METHOD AND SYSTEM FOR CROWD SENSING TO BE USED FOR AUTOMATIC SEMANTIC IDENTIFICATION
First Claim
1. A method, comprising:
- collecting at least one of a trace information data, a location information using a geo positioning system (GPS) data and sensor data that is time stamped as raw data;
preprocessing the raw data using a low-pass filter to the raw data to reduce the effect of phone orientation changes and noise and bogus changes, wherein the noise and bogus changes are one of a sudden breaks used by a vehicle, small changes in the direction while moving and mobile coordinate;
detecting a mode of transportation to collect a transportation data, wherein the mode of transportation is at least one of a vehicle and people who are walking;
extracting a map semantics data as a unique identifier by the semantic detection module; and
clustering the preprocessed raw data, transportation data and map semantics data to automatically map, direct and update using a crowd sensing mechanism from a mobile device for automatic semantic identification.
1 Assignment
0 Petitions
Accused Products
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.
-
Citations
14 Claims
-
1. A method, comprising:
-
collecting at least one of a trace information data, a location information using a geo positioning system (GPS) data and sensor data that is time stamped as raw data; preprocessing the raw data using a low-pass filter to the raw data to reduce the effect of phone orientation changes and noise and bogus changes, wherein the noise and bogus changes are one of a sudden breaks used by a vehicle, small changes in the direction while moving and mobile coordinate; detecting a mode of transportation to collect a transportation data, wherein the mode of transportation is at least one of a vehicle and people who are walking; extracting a map semantics data as a unique identifier by the semantic detection module; and clustering the preprocessed raw data, transportation data and map semantics data to automatically map, direct and update using a crowd sensing mechanism from a mobile device for automatic semantic identification. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
-
-
9. A system, comprising:
-
a processor to house and compute various modules; a trace information data collection module to collect information for a specific location as a time stamped and location stamped raw data; a preprocessing module to gather and filter the raw data; a transportation mode detection module for acquiring a high accuracy differentiated data between the different transportation modes using an energy-efficient inertial sensor; and a semantic detection module performs a clustering algorithm to detect, map and update a precise map location for a vehicular traffic and pedestrian traffic. - View Dependent Claims (10, 11, 12, 13, 14)
-
Specification