Abstract:
The Map++ as a system and method that leverages standard cell-phone sensors in a crowdsensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others is described. Our analysis shows that cell-phones sensors with humans in vehicles or walking get affected by the different road features, which can be mined to extend the features of both free and commercial mapping services. We present the design and implementation of Map++ and evaluate it in a large city. Our results show that we can detect the different semantics accurately with at most 3% false positive rate and 6% false negative rate for both vehicle and pedestrian-based features. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.

Description:
CROSS REFERENCE TO RELATED APPLICATION 
       [0001]    The instant application claims priority and is a continuation of PCT application PCT/US 14/33087 filed on Apr. 4, 2014. The pending PCT application is hereby incorporated by reference in its entireties for all of its teachings. 
     
    
     FIELD OF TECHNOLOGY 
       [0002]    A method and system for crowd sensing that is being used for automatic semantic identification that leverages standard cell-phone and smartphone sensors to automatically enrich digital maps with different road semantics. More specifically it relates to a method and system that defines a differentiation of analysis done on the data from sensors in smartphones for various activities. 
       BACKGROUND 
       [0003]    Recently, digital maps have gained great attention due to their high economic and social impact; they are integrated into our everyday lives in different forms such as navigation systems, traffic estimation services, location based services, asset tracking applications, and many more. Realizing the economic value of this technology, several giant companies are producing commercial map services including Google Map, Yahoo! Map, and Microsoft&#39;s Bing Map, as well as free services such as OpenStreetMaps. These map services attract millions of users daily. In 2013, Google announced that its Google Maps service is accessed by over one billion users every month. 
         [0004]    Typically, these maps are constructed through satellite images, road surveyors, and/or manual entry by trained personnel. However, with the dynamic changes and richness of the physical world, it is hard to keep these digital maps up-to-date and capture all the physical world road semantics. To address this issue, commercial map companies started to provide tools, e.g. Google&#39;s MapMaker and Nokia&#39;s HERE Map Creator, for users to manually send feedback about their maps, i.e. crowd source the map updates. This was even generalized to build entire completely-free editable maps such as OpenStreetMap (OSM) and WikiMapia. However, these services require active user participation and are subject to intentional incorrect data entry by malicious users. 
         [0005]    With the proliferation of today&#39;s sensor-rich mobile devices, cell phones are becoming the bridge between the physical and digital worlds. Researchers leveraged the GPS chips on smart phones to collect traces that can be used automatically to update existing maps and infer new roads. However, GPS is an energy hungry device and these systems focus only on estimating missing road segments. Today, all existing mapping services, both commercial and free, miss a large number of semantic features that are a necessity for many of today&#39;s map-based applications. There is a need for a better method and system to navigate and guide the users. 
       SUMMARY 
       [0006]    In the present disclosure, in one embodiment, a method and system called Map++ that leverages the ubiquitous sensors available in commodity cell-phones to automatically discover new map semantics to enrich digital maps. In another embodiment, a crowd sensing that is being used for automatic semantic identification that leverages standard cell-phone and smartphone sensors to automatically enrich digital maps with different road semantics such as tunnels, bumps, bridges, footbridges, crosswalks and road capacity. In one embodiment, a differentiation of analysis is done on the data from sensors in smartphones for various activities define from those who walk verses those who are in-vehicles. For example, navigation systems relay on important semantics to better guide users to their destinations; a short route may be falsely tempting if traffic lights are hidden from the user, a pedestrian tourist might be deceived when finding out that the road has no sidewalks, city evacuation planning might be ineffective if maps are not tagged with the number of lanes, a driver might be at risk of an accident if his map does not show the road bumps ahead, and a person with disability needs a map that shows the elevator-enabled subway stations. The instant invention overcomes all these shortcomings. 
         [0007]    In one embodiment, as a system the Map++ architecture to automatically crowdsense and identify map semantics from available sensor readings without inferring any overhead on the user and with minimal energy consumption. In another embodiment, a framework for extracting the different map features from both pedestrian and in-vehicle traces is disclosed. In another embodiment, an implementation of Map++ method and system on Android device is done and an evaluation for its accuracy and energy-efficiency in a typical city is performed. 
         [0008]    In one embodiment, the instant system and method depends only on time- and location-stamped inertial sensor measurements, which have a low-energy profile for both road semantics estimation and accurate localization, removing the need for the energy-hungry GPS. For example, a phone going inside a tunnel will experience a drop in the cellular signal strength. This can be leveraged to detect the tunnel location. Map++ uses a classifier-based approach based on the multi-modal phone sensor traces from inside cars to detect tunnels and other road semantics such as bridges, traffic calming devices (e.g. bumps, cat-eyes, etc.), railway crossings, stop signs, and traffic lights. In another embodiment, the system and method uses pedestrians&#39; phone sensor traces to detect map semantics like underpasses (pedestrian tunnels), footbridges (pedestrian bridges), crosswalks, stairs, escalators, and number of lanes. 
         [0009]    In one embodiment, Map++ system architecture as well as the details of its components are disclosed. In another embodiment, implementation of Map++ over different android phone is shown. It has been observed that detection of different map features was performed accurately by using android device resulting in 3% false positive rate and 6% false negative for in-vehicle traces, and 2% false positive rate and 3% false negative rate for pedestrian traces. In one embodiment, Map++ can detect the location of the detected features accurately to within 2 m using as few as 12 samples without using the GPS chip. This comes with a low power consumption of 23 mW, which is 50% less than GPS when run at a 1 minute duty cycle. 
         [0010]    Other features will be apparent from the accompanying Figures and from the detailed description that follows. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0011]    Example embodiments are illustrated by way of example and no limitation in the graph and in the accompanying Figures, like references indicate similar elements and in which: 
           [0012]      FIG. 1  is a diagram illustrating a map of Alexandria, Egypt as an example. 
           [0013]      FIG. 2  depicts the Google maps in the past and that which is geographically specific to Egypt. 
           [0014]      FIG. 3  provides a map from Open Street maps that is generally used for development purposes. 
           [0015]      FIG. 4  shows that both commercial and free mapping services miss a number of semantic features for the next generation maps. 
           [0016]      FIG. 5  shows the Map++ System architecture. 
           [0017]      FIG. 6  shows the decision tree classifier for detecting map features from pedestrian traces. 
           [0018]      FIG. 7  shows the effect of walking through an underpass on the cell-phone sensors measurements. 
           [0019]      FIG. 8  shows the start and end tunnel through which pedestrians walk. 
           [0020]      FIG. 9  shows the step pattern while walking verses the step pattern when going down stairs. 
           [0021]      FIG. 10  compares the variance in acceleration and the steps frequency while walking to when going down on the stairs. 
           [0022]      FIG. 11  illustrates the variance of acceleration and ambient magnetic field while being stationary, walking and using the escalator. 
           [0023]      FIG. 12  shows acceleration variance when going up then walking on the foot bridge then going down the stairs. 
           [0024]      FIG. 13  shows the decision tree classifier for detecting different map features from in-vehicle traces. 
           [0025]      FIG. 14  shows the effect of going inside a tunnel on the cell-phone sensors measurements. 
           [0026]      FIG. 15  shows the effect of the different map features on the Y-axis gravity acceleration. 
           [0027]      FIG. 16  shows the effect of different traffic calming devices on the X, Y and Z gravity acceleration variance in comparison with smooth road and railway crossing. 
           [0028]      FIG. 17  illustrates the difference between a roundabout and an intersection in terms of a car behavior. 
           [0029]      FIG. 18  shows that moving along the curve, the car orientation changes till the end of the curve, where the car returns to its original direction. This is unlike taking a turn, where direction changes after the end of the curving part. 
           [0030]      FIG. 19  shows the effect of samples on the accuracy of estimating the semantic location. 
           [0031]      FIG. 20  shows the energy footprint of Map++ as compared to the systems that use GPS with different duty cycles. 
           [0032]      FIG. 21  is a diagrammatic system view of a computer device view in which any of the embodiments disclosed herein may be performed, according to one embodiment. 
           [0033]      FIG. 22  shows an entire system for processing MAP++ system and method, according to one or more embodiments. 
       
    
    
       [0034]    Other features of the present embodiments will be apparent from the accompanying detailed description that follows. 
       DETAILED DESCRIPTION 
       [0035]    The present disclosure relates to a method and system for crowd sensing that is being used for automatic semantic identification that leverages standard cell-phone and smartphone sensors to automatically enrich digital maps with different road semantics.  FIG. 1  shows the map of Alexandria that is displayed by Google Maps today that is recent. It can be seen that a bridge  102 , a traffic section calming  104 , multiple lanes  106 , an underpass  108  and traffic signs  110  are missed.  FIG. 2  shows the map that with road features normally ignored by the popular map applications such as Google Maps. These features are very useful for people travelling that need extra assistance. 
         [0036]      FIG. 3  shows that Open Street Maps, the genesis for many map applications, also do not provide complete information. We see that  302  is identified as bridge, but others such as multiple lanes  104  and underpass  106  is missing. 
         [0037]      FIG. 4  shows the satellite image of view by the proposed system Map++. It can be clearly seen that the details ignored by popular map applications are indeed covered in Map++. For example, the bridge  302  is clearly identified. Similarly, a roundabout  304 , an underpass  108  and multiple lanes  106  are properly identified. 
         [0038]      FIG. 5  shows the Map++ system architecture. Map++ is based on a crowdsensing approach, where cell phones carried by users submit their data to the Map++ service running in the cloud in a way transparent to the user. The data is first processed by Map++ to reduce the noise. Then, the user mode of transportation is inferred to separate pedestrian and in-vehicle traces from other modes of transportation. Map++ has two core components: one for extracting map features from in-vehicle traces and the other for extracting the map features from pedestrian traces. Map++ takes a classifier approach to determine the different road semantics based on extracted features from the collected sensor traces. 
         [0039]    Traces information data Collection: The trace information data collection module  502  collects time-stamped and location-stamped traces along with sensor measurements. These include available inertial sensors (such as accelerometer, gyroscope and magnetometer) as well as cellular network information (associated cell tower ID and its Received Signal Strength Information (RSSI), plus neighboring cell towers and their associated RSSI). These sensors have a low cost energy profile and they are already running all the time during the standard phone operation to maintain cellular connectivity or to detect phone orientation changes. Therefore, using them consumes zero extra energy. 
         [0040]    To get the location information, Map++ is generic enough to either use GPS coordinates or other energy efficient localization systems such as Dejavu (a system and method for location calculation) that can provide accuracy better than GPS in urban conditions with much lower energy consumption. To achieve this, Dejavu uses a dead-reckoning approach based on the low-energy phone inertial sensors. However, to reduce the accumulated error in dead-reckoning, Dejavu leverages the ample unique physical and logical landmarks in the environments; such as turns, curves, and cellular signal anomalies; as error resetting opportunities. Dejavu can achieve a median distance error of 8.4 m in in-city driving conditions and 16.6 m in highway driving conditions with a 347.4% enhancement in energy consumption compared to the GPS. Therefore in the performance measurement, Map++ energy efficiency is based on Dejavu&#39;s energy-efficient localization and using the inertial and cellular sensors information for its analysis. The Map++ architecture is encompassed in  504  that takes the collected traces and processes to obtain quality data on the road characteristics 
         [0041]    Preprocessing: The first module to receive the traces is the Preprocessing  506  module. This module is responsible for preprocessing the raw data collected from raw sensor measurements to reduce the effect of (a) phone orientation changes and (b) noise and bogus changes, e.g. sudden breaks, or small changes in the direction while moving. To handle the former, we transform the sensor readings from the mobile coordinate system to the world coordinate system leveraging the inertial sensors. To address the latter, we apply a low-pass filter to the raw sensors data using local weighted regression to smooth the data. 
         [0042]    Transportation Mode Detection: Based on the preprocessing the mode of transportation is detected  508 . Map++ is designed to detect two main classes of map semantics; in-vehicle and pedestrian as well as to filter other classes, such as train traces. We start by filtering users inside buildings. Different approaches have been proposed in literature based on the different phone sensors. Map++ uses the IODetector approach due to its accuracy and low-energy profile. 
         [0043]    Similarly, transportation mode detection using the transportation mode detection module for outdoor users has been thoroughly studied in the literature. In the instant method and system provides the data collection which provides high accuracy of differentiation between the different transportation modes based on the energy-efficient inertial sensors. The technique starts by segmenting the location traces using velocity and acceleration upper bounds. Then the following features are used to classify each segment: The stopping rate, the heading and velocity change rate, the segment length, the nth maximum velocity and acceleration, average velocity, and velocity variance. A decision tree classifier  516 ,  526  is applied to identify the transportation mode for each segment. 
         [0044]    Once the mode of transportation is detected, an HMM map matcher is applied to the in-vehicle traces to map the estimated locations to the road network to reduce the localization error  512 ,  522 . Similarly, the UPTIME step detection algorithm that takes into account the different users&#39; profiles and gaits is applied to the pedestrian acceleration signal to detect the user steps. In both cases, features are extracted  514 ,  524  from the traces to prepare for the road semantic classification step. 
         [0045]    Map Semantics Extraction: There are a large number of road semantic features that can be identified based on their unique signature on the different phone sensors. Map++ uses a tree-based classifier  516 ,  526  to identify the different semantics. 
         [0046]    Road Semantic Features Location Estimation: Whenever a road semantic feature is detected by the semantic detection modules (in-vehicle or pedestrians), Map++ determines whether it is a new instance of the road feature or not in addition to its location. To do this, Map++ applies spatial clustering  528  for each type of the extracted semantics. It uses density-based clustering algorithms (DBSCAN). DBSCAN has several advantages as the number of clusters is not required before carrying out clustering; the detected clusters can be represented in an arbitrary shape; and outliers can be detected. The resulting clusters represent map features. 
         [0047]    The location of the newly discovered semantics is the weighted mean of the points inside their clusters. We weight the different locations based on their accuracy reported by Dejavu: In Dejavu, the longer the user trace from the last resetting point, the higher the error in the trace. Therefore, shorter traces have better accuracy. When a new semantic is discovered, if there is a discovered map feature within its neighborhood, we add it to the cluster and update its location. Otherwise, a new cluster is created to represent the new road feature. To reduce outliers, a semantic is not physically added to the map until the cluster size reaches a certain threshold. 
         [0048]    Sensor specifications are different from one phone manufacturer to another, which leads to different sensor readings for the same map feature. To address this issue, Map++ applies a number of techniques including use of scale-independent features (e.g. peak of acceleration) and combining a number of features for detecting the same semantic feature. Map++ does not also require real-time sensor data collection; it can store the different sensor measurements and opportunistically upload them to the cloud for processing; allowing it to save both communication energy and cost. 
         [0049]    Pedestrian traces semantic detection module  510 : To determine the different road semantics, Map++ applies a decision tree classifier to the extracted features from the pedestrian traces.  FIG. 6  shows the decision tree classifier used to extract the different semantic map features from the pedestrian traces. We give the details of the classifier features that can differentiate the different semantic road features (Crosswalk  618 , Underpasses  620 , Footbridge  626 , Walkway  624 , Stairs  628 , Escalator  632 , Stationary  630  and number of lanes). 
         [0050]    Underpasses or pedestrian tunnels are specially constructed for pedestrians beneath a road or railway, allowing them to reach the other side. A pedestrian trace crossing a road may be a crosswalk (e.g. zebra lines), a bridge, or an underpass. We identify the underpasses from other classes by their unique features: Walking inside an underpass, a cell-phone will experience a drop in the cellular signal  702  and also a high variance in the magnetic field around it (both Y  706  and X  704  axes) due to metals and electricity lines inside the tunnel as shown in  FIG. 7 . We can see a real example of the underpass in Alexandria depicted in  FIG. 8 , where the measurement started in  802  and ended in  804 . 
         [0051]    Furthermore, when ascending or descending stairs, the frequency of steps, detected by a simple peak detector ( FIG. 9 ), within the unit distance increases since the user is moving vertically ( FIG. 10 ). The start of the peak and end of peak can be measured and correlated to Stairs Start  902  and Stairs End  904 . 
         [0052]      FIG. 10  illustrates the effect of walking and going down on stairs on the acceleration variance and the steps frequency. When descending stairs, the gravity force affecting the person will lead to a higher peak in acceleration, and hence higher variance  1004 , as compared to walking  1002 ,  1006 . The number of steps can be used, e.g., to determine the height of the pedestrian bridge, which is useful for determining the height limits for the vehicles on the road. 
         [0053]      FIG. 11  illustrates the variance of acceleration and ambient magnetic field while being stationary  1102 , walking  1104  and using Escalator  1106 . When using escalators, users typically keep standing while carried by the moving staircase. Therefore, the acceleration variance remains small compared to walking. However, escalators are often powered by constant-speed alternating current motors, which results in high variance in the magnetic field. 
         [0054]      FIG. 12  illustrates the acceleration variance when going up then walking on the footbridge then going down on stairs. Similar to underpasses, footbridges allows pedestrians to safely cross roads, railways and rivers. A user crossing a footbridge will use stairs/escalators to ascend  1202  and descend  1206 . In between, the user will walk  1204  the length of the footbridge. We separate footbridges from crossroads by detecting the stairs/escalator pattern before/after using them; we separate them from underpasses using the cellular signal which drops in the underpasses case but not in the footbridge case. Similar to others, crosswalks  618  and number of lanes can be detected from pedestrian traces. The number of lanes is detected using the ratio between road width and average lane width. 
         [0055]    In-vehicle traces semantic detection module  520 : We extract the different map semantic features from the traces collected by the in-vehicle users.  FIG. 13 , shows the tree classifier used to detect the different semantics. Based on the rules  1302  on orientation, magnetic variance  1304 , direction changes  1306 , high acceleration variance  1312 , Curves  1332 , high stop density  1320 , low acceleration variances  1324 , gravity acceleration sensor  1326  and high density sensors  1318 , we can deduce if it is a Tunnel  1308 , Curve  1310 , Turn  1316 , Roundabout  1330 , Road  1328 , Cat&#39;s eye  1328 , Traffic light  1330 , Stop sign  1332 , Traffic calming  1334  and Bridge  1336 . 
         [0056]    Similar to the underpass case in pedestrian walk, a car going inside a tunnel will typically experience an attenuated cellular signal  1402 . We also notice a large variance in the ambient magnetic field in the x-direction  1406  (perpendicular to the car direction of motion) while the car is inside the tunnel. This is different from the underpass case, where there is no smooth ramp at the end and hence both the x and y magnetic fields are affected. Therefore, car tunnels have a low variance in the y-axis (direction of car motion) magnetic field  1404  as shown in  FIG. 14 . 
         [0057]      FIG. 15  shows the effect of the different map features on the Y-axis gravity acceleration. Bridges cause the car to go up at the start of the bridge and then go down at the end of the bridge. This is reflected on the Y-gravity or Z-gravity acceleration. Although other classes, such as bumps  1502 , cause the same effect (Y or Z gravity acceleration going up then down), bridges  1504  are unique in having this effect over a longer distance. The bridge is detected at its end. Note that after detecting the end of the bridge, we could identify its starting point. 
         [0058]      FIG. 16  shows the effect of different traffic calming devices on the X, Y, and Z gravity acceleration variance in compared with smooth road and railway crossing. Different traffic calming techniques like bumps, speed humps, and cat&#39;s eyes  1510  all cause the car to move up then down similar to bridges, affecting all gravity acceleration axes. However, unlike bridges, all these classes affect the gravity acceleration over a small distance. Cat&#39;s eyes have the lowest Y and Z variance; bumps have the highest Y and Z variance. To further separate these classes, we employ other sensors. 
         [0059]    Vertical deflection devices (e.g., speed bumps, humps, cushions, and speed tables): As the vehicle hits such devices, large spikes in variance in the Y-axis  1610  and Z-axis  1620  gravity acceleration are sensed compared to the other classes while in motion. Unlike other road anomalies, the cat&#39;s eyes structure does not cause the car moving above them to have high variance in the Y-axis  1616  or Z-axis  1626  gravity acceleration. Railway crossings leads to a medium variance in the Y-axis  1612  and Z-axis  1622  gravity acceleration over a longer distance compared to other road anomalies. In addition, they cross a railway if available on the map. 
         [0060]    A roundabout is a type of circular junction in which road traffic must travel in one direction around a central island. While a four-way intersection are typically two perpendiculars crossing roads ( FIG. 17 ). Roundabouts  1704  can be identified as normal crossings by some commercial services as shown in  FIG. 4 . Noting that a four-way intersection will only have sharp 90 degrees turns  1702 ; while a roundabout  1704  will have both turns and curves ( FIG. 17 ), we can leverage the orientation angle sensor to identify the roundabouts by the differences between their start and end orientation angles.  FIG. 18  clearly shows the difference between roundabout and an intersection in terms of car behavior. 
         [0061]    Road Features Detection Accuracy: Tables I and II show the confusion matrices for detecting the different map semantics from in-vehicle and pedestrian traces, respectively. The tables show that different map features could be detected with small false positive and negative rates due to their unique signatures; we can detect the map semantics accurately with 3% false positive rate and 6% false negative rate from in-vehicle traces, and 2% false positive rate and 3% false negative rate from pedestrian traces. 
         [0000]    
       
         
               
             
               
               
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
               
             
           
               
                 TABLE I 
               
             
             
               
                   
               
               
                 CONFUSION MATRIX FOR CLASSIFYING DIFFERENT ROAD  
               
               
                 SEMANTICS DISCOVERED FROM IN VEHICLE TRACES. 
               
             
          
           
               
                   
                 Cat&#39;s eyes 
                 Bumps 
                 Curves 
                 Rail cross. 
                 Bridges 
                 Tunnels 
                 Turns 
                 unclass. 
                 FP 
                 FN 
                 Total 
               
               
                   
               
             
          
           
               
                 Cat&#39;s eyes 
                 22 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 5 
                 0 
                 0.18 
                 27 
               
               
                 Bumps 
                 0 
                 30 
                 0 
                 3 
                 0 
                 0 
                 0 
                 0 
                 0.03 
                 0.09 
                 33 
               
               
                 Curves 
                 0 
                 0 
                 20 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 20 
               
               
                 Rail cross. 
                 0 
                 1 
                 0 
                 13 
                 0 
                 0 
                 0 
                 0 
                 0.21 
                 0.07 
                 14 
               
               
                 Bridges 
                 0 
                 0 
                 0 
                 0 
                 9 
                 0 
                 0 
                 1 
                 0 
                 0.1 
                 10 
               
               
                 Tunnels 
                 0 
                 0 
                 0 
                 0 
                 0 
                 11 
                 0 
                 0 
                 0 
                 0 
                 11 
               
               
                 Turns 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 41 
                 0 
                 0 
                 0 
                 41 
               
               
                 Overall 
                   
                   
                   
                   
                   
                   
                   
                   
                 0.03 
                 0.06 
                 156 
               
               
                   
               
             
          
         
       
     
         [0000]    
       
         
               
             
               
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
             
           
               
                 TABLE II 
               
             
             
               
                   
               
               
                 CONFUSION MATRIX FOR CLASSIFYING DIFFERENT ROAD 
               
               
                 SEMANTICS DISCOVERED FROM PEDESTRIAN TRACES. 
               
             
          
           
               
                   
                 Underpass 
                 Stairs 
                 Escalator 
                 Footbridge 
                 Walking 
                 Stationary 
                 Crosswalk 
                 FP 
                 FN 
                 Σ 
               
               
                   
               
             
          
           
               
                 Underpass 
                 11 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 11 
               
               
                 Stairs 
                 0 
                 14 
                 0 
                 0 
                 2 
                 0 
                 0 
                 0 
                 0.13 
                 16 
               
               
                 Escalator 
                 0 
                 0 
                 15 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 15 
               
               
                 Footbridge 
                 0 
                 0 
                 0 
                 16 
                 0 
                 0 
                 1 
                 0 
                 0.06 
                 17 
               
               
                 Walking 
                 0 
                 0 
                 0 
                 0 
                 32 
                 0 
                 0 
                 0.06 
                 0 
                 32 
               
               
                 Stationary 
                 0 
                 0 
                 0 
                 0 
                 0 
                 15 
                 0 
                 0 
                 0 
                 15 
               
               
                 Crosswalk 
                 0 
                 0 
                 0 
                 0 
                 0 
                 0 
                 10 
                 0.1 
                 0 
                 10 
               
               
                 Overall 
                   
                   
                   
                   
                   
                   
                   
                 0.02 
                 0.03 
                 116 
               
               
                   
               
             
          
         
       
     
         [0062]    Discovered Semantic Road Features Location Accuracy:  FIG. 19  shows the effect of number of samples on the accuracy of estimating the semantic location. We can see that the errors in the location of the discovered map feature drop quickly as we increase the number of crowd-sensed samples. We can consistently reach an accuracy of less than 2 m using as few as 12 samples for all discovered map features. 
         [0063]    Power Consumption:  FIG. 20  shows the power consumption of Map++, which is based on the inertial sensors for both road semantic detection and localization based on Dejavu, as compared to systems that detects the missing road segments only, based on the GPS traces with different duty-cycles. The power is calculated using the PowerTutor profiler and the android APIs using the HTC Nexus One cell phone.  FIG. 20  shows that Map++ has a significantly lower energy profile  2008  compared to systems that are based on the GPS chip. GPS with 1 sec duty cycle  2002  has the largest power consumption, followed by 5 sec duty cycle  2004 , and 60 sec duty cycle  2006 . 
         [0064]      FIG. 21  is a diagrammatic system view  2100  of a computer device view in which any of the embodiments disclosed herein may be performed, according to one embodiment. Particularly, the computer system view  2100  illustrates a processor  2102 , a main memory  2104 , a static memory  2106 , a bus  2112 , a video display  2120 , an alpha-numeric input device  2122 , a cursor control device  2124 , a drive unit  2126 , a signal generation device  2128 , a network interface device  2108 , a machine readable medium  2130 , instructions  2132 , and a network  2101 , according to one embodiment. 
         [0065]    The computer system view  2100  may indicate a personal computer and/or a data processing system (e.g., server) in which one or more operations disclosed herein are performed. The processor  2102  may be microprocessor, a state machine, an application specific integrated circuit, a field programmable gate array, etc. The main memory  2104  may be a dynamic random access memory and/or a primary memory of a computer system. The static memory  2106  may be a hard drive, a flash drive, and/or other memory information associated with the computer system. The bus  2112  may be an interconnection between various circuits and/or structures of the computer system. The video display  2120  may provide graphical representation of information on the data processing system. The alpha-numeric input device  2122  may be a keypad, keyboard and/or any other input device of text (e.g., a special device to aid the physically handicapped). The cursor control device  2124  may be a pointing device such as a mouse. 
         [0066]    The drive unit  2126  may be a hard drive, a storage system, and/or other longer term storage subsystem. The signal generation device  2128  may be a bios and/or a functional operating system of the data processing system. The network interface device  2108  may be a device that may perform interface functions such as code conversion, protocol conversion and/or buffering required for communication to and from a network  2101 . The machine readable medium  2130  may provide instructions on which any of the methods disclosed herein may be performed. The instructions  2132  may provide source code and/or data code to the processor  2102  to enable any one/or more operations disclosed herein. 
         [0067]    The instant system, method and process enables the right information at the right time to be intelligently and securely updated, maintained, and recombined dynamically across databases and delivery channels. The constraints and rules may be implemented in compliance to any user/users organization. The system, method and process eliminate information senescence and mutation, ensuring that internal and external user/customer gets the information they need to achieve their objectives. Even though the software is platform agnostic the display also is platform agnostic. The additional security enables the user of different professions to be comfortable to use it on any device including mobile devices. 
         [0068]      FIG. 22  is a systematic view of an integrated system for data management  2100  illustrating communication between user and the server through a network, according to one embodiment. In one embodiment a user or multiple users may connect to the server that hosts the multimedia tool in the system. In another embodiment, the user hard ware such as a PDA, mobile device such as tablets etc., computer or a mobile phone or any wireless device, or an electronic book (e-book) may be connected with each other or work independently to allow the user to use the multimedia tool for education, learning, and/or interactively playing games. The network  2101  may be a LAN, WAN, mobile, telecommunications, internet, intranet, WiFi and/or ZigBee network, etc. The user/individual  2205 ,  2204  and  2203 , a database  2202  to store all the information and so on may be an individual, a parent, a scientist, an author, but not limited to these group of folks only. The user and individual are used interchangeably and mean the same. The user may be any person who accesses the data management system for various activities as discussed in different case scenarios in the supporting figures. The cloud server may also be used for storing data and processing. The data management tool may be accessed to search, create content, upload content, view content, use the content and save and/or delete the content. The server may be stand alone, cloud based or hosted services. 
         [0069]    In this disclosure Map++: a system for automatically enriching digital maps via a crowdsensing approach based on standard cell phones is explained. For energy efficiency, Map++ uses only low-energy sensors and sensors that are already running for other purposes. We also disclose the Map++ architecture as well as the features and classifiers that can accurately detect the different road features such as tunnels, bridges, crosswalks, stairs, and footbridges from the user traces. In this document, we showed the evidence through measurements how useful traffic and road features can be added to real-world maps. Map++ has a significantly lower energy profile compared to systems that are based on GPS. 
       INDUSTRIAL APPLICABILITY 
       [0070]    Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The instant disclosure is valid for cell phone networks and general wireless network that works under IEEE 802.11 a/b/g/n/ac standards. The instant disclosure works with all smart phones that are equipped with standard sensors including GPS. The instant disclosure does not require any special permission to be generated on the smart devices. The hall mark of the instant disclosure is that the innovation works seamlessly and silently in the background without any disturbance to the smart device owners to carry on the sensor data and updating the maps. The instant disclosure is directly applicable to industry as majority of the smart devices have WiFi interfaces and can be used immediately. The procedure works well with smart devices. The instant disclosure is directly applicable to the map industry where accurate maps are needed at ground level for people to move about and those with disability.