examples, the body control module only enables the unlocking of certain doors. In some such examples, the body control module only enables the unlocking of doors associated with blind spots (e.g., rear doors, liftgates, etc.). In some such examples, when the location of the moving device is determined (e.g., using trilateration, etc.), the body control module enables the unlocking of doors associated with the side of the vehicle indicated by the trajectory of the moving device. For example, if the past location of the moving device indicates that its trajectory leads to the driver's side of the vehicle, then the body control module only enables the unlocking of the driver's side door. In some examples, when the moving device leaves the blind spot, allowing (multiple) antenna modules to accurately measure the signal from the moving device, the wireless communication module acts based on the signal strength of the moving device, regardless of the decisions made when the moving device was in the blind spot.

[0016] Figure 1 A vehicle 100 operating in accordance with the teachings of this disclosure is shown. Vehicle 100 may be a standard gasoline-powered vehicle, a hybrid vehicle, an electric vehicle, a fuel cell vehicle, and / or any other type of mobility tool. Vehicle 100 includes mobility-related components, such as a powertrain having an engine, transmission, suspension, drive axle, and / or wheels. Vehicle 100 may be non-autonomous, semi-autonomous (e.g., some conventional motion functions are controlled by vehicle 100), or autonomous (e.g., motion functions are controlled by vehicle 100 without direct driver input). In the illustrated example, vehicle 100 includes a body control module (BCM) 102, an antenna module 104, and a wireless communication module (WM) 106.

[0017] The body control module 102 controls various subsystems of the vehicle 100. For example, the body control module 102 can control anti-theft systems and / or power mirrors, etc. The body control module 102 is electrically connected to circuits that, for example, drive relays (e.g., to control windshield washer fluid, etc.), drive brushed DC motors (e.g., to control power seats, power locks, power windows, windshield wipers, etc.), drive stepper motors, and / or drive LEDs, etc. Specifically, the body control module 102 controls door control units, which control the electronic locks and power windows of the vehicle 100. In addition, the body control module 102 controls the exterior lights of the vehicle 100. When an authorized mobile device 108 is within the welcome area (e.g., within three meters of the vehicle 100), the body control module 102 anticipates the operator 110 entering the vehicle 100 and controls the activation of various subsystems of the vehicle 100. For example, the body control module 102 can activate the exterior lights and change the position of the driver's seat in the vehicle 100. When the mobile device 108 is within the passive entry zone (e.g., within two meters of vehicle 100), the body control module 102 prepares one or more doors 112a and 112b and / or liftgate doors 114 of vehicle 100 to unlock when sensors (e.g., capacitive hold sensors, infrared sensors, cameras, etc.) detect that the operator 110 touches the corresponding door / liftgate handle. In some examples, the body control module 102 prepares only certain doors 112a, 112b and / or liftgate doors 114. In some such examples, which doors 112a, 112b and 114 are prepared by the body control module 102 is based on the trajectory of the mobile device 108. For example, if the mobile device 108 approaches vehicle 100 from the driver's side, the body control module 102 may prepare only the driver's side doors 112a and 112b. In some examples, when the mobile device 108 is within the blind spot 116 and the intended location of the mobile device 108 is near one of the doors 112a and 112b and / or the liftgate 114, the body control module 102 prepares only the door 112b and / or the liftgate 114 that is near the intended location. For example, when the wireless communication module 106 (discussed below) estimates that the mobile device 108 is in the area around the liftgate 114 and simultaneously in the corresponding blind spot 116, the body control module 102 may prepare only the liftgate 114 to unlock.

[0018] Antenna module 104 includes hardware (e.g., processor, memory, storage device, antenna, etc.) and software for controlling (multiple) wireless network interfaces. Antenna module 104 includes components for personal or local area networks (e.g., Low power (BLE) The communication controller (etc.). In some examples, when antenna module 104 is configured to implement BLE, antenna module 104 may be referred to as a "BLE antenna module (BLEAM)". Antenna module 104 is communicatively coupled to mobile device 108 and measures and / or receives measurements of the signal strength broadcast by mobile device 108. In some examples, vehicle 100 includes one or more internal antenna modules (not shown) located within the vehicle compartment of vehicle 100 to help determine when mobile device 108 is within the vehicle compartment of vehicle 100 (e.g., to enable passive start function of vehicle 100).

[0019] In the illustrated example, antenna module 104 receives signals from mobile device 108 within coverage area 118. As used herein, coverage area 118 encompasses the location where antenna module 104 can accurately measure signals from mobile device 108 (e.g., signals that are substantially undistorted, unabsorbed, and / or reflected). The shape of coverage area 118 is defined by the characteristics of the antenna, the mounting location of antenna module 104 on vehicle 100, and the geometry of conductive portions of vehicle 100. In the illustrated example, the area around vehicle 100 not covered by one or more of coverage areas 118 is blind zone 116, where antenna module 104 cannot accurately measure broadcasts from mobile device 108. The area covered by blind zone 116 is also affected by the antenna of mobile device 108. In the illustrated example, the handles of rear door 112b and liftgate 114 are within blind zone 116.

[0020] Wireless communication module 106 is communicatively coupled to antenna module 104 to track the position of mobile device 108 relative to vehicle 100. In some examples, wireless communication module 106 may be referred to as a "BLE module (BLEM)" when antenna module 104 is configured to implement BLE. Wireless communication module 106 receives and analyzes signal strength measurements between antenna module 104 and mobile device 108; based on these measurements, wireless communication module 106: (a) determines whether mobile device 108 is within range of vehicle 100, (b) if within range, determines the position of mobile device 108 relative to vehicle 100, (c) determines whether mobile device 108 is within one of blind zones 116, and (d) if within one of blind zones 116, determines the estimated position of mobile device 108. In the illustrated example, wireless communication module 106 includes blind zone manager 120.

[0021] The blind spot manager 120 analyzes signal strength measurements received by the antenna module 104 from the mobile device 108. Periodically (e.g., every 500 milliseconds, every second, etc.), the blind spot manager 120 stores samples of the signal strength measurements in memory (e.g., stored below). Figure 2(In memory 206). In some examples, the blind spot manager 120 collects signal strength measurements starting when the moving device 108 is within range and ending when one of the doors 112a and 112b and / or the liftgate door 114 is opened. When the moving device 108 is in one or more of the coverage areas 118, the blind spot manager 120 calculates the distance between the moving device 108 and the vehicle 100. In some examples, the blind spot manager 120 estimates the radial distance when the moving device 108 is detected by one of the antenna modules 104. In some examples, when the signal from the moving device is detected by multiple antenna modules 104, the blind spot manager 120 uses triangulation or trilateration to determine the position of the moving device 108. Using the position of the moving device 108, the blind spot manager 120 determines when the moving device 108 is within the region of interest (e.g., welcome zone, passive start zone, etc.) and sends a message indicating this to the body control module 102.

[0022] Blind spot manager 120 determines when mobile device 108 enters one of the blind spots 116. In some examples, blind spot manager 120 determines that mobile device 108 has entered one of the blind spots 116 when a previous signal strength measurement indicates that mobile device 108 previously moved toward vehicle 100 and mobile device 108 is no longer near the edge of the coverage area 118. In some examples, blind spot manager 120 determines that mobile device 108 has entered one of the blind spots 116 when blind spot manager 120 cannot determine which antenna module 104 mobile device 108 is closest to based on multiple signal strength measurements from antenna module 104. When mobile device 108 enters one of the blind spots, blind spot manager 120 estimates the current position of mobile device 108 based on past signal strength values ​​associated with mobile device 108 stored in memory 206.

[0023] Blind spot manager 120 uses models to generate one or more predicted values ​​of the location of mobile device 108. These models are generated using data collected by mapping the signal strength of the mobile device 108 as it transitions from various ranges of interest (e.g., welcome zone, passive entry zone, exit zone, in blind spot, etc.). When generating more than one predicted value, blind spot manager 120 uses different models to generate each of the predicted values. In some examples, the behavior of mobile device 108 is determined by a third party (e.g., a car manufacturer, component manufacturer, etc.). In some such examples, the behavior is measured for different models of mobile devices to generate a specific model for a particular model and / or manufacturer of mobile device 108. Alternatively, in some examples, the behavior of a particular mobile device 108 is measured over time as the mobile device 108 interacts with antenna module 104. In such examples, the model is formed over time, and the model is customized for the mobile device 108 of operator 110. In some examples, blind spot manager 120 includes at least two models generated using different algorithms.

[0024] In some examples, the following are used to generate the model: quadratic linear equations, Kalman filter algorithms, double exponential smoothing algorithms, Markov chain algorithms, Bayesian network algorithms, neural network algorithms, and / or perceptron algorithms, etc. The model uses information about the mobile device 108 to generate predicted values, such as (a) the previous state or zone of the mobile device 108 (e.g., welcome zone, passive entry zone, within range of antenna module 104, etc.), (b) the history of signal strength measurements of antenna module 104, (c) the current signal strength measurement of antenna module 104, and / or (d) sensor measurements that detect the presence of operator 110 (e.g., door handle sensor, etc.). For example, a Markov chain model could define five states characterizing the relationship between the mobile device 108 and the vehicle 100. In such an example, the Markov chain model can define states within range (e.g., mobile device 108 is within the communication range of vehicle 100), states outside range (e.g., mobile device 108 is not within the communication range of vehicle 100), proximity detection states (e.g., mobile device 108 is within a welcome zone), passive entry states (e.g., mobile device 108 is within a passive entry zone), and uncertain states. In such an example, the Markov chain model can define transitions between the defined states based on: (a) the previous state or zone of mobile device 108 (e.g., welcome zone, passive entry zone, within range of antenna module 104, etc.), (b) the history of signal strength measurements of antenna module 104, (c) the current signal strength measurement of antenna module 104, and / or (d) sensor measurements detecting the presence of operator 110 (e.g., door handle sensor, etc.). Transition probabilities are experimentally determined under various conditions known to lead to dumbness. In such an example, to generate predicted values, the inputs to the Markov chain model produce the final state. For example, when the final state is a passive entry state, the predicted value indicates that the mobile device 108 is in the passive entry zone, even when the wireless communication module 106 cannot determine the current location of the mobile device 108 because the mobile device 108 is in the blind zone 116.

[0025] Blind spot manager 120 uses a model to generate one or more predicted values, which are estimates of the position of mobile device 108 within one of the blind spots 116. In some examples, blind spot manager 120 generates a single predicted value and uses it to determine whether mobile device 108 is within a passive entry zone. In some examples, blind spot manager 120 generates multiple predicted values. For example, a Kalman filter-based model can be used to generate the first predicted value and a Markov chain-based model can be used to generate the second predicted value. When the predicted values ​​substantially match (e.g., within 0.25 meters of the same location, indicating that mobile device 108 is in the same zone, etc.), blind spot manager 120 estimates the position of mobile device 108. Blind spot manager 120 then treats mobile device 108 as if it were at the location indicated by the predicted value. Blind spot manager 120 uses the position to determine which zone (if any) mobile device 108 is in. Blind spot manager 120 continues to estimate the position of moving device 108 until it determines that moving device 108 is no longer within one of the blind spots 116. In some examples, blind spot manager 120 generates predictions periodically (e.g., every 500 milliseconds, every second, etc.) and estimates the position of moving device 108 relative to vehicle 100 regardless of whether moving device 108 is within one of the blind spots 116. In such examples, blind spot manager 120 uses most of the previously generated predictions to estimate the position of moving device 108.

[0026] In some examples, when multiple antenna modules 104 receive a broadcast from mobile device 108 while the mobile device is not in one of the blind zones 116, the blind zone manager 120 estimates the position of mobile device 108 in two dimensions of the horizontal plane using trilateration based on the known distances between the antenna modules 104. Additionally, the distance to mobile device 108 is calculated from a line orthogonal to the line between two antenna modules 104. When mobile device 108 enters one of the blind zones 116, the blind zone manager 120 estimates the position based on the previously known position (D). N The current position (D) is determined by the time difference (Δt) between the time at the previous position and the time at the current position (sometimes called the "time step"). P In some examples, the following equation (1) is used to calculate the current position (D). P ).

[0027] D P =D N -V N *Δt+A N *(Δt) 2

[0028] Equation (1)

[0029] In the above equation (1), V N It is the speed of the moving device 108 and A N This refers to the acceleration of the mobile device 108 (e.g., both are measured based on previous location determination, etc.). In some examples, broadcasts from the mobile device 108 are received by three or more antenna modules 104 that facilitate the performance of redundant trilateration measurements. In such examples, the blind spot manager 120: (a) averages the estimated location, (b) uses the highest signal strength measurement, and / or (c) calculates a weighted average of the signal strength measurements based on the location of the corresponding antenna module 104 on the vehicle 100. In some examples, the blind spot manager 120 uses this method to determine when the mobile device 108 enters one of the blind spots 116. In some examples, when the mobile device 108 is not in one of the blind spots 116, the blind spot manager 120: (a) uses the signal strength measurements to calculate the location of the mobile device 108, (b) uses Equation (1) above to predict the location of the mobile device 108, and (c) averages the calculation and the prediction to determine the location of the mobile device 108.

[0030] Figure 2 yes Figure 1 A block diagram of the electronic components 200 of the vehicle 100. In the example shown, the electronic components 200 include a body control module 102, an antenna module 104, a wireless communication module 106, and a vehicle data bus 202.

[0031] The wireless communication module 106 includes a processor or controller 204 and a memory 206. In the illustrated example, the wireless communication module 106 is configured to include a blind spot manager 120. Alternatively, in some examples, the blind spot manager 120 is incorporated into another electronic control unit (ECU), such as the body control module 102, which itself has a processor and memory. The processor or controller 204 can be any suitable processing device or group of processing devices, such as, but not limited to, a microprocessor, a microcontroller-based platform, a suitable integrated circuit, one or more field-programmable gate arrays (FPGAs), and / or one or more application-specific integrated circuits (ASICs). The memory 206 can be volatile memory (e.g., RAM, which may include non-volatile RAM, magnetic RAM, ferroelectric RAM, and any other suitable form); non-volatile memory (e.g., disk storage, flash memory, EPROM, EEPROM, non-volatile solid-state memory, etc.), immutable memory (e.g., EPROM), read-only memory, and / or mass storage devices (e.g., hard disk drives, solid-state drives, etc.). In some examples, the memory 206 includes a variety of memories, particularly volatile and non-volatile memories.

[0032] Memory 206 is a computer-readable medium on which one or more instruction sets, such as software for operating the methods of this disclosure, may be embedded. Instructions may embody one or more of the methods or logic described herein. In certain embodiments, instructions may reside wholly or at least partially within any one or more of memory 206, the computer-readable medium, and / or processor 204 during execution of the instructions.

[0033] The terms “non-transitory computer-readable storage medium” and “tangible computer-readable medium” should be understood to include a single medium or multiple media (such as a centralized or distributed database storing one or more instruction sets and / or associated caches and servers). The terms “non-transitory computer-readable medium” and “tangible computer-readable medium” also include any tangible medium capable of storing, encoding, or carrying instruction sets for execution by a processor or causing a system to perform any one or more methods or operations disclosed herein. As used herein, the term “tangible computer-readable medium” is explicitly defined to include any type of computer-readable storage device and / or storage disk and excludes propagated signals.

[0034] The vehicle data bus 202 communicatively couples the body control module 102 to the wireless communication module 106. In some examples, the vehicle data bus 202 includes one or more data buses. The vehicle data bus 202 can be configured according to the Controller Area Network (CAN) bus protocol, the Media-Oriented System Transport (MOST) bus protocol, the CAN Flexible Data (CAN-FD) bus protocol (ISO 11898-7), and / or the K-line bus protocol (ISO 9141 and ISO 14230-1) as defined by the International Organization for Standardization (ISO) 11898-1, and / or Ethernet. TM It is implemented using bus protocols such as IEEE 802.3 (since 2002).

[0035] Figure 3 It can be made by Figure 2The flowchart illustrates a method implemented by electronic component 200 for mitigating passive entry into blind zone 116. Initially, at block 302, blind zone manager 120 waits until mobile device 108 is within range. At block 304, when mobile device 108 is within range, blind zone manager 120 stores a signal strength measurement from mobile device 108 in memory. At block 306, blind zone manager 120 determines whether the location or zone of mobile device 108 can be determined based on signal strength measurements from antenna modules 104 (e.g., whether mobile device 108 might be within one of the blind zones 116). For example, blind zone manager 120 may not be able to determine the location or zone of mobile device 108 if signal strength measurements from antenna modules 104 are close enough that blind zone manager 120 cannot determine which antenna modules 104 are closest to mobile device 108. When the location of mobile device 108 is determined, the method continues to block 308. Otherwise, if the location of mobile device 108 is not determined, the method continues at block 310. At box 306, blind spot manager 120 determines the position of mobile device 108 relative to vehicle 100 based on signal strength measurements.

[0036] At block 310, blind spot manager 120 determines whether the model of mobile device 108 requires blind spot management. For example, wireless communication module 106 may store a list of mobile devices 108 with connectivity issues contributing to blind spot 116 in memory. Optionally or additionally, in some examples, blind spot manager 120 learns over time whether a particular mobile device 108 has connectivity issues. For example, blind spot manager 120 may generate a mapping of signal strength measurements received from mobile device 108 and determine that mobile device 108 has low signal strength measurements at certain locations near vehicle 100. As another example, when setting up the PaaK system, blind spot manager 120 may instruct operator 110 to walk a predefined route around vehicle 100 via an application running on mobile device 108 to determine if blind spot 116 exists around vehicle 100. When mobile device 108 has connectivity issues, the method continues at block 312. Otherwise, when mobile device 108 does not have connectivity issues, the method returns to block 302.

[0037] At box 312, blind zone manager 120 generates a first estimated position of mobile device 108 based on a first predicted value. A first model, such as a Kalman filter, an enhanced Kalman filter, or a double exponential smoothing algorithm, is used to generate the first predicted value. At box 314, blind zone manager 120 generates a second estimated position of mobile device 108 based on a second predicted value, which is different from the first predicted value. A second model, such as a Markov chain algorithm, a Bayesian network algorithm, or a neural network, is used to generate the second predicted value. At box 316, blind zone manager 120 determines whether the first position matches the second position. For example, the first position matches the second position when the first position and the second position are within a threshold distance of each other, or when the first predicted value and the second predicted value indicate that mobile device 108 is in the same area. Alternatively, in some examples, the first position matches the second position, for example, when the first position and the second position are within the same blind zone in blind zone 116. When the first position matches the second position, the method continues at box 318. Otherwise, if the first position and the second position do not match, the method continues at box 302.

[0038] At box 318, blind spot manager 120 determines whether mobile device 108 is within the passive entry zone based on the estimated position of mobile device 108. If mobile device 108 is within the passive entry zone, the method continues at box 320. Otherwise, if mobile device 108 is not within the passive entry zone, the method continues at box 324. At box 320, blind spot manager 120 determines whether one of the vehicle doors 112a and 112b and / or the liftgate 114 of vehicle 100 is activated and / or touched within a threshold time period (e.g., three seconds, five seconds, ten seconds, etc.). When one of the vehicle doors 112a and 112b and / or the liftgate 114 of vehicle 100 is activated and / or touched within the threshold time period, the method continues to box 322. Otherwise, the method continues at box 324 when one of the doors 112a and 112b of vehicle 100 and / or the liftgate 114 is not activated and / or touched during the threshold time period.

[0039] At box 322, the blind spot manager 120 instructs the body control module 102 to enable passive entry (e.g., unlocking doors 112a and 112b or liftgate 114). At box 324, the blind spot manager 120 provides a warning to the operator 110. In some examples, the nature of the warning indicates which error condition is being conveyed (e.g., a first position does not match a second position, the moving device is not in the passive entry zone, the door is not activated within a threshold time, etc.). In some examples, to provide the warning, the blind spot manager 120 instructs the body control module 102 to generate an audio or visual warning using the lights of the vehicle 100 and / or sound generating devices on the vehicle 100 (e.g., horn, loudspeaker, etc.).

[0040] At box 326, the blind spot manager 120 determines whether the location or area of ​​the mobile device 108 is determinable based on signal strength measurements. For example, since the last determination, operator 110 may have moved the mobile device 108 out of blind spot 116. When the location or area of ​​the mobile device 108 is determinable, the method continues to box 308. Otherwise, when the location or area of ​​the mobile device 108 is not determinable, the method returns to box 310.

[0041] Figure 3 The flowchart represents the data stored in memory (such as, Figure 2 Machine-readable instructions in memory 206, the machine-readable instructions comprising one or more programs, the programs being processed by a processor (such as, Figure 2 When the processor 204 executes, it causes the wireless communication module 106 and / or more generally the vehicle 100 to implement Figure 1 and Figure 2 Example blind spot manager 120. Furthermore, although the reference... Figure 3 The flowcharts shown depict (multiple) example programs, but many other methods of implementing the example blind spot manager 120 may be used optionally. For example, the execution order of the boxes may be changed, and / or some of the boxes described may be changed, eliminated, or combined.

[0042] In this application, the use of the conjunction "or" is intended to include conjunctions. The use of definite or indefinite articles is not intended to indicate cardinality. Specifically, references to "the" object or "an" and "a" object also indicate one of a possible plurality of such objects. Furthermore, the conjunction "or" can be used to convey concurrent features rather than mutually exclusive alternatives. In other words, the conjunction "or" should be understood to include "and / or". As used herein, the terms "module" and "unit" refer to hardware having circuitry for providing communication, control, and / or monitoring capabilities, typically in conjunction with sensors. "Module" and "unit" may also include firmware executed on the circuitry. The terms "includes", "including", and "include" are inclusive and have the same scope as "comprises", "comprising", and "comprise", respectively.

[0043] The above embodiments, especially any "preferred" embodiments, are possible examples of implementations and are merely illustrative for the purpose of clearly understanding the principles of the invention. Many variations and modifications can be made to (the various embodiments) above-described without substantially departing from the spirit and principles of the technology described herein. All modifications are intended to be included within the scope of this disclosure and are protected by the appended claims.

[0044] According to the present invention, a vehicle is provided, the vehicle comprising: an antenna module for determining the signal strength for communicating with a mobile device; a wireless module for: generating a first predicted value and a second predicted value when the mobile device is in a blind spot; enabling passive entry when the first predicted value matches the second predicted value and indicates that the mobile device is in a passive entry zone, and a sensor detects a user; and a body control module for unlocking the doors when passive entry is enabled.

[0045] According to an embodiment, the blind zone is the area where the location of the mobile device cannot be determined based on the signal strength determined by the antenna module.

[0046] According to an embodiment, the coverage area of ​​the antenna module defines the blind zone.

[0047] According to an embodiment, before the mobile device enters the blind zone, the wireless module is used to track the location of the mobile device based on measured signal strength.

[0048] According to an embodiment, the wireless module is used to store the measured signal strength to generate the first predicted value and the second predicted value.

[0049] According to an embodiment, the wireless module is configured to: generate the first predicted value using a first model; and generate the second predicted value using a second model, wherein the second model is generated using a different technique than the first model.

[0050] According to the embodiments, the first model and the second model are generated using at least two of the following: Kalman filter, Bayesian network, double exponential smoothing algorithm, Markov chain, or linear quadratic equation.

[0051] According to an embodiment, the first predicted value and the second predicted value are the estimated positions of the mobile device within the blind zone.

[0052] According to an embodiment, when the first predicted value matches the second predicted value, the wireless module is used to enable passive entry for a limited amount of time.

[0053] According to an embodiment, when the first predicted value matches the second predicted value, the wireless module is used to enable passive entry for the door approaching the blind spot.

[0054] According to the present invention, a method includes: determining a signal strength measurement of a broadcast from a mobile device using an antenna module of a vehicle; generating a plurality of predicted values ​​using a wireless module including a processor and a memory when the mobile device enters a blind zone near the vehicle; enabling passive entry using the wireless module when the plurality of predicted values ​​match and indicate that the mobile device is in a passive entry zone; and unlocking a door using a body control module when passive entry is enabled and a sensor detects that a user touches a door handle of the vehicle.

[0055] According to an embodiment, the blind spot is the area close to the vehicle, where the antenna module cannot accurately measure the signal strength of the broadcast from the mobile device.

[0056] According to an embodiment, the coverage area of ​​the antenna module defines the blind zone.

[0057] According to an embodiment, the wireless module tracks the mobile device based on the signal strength measurement before the mobile device enters the blind zone.

[0058] According to an embodiment, the method includes: storing the signal strength measurement in a memory for generating the plurality of predicted values.

[0059] According to an embodiment, the method includes: using a first model to generate a first predicted value among the plurality of predicted values; and using a second model to generate a second predicted value among the plurality of predicted values, the second model being generated using a different technique than the first model.

[0060] According to an embodiment, the first model and the second model are generated using at least two of the following: Kalman filter, Bayesian network, double exponential smoothing algorithm, Markov chain, or linear quadratic equation.

[0061] According to the embodiment, each of the plurality of predicted values ​​is generated using different models.

[0062] According to an embodiment, the method includes: when the plurality of predicted values ​​match, passively entering a limited period of time for activation.

[0063] According to an embodiment, the method includes: when the plurality of predicted values ​​match, enabling passive entry for the door that is close to the blind spot.

Claims

1. A vehicle, the vehicle comprising: Antenna module, the antenna module being used to measure the signal strength of broadcasts from a mobile device; The wireless module is used for: When the mobile device is in a blind zone, a first prediction value and a second prediction value are generated, the first prediction value and the second prediction value being the estimated position of the mobile device in the blind zone, the blind zone being the area in which at least one of the antenna modules fails to properly receive the broadcast from the mobile device; Passive entry is enabled when the first predicted value matches the second predicted value and indicates that the mobile device is in the passive entry zone, and the sensor detects the user. as well as A body control module for unlocking the doors when passive entry is enabled.

2. The vehicle of claim 1, wherein the coverage area of ​​the antenna module defines the blind zone.

3. The vehicle of claim 1, wherein the wireless module is used to track the mobile device based on the measured signal strength before the mobile device enters the blind spot.

4. The vehicle of claim 3, wherein the wireless module is used to store the measured signal strength for generating the first predicted value and the second predicted value.

5. The vehicle of claim 1, wherein the wireless module is used for: The first model is used to generate the first predicted value; and The second model is used to generate the second predicted value, and the second model is generated using a different technique than the first model.

6. The vehicle of claim 5, wherein the first model and the second model are generated using at least two of the following: Kalman filter, Bayesian network, double exponential smoothing algorithm, Markov chain, or linear quadratic equation.

7. The vehicle of claim 1, wherein when the first predicted value matches the second predicted value, the wireless module is configured to enable passive entry for a limited amount of time.

8. The vehicle of claim 1, wherein when the first predicted value matches the second predicted value, the wireless module is configured to enable passive entry for the door approaching the blind spot.

9. A method for mitigating passive entry blind spots, the method comprising: The signal strength measurement of broadcasts from mobile devices is determined using the vehicle's antenna module; When the mobile device enters the blind spot near the vehicle, it generates multiple predicted values ​​using a wireless module including a processor and memory. When the multiple predicted values ​​match and indicate that the mobile device is in the passive entry zone, passive entry is enabled using the wireless module; as well as When passive entry is enabled and sensors detect that a user touches the vehicle's door handle, the vehicle's body control module unlocks the door.

10. The method of claim 9, wherein the method comprises: The first model is used to generate the first predicted value among the plurality of predicted values; as well as A second model is used to generate a second predicted value among the plurality of predicted values, the second model being generated using a different technique than the first model.

11. The method of claim 10, wherein the first model and the second model are generated using at least two of the following: Kalman filter, Bayesian network, double exponential smoothing algorithm, Markov chain, or linear quadratic equation.

12. The method of claim 9, wherein each of the plurality of predicted values ​​is generated using different models.

13. The method of claim 9, wherein the method comprises: When the multiple predicted values ​​match, the door near the blind spot will be passively activated for a limited time.

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