Patent Publication Number: US-11661109-B2

Title: Motor vehicle with turn signal-based lane localization

Description:
INTRODUCTION 
     The present disclosure relates to methods and systems for increasing the fidelity of an autonomous lane localization function aboard a motor vehicle. Motor vehicles often come equipped with lane localization systems having logic and associated hardware. Working together, such systems facilitate responsive dynamic steering control actions. Modern lane localization systems tend to be highly reliant on digital geospatial mapping data and real-time GPS information when determining a motor vehicle&#39;s present location. Autonomous steering control decisions may also be informed by data from ever-evolving vision systems such as video cameras, radar and/or lidar sensors, and the like. Boundaries of paved road surfaces are usually demarcated by detectable broken-line or solid-line lane markings. Onboard processing of a variety of real-time lane localization data enables an onboard controller to interpret the surrounding environment and make informed control decisions, such as when automatically adjusting a steering angle to maintain the motor vehicle&#39;s current lane position or change lanes. 
     As understood in the art, lane localization functions employed by an Advanced Driver Assistance System (ADAS) of a modern day motor vehicle utilize a suite of onboard sensors to collect different data types, with the collected data collectively describing the surrounding environment. Lane localization functions also enable a driver of the motor vehicle to selectively offload certain driving tasks to the onboard controller. In the context of automated lane keeping/centering, for instance, relevant data includes the above-noted GPS-informed/geocoded locations of detected lane markings and other road surface boundaries. The controller continuously calculates a level of error between the present position/heading of the motor vehicle and a trajectory of the detected lane markings, and responds with autonomous steering control signals to produce a situation-appropriate steering response. Lane change assist systems likewise use the detected lane markings and other available sensor inputs to inform an autonomous steering maneuver when departing a current lane, e.g., when passing another vehicle, while turning, or when merging onto an offramp at a highway exit. 
     SUMMARY 
     The present disclosure pertains to the real-time operational control of a motor vehicle or another mobile platform using a lane localization function of the type generally noted above. Automatic lane centering, lane change assist, and other Advanced Driver Assistance System (ADAS)-executed control functions rely on a lane localization algorithm and associated hardware. However, such algorithms tend to be heavily dependent on lateral GPS accuracy when making a host lane assignment, i.e., when identifying a particular roadway lane within which the motor vehicle is presently traveling. GPS data has built-in error, and therefore an incorrect host lane assignment based solely or primarily on GPS data may lead to a suboptimal steering control performance. 
     Unlike traditional lane localization approaches, the present methodology incorporates an ON/OFF position and corresponding directional state (“turn signal state”) of a turn signal lever as an additional lane localization input. Associated logic enabled by various electrooptical video cameras, remote sensing devices such as radar and/or lidar systems, digital mapping data, etc., are also used to reduce instances of false or inadvertent turn signal indications, thereby increasing overall fidelity and robustness of the disclosed solution. 
     In a particular embodiment, a method for increasing fidelity of a lane localization function aboard a motor vehicle having a turn signal lever includes receiving input signals indicative of a relative position of the motor vehicle with respect to a roadway, which occurs via an onboard controller. The received input signals include GPS data and geocoded mapping data. In response to a set of enabling conditions, the method further includes receiving an electronic turn signal as an additional component of the input signals. The electronic turn signal is indicative of a present activation state of the turn signal lever, i.e., an indication of an impending right-hand or left-hand turn. Multiple sensor-specific lane probability distributions are then calculated via the lane localization function. This statistical calculation occurs using each of the input signals inclusive of the electronic turn signal. 
     Thereafter, the present method includes automatically fusing the various lane probability distributions, once again using the lane localization function, to thereby generate a host lane assignment of the motor vehicle. The host lane assignment corresponds to a lane of the roadway having a highest probability among a set of possible lane assignments. The controller, via operation of an ADAS, then executes an autonomous steering control action aboard the motor vehicle in response to the host lane assignment, thus changing the dynamic state of the motor vehicle. 
     The motor vehicle in some configurations may include a video camera configured to collect real-time video image data of the roadway. In such an embodiment, the input signals include the real-time video image data. 
     The method may also include determining a lane marker type, via the controller, using the real-time video image data of the roadway. The enabling conditions in this particular instance may include a predetermined “crossable” lane marker type on a side of a lane matching a direction of the turn signal, e.g., a broken or dashed line demarcating a crossable boundary of a given lane in accordance with prevailing traffic laws. 
     The motor vehicle in other embodiments may include a remote sensing system configured to collect radar data and/or lidar data of the roadway, with the input signals including the radar data and/or the lidar data. 
     The method may include automatically fusing the radar data and/or the lidar data with the video image data using an object fusion logic block of the controller, or fusing the radar data with the lidar data. 
     In an exemplary non-limiting embodiment of the present method, the lane localization function includes a Markov localization function. Calculating the plurality of lane probability distributions in this instance includes using the Markov localization function. 
     The enabling conditions contemplated herein may also include a lane change value indicative of an elapsed time since a last-detected lane change in a direction of the turn signal and/or an elapsed time since the turn signal has been set in a particular direction. 
     In an aspect of the disclosure, the method includes determining a driver attention score via the controller. In this particular scenario, the enabling conditions may include the driver attention score exceeding a calibrated threshold attention score. 
     The enabling conditions may also include a look-ahead value indicative of an existence of and/or an estimated width of an upcoming lane of the roadway, e.g., as reported to the controller from external applications, the mapping data, crowdsourced apps, etc. 
     Executing an autonomous steering control action may include executing one or more of a lane centering control maneuver, a driver-requested automatic lane change maneuver, and/or a controller-initiated automatic lane change maneuver. 
     A motor vehicle is also described herein as having a set of (one or more) road wheels connected to a vehicle body, a turn signal lever, an ADAS configured to control a dynamic state of the motor vehicle based on a host lane assignment, and a controller. The controller in turn is configured to execute instructions for increasing fidelity of an autonomous lane localization function aboard the motor vehicle using turn signals, with the turn signals generated by activation of the turn signal lever. This occurs via execution of the above-summarized method. 
     Also disclosed herein is a computer-readable medium on which is recorded instructions for selectively increasing fidelity of an autonomous lane localization function aboard a motor vehicle having a turn signal lever. The instructions are selectively executed by a processor of the motor vehicle in response to the enabling conditions to thereby cause the processor to execute the present method in its different disclosed embodiments. 
     The above features and advantages, and other features and attendant advantages of this disclosure, will be readily apparent from the following detailed description of illustrative examples and modes for carrying out the present disclosure when taken in connection with the accompanying drawings and the appended claims. Moreover, this disclosure expressly includes combinations and sub-combinations of the elements and features presented above and below. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a schematic illustration of a representative motor vehicle configured with turn signal-based lane localization logic in accordance with the present disclosure. 
         FIG.  2    is a plan view illustration of an exemplary lane change maneuver in which the motor vehicle of  FIG.  1    activates a turn signal when initiating the lane change maneuver. 
         FIG.  3    is a flow chart describing an exemplary embodiment of a method in accordance with the present disclosure. 
         FIG.  4    is a schematic logic flow diagram of exemplary control logic usable by the motor vehicle of  FIG.  1   . 
         FIG.  5    is a representative series of probability distributions describing an example application of the use of turn signal when performing a lane localization function aboard the motor vehicle shown in  FIG.  1   . 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure is susceptible of embodiment in many different forms. Representative examples of the disclosure are shown in the drawings and described herein in detail as non-limiting examples of the disclosed principles. To that end, elements and limitations described in the Abstract, Introduction, Summary, and Detailed Description sections, but not explicitly set forth in the claims, should not be incorporated into the claims, singly or collectively, by implication, inference, or otherwise. 
     For purposes of the present description, unless specifically disclaimed, use of the singular includes the plural and vice versa, the terms “and” and “or” shall be both conjunctive and disjunctive, “any” and “all” shall both mean “any and all”, and the words “including”, “containing”, “comprising”, “having”, and the like shall mean “including without limitation”. Moreover, words of approximation such as “about”, “almost”, “substantially”, “generally”, “approximately”, etc., may be used herein in the sense of “at, near, or nearly at”, or “within 0-5% of”, or “within acceptable manufacturing tolerances”, or logical combinations thereof. 
     Referring to the drawings, wherein like reference numbers refer to like features throughout the several views, a motor vehicle  10  is depicted in  FIG.  1    having a controller (C)  50  programmed to execute a method  100 . Execution of computer-readable instructions embodying the method  100 , an embodiment of which is shown in  FIG.  3    and described in detail below, enables the controller  50  to selectively increase fidelity of an onboard lane localization function  51  by situationally incorporating a driver-initiated turn signal ON/OFF and directional state (“turn signal state”) into its lane probability calculations. Increased fidelity in the present teachings refers to the improved accuracy of lane localization predictions relative to an actual position of the motor vehicle, relative to approaches that do not incorporate turn signal information as set forth herein. 
     For illustrative simplicity, select components of the motor vehicle  10  are shown and described while other components are omitted. In the depicted representative embodiment of  FIG.  1   , the motor vehicle  10  includes road wheels  12  positioned with respect to a vehicle body  14 , with the road wheels  12  in rolling contact with a road surface  16 , with at least one of the road wheels  12  being powered/driven. The actual number of road wheels  12  may vary with the particular configuration of the motor vehicle  10 . That is, as few as one driven road wheel  12  is possible, for instance in the context of motorcycles, scooters, or electric bicycles/e-bikes, with two or more driven road wheels  12  being possible in other configurations, e.g., gasoline powered and/or electrically powered passenger or commercial sedans, crossover vehicles, sport utility vehicles, trucks, etc. 
     Within the scope of the present disclosure, the motor vehicle  10  is equipped with a plurality of lane localization input sensors and/or devices, hereinafter referred to as a lane localization suite  18  for simplicity. Collectively, the constituent components of the lane localization suite  18  provide input signals (arrow  30 ) to the controller  50  indicative of a relative position of the motor vehicle  10  with respect to/on a roadway having the road surface  16 . The capabilities of the lane localization suite  18  are thus relied upon in real-time by the controller  50  when performing autonomous or semi-autonomous steering functions, such as but not necessarily limited to lane keep assist with lane departure warning, lane change assist, lane centering, etc. 
     The composition of the lane localization suite  18  will vary with the particular equipment configuration of the motor vehicle  10 . Typically, however, the lane localization suite  18  will include or have access to at least a geocoded mapping database  22  and a GPS receiver  24 , the latter of which receives GPS signals  25  from an orbiting constellation of GPS satellites (not shown), as is well understood in the art. Thus, the input signals (arrow  30 ) typically include geocoded mapping data and the GPS signals  25  from the respective geocoded mapping database  22  and GPS receiver  24 , with the mapping data provided by such sources displayed to the driver of the motor vehicle  10  via a touch screen (not shown) or other suitable display arranged in a center stack or other convenient location within the motor vehicle  10 , or on a similar touch screen of a smartphone or other portable electronic device. 
     Additionally, the lane localization suite  18  may include a video camera  20  and one or more remote sensing transceivers  26 , e.g., a radar sensor and/or a lidar sensor. With respect to the video camera  20 , such a device may be securely connected to the vehicle body  14  at a suitable forward-facing location thereof, such as behind a rearview mirror  17  attached to a windshield  19 , to a dashboard (not shown), or at another application-suitable location providing good visibility of the roadway lying before the motor vehicle  10 . The video camera  20  is configured to collect real-time video image data of the roadway, with the input signals (arrow  30 ) including the real-time video image data. The remote sensing transceiver(s)  26  in turn are configured to transmit electromagnetic energy at sensor-specific wavelengths toward a target as an interrogation signal, and to receive a reflected portion of the electromagnetic waveform from the target as a response signal. In  FIG.  1   , such interrogation and response signals are abbreviated WW for illustrative simplicity. 
     In addition to the video camera  20 , the geocoded mapping database  22 , the GPS receiver  24 , and the remote sensing transceivers  26 , the method  100  and controller  50  of  FIG.  1    also selectively rely on the turn signal state of a turn signal lever  28  as an additional constituent component of the lane localization suite  18 . As understood in the art, such a turn signal lever  28  is situated in close proximity to a steering wheel  29  of the motor vehicle  10 . When a driver (DD) decides to change lanes or turn, the driver (DD) signals the direction of the impending maneuver by moving the turn signal lever  28  up or down. Within a corresponding turn signal circuit, which is omitted for illustrative simplicity, such activation of the turn signal lever  28  enables an electric current to energize a turn signal lamp  31  disposed at the various corners of the motor vehicle  10 . Blinking action of the turn signal lamp  31 , represented at TS in  FIG.  1    for a representative left hand turn, visually alerts other drivers or pedestrians in proximity to the motor vehicle  10  of the impending turn direction. Thus, the turn signal state is selectively used herein, subject to certain entry conditions, as an additional available input to the controller  50  for the purpose of improving upon the fidelity of the lane localization function  51 . 
     For the purposes of executing the method  100 , the controller  50  shown schematically in  FIG.  1    is equipped with application-specific amounts of the volatile and non-volatile memory (M) and one or more of processor(s) (P), e.g., microprocessors or central processing units, as well as other associated hardware and software, for instance a digital clock or timer, input/output circuitry, buffer circuitry, Application Specific Integrated Circuits (ASICs), systems-on-a-chip (SoCs), electronic circuits, and other requisite hardware as needed to provide the programmed functionality. The method  100  may be implemented in response to the input signals (arrow  30 ) through a computer-executable program of instructions, such as program modules, generally referred to as software applications or application programs executed by the controller  50  or variations thereof. The controller  50  may thereafter transmit output signals (arrow CCo) as part of the method  100 , e.g., to a collection of Automated Driver Assistance System (ADAS) equipment  70 . 
     Software may include, in non-limiting examples, routines, programs, objects, components, and data structures that perform particular tasks or implement particular data types. The software may form an interface to allow a computer to react according to a source of input. The software may also cooperate with other code segments to initiate a variety of tasks in response to data received in conjunction with the source of the received data. The software may be stored on a variety of memory (M), such as but not limited to CD-ROM, magnetic disk, solid-state memory, etc. Similarly, the method  100  or parts thereof may be executed by a device other than the controller  50  and/or embodied in firmware or dedicated hardware in an available manner, such as when implemented by an ASIC, a programmable logic device, a field programmable logic device, discrete logic, etc. 
     Still referring to  FIG.  1   , as part of the present solutions, the controller  50  is equipped to execute a control action downstream of the disclosed lane localization probability determination described below. To that end, the ADAS equipment  70  may include, by way of example and not of limitation, a Lane Keep Assist (LKA) system  52  and a Lane Departure Warning (LDW) system  54 , which may operate alone or in conjunction with each other in different situations. Electronic control and feedback signals (arrow  30 - 1  and arrow  30 - 2 ) may be exchanged between the controller  50 , the LKA system  52 , and the LDW system  54  in response to the method  100  as the controller  50  executes a dynamic control action. The input signals (arrow  30 ) thus may include the corresponding electronic control and feedback signals (arrow  30 - 1  and  30 - 2 ). The motor vehicle  10  may also be equipped with these or a number (N) of additional ADAS components, i.e., ADAS N    58 , with corresponding electronic control and feedback signals (arrow  30 -N) to enable the controller  50  to execute an autonomous steering control action, including a lane centering control maneuver, a driver-requested automatic lane change maneuver, and/or a system/controller  50 -initiated automatic lane change maneuver. 
     Referring now to  FIG.  2   , the motor vehicle  10  is depicted in the process of executing a representative lane change maneuver while traveling on multi-lane driving surface  40 . In this instance, the multi-lane driving surface  40  is represented as a stretch of highway or freeway having four parallel lanes, i.e., L 1 , L 2 , L 3 , and L 4 , separated from each other by inner lane markings  44 , in this instance broken-line and thus crossable in accordance with prevailing traffic laws. The outer boundaries of the driving surface are demarcated by outer lane markings/boundary lines  144 . Such boundary lines  144  typically are solid to delineate the start of a shoulder at the edge of the multi-lane driving surface  40 , but also may be other non-crossable lines, e.g., centerlines, or on inclines or hills, or on extended stretches of the multi-lane driving surface having limited forward visibility and/or obstructed views. 
     In the illustrated scenario, a driver of the motor vehicle  10  traveling in lane L 2  may decide to merge into lane L 1 , e.g., in preparation for an upcoming offramp or when passing another vehicle. Such a lane change maneuver is indicated in  FIG.  2    by arrow AA. The controller  50  of  FIG.  1    may assist in this effort as part of an autonomous or semi-autonomous steering control maneuver. However, in order to do so the controller  50  requires accurate real-time data indicative of the present location of the motor vehicle  10  relative to the multi-lane driving surface  40  and its lanes L 1 , L 2 , L 3 , and L 4 . This determination is made automatically by the controller  50  based on a statistical probability analysis, as appreciated in the general art and described in further detail below. In accordance with the method  100 , and to increase fidelity of the onboard lane localization function  51  of  FIG.  1   , the controller  50  selectively incorporates the activation state of the turn signal lever  28  of  FIG.  1    into its lane localization calculations. 
     Referring to  FIG.  3   , an exemplary embodiment of the method  100  is described in terms of programmed steps or algorithm logic blocks (“blocks” for simplicity). Method  100  commences at block B 102  (“REC  30 ”) with the controller  50  of  FIG.  1    receiving the input signals (arrow  30 ), inclusive of the electronic control and feedback signals (arrows  30 - 1 ,  30 - 2 , . . . ,  30 -N of  FIG.  1   ) and thus indicative of a relative position of the motor vehicle  10  with respect to a roadway, e.g., the multi-lane driving surface  40  of  FIG.  2   . In a possible implementation of the method  100 , block B 102  includes receiving GPS data and geocoded mapping data from the GPS receiver  24  and the mapping database  22 , respectively, as part of the input signals (arrow  30 ). The method  100  proceeds to block B 104  upon receipt of the input signals (arrow  30 ). 
     At block B 104  (“TS-ENBL Cond?”), the controller  50  of  FIG.  1    may determine whether certain turn signal-based fidelity enhancement enabling conditions are satisfied. Block B 104  is used to ensure that the turn signal state of the turn signal lever  28  of  FIG.  1    is used situationally, i.e., when appropriate and likely to be informative, and not when consideration of the turn signal information could reduce the accuracy of resident lane probability determinations of the lane localization function  51 . 
     As part of the present method  100 , the controller  50  of  FIG.  1    may receive and evaluate an electronic signal indicative of activation state of the turn signal lever  28 . Such a signal may be a current or voltage signal representing a left-hand or right-hand turn state of the turn signal lever  28 , for example, or a measured or detected switch position indicative of the same. By way of example and not of limitation, the enabling conditions could include a determination by the controller  50  that a lane change was not recently detected in the present turn direction of the turn signal lever  28 , or that a turn signal was not recently set in the opposite direction of the current turn signal direction. The controller  50  could also determine whether the turn signal lever  28  has been set in a particular direction for a calibratable amount of time, i.e., more than a minimum but less than maximum amount of time, e.g., by comparing an elapsed time in a given left or right turn state to the calibratable amount of time. 
     In an optional embodiment of the motor vehicle  10  in which an interior camera and/or other hardware and associated software evaluates and assigns a numeric score to a driver&#39;s attention level, e.g., a gaze-tracking camera collocated with the video camera  20  on the windshield  19  of  FIG.  1    or mounted to a dashboard or other interior location of the motor vehicle  10 , or sensor(s) tracking erratic steering or braking inputs indicative of reduced attention, such that the controller  50  is made aware of the driver&#39;s present attention level as a reported score, the enabling conditions could also include a threshold minimum driver attention score. 
     Still other exemplary enabling conditions usable as part of block B 104  include a particular detected lane marker type. More particularly, the controller  50  could evaluate, for instance using resident image processing software, whether lines detected on the side of a lane L 1 , L 2 , L 3 , or L 4  matching a turn signal direction of the turn signal lever  28  are dashed or another crossable line type, and/or that a line marker located one over from a side of the lane matching the direction of the turn signal is valid, i.e., is not a road edge corresponding to the boundary lines  144  of  FIG.  2   . Information such as whether an adjacent lane in the direction of the turn signal has an expected lane width within a calibrated tolerance may also be used, e.g., lane L 1  of  FIG.  2   . Look-ahead data could likewise be used, such as information that a new lane is not being added on a side of the multi-lane driving surface  40  of  FIG.  2    in a direction matching that of the turn signal within some predetermined distance. The method  100  proceeds to block B 105  when such turn signal fidelity enhancement enabling conditions are not satisfied, and to block B 106  in the alternative. 
     At block B 105  (“DISBL CC TS ”), the controller  50  temporarily disables use of the turn signal state by preventing its use in the lane localization function  51 , with “CC TS ” shown in  FIG.  3    to abbreviate lane localization-based control in accordance with turn signal state information. The method  100  proceeds to block B 106  with turn signal-based fidelity enhancement disabled. 
     Block B 106  (“CALC Bel(L T =1)”) includes calculating multiple lane probability distributions using the lane localization function  51  shown in  FIG.  1   , i.e., using the control and feedback signals (arrows  30 - 1 ,  30 - 2 , . . . ,  30 -N). When block B 106  is arrived at from block B 104  directly, such signals will include the electronic turn signal triggered by actuation of the turn signal lever  28 . Block B 106  also includes automatically fusing the lane probability distributions, again via the lane localization function  51 , to thereby generate a host lane assignment of the motor vehicle  10 . Various fusing techniques could be used to this end, including weighting the various lane probability distributions in a particular manner. As appreciated in the art, the resulting host lane assignment corresponds to a lane of the roadway having a highest probability among a set of possible lane assignments. 
     With respect to lane localization and its related statistical probability analysis, in general the controller  50  performs real-time calculation of the probability of the motor vehicle  10  being present in a particular lane at a given moment in time. Referring briefly again to  FIG.  2   , for example, such a calculation would result in a lane probability for each respective one of the representative lanes L 1 , L 2 , L 3 , and L 4 , for each sensor or combinations thereof in the lane localization suite  18  of  FIG.  1   . Various localization algorithms may be used to such ends, including but not limited to Markov localization, Monte Carlo localization, etc. In terms of application, available sensor updates may be applied in the same way for each sensor type by multiplying the current probability distribution by a prior distribution/prior belief, and then normalizing to derive an updated probability distribution as a new belief. 
     Precisely how a given input probability distribution (P( ST |l)) is calculated in a given application may differ depending on the sensor being considered, i.e., the various sensors or components in the lane localization suite  18  of  FIG.  1   . The logic  50 L of  FIG.  4    illustrates one possible implementation as explained below. For example, the distribution probability based on a GPS input from the GPS receiver  24  shown in  FIG.  1    may assign a higher priority to detected lanes lying closer to a GPS prediction. For lane marker inputs, lanes having the best/most lane marker type matches to lane markers detected by the video camera  20  will likewise have a higher probability, whereas lanes with fewer/poorer matches will have a lower probability. 
     For the turn signal indication contemplated herein for the purposes of increasing fidelity of the lane localization function  51 , lanes meeting the criteria of having an adjacent available lane in the direction of the turn signal, for instance indicating that a lane is present on the left of the motor vehicle  10  when the turn signal lever  28  is used to signal a left hand turn, are given a higher probability than lanes that do not meet this criteria. The exact number of a high or low probability result may be tunable in order to adjust weight of a given sensor or sensor input. Once a given sensor input distribution is calculated, it may be applied in the same way as other available sensor inputs to produce a final belief/probability distribution. 
     As part of block B 106  of  FIG.  3   , when turn signal-based fidelity enhancement is enabled, the controller  50  next calculates the lane probability based on the turn signal P(S T |l) given each lane (l), e.g.: 
     
       
         
           
             
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     where S T  is the turn signal direction at time T, and L T  is the set of available lanes at time T, e.g., Lanes L 1 , L 2 , L 3 , and L 4  of  FIG.  2   . 
     Referring briefly to the representative probability sequence  80  of  FIG.  5   , a probability distribution  82  exists for lanes L 1 -L 4 , with the probability score ranging from 0 (extremely unlikely) to 1 (highly likely) for each lane L 1 , L 2 , L 3 , and L 4 . For a sensor update calculation, e.g., for the turn signal, the controller  50  calculates: 
     
       
         
           
             
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     The probability distribution  82  from a sensor update is then applied to the prior probability distribution  84  at time T−1, i.e., Belief(L T −1=1). Thus, the controller  50  multiples the probability distributions  82  and  84  together to calculate an updated probability distribution  86 , i.e., Belief(L T =1), at the present time T. The effect in the representative scenario of  FIG.  5    is that, at time T−1, lane L 1  is returned as the most probable lane, with a normalized probability of about 0.5 in this illustrative example. One iteration of the method  100  enhanced by turn signal information, however, results in the updated probability distribution  86  in which lane L 2  is now identified as the most probable lane. As a result, the new host lane assignment using turn signal information would be lane L 2 , with the controller  50  using this new lane assignment as a basis for executing a number of possible autonomous steering control actions. 
     Referring again to  FIG.  3   , block B 108  (“EXEC CA”) in the depicted exemplary embodiment of the method  100  includes executing an autonomous steering control action aboard the motor vehicle  10 , via the controller  50  of  FIG.  1   , in response to the host lane assignment determined in block B 106 . Exemplary steering control actions taken as part of block B 108  may include one or more of a lane centering control maneuver, a driver-requested automatic lane change maneuver, and/or a system-initiated automatic lane change maneuver, among other possible control actions. 
     Execution of the method  100  described above could be facilitated using the representative control logic  50 L as depicted schematically in  FIG.  4   . The various components or sensors of the lane localization suite  18  receive the above-noted input signals  30  of  FIG.  1   , here represented as CC 30  from the video camera  20 , the geocoded mapping database  22  (“HD Map”), the GPS receiver  24  (“GPS”), the remote sensing transceivers  26  (“Radar/Lidar”), and the turn signal lever  28  (“Turn Signal”). Corresponding output signals from each constituent sensor of the lane localization suite  18  are transmitted to the lane localization function  51  (“LLA”). 
     In a possible signal configuration, for example, the video camera  20  may output camera data CC 20B  indicative of detected lane marker types, camera data CC 20A  indicative of the presence, size, and shape of, and range to detected objects in proximity to the motor vehicle  10 , and camera data CC 20C  indicative of lateral positions of detected broken or solid line lane markers, e.g., lines  44  and  144  of  FIG.  2   , respectively. The geocoded mapping database  22  outputs lane data CC 22A , such as the GPS location and width of detected lanes, lane marker data CC 22B  indicative of detected lane marker types, and lane layout data CC 22C  indicative of a detected lane layout. The GPS receiver  24  likewise outputs GPS position data CC 24  as a present GPS position of the motor vehicle  10 . Remaining data may include remote sensing data CC 26  from the remote sensing transceivers  26  indicative of the positions and range to radar and/or lidar-detected objects, while turn signal data CC 28  from the turn signal lever  28  are indicative of an impending righthand turn, left hand turn, or lane change direction. 
     The lane localization function  51 , i.e., an encoded or programmed implementation of the present method  100 , is thereafter used to generate multiple lane probability distributions. Specifically, logic blocks  60 ,  62 ,  64 ,  66 , and  68  may be used to independently generate corresponding sensor-specific probability distributions, which are then collectively processed via a fusion block  69  (“Fusion”) to generate the above-noted host lane assignment. The ADAS equipment  70  is then informed by the host lane assignment, with the controller  50  thereafter executing a corresponding control action aboard the motor vehicle  10  of  FIG.  1    using the ADAS equipment  70  based on the host lane assignment. 
     Logic block  60  may receive fused object data CC 27  from an object fusion block  27  and lane layout data CC 22C  from the geocoded mapping database  22 , and then output a lane probability distribution (arrow P 60 ) indicative of relative lanes of fused objects versus a map-based lane layout. In terms of data fusion at block  27 , which in general may be implemented in an analogous manner to implementation of the fusion block  69  described below, various possibilities exist within the scope of the disclosure, including fusing video image data, radar data, and/or lidar data, i.e., any or all available sensor data depending on sensor availability and application requirements. Logic block  62  similarly may determine a lane probability distribution (arrow P 62 ) indicative of lane marker type using the video camera  20  and the geocoded mapping database  22 . Likewise, logic block  64  may produce a lane probability distribution (arrow P 64 ) indicative of the GPS location of the motor vehicle  10 , while logic block  66  produces a lane probability distribution (arrow P 66 ) based on a detected lane change informed solely by the video camera  20 . To account for turn signal information, the logic block  68  produce a lane probability distribution (arrow P 68 ) based solely on the state of the turn signal lever  28  of  FIG.  1   . 
     As noted above, sensor updates are applied in the same way, i.e., by multiplying probability distributions by a prior belief and thereafter normalizing to produce a new belief. Input probability distributions, e.g., P 60 , P 62 , P 64 , P 66 , P 68  in  FIG.  4   , are calculated differently based on the particular sensor(s) being used. Using the turn signal lever  28  as an input, for instance, lanes having criteria of an adjacent available lane in the direction of the turn signal maybe given a higher priority at logic block  68 . In fusion block  69 , the relative weight of each input can be assigned as a calibratable value to produce a desired control result for the ADAS equipment  70 . 
     Those skilled in the art will appreciate that, by using the present method  100 , the fidelity of the lane localization function  51  may be situationally improved aboard the motor vehicle  10  of  FIG.  1    having the turn signal lever  28 , and similar mobile systems. By generating turn signal-based lane probability distributions informed by the state of the turn signal lever  28 , the controller  50  of  FIG.  1    is able to generate a host lane assignment of the motor vehicle  10  in a more accurate manner. While generation of the host lane assignment by the controller  50  is a logical change of state having various benefits, including enabling a possible real-time depiction of the relative position of the motor vehicle  10  on a heads-up display screen (not shown) of the motor vehicle  10 , dynamic control actions are also enabled. For example, the controller  50  may command an autonomous steering control action in response to the turn signal-based host lane assignment, or the controller  50  may perform a variety of other dynamic control actions informed thereby. These and other benefits will be readily appreciated by one skilled in the art in view of the foregoing disclosure. 
     The detailed description and the drawings or figures are supportive and descriptive of the present teachings, but the scope of the present teachings is defined solely by the claims. While some of the best modes and other embodiments for carrying out the present teachings have been described in detail, various alternative designs and embodiments exist for practicing the present teachings defined in the appended claims. Moreover, this disclosure expressly includes combinations and sub-combinations of the elements and features presented above and below.