Patent Publication Number: US-2022236363-A1

Title: Time-Of-Flight Vehicle User Localization Distance Determination

Description:
TECHNICAL FIELD 
     The present disclosure relates to mobile device localization, and more particularly, to mobile device localization using a Time-of-Flight (ToF) localization device. 
     BACKGROUND 
     Some antenna transmission radio technologies utilize low energy levels for short-range, high-bandwidth communications over a large bandwidth (e.g., &gt;500 MHz) of the unlicensed radio spectrum. Some wireless applications are used for target sensor data collection, precision localization of devices, and tracking applications. 
     One example wireless technology uses radio device localization by determining a ToF of the transmission at various frequencies using radio systems. In some conventional applications, human body signal absorption and other signal impediments may significantly impact system performance, which can skew the Received Signal Strength Indicator (RSSI) measurement, which can adversely affect device localization. 
     With a cooperative symmetric two-way metering technique, distances can be measured to high resolution and accuracy. This feature may overcome signal loss due to human absorption, among other causes of signal loss. The use of multiple antenna systems may increase transmission range by exploiting distributed antennas among multiple nodes in an antenna array. Distributed antenna arrays may increase the transmission range and increase system throughput and reception reliability. 
     It is with respect to these and other considerations that the disclosure made herein is presented. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The detailed description is set forth with reference to the accompanying drawings. The use of the same reference numerals may indicate similar or identical items. Various embodiments may utilize elements and/or components other than those illustrated in the drawings, and some elements and/or components may not be present in various embodiments. Elements and/or components in the figures are not necessarily drawn to scale. Throughout this disclosure, depending on the context, singular and plural terminology may be used interchangeably. 
         FIG. 1  depicts an example computing environment in which techniques and structures for providing the systems and methods disclosed herein may be implemented. 
         FIG. 2  depicts a functional schematic of a Driver Assist Technologies (DAT) controller in accordance with the present disclosure. 
         FIG. 3  illustrates an example automotive computer disposed in communication with a multiple input multiple output (MIMO) antenna array in accordance with the present disclosure. 
         FIG. 4  depicts a flow diagram of localizing a user device using ToF technology in accordance with the present disclosure. 
         FIG. 5  depicts another flow diagram of localizing a user device using ToF in accordance with the present disclosure. 
         FIG. 6  is a table of measurement tag distances at 0° with respect to MIMO antenna arrays in accordance with the present disclosure. 
         FIG. 7  is a table of measurement tag distances at 0° with respect to MIMO antenna arrays in accordance with the present disclosure. 
         FIG. 8  depicts another functional flow diagram of localizing a user using ToF in accordance with the present disclosure. 
         FIGS. 9A and 9B  depict localization of a mobile device using trilateration in accordance with the present disclosure. 
         FIG. 10  depicts localization of a mobile device using trilateration in accordance with the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     The disclosure will be described more fully hereinafter with reference to the accompanying drawings, in which example embodiments of the disclosure are shown, and not intended to be limiting. 
     Disclosed is a method to determine a specific region in which a wireless device (e.g., a user&#39;s phone) is located with respect to a vehicle. Incorporating ToF enables the use of Time-of-Flight (ToF) between two ToF devices, which communicates between the devices at a high frequency. ToF measures the time taken for the high frequency signal to go back and forth between the two devices. In some aspects, the ToF may be based on Roundtrip Time (RTT), less an empirically established and known fixed delay time in the transponder module of the sending and receiving devices. The method implements multiple ToF modules disposed at the vehicle that use ToF to develop an algorithm that locates a ToF device for 360° of coverage around the vehicle, and determines if that device is inside or outside of the vehicle. The algorithm may use, for example, seven modules to do this, but it may still be able to locate and tag the ToF device even if some of the modules fail to communicate with it. These determinations may differ based on whether the user is within or outside of a predetermined threshold distance of the vehicle. 
     Although described as processes and infrastructures for localization of a user device using ToF where a user may be carrying the user device, it should be appreciated that the disclosed system and methods may interchangeably refer to localization of the user carrying the user device, and/or localization of the user device where the user may or may not be present. 
     For localization outside the predetermined threshold, the system may test four operating range logical OR conditions to determine if the device is at the 0° region (front of the vehicle). If one of the four checks pass, the user is determined to be at the 0° region. If all four checks fail, the system may determine that the user device is not positioned at the 0° region. The system may then cause the algorithm to check if the user is at the next region, the 45° region. 
     The OR conditions start with check 1, which determines if the user device distance to Tag 0 is less than the user&#39;s distance to all interior Tags, AND the user&#39;s distance to Tag 2 is less than the user device distance to all interior Tags, AND the user device distance to Tag 6 is less than 7 meters, for example, AND Tag 1 &amp; Tag 3 fail to communicate with the user device If yes (e.g., all of the Check #1 conditions are met), the system determines that the user device is localized at the 0° region at the front of the vehicle, and the system proceeds with a distance calculation. If no, the system can proceed to check 2. 
     Check 2 determines if the user device distance to Tag 5 is the maximum distance out of all seven Tags. Responsive to an affirmative determination of distance, the system determines that the user device is positioned at the front of the vehicle. Responsive to a negative determination, the system proceeds to Check 3. 
     Check 3 determines if the user&#39;s distance to Tag 6 is the maximum distance out of all seven Tags AND Tag 1 &amp; Tag 3 fail to communicate with the user&#39;s ToF device. Responsive to a positive determination, the system determines that the user device is positioned at the front of the vehicle. 
     Check 4 determines whether the distance to the Tag 0 is greater than a predetermined threshold distance (e.g., 11 m, 10.5 m, 10 m, etc.). Responsive to a positive determination that the distance from Tag 0 to the user device exceeds the threshold distance, the system determines that the user device is localized at 0°, and proceeds to perform a distance calculation. 
     Responsive to a negative determination (e.g., the distance between Tag 0 and the user device is greater than the threshold distance), the system determines that the user device is not localized at 0°. The system may then move to the next 45° position. 
     For localization at distances under the predetermined threshold, the system may further test four AND conditions. Unlike the localization performed when the device is over the predetermined threshold, if all of the four checks pass, the user is determined to be at the 0° region. If any of the four checks fail, the user is determined not to be at the 0° region. The algorithm then moves on to check if the user is at the next region, the 45° region. Responsive to determining that the user is proximate to the vehicle at the 45° region, the system may trigger an unlock action that provides the user with vehicle access. 
     Illustrative Embodiments 
       FIG. 1  depicts an example computing environment  100  that can include a vehicle  105 . The vehicle  105  may include an automotive computer  145 , and a Vehicle Controls Unit (VCU)  165  that can include a plurality of electronic control units (ECUs)  117  disposed in communication with the automotive computer  145 . A mobile device  120 , which may be associated with a user  140  and the vehicle  105 , may connect with the automotive computer  145  using wired and/or wireless communication protocols and transceivers. The mobile device  120  may be communicatively coupled with the vehicle  105  via one or more network(s)  125 , which may communicate via one or more wireless connection(s)  130 , and/or may connect with the vehicle  105  directly using near field communication (NFC) protocols, Bluetooth® protocols, Wi-Fi, Ultra-Wide Band (ToF), and other possible data connection and sharing techniques. 
     To categorize the position of the user when localization occurs, eight vehicle regions  106  may be defined. The vehicle regions  106  are depicted as dashed ovals surrounding the vehicle  105 . These eight regions come from the 360° coverage associated with seven ToF modules  111  as illustrated by region numbers demarcated as numbered diamonds. The ToF modules  111  may provide vehicle surrounding location coverage in each of the respective vehicle regions  106  (e.g., 0°, 45°, etc.). Accordingly, each region covers approximately 45° of coverage. For example, the user  140  is shown walking into the 0° region. 
     The vehicle  105  may also receive and/or be in communication with a Global Positioning System (GPS)  175 . The GPS  175  may be a satellite system (as depicted in  FIG. 1 ) such as the global navigation satellite system (GLNSS), Galileo, or navigation or other similar system. In other aspects, the GPS  175  may be a terrestrial-based navigation network. In some embodiments, the vehicle  105  may utilize a combination of GPS and Dead Reckoning responsive to determining that a threshold number of satellites are not recognized. 
     The automotive computer  145  may be or include an electronic vehicle controller, having one or more processor(s)  150  and memory  155 . The automotive computer  145  may, in some example embodiments, be disposed in communication with the mobile device  120 , and one or more server(s)  170 . The server(s)  170  may be part of a cloud-based computing infrastructure, and may be associated with and/or include a Telematics Service Delivery Network (SDN) that provides digital data services to the vehicle  105  and other vehicles (not shown in  FIG. 1 ) that may be part of a vehicle fleet. 
     Although illustrated as a sport vehicle, the vehicle  105  may take the form of another passenger or commercial automobile such as, for example, a car, a truck, a sport utility, a crossover vehicle, a van, a minivan, a taxi, a bus, etc., and may be configured and/or programmed to include various types of automotive drive systems. Example drive systems can include various types of internal combustion engines (ICEs) powertrains having a gasoline, diesel, or natural gas-powered combustion engine with conventional drive components such as, a transmission, a drive shaft, a differential, etc. In another configuration, the vehicle  105  may be configured as an electric vehicle (EV). More particularly, the vehicle  105  may include a battery EV (BEV) drive system, or be configured as a hybrid EV (HEV) having an independent onboard powerplant, a plug-in HEV (PHEV) that includes a HEV powertrain connectable to an external power source, and/or includes a parallel or series hybrid powertrain having a combustion engine powerplant and one or more EV drive systems. HEVs may further include battery and/or supercapacitor banks for power storage, flywheel power storage systems, or other power generation and storage infrastructure. The vehicle  105  may be further configured as a fuel cell vehicle (FCV) that converts liquid or solid fuel to usable power using a fuel cell, (e.g., a hydrogen fuel cell vehicle (HFCV) powertrain, etc.) and/or any combination of these drive systems and components. 
     Further, the vehicle  105  may be a manually driven vehicle, and/or be configured and/or programmed to operate in a fully autonomous (e.g., driverless) mode (e.g., Level-5 autonomy) or in one or more partial autonomy modes which may include driver assist technologies. Examples of partial autonomy (or driver assist) modes are widely understood in the art as autonomy Levels 1 through 4. 
     A vehicle having a Level-0 autonomous automation may not include autonomous driving features. 
     A vehicle having Level-1 autonomy may include a single automated driver assistance feature, such as steering or acceleration assistance. Adaptive cruise control is one such example of a Level-1 autonomous system that includes aspects of both acceleration and steering. 
     Level-2 autonomy in vehicles may provide driver assist technologies such as partial automation of steering and acceleration functionality, where the automated system(s) are supervised by a human driver that performs non-automated operations such as braking and other controls. In some aspects, with Level-2 and greater autonomous features, a primary user may control the vehicle while the user is inside of the vehicle, or in some example embodiments, from a location remote from the vehicle but within a control zone extending up to several meters from the vehicle while it is in remote operation. 
     Level-3 autonomy in a vehicle can provide conditional automation and control of driving features. For example, Level-3 vehicle autonomy may include “environmental detection” capabilities, where the autonomous vehicle (AV) can make informed decisions independently from a present driver, such as accelerating past a slow-moving vehicle, while the present driver remains ready to retake control of the vehicle if the system is unable to execute the task. 
     Level-4 AVs can operate independently from a human driver, but may still include human controls for override operation. Level-4 automation may also enable a self-driving mode to intervene responsive to a predefined conditional trigger, such as a road hazard or a system failure. 
     Level-5 AVs may include fully autonomous vehicle systems that require no human input for operation, and may not include human operational driving controls. 
     The mobile device  120  can include a memory  123  for storing program instructions associated with an application  135  that, when executed by a mobile device processor  121 , performs aspects of the disclosed embodiments. The application (or “app”)  135  may be part of a ToF localization system  107 , or may provide information to the ToF localization system  107  and/or receive information from the ToF localization system  107 . 
     In some aspects, the mobile device  120  may communicate with the vehicle  105  through the one or more wireless connection(s)  130 , which may be encrypted and established between the mobile device  120  and a Telematics Control Unit (TCU)  160 . The mobile device  120  may communicate with the TCU  160  using a wireless transmitter (not shown in  FIG. 1 ) associated with the TCU  160  on the vehicle  105 . The transmitter may communicate with the mobile device  120  using a wireless communication network such as, for example, the one or more network(s)  125 . The wireless connection(s)  130  are depicted in  FIG. 1  as communicating via the one or more network(s)  125 , and via one or more wireless connection(s)  133  that can be direct connection(s) between the vehicle  105  and the mobile device  120 . The wireless connection(s)  133  may include various low-energy protocols including, for example, Bluetooth®, Bluetooth® Low-Energy (BLE®), ToF, Near Field Communication (NFC), or other protocols. 
     The network(s)  125  illustrate an example communication infrastructure in which the connected devices discussed in various embodiments of this disclosure may communicate. The network(s)  125  may be and/or include the Internet, a private network, public network or other configuration that operates using any one or more known communication protocols such as, for example, transmission control protocol/Internet protocol (TCP/IP), Bluetooth® BLE®, Wi-Fi based on the Institute of Electrical and Electronics Engineers (IEEE) standard 802.11, ToF, and cellular technologies such as Time Division Multiple Access (TDMA), Code Division Multiple Access (CDMA), High Speed Packet Data Access (HSPDA), Long-Term Evolution (LTE), Global System for Mobile Communications (GSM), and Fifth Generation (5G), to name a few examples. 
     The automotive computer  145  may be installed in an engine compartment of the vehicle  105  (or elsewhere in the vehicle  105 ) and operate as a functional part of the ToF localization system  107 , in accordance with the disclosure. The automotive computer  145  may include one or more processor(s)  150  and a computer-readable memory  155 . 
     The one or more processor(s)  150  may be disposed in communication with one or more memory devices disposed in communication with the respective computing systems (e.g., the memory  155  and/or one or more external databases not shown in  FIG. 1 ). The processor(s)  150  may utilize the memory  155  to store programs in code and/or to store data for performing aspects in accordance with the disclosure. The memory  155  may be a non-transitory computer-readable memory storing a ToF localization program code. The memory  155  can include any one or a combination of volatile memory elements (e.g., dynamic random access memory (DRAM), synchronous dynamic random-access memory (SDRAM), etc.) and can include any one or more nonvolatile memory elements (e.g., erasable programmable read-only memory (EPROM), flash memory, electronically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), etc. 
     The VCU  165  may share a power bus  178  with the automotive computer  145 , and may be configured and/or programmed to coordinate the data between vehicle  105  systems, connected servers (e.g., the server(s)  170 ), and other vehicles (not shown in  FIG. 1 ) operating as part of a vehicle fleet. The VCU  165  can include or communicate with any combination of the ECUs  117 , such as, for example, a Body Control Module (BCM)  193 , an Engine Control Module (ECM)  185 , a Transmission Control Module (TCM)  190 , the TCU  160 , a Driver Assistances Technologies (DAT) controller  199 , etc. The VCU  165  may further include and/or communicate with a Vehicle Perception System (VPS)  181 , having connectivity with and/or control of one or more vehicle sensory system(s)  182 . In some aspects, the VCU  165  may control operational aspects of the vehicle  105 , and implement one or more instruction sets received from the application  135  operating on the mobile device  120 , from one or more instruction sets stored in computer memory  155  of the automotive computer  145 , including instructions operational as part of the ToF localization system  107 . 
     The TCU  160  can be configured and/or programmed to provide vehicle connectivity to wireless computing systems onboard and offboard the vehicle  105 , and may include a Navigation (NAV) receiver  188  for receiving and processing a GPS signal from the GPS  175 , a BLE® Module (BLEM)  195 , a Wi-Fi transceiver, a ToF transceiver, and/or other wireless transceivers (not shown in  FIG. 1 ) that may be configurable for wireless communication between the vehicle  105  and other systems, computers, and modules. The TCU  160  may be disposed in communication with the ECUs  117  by way of a bus  180 . In some aspects, the TCU  160  may retrieve data and send data as a node in a CAN bus. 
     The BLEM  195  may establish wireless communication using Bluetooth® and BLE® communication protocols by broadcasting and/or listening for broadcasts of small advertising packets, and establishing connections with responsive devices that are configured according to embodiments described herein. For example, the BLEM  195  may include Generic Attribute Profile (GATT) device connectivity for client devices that respond to or initiate GATT commands and requests, and connect directly with the mobile device  120 , and/or one or more keys (which may include, for example, the fob  179 ). 
     The bus  180  may be configured as a Controller Area Network (CAN) bus organized with a multi-master serial bus standard for connecting two or more of the ECUs  117  as nodes using a message-based protocol that can be configured and/or programmed to allow the ECUs  117  to communicate with each other. The bus  180  may be or include a high speed CAN (which may have bit speeds up to 1 Mb/s on CAN, 5 Mb/s on CAN Flexible Data Rate (CAN FD)) device, and can include a low-speed or fault tolerant CAN (up to 125 Kbps) device, which may, in some configurations, use a linear bus configuration. In some aspects, the ECUs  117  may communicate with a host computer (e.g., the automotive computer  145 , the ToF localization system  107 , and/or the server(s)  170 , etc.), and may also communicate with one another without the necessity of a host computer. The bus  180  may connect the ECUs  117  with the automotive computer  145  such that the automotive computer  145  may retrieve information from, send information to, and otherwise interact with the ECUs  117  to perform steps described according to embodiments of the present disclosure. The bus  180  may connect CAN bus nodes (e.g., the ECUs  117 ) to each other through a two-wire bus, which may be a twisted pair having a nominal characteristic impedance. The bus  180  may also be accomplished using other communication protocol solutions, such as Media Oriented Systems Transport (MOST) or Ethernet. In other aspects, the bus  180  may be a wireless intra-vehicle bus. 
     The VCU  165  may control various loads directly via the bus  180  communication or implement such control in conjunction with the BCM  193 . The ECUs  117  described with respect to the VCU  165  are provided for example purposes only, and are not intended to be limiting or exclusive. Control and/or communication with other control modules not shown in  FIG. 1  is possible, and such control is contemplated. 
     In an example embodiment, the ECUs  117  may control aspects of vehicle operation and communication using inputs from human drivers, inputs from an autonomous vehicle controller, the ToF localization system  107 , and/or via wireless signal inputs received via the wireless connection(s)  133  from other connected devices such as the mobile device  120 , among others. The ECUs  117 , when configured as nodes in the bus  180 , may each include a central processing unit (CPU), a CAN controller, and/or a transceiver (not shown in  FIG. 1 ). For example, although the mobile device  120  is depicted in  FIG. 1  as connecting to the vehicle  105  via the BLEM  195 , it is possible and contemplated that the wireless connection  133  may also or alternatively be established between the mobile device  120  and one or more of the ECUs  117  via the respective transceiver(s) associated with the module(s). 
     The BCM  193  generally includes integration of sensors, vehicle performance indicators, and variable reactors associated with vehicle systems, and may include processor-based power distribution circuitry that can control functions associated with the vehicle body such as lights, windows, security, door locks and access control, and various comfort controls. The BCM  193  may also operate as a gateway for bus and network interfaces to interact with remote ECUs (not shown in  FIG. 1 ). 
     The BCM  193  may coordinate any one or more functions from a wide range of vehicle functionality, including energy management systems, alarms, vehicle immobilizers, driver and rider access authorization systems, Phone-as-a-Key (PaaK) systems, driver assistance systems, AV control systems, power windows, doors, actuators, and other functionality, etc. The BCM  193  may be configured for vehicle energy management, exterior lighting control, wiper functionality, power window and door functionality, heating ventilation and air conditioning systems, and driver integration systems. In other aspects, the BCM  193  may control auxiliary equipment functionality, and/or be responsible for integration of such functionality. 
     The DAT controller  199  may provide Level-1 through Level-3 automated driving and driver assistance functionality that can include, for example, active parking assistance, trailer backup assistance, adaptive cruise control, lane keeping, and/or driver status monitoring, among other features. The DAT controller  199  may also provide aspects of user and environmental inputs usable for user authentication. Authentication features may include, for example, biometric authentication and recognition. 
     The DAT controller  199  can obtain input information via the sensory system(s)  182 , which may include sensors disposed on the vehicle interior and/or exterior (sensors not shown in  FIG. 1 ). The DAT controller  199  may receive the sensor information associated with driver functions, vehicle functions, and environmental inputs, and other information. The DAT controller  199  may characterize the sensor information for identification of biometric markers stored in a secure biometric data vault (not shown in  FIG. 1 ) onboard the vehicle  105  and/or via the server(s)  170 . 
     In other aspects, the DAT controller  199  may also be configured and/or programmed to control Level-1 and/or Level-2 driver assistance when the vehicle  105  includes Level-1 or Level-2 autonomous vehicle driving features. The DAT controller  199  may connect with and/or include a Vehicle Perception System (VPS)  181 , which may include internal and external sensory systems (collectively referred to as sensory systems  182 ). The sensory systems  182  may be configured and/or programmed to obtain sensor data usable for biometric authentication, and for performing driver assistance operations such as, for example, active parking, trailer backup assistance, adaptive cruise control and lane keeping, driver status monitoring, and/or other features. 
     The vehicle PaaK system (not shown in  FIG. 1 ) determines and monitors a location for a PaaK-enabled mobile device relative to the vehicle location in order to time broadcasting a pre-authentication message to the mobile device  120 , or other passive key device such as a fob  179 . As the mobile device  120  approaches a predetermined communication range relative to the vehicle position, the mobile device may transmit a preliminary response message to the PaaK-enabled vehicle. The vehicle PaaK system may cache the preliminary response message until a user associated with the authenticating device performs an unlock action such as actuating a vehicle door latch/unlatch mechanism by pulling a door handle, for example. The PaaK system may unlock the door using data already sent to the pre-processor to perform a first level authentication without the delay associated with full authentication steps. 
     After actuation of the door latch, the PaaK system may perform post-authentication confirmation using a secure processor, by transmitting, to the requesting device, a validation message that includes a challenge value requiring a validation response from the requesting device, and authenticating responsive validation messages using the secure processor. Responsive messages that correctly answer the validation message may confirm authenticity of the requesting device, and no further mitigating action is taken. 
     The processor(s)  150  may provide initial access to the vehicle  105  when the mobile device  120  is within the passive-entry-passive-start (PEPS) zone. Determining that the mobile device  120  is proximate to the vehicle  105  and within the PEPS zone, in conjunction with one or more other triggers, may cause pre-authorization steps to begin. For example, the processor(s)  150  may generate a secure processor initialization instruction responsive to a door latch opening, or a user touching the sensory area of a door handle or keyless entry keypad, or presence detection through cameras, electromagnetic sensing, or other methods. The processor(s)  150  may receive a sensor output that indicates an attempt to enter the vehicle. 
     The handle touch, by itself, would not trigger an unlock instruction. Rather, in an example embodiment, the touch to the door handle, plus the proximity indication associated with the position of the mobile device  120  with respect to the vehicle  105 , may cause a door handle sensor (not shown in  FIG. 1 ) to transmit sensor output to the processor(s)  150 . The processor(s)  150  may receive the vehicle sensor output associated with the actuation of the door handle (not shown in  FIG. 1 ) (and more precisely, associated with an actuation of a door latch mechanism (not shown in  FIG. 1 ) of the door handle, and generate a secure processor initialization instruction to the secure processor(s)  150  in response. 
     The processor(s)  150  may also provide access to the vehicle  105  in conjunction with the secure processor(s)  150  by unlocking the door  198  (not shown in  FIG. 1 ), based on the key-on request generated and/or the authentication message (key-on request and authentication message not shown in  FIG. 1 ) stored in the cache memory of the automotive computer  145 . The secure processor initialization instruction may initialize the secure processor(s)  150 , by sending instructions that “wake up” the secure processor(s)  150  by changing a power mode profile from a low-energy state to a higher-energy state. Once initialized, the secure processor(s)  150  may verify the authentication message stored in the cache memory of the automotive computer  145  before unlocking the door  198 . 
     The computing system architecture of the automotive computer  145 , VCU  165 , and/or the ToF localization system  107  may omit certain computing modules. It should be readily understood that the computing environment depicted in  FIG. 1  is an example of a possible implementation according to the present disclosure, and thus, it should not be considered limiting or exclusive. 
     The automotive computer  145  may connect with an infotainment system  110  that may provide an interface for the navigation and GPS receiver  188 , and the ToF localization system  107 . The infotainment system  110  may include voice recognition features, biometric identification capabilities that can identify users based on facial recognition, voice recognition, fingerprint identification, or other biological identification means. In other aspects, the infotainment system  110  may provide user identification using mobile device pairing techniques (e.g., connecting with the mobile device  120 ), a Personal Identification Number (PIN) code, a password, passphrase, or other identifying means. 
       FIG. 2  depicts an example DAT controller  199 , in accordance with an embodiment. As explained in prior figures, the DAT controller  199  may provide automated driving and driver assistance functionality and may provide aspects of user and environmental assistance. The DAT controller  199  may facilitate user authentication, such as biometric authentication that can include facial recognition, fingerprint recognition, voice recognition, gait recognition, and other unique and non-unique biometric aspects. The DAT controller  199  may further provide vehicle monitoring, and multimedia integration with driving assistances. 
     In one example embodiment, the DAT controller  199  may include a sensor I/O module  205 , a chassis I/O module  207 , a Biometric Recognition Module (BRM)  210 , a ToF positioning module  215 , an active parking assist module  220 , a blind spot information system (BLIS) module  225 , a trailer backup assist module  230 , a lane keeping control module  235 , a vehicle camera module  240 , an adaptive cruise control module  245 , a driver status monitoring system  250 , and an augmented reality integration module  255 , among other systems. It should be appreciated that the functional schematic depicted in  FIG. 2  is provided as an overview of functional capabilities for the DAT controller  199  and should not be considered limiting. In some embodiments, the vehicle  105  may include more or fewer modules and control systems. 
     The DAT controller  199  can obtain input information via the sensory system(s)  182 , which may include the external sensory system  281  and the internal sensory system  283  sensors disposed on the vehicle  105  interior and/or exterior, and via the chassis I/O module  207 , which may be in communication with the ECUs  117 . The DAT controller  199  may receive the sensor information associated with driver functions, and environmental inputs, and other information from the sensory system(s)  182 . 
     In other aspects, the DAT controller  199  may also be configured and/or programmed to control Level-1 and/or Level-2 driver assistance when the vehicle  105  includes Level-1 or Level-2 autonomous vehicle driving features. The DAT controller  199  may connect with and/or include a Vehicle Perception System (VPS)  181 , which may include internal and external sensory systems (collectively referred to as sensory systems  182 ). The sensory systems  182  may be configured and/or programmed to obtain sensor data usable for biometric authentication, and for performing driver assistance operations such as, for example, active parking, trailer backup assistance, adaptive cruise control and lane keeping, driver status monitoring, and/or other features. 
     The DAT controller  199  may be configured and/or programmed to provide biometric authentication control for the vehicle  105 , including, for example, facial recognition, fingerprint recognition, voice recognition, and/or provide other authenticating information associated with characterization, identification, occupant appearance, occupant status, and/or verification for other human factors such as gait recognition, body heat signatures, eye tracking, etc. The DAT controller  199  may obtain the sensor information from an external sensory system  281 , which may include sensors disposed on a vehicle exterior and in devices connectable with the vehicle  105  such as the mobile device  120  and/or the fob  179 . 
     The DAT controller  199  may further connect with the sensory system  182 , which can include an internal sensory system  283 , and which may include any number of sensors configured in the vehicle interior (e.g., the vehicle cabin, which is not depicted in  FIG. 2 ). The external sensory system  281  and internal sensory system  283  can connect with and/or include one or more inertial measurement units (IMUS)  284 , camera sensor(s)  285 , fingerprint sensor(s)  287 , and/or other sensor(s)  289 , and obtain biometric data usable for characterization of the sensor information for identification of biometric markers stored in a secure biometric data vault (not shown in  FIG. 2 ) onboard the vehicle  105 , and to obtain environmental data for providing driver assistance features. The DAT controller  199  may obtain, from the external and internal sensory systems  281  and  283 , sensory data that can include external sensor response signal(s) and internal sensor response signal(s) (collectively referred to as sensory data), via the sensor I/O module  205 . The DAT controller  199  (and more particularly, the biometric recognition module  210 ) may characterize the sensory data, and generate occupant appearance and status information to an occupant manager, which may use the sensory data according to described embodiments. 
     The internal and external sensory systems  283  and  281  may provide the sensory data obtained from the external sensory system  281  and the sensory data from the internal sensory system. The sensory data may include information from any of the sensors  284 - 289 , where the external sensor request message and/or the internal sensor request message can include the sensor modality with which the respective sensor system(s) are to obtain the sensory data. 
     The camera sensor(s)  285  may include thermal cameras, RGB (Red Green Blue) cameras, NIR (Near Infrared) cameras and/or a hybrid camera having thermal, RGB, NIR, or other sensing capabilities. Thermal cameras may provide thermal information of objects within a frame of view of the camera(s), including, for example, a heat map figure of a subject in the camera frame. A standard camera may provide color and/or black-and-white image data of the target(s) within the camera frame. The camera sensor(s)  285  may further include static imaging, or provide a series of sampled data (e.g., a camera feed) to the biometric recognition module  210 . 
     The IMU(s)  284  may include a gyroscope, an accelerometer, a magnetometer, or other inertial measurement devices. The fingerprint sensor(s)  287  can include any number of sensor devices configured and/or programmed to obtain fingerprint information. The fingerprint sensor(s)  287  and/or the IMU(s)  284  may also be integrated with and/or communicate with a passive key device, such as, for example, the mobile device  120  and/or the fob  179 . The fingerprint sensor(s)  287  and/or the IMU(s)  284  may also (or alternatively) be disposed on a vehicle exterior space such as the engine compartment (not shown in  FIG. 2 ), door panel (not shown in  FIG. 2 ), etc. In other aspects, when included with the internal sensory system  283 , the IMU(s)  284  may be integrated in one or more modules disposed within the vehicle cabin or on another interior surface of the vehicle. 
       FIG. 3  illustrates the vehicle  105  disposed with the automotive computer  145  (not shown in  FIG. 3 ). The automotive communication is operatively connected with and in communication with an antenna array  305 , which may include, for example, a plurality of Time-of-Flight (ToF) modules  111 , which may include Tags (ToF modules)  111 A,  111 B,  111 C,  111 D,  111 E,  111 F, and  111 G. The ToF modules are illustrated in  FIG. 3  as diamonds numbered 0-6 disposed at various locations on the vehicle  105 , in accordance with the present disclosure. 
     To categorize the position of the user  140  (not shown in  FIG. 3 ) when localization occurs, eight vehicle regions  106  may be defined. The vehicle regions  106  are depicted as dashed ovals surrounding the vehicle  105 , including vehicle regions  106 A,  106 B,  106 C,  106 D,  106 E,  106 F,  106 G and  106 H. These eight regions come from the 360° coverage associated with seven ToF modules  111  as illustrated by region numbers demarcated as numbered diamonds. The ToF modules  111  may provide vehicle surrounding location coverage in each of the respective vehicle regions  106  (e.g., 0°, 45°, etc.). Accordingly, each region covers approximately 45° of coverage. 
     When using conventional localization methods based on signal amplitude, (i.e., of BLE, ToF, UHF, etc.) signal interference that results in absorption, reflection or loss of a signal will cause inconsistencies when determining the position and/or distance of a ToF capable Smartphone or other device (i.e. a ToF capable watch, a Key Fob such as the fob  179  as shown in  FIG. 1 , or mobile device  120  which may be, for example, a smartphone, a tablet, or another such device) relative to the vehicle  105 , thus preventing robust performance of the PaaK to execute required operations such as lock, unlock, or PEPS. This is due to such absorption or reflection altering the expected signal amplitude of a signal for a known distance of a transmitter from a receiver and, in part, due to conventional systems utilizing a single module and/or antenna to localize the ToF device thus depending on only one measurement of amplitude versus multiple measurements. 
     Conventional user approach and localization methods based on the angle (position) around a vehicle, using angle of arrival by communicating with a single module and multiple antennas, can provide better performance than amplitude-based approaches. However, in scenarios where the line of sight is not consistently available, the angle of arrival approach is also problematic for conventional localization systems using BLE, ToF, or UHF angle of arrival. The above described shortcomings with both amplitude and/or angular based measurement of arriving signals can be avoided by measuring the time of propagation for the signal since time of propagation is not affected by absorption and line-of sight received versus reflected (multi-path) received signals since they can be identified by their relative time markers. 
     According to an embodiment of the present disclosure, the ToF localization system  107  may utilize the ToF modules  111  to determine the angle at which a ToF device approaches the vehicle  105  using ToF. The ToF localization system  107  may detect and measure ToF using, at least in part, a combination of the ToF modules  111  to accurately localize a ToF device (e.g., the mobile device  120  or the fob  179 , as shown in  FIG. 1 ) for 360° of coverage around the vehicle  105 . 
     The ToF localization system  107  may determine a region around the vehicle  105  where a user (not shown in  FIG. 3 or 4 ) may be standing or approaching. Example use case scenarios can include, for example, line of sight in an empty parking lot, non-line of sight in an empty parking lot, line of sight in a crowded parking lot, non-line of sight in a crowded parking lot, line of sight in a garage, and non-line of sight in a garage. Other scenarios are possible and such scenarios are contemplated. 
       FIG. 4  depicts a flow diagram illustrating localizing a user device using ToF according to these various use case scenarios, in accordance with the present disclosure.  FIGS. 3 and 4  will be considered together in the following section. 
     The location strategy can be initiated in many different ways. Initiation means that the process of localizing the mobile device  120  via PaaK to enable passive features has begun. Therefore, when the user device is actuated via a button or screen actuation, or when the user  140  presses/places a hand to a vehicle handle (not shown in  FIG. 3 or 4 ), the chassis I/O module  207  may cause one or more vehicle doors to be unlocked. 
     The ToF localization system  107  may begin initialization at step  405  in various ways. For example, in one aspect, the ToF localization system may actuate upon user  140 /mobile device  120  approach when the ToF localization system  107  causes the localization algorithm to determine that the approaching user is 2 meters or less from the skin of the vehicle  105 . This method consumes the most power by both the vehicle  105  and the smartphone or other mobile device  120 , as the vehicle  105  and mobile device  120  are (in most cases) in constant communication, and the vehicle  105  is localizing all or substantially all of the time. 
     In another aspect, the initialization step  405  commences when the mobile device  120 /vehicle  105  can successfully receive and interpret a signal from all seven of the ToF modules  111  (e.g., when all the ToF modules  111  are able to send packets to and receive packets from the mobile device  120 ). 
     In another embodiment, the initialization step  405  begins when all of the ToF modules  111  report a distance to mobile device value to the automotive computer  145 , and the maximum distance measurement from all the modules is less than 7.3 meters. 
     In yet another embodiment, the initialization step  405  commences when the user presses/places a hand to the handle of the vehicle  105  (which is the traditional and possibly the slowest method for the user  140  to trigger a passive entry). 
     Using the previously mentioned handle touch test method, the ToF localization system  107  is able to determine a vehicle region of the plurality of vehicle regions  108  in which the mobile device  120  or other ToF device (e.g., the fob  179 , etc.) is localized, as well as calculate the distance between the mobile device  120  or other ToF device and the vehicle  105 . The ToF localization system  107  performs this check regardless of the distance of the user  140 /mobile device  120  from the vehicle  105 . The first part of the algorithm (similar to a conventional operation PEPS localization) is initiated to determine if Passive features should be made available to provide vehicle access. Determining the user&#39;s  140  position and distance with respect to the vehicle  105  is still useful for continuous localizing, even when the user is more than 2 meters from the vehicle  105 . The procedure shown in  FIG. 4  describes the disclosed localization method when a user location is determined to be 2 meters and under from the vehicle. Another procedure is described with respect to  FIG. 5  for device localization using ToF when the user  140 /device  120  is determined to be a distance greater than 2 meters from the skin of the vehicle  105 . 
     At step  405 , the ToF localization system  107  may determine if the mobile device  120  is less than a predetermined threshold distance from the vehicle  105 . For example, in one embodiment, the ToF localization system  107  determines if the mobile device  120 , which can serve as an anchor point, is located more or less than 2 meters from the vehicle  105 . 
     At step  410  the ToF localization system  107  determines the distance from the anchor point (mobile device  120 ) to all seven of the ToF modules  111 . Responsive to a negative determination, at step  415 , the ToF localization system  107  may determine that the mobile device  120  is not localized at 0°, and advance to assessing localization at a second module location at 45° (step  416 ). It should be appreciated that the step  416  represents the entire algorithm of Checks 1, 2, 3, and 4 but at the 45° module instead of at 0°, then at 90° instead of 45°, etc., throughout the entire periphery of the vehicle  105 . 
     At step  420  the ToF localization system  107 , responsive to an affirmative localization determination at step  410 , may perform a first check (Check #1) to determine whether the mobile device  120  distance to Tag 0  111 A is less than the user&#39;s distance to all interior Tags  111 E- 111 G. Responsive to an affirmative determination, the ToF localization system  107  proceeds from step  420  to Check #2. Responsive to a negative determination at step  420 , the system returns to step  415 , where the ToF localization system  107  may determine that the mobile device  120  is not localized at the present position (currently at 0°  106 A), and then proceeds to a round of checking at a second location at 45° (step  416 ). It should be appreciated that the step  416  represents the entire algorithm of Checks 1, 2, 3, and 4 but rather at a next position at 45° instead of at 0°, then at 90° instead of 45°, etc., throughout the entire periphery of the vehicle  105 . 
     At step  425  the ToF localization system  107  performs a second check (Check #2), to determine whether the mobile device  120  distance to Tag 2  111 C is less than the user&#39;s distance to all interior Tags  111 E- 111 G. Responsive to a positive determination, the process proceeds to Check #3. Responsive to a negative determination at step  425 , the ToF localization system  107  may determine that the mobile device  120  is not localized at the present position (currently at 0°  106 A), and moves to a round of checking at a second location at 45° (step  416 ). 
     At step  430  the ToF localization system  107  performs a third check (Check #3) and determines whether the mobile device  120  distance to Tag 4  111 E is less than the user&#39;s distance to the other two interior Tags  111 F and  111 G. Responsive to a positive determination, the process proceeds to Check #4. Responsive to a negative determination, at step  436 , the ToF localization system  107  may determine that the mobile device  120  is not localized at the present position (currently at 0°  106 A), and moves to a round of checking at a second location at 45° (step  416 ). 
     At step  435  the ToF localization system  107  performs a fourth check (Check #4) and determines whether the mobile device  120  distance to Tag 5  111 F is less than the user&#39;s distance to Tag 6  111 G, AND the mobile device  120  is less than a predetermined distance (e.g., 1.5 meters, 1.8 meters, etc.) to Tag 0  111 A. Responsive to a negative determination, at step  435 , the ToF localization system  107  may determine that the mobile device  120  is not localized at the present position (currently at 0°  106 A), and moves to a round of checking at a second location at 45° (step  416 ). 
     At step  440  the ToF localization system  107  determines that the mobile device  120  is at the front of the vehicle (e.g.,  106 A), and proceeds to performance of a distance calculation at step  445 . 
     Using the logic conditions from the procedure above ensures that the mobile device  120  is in one of the eight specified regions  106 A- 106 H with a relatively higher confidence than if fewer conditions were used. Successfully determining the location of the user device (e.g., the mobile device  120 ) and thus, the user  140 , also guarantees that the final part of the algorithm runs correctly to determine the distance the user  140  is from the vehicle  105 . Using the closest tags to the specific region, and comparing distances to the other tags over the four checks (Check 1 through Check 4) helps maintain accuracy and precision in locating the user  140  using ToF. 
     In the case that the ToF device (e.g., the mobile device  120 ) is not in the 0° region  106 A, the logic and flowchart to check if the device is at the 45° vehicle region  106 B, 90° vehicle region  106 C, or any of the other positions around the vehicle (e.g.,  106 D- 106 H), the logic and flowchart use the same approach. The mobile device  120  distances to various tags are measured and compared to the distance from the closest Tags to the particular region that is being checked. For example, if the device  120  is being checked at the 45° region  106 B, distances to other Tags are compared to Tags 0  111 A, 1  111 B, 4  111 E, &amp; 5  111 F. If the device  120  is being checked at the 225° region  106 F, distances to other Tags are compared to Tags 2  111 C, 3  111 D, 5  111 F, &amp; 6  111 G. 
       FIG. 5  depicts another flow diagram of localizing a user device using ToF when the user device is determined to be over 2 meters from the vehicle  105  in accordance with the present disclosure. 
     These four checks rely on Tags 0  111 A, 1  111 B, 2  111 C, 3  111 D, 5  111 F, &amp; 6  111 G. If one of the four checks (e.g., Checks #1-4 as shown in  FIG. 5 ) pass, the mobile device  120  (and thus, the user  140 ) is determined to be at the 0° vehicle region  106 A. If all of the four checks fail, the mobile device  120  is determined not to be at the 0° vehicle region  106 A. The algorithm then moves on to check if the user is at the next subsequent region, (e.g., the 45° region  106 B, etc.) until all the vehicle regions  106  are checked. 
     At step  505  the ToF localization system  107  may determine if the mobile device  120  is greater than a predetermined threshold distance from the vehicle  105 . One example threshold distance is 2 meters. In another embodiment, the threshold distance may be 2.5 meters, 3 meters, etc. In one embodiment, starting at the 0° vehicle region  106 A, the ToF localization system  107  determines whether the mobile device  120  is located more or less than 2 meters from the vehicle  105 . 
     Responsive to determining that the mobile device  120  is more than 2 meters from the vehicle  105 , the ToF localization system  107  proceeds to step  510 , to check the mobile device  120  position at region 0°  106 A with respect to the vehicle  105 . Accordingly, the ToF localization system  107  performs a first check (e.g., Check 1) to determine the distance from an anchor point (e.g., the mobile device  120 ) to all seven of the ToF modules  111 . 
     At the first check (Check #1) at step  515 , if the mobile device  120  distance to Tag 0  111 A is less than the user device&#39;s distance to all interior Tags  111 E,  111 F, and  111 G, AND the user device distance to Tag 2  111 C is less than the user device distance to all of the interior Tags  111 E,  111 F, and  111 G, AND the user device distance to the Tag 6  111 G is less than 7 meters, AND the Tag 1  111 B and the Tag 3  111 D fail to communicate with the user&#39;s ToF device  120 , then the user is located at the front of the vehicle  105  (e.g., the vehicle region  106 A). Responsive to determining that the mobile device  120  is not at the front of the vehicle in vehicle region  106 A, then the ToF localization system  107  proceeds to the next step. 
     At the second check (Check #2) at step  520 , the ToF localization system  107  determines if the Tag 5  111 F value is the maximum value of all the values associated with the tags  111 . Responsive to an affirmative determination, the ToF localization system  107  determines that the user  140  is positioned at the front of the vehicle (e.g., the vehicle region  106 A). Responsive to a negative determination, the ToF localization system  107  proceeds to the third check. 
     At the third check (Check #3) at step  525 , if the mobile device  120  distance to Tag 6  111 G is greater than the user device&#39;s  120  distance to all of the other tags  111 A- 111 F, AND Tag 1  111 B and Tag 3  111 D have failed to communicate with the mobile device  120 , then the ToF localization system  107  determines that the user  140  is positioned at the front of the vehicle (e.g., the vehicle regions  106 A). Responsive to a negative determination (e.g., there was communication at one of the tested Tag locations), then the system proceeds to block  530  which is fourth check (check #4). Responsive to a positive determination, the ToF localization system  107  proceeds to block  535 . 
     At block  530  the ToF localization system  107  performs the fourth check (Check #4), to determine whether the distance to the Tag 0  111 A is greater than a predetermined threshold distance (e.g., 11 m, 10.5 m, 10 m, etc.). Responsive to a positive determination, the ToF localization system  107  determines that the mobile device  140  is localized at 0° (step  535 ). Responsive to a negative determination, the ToF localization system determines that the user is not localized at 0° at step  545 . The system may then move to the next 45° position as shown at block  550 . 
     Again, multiple conditions confirm the mobile device  120  location at a higher level of confidence than if fewer conditions were used. Correctly determining the vehicle region  106  in which the mobile device  120  is localized may confirm that the final portion of the algorithm runs properly when determining the distance, the user  140  is from the vehicle  105 . Similar to the short-range localization, the long-range localization uses tags that are closest to the specific vehicle region  106  being evaluated for localization, and compares them to the distances to other tags. In addition, a few other logic checks are in place, in the case one of the previous conditions fails. The long-range process runs through all eight regions each time to maintain accuracy and precision. 
       FIG. 6  is a table of tag distances at 0° with respect to the MIMO antenna array (e.g., the ToF modules  111 ) in accordance with the present disclosure. The table of  FIG. 6  depicts data for longer distances (e.g., 2 m, 3 m, 4 m, etc.). There are four OR checks performed to determine if the user of an ToF device is at the 0° (front of the vehicle) region  106 A.  FIG. 7  is a table of measurement tag distances at 0° with respect to the MIMO antenna array (e.g., the ToF modules  111 ) in accordance with the present disclosure. The table of  FIG. 7  depicts data for shorter distances (e.g., 1 ft, 1 m, etc.). 
     The tables depicted in  FIG. 6  and  FIG. 7  include data collected during testing at different distances to the vehicle  105 . There are some differences between short and long range. At close distances, all seven tags  111  may communicate with the ToF anchor (e.g., the mobile device  120 ). At long distances, this may not be the case. Due to these differences, the logic checks may be advantageously differentiated from one another to confirm the user&#39;s  140  position. This is also the reason that the first part of the algorithm includes performance of a check to determine if the mobile device  120  is over or under two meters from the vehicle  105 . Regardless of distance, the ToF localization system  107  accurately reports the distance from anchor (e.g., the mobile device  120 ) to Tag  111 , and the change in testing distance is in the results. 
       FIG. 8  illustrates a flow diagram  800  showing the steps the algorithm takes to calculate the mobile device  120  distance to the vehicle  105 , according to another embodiment.  FIGS. 9A and 9B  depict localization of the mobile device  120  using three methods of trilateration, in accordance with the present disclosure.  FIG. 8  and  FIG. 9  are considered together in the following section. 
     With reference now to  FIG. 8 , at step  805 , the ToF localization system  107  may determine a particular region proximate to the vehicle  105  in which the ToF device (e.g., the mobile device  120 ) is localized, as well as calculate the distance the smartphone or other ToF device is from the vehicle  105 . This is done regardless of being over or under two meters from the skin of the vehicle  105 . Even when determined to be over two meters, the ToF localization system  107  can still track the approach of the user device by repeating the localization algorithm. The distance calculations are the same, regardless if the user  140  is determined to be over or under two meters. There are three different types of calculation methods. One uses direct distance measurements to the tags. The second creates right triangles based on the tag position as well as different distances, inherent to the vehicle itself such as width. The third method uses a 2D Trilateration method to calculate distance. Note that when the user is determined to be over 2 meters from the vehicle, 2D trilateration calculations are not computed, due to possible failure to communicate with some tags. After all calculations are complete, a confidence percentile is determined based on the how many of the total calculations are within a predetermined range. 
     At step  810  the ToF localization system  107  determines a position of the mobile device  120  at position 0°  106 A (as depicted in  FIG. 3 ). At steps  815 ,  820 ,  825 ,  830 ,  835 ,  840 ,  845 ,  850 , and  860 , the ToF localization system  107  performs a series of calculations, then performs at step  865  a series of trilateration calculations (represented in  FIG. 8  as step  865  and described in detail with respect to  FIG. 9 ), performs a confidence determination step at Step  870 , and finally reiterates the process at subsequent 45° intervals at step  875 . 
     With reference now to  FIGS. 9A and 9B , Methods 1-9 are described pictorially, where  FIG. 9A  illustrates trilateration using Methods #1 and #2, and  FIG. 9B  illustrates trilateration using Methods #3-9. 
     Method #1 and Method #2 take direct measurements from Tag 0  111 A and Tag 2  111 C (as shown in  FIG. 3 ), respectively. Using a known half width of the vehicle  105  to create a right triangle, and the Pythagorean Theorem, a distance is calculated for the mobile device  120  from the vehicle  105 . This distance is adjusted by subtracting the physical distance of where Tag 0  111 A and Tag 2  111 C are positioned, from the front of the vehicle  105  for Method 1 and 2, respectively. 
     For example, if Tag 0  111 A measures the user to be 3.3 meters from the skin of the vehicle  105 , this is classified as variable ‘C’. If the known half width of the vehicle  105  is 0.9 meters, this is classified as variable ‘A’. By Pythagorean Theorem for right triangles, A 2 +B 2 =C 2 . Rearranging this to get the unknown side B=√(C 2 −A 2 ). The algorithm may then take the difference between B and the distance from the front of the vehicle  105  to Tag 0  111 A. If this value is 1 m then the user  140  is calculated to be √( 3.3 ) 2 − 0.9   2 )−1=2.1 m from the vehicle  105 , according to Method #1. The same approach may also be utilized to perform Method #2. 
     Method #&#39;s 3-9 may include calculation of the user&#39;s  140  (and more precisely, distance of the mobile device  120 ) from the vehicle  105  in the same way. Method 3, 4, 5, 6, 7, 8, and 9 may take direct measurements to Tags 0  111 A, 1  111 B, 2  111 C, 3  11 D, 4  111 E, 5  111 F, and 6  111 G, respectively. 
     The ToF localization system  107  may next take a difference from the measured distance and the physical distance of where Tags 0-6 ( 111 A- 111 G) are from the front of the vehicle  105 . For example, if Tag 4  111 E measures a distance to the mobile device  120  to be 2.4 meters from the skin of the vehicle  105 , and Tag 4  111 E is 1.8 meters inward from the front of the vehicle  105 , then the mobile device  120  is calculated to be 4.1-1.8=2.3 m from the vehicle  105 . 
       FIG. 10  depicts localization of the mobile device  120  using trilateration, in accordance with the present disclosure. The last calculation is a 2D Trilateration calculation. This calculation method uses two Cartesian dimensions, analytical geometry and a station-based coordinate frame. 
     The 2D Trilateration calculation uses two tags on the vehicle  105 . These two tags act as the two circle centers (C1  1005  and C2  1010 ). For the 0° vehicle region  106 A (as depicted in  FIG. 3 ), let C1  1005  be Tag 0 and C2 be Tag 2. These two circles have a known separation U. Let U be the known distance between Tag 0  111 A and Tag 2  111 C. In this case, U is the full width of the vehicle  105 . Where these two circles  1005  and  1010  intersect is point P, which may be an (x, y) coordinate relative to the (0, 0) coordinate frame of the center of C1. The radii of the two circles are defined as: 
     
       
      
       r 
       1 
       2 
       =x 
       2 
       +y 
       2  
      
     
         r   2   2 =( U−x ) 2   +y   2    
     They can be rearranged to the following form: 
     
       
         
           
             x 
             = 
             
               
                 
                   r 
                   1 
                   2 
                 
                 - 
                 
                   r 
                   2 
                   2 
                 
                 + 
                 
                   U 
                   2 
                 
               
               
                 2 
                 ⁢ 
                 U 
               
             
           
         
       
       
         
           
             y 
             = 
             
               ± 
               
                 
                   
                     r 
                     1 
                     2 
                   
                   - 
                   
                     x 
                     ⋀ 
                     2 
                   
                 
               
             
           
         
       
     
     This (x, y) coordinate pair represents the position of the mobile device  120  based on the trilateration calculation. The y coordinate represents the distance from the vehicle  105 . The difference of the y value and the physical distance Tag 0  111 A and Tag 2  111 C are from the front of the vehicle  105  is taken to get the final distance the mobile device  120  is from the vehicle  105 . 
     For example, if the half width of the vehicle  105  is 0.9652 m and the user&#39;s distance is determined to be 2.1 m, then the 2D coordinate pair is (0.9652, 2.1). The distance from the front of the vehicle  105  to Tag 0  111 A and Tag 2  111 C is 1.016 m. Using they coordinate point, the user is 2.1−1.016=1.084 m from the vehicle  105 . 
     The result is based on 10 total calculations once the user (e.g., the mobile device  120 ) has been determined to be at the region 0° vehicle region  106 A (depicted in  FIG. 3 ). 
     With reference again to  FIG. 8 , at step  870 , the ToF localization system  107  determines a confidence percentile based on all 10 results. The majority of the calculations that fall into a specified range may result in a distance from the vehicle  105  from which the user is located. For example, if 8 out of 10 of the 10 results evaluate within the range of 1.75-2.75, then the user  140  is determined to be 2 m from the vehicle  105  with an 80% confidence metric. 
     In addition to the calculations performed for the specific vehicle region  106  (as shown in  FIG. 3 ) the location in which user  140  is positioned, the ToF localization system  107  may evaluate results for both neighboring regions to assure a distance from the vehicle  105  is relatively accurate to a particular region localized by Part  3 . For example, if Part  3  determined the user  140  is localized in the 0° vehicle region  106 A, the ToF localization system  107  may execute all calculation methods for the 45° vehicle region  106 B and the 315° vehicle region  106 H (see  FIG. 3 ). These neighboring regions to the region— 106 A may also be included in the percent confidence calculation. 
     The calculation methods for the other 7 regions  106 B- 106 H around the vehicle  105  are the same. A combination of direct measurements to the Tags  111 , creating triangles based on known dimensions on the vehicle  105  and tag locations, and 2D trilateration. The ToF localization system  107  may calculate percent intervals at all eight regions around the vehicle  105  and at all distances, regardless of whether the ToF device (mobile device  140 ) is over or under 2 meters form the skin of the vehicle  105 . 
     In the above disclosure, reference has been made to the accompanying drawings, which form a part hereof, which illustrate specific implementations in which the present disclosure may be practiced. It is understood that other implementations may be utilized, and structural changes may be made without departing from the scope of the present disclosure. References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, when a feature, structure, or characteristic is described in connection with an embodiment, one skilled in the art will recognize such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. 
     Further, where appropriate, the functions described herein can be performed in one or more of hardware, software, firmware, digital components, or analog components. For example, one or more application specific integrated circuits (ASICs) can be programmed to carry out one or more of the systems and procedures described herein. Certain terms are used throughout the description and claims refer to particular system components. As one skilled in the art will appreciate, components may be referred to by different names. This document does not intend to distinguish between components that differ in name, but not function. 
     It should also be understood that the word “example” as used herein is intended to be non-exclusionary and non-limiting in nature. More particularly, the word “example” as used herein indicates one among several examples, and it should be understood that no undue emphasis or preference is being directed to the particular example being described. 
     A computer-readable medium (also referred to as a processor-readable medium) includes any non-transitory (e.g., tangible) medium that participates in providing data (e.g., instructions) that may be read by a computer (e.g., by a processor of a computer). Such a medium may take many forms, including, but not limited to, non-volatile media and volatile media. Computing devices may include computer-executable instructions, where the instructions may be executable by one or more computing devices such as those listed above and stored on a computer-readable medium. 
     With regard to the processes, systems, methods, heuristics, etc. described herein, it should be understood that, although the steps of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes could be practiced with the described steps performed in an order other than the order described herein. It further should be understood that certain steps could be performed simultaneously, that other steps could be added, or that certain steps described herein could be omitted. In other words, the descriptions of processes herein are provided for the purpose of illustrating various embodiments and should in no way be construed so as to limit the claims. 
     Accordingly, it is to be understood that the above description is intended to be illustrative and not restrictive. Many embodiments and applications other than the examples provided would be apparent upon reading the above description. The scope should be determined, not with reference to the above description, but should instead be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. It is anticipated and intended that future developments will occur in the technologies discussed herein, and that the disclosed systems and methods will be incorporated into such future embodiments. In sum, it should be understood that the application is capable of modification and variation. 
     All terms used in the claims are intended to be given their ordinary meanings as understood by those knowledgeable in the technologies described herein unless an explicit indication to the contrary is made herein. In particular, use of the singular articles such as “a,” “the,” “said,” etc. should be read to recite one or more of the indicated elements unless a claim recites an explicit limitation to the contrary. Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments could include, while other embodiments may not include, certain features, elements, and/or steps. Thus, such conditional language is not generally intended to imply that features, elements, and/or steps are in any way required for one or more embodiments.