Patent Abstract:
The automated lane management assist method, data structure and system receive unprocessed lane-specific limited-access highway information, including lane use and speed limits, from traffic detectors in the roadway or from other sources, process and develop predicted information from these sources and substitute the predicted information for the current information where appropriate, thus improving the timeliness of the information in a form that assists in the selection of driving lanes and target speeds for vehicles, including in partially and fully automated vehicles, and communicate the processed predicted information to the vehicles by suitable means.

Full Description:
CROSS REFERENCE OF RELATED APPLICATIONS 
       [0001]    This patent application is a nonprovisional patent application of and claims priority from the provisional patent application Ser. No. 62/333,352 filed on May 9, 2016, and this patent application also claims the benefit of the provisional patent application Ser. No. 62/342,532 filed on May 27, 2016, both of which are hereby incorporated by reference in their entirety. 
     
    
     FIELD OF THE INVENTION 
       [0002]    This invention was not made pursuant to any federally-sponsored research and/or development. 
         [0003]    A prior invention (Management Center Module for Advanced lane Management Assist—U.S. Pat. No. 9,053,636) provides a method and system for assisting the drivers of vehicles, and the intelligent in-vehicle systems in partially and fully automated vehicles to select a specific lane for vehicle travel on limited access highways as well as a recommended speed. That patent employs current lane specific information from traffic detectors in the roadway or from other sources. The present invention develops predicted information from these sources and substitutes it for the current information where appropriate, thus improving the timeliness of the information. 
       BACKGROUND 
       [0004]    This patent application extends the usefulness of the following two prior patents, the disclosures of which are hereby incorporated by reference in their entirety, as if fully set forth herein:
       U.S. Pat. No. 9,053,636 Management Center Module for Advanced Lane Management Assist for Automated Vehicles and Conventionally Driven Vehicles (ALMAMC)   U.S. Pat. No. 9,286,800 Guidance Assist Vehicle Module (ALMAVM)       
 
         [0007]    These patents describe a methodology (ALMA) for using traffic management center (TMC) information to select a most appropriate freeway lane for a driver or automated vehicle and to provide a target speed for that lane. The TMC traffic condition information, is essentially current information on traffic speed and other variables for each through traffic lane. The information is organized according to a data structure described in in the ALMAMC patent that considers the physical and functional features of the freeway as well as traffic information devices. The information is transmitted to the vehicle where it is further processed (ALMAVM). This additional processing develops guidance on the best lane and target speed by looking at traffic speeds for several miles ahead (downstream) of the vehicle&#39;s current position. 
         [0008]    Since the vehicle may not reach the look-ahead distance for a few minutes, the current patent improves the performance of the prior patents by using predicted traffic speed in place of current traffic speed for lane selection and target speed recommendations. To obtain lane based speed information, TMCs may use sources such as roadway based traffic detectors and reports from connected vehicles that include position, speed and lane identification. Prediction for other key parameters provided by the ALMAMC patent is provided. 
         [0009]    Pan et al 1  provide a review of traffic prediction techniques. Examples of prediction techniques include:  1  BEI PAN, UGUR DEMIRYUREK, and CYRUS SHAHABI, Utilizing Real-World Transportation Data for Accurate Traffic Prediction, Integrated Media System Center, University of Southern California.
       1. Simulation Models—Traffic prediction using microscopic simulation models in conjunction with traffic detector measurements. References include Gehrke and J. Wojtusiak 2  and Ben Akiva et al 3 .  2  JAN D. GEHRKE and JANUSZ WOJTUSIAK, A Natural Induction Approach to Traffic Prediction for Autonomous Agent-based Vehicle Route Planning, Machine Learning and Inference Laboratory, MLI 08-1, George Mason University, Feb. 17, 2008. 3  MOSHE BEN-AKIVA, MICHEL BIERLAIRE, HARTS KOUTSOPOULOS, and RABI MISHALANI, DynaMIT: a simulation-based system for traffic prediction, Massachusetts Institute of Technology Intelligent Transportation Systems Program, Presented Paper presented at the DACCORD Short Term Forecasting Workshop February, 1998.   2. Data Mining Techniques—This class analyzes the data collected by traffic detectors. Various analysis approaches include:
           A. Auto-Regressive Integrated Moving Average (ARIMA) Model. 4  The Exponential Smoothing model is a special case of ARIMA that has been extensively used for traffic data applications. Strictly speaking these models are just estimators of current conditions that are used as predictors. Although not discussed by Pan, The ALMAMC software uses a Kalman Filter in this manner to process lane specific information developed by the TMC from traffic detectors and other sources.  4  G. BOX and G. JENKINS, Time series analysis: Forecasting and control. San Francisco: Holden-Day, 1970 (book).   B. Neural Network Models have been used for traffic prediction 5  as have genetic algorithms.  5  SHERIF ISHAK and CIPRIAN ALECSANDRU, Optimizing Traffic Prediction Performance of Neural Networks under Various Topological, Input, and Traffic Condition Settings, JTE&#39;04, Volume 130.   C. Historical Models—These models process historical data and provide prediction by reference to a future time period.   
               
 
         [0015]    Pan concludes that he ARIMA/Exponential Smoothing models ae best for short term prediction (our interest) and that historical models are best for long term prediction. Pan provides an algorithm based on error characteristics to select between them. 
       SUMMARY OF THE INVENTION 
       [0016]    The ALMAMC patent provides traffic speeds and other traffic variables according to a geographically related data structure described in the patent. The current patent replaces these current variables with predicted values when the prediction is estimated to be sufficiently accurate. Prediction periods are typically two minutes in duration, and typically predictions for three such periods may be provided, for a total prediction time of typically up to six minutes. 
         [0017]    Prediction is provided in the current patent only when historic and current traffic conditions and estimated errors indicate that the prediction is likely to be accurate. When these conditions are not present, current traffic variables as provided by the processes in the ALMAMC patent are used. 
         [0018]    The ALMAMC patent provides for filtering of the TMC traffic detector lane speed data. Occupancy and volume data if available are similarly processed. As an example, that patent describes the filter process using Kalman Filters. In ALMAMC, only the current values of these quantities are employed. 
         [0019]    An example of the prediction methodology (ALMAPR) that may be used in the current patent and described in some detail is to use the predictive capability of Kalman Filters to predict the future lane based values of speed, volume and occupancy for the first prediction period and to extrapolate the rate of speed, volume and occupancy change into subsequent prediction periods. The estimate of the error in the current traffic variables is provided by the Kalman Filter and is used by the ALMAPR patent to assist in determining when the use of prediction is appropriate. Other data characteristics are also used for this purpose. 
         [0020]    It is an object of the present invention to achieve, provide and facilitate Prediction for Lane Guidance Assist to supplement and/or replace modules in the ALMAMC and ALMAVM patents as follows:
       Short term prediction of lane-specific traffic parameters including speed and others for providing guidance for the selection of a preferred lane and target speed for that lane; Such data is used by the ALMAVM module but may be used for other lane selection processes.   Utilizing traffic management centers (TMCs), an ALMA Management Center and/or other information sources as possible data sources for the prediction model.   Prediction traffic parameters may conform to a data structure described in the ALMAMC patent.   Process checks may be performed for the quality of the predicted data; if the predicted data is not of sufficient quality, current data is substituted. Quality checks may include periods when changes in speed are high, data is erratic, estimation error is excessive and data that may be affected by traffic incidents. The predicted data may be an improvement over the current data because the predicted data is available in advance.   Predictions are provided in prediction time intervals of appropriate duration.   Provides average predicted lane speeds for vehicles or drivers for a look ahead (downstream) distance from the vehicle.       
 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0027]    The features, aspects and advantages of the novel Prediction for Lane Guidance Assist will become further understood with reference to the following description and accompanying drawings where 
           [0028]      FIG. 1  is the flowchart of the ALMA simplified data flow; 
           [0029]      FIG. 2  is the graphical representation of Key Time Sequence Relationships; 
           [0030]      FIG. 3 . Is ALMA Management Center Flow Chart for Additions Required by ALMAPR; and 
           [0031]      FIG. 4  is Example of Look Ahead Time Development Process. 
       
    
    
     DESCRIPTION 
       [0032]    Introduction. ALMA as described in U.S. Pat. Nos. 9,053,636 (ALMAMC) and 9,286,800 (ALMAVM) provides information to conventional and partly or fully automated vehicles to enable them to respond to freeway lane selection and target speed selection information originating from a freeway traffic management center in a way that is superior to the way that an unaided human driver or automated vehicle would respond to that information. The disclosures of these patents describe how speed, volume and occupancy information collected at a traffic management center (TMC) is processed by the ALMA Management Center (ALMAMC) and transmitted to the vehicle. Using this information in conjunction with routing, speed and location information supplied by the vehicle and information from the vehicle operator, the ALMA Vehicle Module (ALMAVM) recommends the most appropriate freeway lane and a target speed for that lane. 
         [0033]      FIG. 2  of the existing ALMAMC patent illustrates the ALMA architecture.  FIG. 1  shows a simplified view of this architecture. Using roadway traffic sensors or other means to detect individual vehicles, a traffic management center (TMC)  102  develops traffic data such as speed, volume and occupancy. TMCs also detect and manage traffic incidents and provide traffic information to motorists by means of roadway devices such as dynamic message signs (DMS) and other means. The ALMA traffic management center  103  further processes this information, organizes it and transforms it using a prescribed data structure as described in the ALMAMC patent. Processed traffic parameters suitable for the presentation of lane conditions and other information are the transmitted by means of a communication system  104  such as a smartphone with an appropriate vehicle head unit. This information is used by the ALMA Vehicle Module  107  in conjunction with vehicle position, speed and routing information from the vehicle  105  and driving preferences and other information from the motorist  106  to provide recommended lane and target speed information to the motorist display and/or the partially or fully automated vehicle management system  101 . 
         [0034]    The ALMAMC patent also describes the geometric data structure to which the data is referenced. In summary, the freeway is divided into barrels and zones. A barrel represents a set of travel lanes in a roadway. It is physically or functionally separated from other parallel lane sets. Barrel boundaries are determined by changes in the physical roadway configuration and by permanent changes along the roadway in the regulatory use of the roadway or its lanes. A barrel is divided into zones. Zone boundaries are determined by a number of factors including traffic conditions, placement of motorist information devices and regulatory devices that provide changeable information. 
         [0035]    The existing ALMAMC patent provides information based on its estimate of current traffic conditions. The current patent provides this information based on short term predictions of traffic conditions. This will improve performance under some traffic conditions. The geometric data structure of the ALMAMC patent is preserved in the current patent application. 
         [0036]    The current ALMAPR patent application describes a set of modules that replace certain ALMAMC and ALMAVM modules when confidence in the prediction accuracy is high. These are described in the following sections. The ALMAMC modules to be replaced include five outputs in Table 3 of the ALMAMC patent. These are described in Appendix B. Appendix A identifies the symbols used in this patent. Essentially the new functions use the prediction feature of the Kalman Filter for the prediction period that follows the current period. An extrapolation process provides prediction for subsequent prediction periods. 
         [0037]    For predictive purposes, the computation of the number of look-ahead zones and the look ahead speed is more complex that for Modules 4.3R.2.5 and 4.3R.2.6 of the ALMAVM patent. These modifications are described in subsequent sections of this patent. 
         [0038]    Temporal Relationships. The current patent (ALMAPR) predicts traffic variables for several future time periods.  FIG. 2  shows the relationship among the key time sequences employed. Horizontal axis D  204  is a time scale that relates the other horizontal axes to clock time. 
         [0039]    The top horizontal axis (A)  201  represents the intervals (n) after the current interval for which the data is received by the ALMAMC from the TMC. It is represented as one half minute in the figure although some TMCs may provide different intervals. The second horizontal axis (B)  202  represents the periods (r) for which ALMAPR will predict the speed. The duration of these periods is shown as two minutes in the figure. 
         [0040]    As will be subsequently described, ALMAPR employs historic traffic speed data. The third horizontal axis (C)  203  represents the time periods (j) for which the historic data is compiled from TMC data. Five minute intervals as shown are typically employed. 
         [0041]    The current patent ALMAPR provides a major addition to the modules in the ALMAMC and ALMAVM patents. This addition provides:
       The type of traffic parameter outputs shown in Table 3 of the ALMAMC patent in the form of predicted parameters. The current patent&#39;s outputs are shown in Appendix B.   Modifications to the spatial-temporal data structure in the ALMAVM patent to enable the predicted traffic parameters to be effectively employed in the vehicle.       
 
         [0044]    Example of Prediction Process. As described earlier, a number of prediction concepts may be used. An example of the prediction process, and the constraints on its use, is described in the following discussion with the assistance of  FIG. 3 . 
         [0045]    The speed data SPINT(Z,L,) from the TMC  301  is averaged into stored historic data reference periods (j)  203 . 
         [0046]    In the Historic Speed Data process  302 , averages for the most recent K 3  days are obtained for each time period for weekdays, and weekend days. Exception days (days excluded from the averaging period) may be identified by the ALMAMC manager. 
         [0047]    The historic data is processed for the purpose of eliminating Kalman prediction data  305  for periods that history has shown to be unsuitable. This includes:
       Periods when changes in the speed in a zone averaged over all lanes exceed a threshold value (K 2 ) render Kalman prediction unsuitable  303 .   Detector stations that frequently provide erratic data  304 . These stations can be identified when the total number of stored daily periods (j) for which the average zone speed change exceeds K 5  for representative days is greater than K 4 .       
 
         [0050]    Similar to the process described for the ALMAMC patent the prediction process uses a Kalman Filter process  305  to estimate the current speed (SPFIL(Z,L,r)). See, for example Welch, G and T R Bishop, “An Introduction to the Kalman Filter”, University of North Carolina Department of Computer Science, TR 95-041, 2006. 
         [0051]    The prediction process in this patent modifies the estimation process in the ALMAMC patent as follows. Prediction for the first prediction interval r 0 +1  205  and designated as SPFILPR(B,Z,L,r 0 +1) is available from the Kalman iteration that follows the estimate for the current interval. Prediction for subsequent prediction intervals (r&gt;r 0 +1) will be performed by using the rate of change of speed for interval r+1 for the subsequent intervals. The rate of speed change is computed as 
         [0000]        RSC =( SPFIL ( B,L,Z,r )− SPFIL ( B,L,Z,r− 1))/ LR  
       where LR is the duration of the prediction interval (r)   The predicted speed when converted to space-mean-speed is designated as SPSPPR(B,Z,L) for all prediction intervals.       
 
         [0054]    The estimation error for speed (SPE(L,Z,r)) for the current interval is used in module  307 . 
         [0055]    Module  306  interprets incident related information from the TMC to identify the presence of an incident. The module identifies zones affected by the incident. This information is sent to Module  307 . 
         [0056]    Under certain conditions the predicted value for speed may be unreliable. Module  307  substitutes the current value of zone speed (SPFILTOT(Z,r)) for each lane for the values for the prediction intervals when the following conditions are present:
       Speed estimation error (SPE(L,Z,r) exceeds an acceptable threshold K6.   Zone employs a detector station that provides erratic data ( 304 ).   Zones affected by an incident have been identified ( 306 ).       
 
         [0060]    As described in the ALMAMC patent, lane based data that originates from point detectors can be processed to provide key fundamental traffic parameters such as volume and occupancy (which may be further processed to provide density.) TMC data that originates from such sources as infrastructure based probes or vehicle based sensors working in conjunction with vehicle to infrastructure communications cannot be effectively processed in a similar manner because all of the necessary variables cannot be measured by these techniques. Module  308  controls the steps that implement this distinction. 
         [0061]    If the TMC speed data originated from point detectors, Module  308  directs the computation to a path that will provide the additional parameters described in the ALMAMC patent (Module 6 and Table 3). 
         [0062]    Module  309  converts the predicted time mean speed originating from point detectors to predicted space mean speed using the relationship shown in Equation 8 of the ALMAMC patent. 
         [0063]    Predicted volume and occupancy  310  are computed by Kalman Filters in the prediction mode similar to that used to compute time mean speed. Predicted density  310  is computed using the relationships in Appendix B and predicted compensated occupancy  310  is computed using Equation 10 of the ALMAMC patent in conjunction with the prediction processes described for speed in the current patent. 
         [0064]    The relationships required to compute the remaining parameters  311  (predicted average headway, predicted average vehicle length, predicted passenger car equivalent volume) identified in Table 3 of the ALMAMC patent are computed as shown in Appendix B. 
         [0065]    When point detectors are used as the data source, the full parameter set is provided to the ALMAVM module in the vehicle  313 . When other types of information (such as probe based information) are used as the data source  312 , only predicted space mean speed is provided to the ALMAVM module  313 . 
         [0066]    Spatial—Temporal Relationships. The prior discussion describes the additions to the ALMA Management Center required to support predictive capability. The following discussion describes additions and modifications to the ALMA Vehicle Module required to support prediction. 
         [0067]    ALMAVM identifies the number of downstream zones that should be employed to estimate a “look ahead” speed (ALMAVM Module 4.3R.2.5). In that module, the look ahead speed is calculated as the current speed for each zone weighted by the length of each zone. 
         [0068]    Where predicted speeds are used in place of current speeds, it becomes necessary to identify the appropriate time interval to be used for traversing each zone. An approach for doing this is described with the assistance of  FIG. 4 . The solid line represents the time-space plot of a vehicle after it enters the first look ahead zone. The slope of each segment of the trace represents the average speed of the vehicle for that zone and prediction interval. This slope changes as the vehicle traverses each zone in the look ahead distance. The first segment shown in the  FIG. 401  uses the speed for Zone Z+1 for the current time interval. The line segment  402  for the first prediction interval (r+1) lies entirely within zone Z+1. Table 1 shows the predicted speed to be used for each trace segment to fully develop a similar figure for each vehicle. In this way each line segment serves as the base for the next line segment when this process is completed, the value for look ahead time (LAT(L)) is obtained. 
         [0000]    
       
         
               
             
               
               
             
               
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 Predicted Speed for Vehicle 
               
             
          
           
               
                   
                 Look ahead zones 
               
             
          
           
               
                 Time interval 
                 Z + 1 
                 Z + 2 
                 Z + 3 
               
               
                   
               
               
                 Current interval r 0   
                 Slope of  
                   
                   
               
               
                   
                 segment 401 
                   
                   
               
               
                 Prediction Interval r 0  + 1 
                 Slope of  
                   
                   
               
               
                   
                 segment 402 
                   
                   
               
               
                 Prediction Interval r 0  + 2 
                 Slope of  
                 Slope of  
                 Slope of  
               
               
                   
                 segment 403 
                 segment 404 
                 segment 405 
               
               
                 Prediction Interval r 0  + 3 
                   
                   
                 Slope of  
               
               
                   
                   
                   
                 segment 406 
               
               
                   
               
             
          
         
       
     
         [0069]    These concepts will be used to replace Module 4.3R.2.6 in the GAVM program. In that module ZWAS(L) represents the look ahead speed for each lane using current zone speeds. The predictive replacement is provided by the expression: 
         [0000]        ZPWAS ( L )= DLA/LAT ( L )       where DLA is the look ahead distance  407  shown in  FIG. 4  and LAT(L) is the look ahead time  408  for lane L.         
       APPENDIX A 
       [0071]      
         [0000]    
       
         
               
             
               
               
             
           
               
                   
               
               
                 Definition of Symbols 
               
             
          
           
               
                 Symbol 
                 Definition 
               
               
                   
               
               
                 AHWPR(B, Z, L) 
                 Predicted average headway 
               
               
                 AVL 
                 Average vehicle length 
               
               
                 d 
                 Detection zone 
               
               
                 DENFILPR (B, Z, L) 
                 Predicted lane density 
               
               
                 DLA 
                 Look ahead distance 
               
               
                 F 
                 Scaling coefficient 
               
               
                 j 
                 Stored historical period 
               
               
                 K2 
                 Threshold for excessive changes in speed 
               
               
                 K3 
                 Number of averaging days 
               
               
                 K4 
                 Limit on number of prediction intervals for  
               
               
                   
                 which speed change is unsuitable 
               
               
                 K5 
                 Threshold for speed change unsuitability 
               
               
                 K6 
                 Acceptable estimated error for speed 
               
               
                 LAT(L) 
                 Predicted look ahead time 
               
               
                 LL 
                 Length of detector sensing area in lane 
               
               
                 LR 
                 Duration of prediction interval 
               
               
                 NOPREDDAYCLASS(d) 
                 Detection zones to be eliminated because of  
               
               
                   
                 erratic data 
               
               
                 OCCFILPR (Det, L) 
                 Predicted occupancy 
               
               
                 PCE 
                 Passenger car equivalent volume 
               
               
                 PCEPR (B, L, Z) 
                 Predicted passenger car equivalent volume 
               
               
                 r 
                 Computation and prediction interval 
               
               
                 r 0   
                 Current interval 
               
               
                 RSC 
                 Rate of speed change 
               
               
                 SPE(L, Z, r) 
                 Estimated error in speed 
               
               
                 SPFIL(L, Z, r) 
                 Kalman filtered lane speed 
               
               
                 SPFILTOT(Z, r) 
                 Filtered speed for all through lanes in zone 
               
               
                 SPINT(Z, L) 
                 Speed by lane from TMC 
               
               
                 SPSPPR(B, Z, L) 
                 Predicted lane speed 
               
               
                 VOLFIL(BZL) 
                 Filtered detector volume in TMC reference 
               
               
                 VOLFILPR(B, Z, L) 
                 Predicted lane volume 
               
               
                 Z 
                 Zone 
               
               
                 ZPWAS(L) 
                 Predicted look ahead speed 
               
               
                 ZWAS(L) 
                 Look ahead speed using current speed 
               
               
                   
               
             
          
         
       
     
       APPENDIX B 
     ALMAPR Output Parameters 
       [0072]    When appropriate as described in this patent the parameters shown inn Table B1 are provided using the equations that follow the table. The background for these equations is the same as that provided for Equations 8, 9, 18, 19 and 20 in the ALMAMC patent. Symbol definitions are provided in Appendix A. 
         [0000]    
       
         
               
             
               
               
               
             
           
               
                 TABLE B1 
               
             
             
               
                   
               
               
                 ALMAPR output parameters from the ALMA Management Center 
               
             
          
           
               
                   
                   
                 Detectors with Accurate 
               
               
                   
                   
                 Volume and Speed Data (may 
               
               
                   
                 Detectors with Accurate 
                 or May Not Include Accurate 
               
               
                 Traffic Parameter 
                 Volume and Occupancy Data 
                 Occupancy Data) 
               
               
                   
               
               
                 Predicted Lane Volume 
                 VOLFILPR(B, Z, L)—Volume 
                 VOLFILPR(B, Z, L)—Volume 
               
               
                 (vehicles/hr) 
                 prediction process output 
                 prediction process output 
               
               
                   
                 converted to ALMA data 
                 converted to ALMA data 
               
               
                   
                 structure 
                 structure 
               
               
                 Predicted Average Headway 
                 AHWPR(B, Z, L) =  
                 AHWPR(B, Z, L) =  
               
               
                 (hours/vehicle) 
                 1/VOLFILPR(B, Z, L) converted 
                 1/VOLFILPR(B, Z, L) converted 
               
               
                   
                 to ALMA data structure 
                 to ALMA data structure 
               
               
                 Predicted Passenger Car 
                 PCEPR (B, L, Z)—Equation 
                 PCEPR (B, L, Z)—Equation 
               
               
                 Equivalent Volume 
                 B1 converted to ALMA data 
                 B1 converted to ALMA data 
               
               
                   
                 structure 
                 structure 
               
               
                 Predicted Lane Speed 
                 SPSPPR(B, Z, L)—Equation 
                 SPSPPR(B, Z, L)—Speed 
               
               
                   
                 B3 converted to ALMA data 
                 prediction process (307, 308, 
               
               
                   
                 structure 
                 309) converted to ALMA data 
               
               
                   
                   
                 structure 
               
               
                 Predicted Lane Density 
                 DENFILPR (B, Z, L)—Equation  
                 DENFILPR (B, Z, L)—Equation  
               
               
                   
                 B4 converted to ALMA data  
                 B2 
               
               
                   
                 structure 
               
               
                   
               
             
          
         
       
     
       Equations 
       [0073]        PCEPR ( Det,L )= PCE*VOLFILPR ( B,Z,L )/ VOLFIL ( B,Z,L )   B1
 
         [0000]        DENFILPR ( B,Z,L )= VOLFILPR ( B,Z,L )/ SPSPPR ( B,Z,L )   B2
 
         [0000]        SPSPPR ( B,Z,L )= VOLFILPR ( B,Z,L )/ DENFILPR ( B,Z,L )   B3
 
         [0000]        DENFILPR ( Det,L )=( F*OCCFILPR ( Det,L ))/( LL+AVL ( Det,L ))   B4

Technology Classification (CPC): 1