METHOD AND A SYSTEM FOR GENERATING A MACHINE LEARNING MODEL FOR REDUCING A LOCATION ERROR OF READINGS OF ROAD SIGNS SENSED BY CONNECTED VEHICLES

A method and a system for generating a machine learning model for reducing a location error of readings of road signs sensed by connected vehicles, said method comprising: obtaining an incoming stream of readings of road signs captured by connected vehicles traveling along roads, wherein each reading contains a location of a road sign and road sign metadata associated therewith; applying a clustering algorithm to group together readings associated with a common road sign in separate groups; comparing the locations of each group to respective tagged road-signs dataset associated with a correct location, to yield a correction vector for each cluster; and training a machine learning model to correct the location of the readings in each group by learning the correction vectors.

FIELD OF THE INVENTION

The present invention relates generally to a computerized automotive marketplace, and more particularly, to processing automotive data of connected vehicles on same.

BACKGROUND OF THE INVENTION

Prior to the background of the invention being set forth, it may be helpful to provide definitions of certain terms that will be used hereinafter.

The term “connected vehicle” or “connected car” as used herein is defined as a car or any other motor vehicle such as a drone or an aerial vehicle that is equipped with any form of wireless network connectivity enabling it to provide and collect data from the wireless network. The data originated from and related to connected vehicles and their parts is referred herein collectively as “automotive data”.

The term “data marketplace” or “data market” as used herein is defined as an online platform preferably implemented on a cloud that enables a plurality of users (e.g. subscribers or consumers) to access and consume data originated by various data sources (e.g. data providers). Data marketplaces typically offer various types of data for different markets and from different sources. Common types of data consumers include business intelligence, financial institutions, demographics, research and market data. Data types can be mixed and structured in a variety of ways. Data providers may offer data in specific formats for individual clients.

Data consumed in these marketplaces is used by businesses of all kinds, fleets, business and safety applications and many types of analysts. Data marketplaces have proliferated with the growth of big data, as the amount of data collected by municipalities and smart cities, businesses, websites, and services has increased, and all that data has become increasingly recognized as an asset.

The road-sign data that is produced by connected-vehicles, proves to be noisy and inaccurate. It would be desirable to improve the accuracy of the data that is obtained from road signs and captured via the connected vehicles.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a method and a system for generating a machine learning model for reducing a location error of readings of road signs sensed by connected vehicles. The method may include the following steps: obtaining an incoming stream of readings of road signs captured by connected vehicles traveling along roads, wherein each reading contains a location of a road sign and road sign metadata associated therewith; using a clustering algorithm to cluster together readings associated with a common road sign in respective; comparing the locations of each group to respective tagged road-signs dataset having a correct location, to yield a correction vector; and training a machine learning model to correct the location of the readings in each group by learning the correction vectors.

DETAILED DESCRIPTION OF THE INVENTION

The automotive marketplace in accordance with embodiments of the present invention receives large amounts of road-sign data that is inaccurate and noisy. These are the steps needed to be able to “fix” the location data and generate an accurate output with road-sign locations:A. Create a machine learning model that would “repair” all individual data items.B. Filter out the raw data by location to match the area where our reference data exists.C. Use a clustering algorithm to isolate reports about a single sign (by the signs location and the sign metadata)D. Compare the resulting signs' locations and type to the reference signs in our external road-signs dataset. Train a machine learning model to “fix” raw data items using the metadata of the raw data.E. Run the newly created machine learning model on the raw data and repeat stages B and C until the resulting machine model stops improving (gradient descent style).F. Using the newly created machine model, “fix” the entire raw dataset.G. Run the same clustering method to produce the location of signs from all over the raw dataset.

FIG.1is a block diagram illustrating non-limiting exemplary architecture of the system in accordance with some embodiments of the present invention. Automotive data marketplace100comprises at least one automotive data server110which may, in preferable embodiments, be a secured automotive data server fully compliant with data protection and privacy regulations.

Server110may be implementing a data marketplace and connected via network30to a plurality of data consumers40A-40D. Vehicle related data, possibly obtained from various sensors may be stored in raw format on a plurality of vehicle related data sources such as automotive data providers10A-10N and are accessed by server110via a secured data link20. Server110may include a data processing module130implemented by a computer readable code running on computer processor120that stores processed automotive data records on data store140.

In accordance with some embodiments of the present invention, a system for generating a machine learning model for reducing a location error of readings of road signs sensed by connected vehicle may be implemented on automotive data marketplace100. A data processing module130may be configured to obtain an incoming stream of readings of road signs captured by connected vehicles traveling along roads, e.g., from processed automotive data store140, wherein each reading contains a location of a road sign and road sign metadata associated therewith.

In accordance with some embodiments of the present invention, a clustering module180may be configured to use a clustering algorithm to group together readings associated with a common road sign in clusters.

In accordance with some embodiments of the present invention, a comparing module190may be configured to compare the locations of each cluster to respective tagged road-signs dataset associated with a correct location, to yield a respective correction vector for each cluster.

In accordance with some embodiments of the present invention, a training module170may be configured to train a machine learning model to correct the location of the readings in each group by learning the correction vectors.

In accordance with some embodiments of the present invention, a trained machine learning model150may be used to repair the entire raw dataset by applying it via training module170.

FIG.2is a high-level flowchart illustrating non-limiting exemplary method in accordance with embodiments of the present invention. A method200of A method and a system for generating a machine learning model for reducing an error of readings of road signs sensed by connected vehicles, said method comprising: obtaining an incoming stream of readings of road signs captured by connected vehicles traveling along roads, wherein each reading contains a location of a road sign and road sign metadata associated therewith210; using a clustering algorithm to group together readings associated with a common road sign in separate groups220; comparing the locations of each group to respective tagged road-signs dataset, to yield a correction vector230; and training a machine learning model to correct the location of the readings in each group by learning the respective correction vectors240.

FIG.3is an exemplary diagram illustrating the clustered readings and the corrections vectors used for training in accordance with embodiments of the present invention on a roadmap300. Vehicles positions (e.g., GPS)310and360are collected and each such reading is associated with its respective road sign reading shown collectively in clusters330and370respectively. These readings are what the vehicle sensors are capable of provide as measures location (usually relative distance) from the road sign itself (not shown). These readings are clusters per common road sign. Then each of the readings in clusters330and370are compared with tagged data set that includes correct and validated position of the respective road sign, so as to provide corrected locations in clusters340and380respectively, thus producing correction vectors350and380respectively.

As explained above, these correction vectors are fed into a training module which generates a machine learning model capable of fixing the location error of the vehicle-obtained readings for the road signs. As can be shown, a major error may be caused by the drift in the sensors reading along the direction of the rise of the vehicles, but other errors occur and their pattern is studied by the machine learning algorithm so that a meaningful fix may be applied to the entire dataset of reading thus significantly improving the accuracy of automotive data as a source for road signs mapping.

In order to implement method and system according to embodiments of the present invention, a computer processor may receive instructions and data from a read-only memory or a random-access memory or both. At least one of aforementioned steps may be performed by at least one processor associated with a computer. The essential elements of a computer are a processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer will also include, or be operatively coupled to communicate with, one or more mass storage devices for storing data files. Storage modules suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices and magneto-optic storage devices.

Aspects of the present invention are described above with reference to flowchart illustrations and/or portion diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each portion of the flowchart illustrations and/or portion diagrams, and combinations of portions in the flowchart illustrations and/or portion diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or portion diagram portion or portions.

The aforementioned flowchart and diagrams illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each portion in the flowchart or portion diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the portion may occur out of the order noted in the figures. For example, two portions shown in succession may, in fact, be executed substantially concurrently, or the portions may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each portion of the portion diagrams and/or flowchart illustration, and combinations of portions in the portion diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

It is to be understood that where the claims or specification refer to “a” or “an” element, such reference is not construed that there is only one of that elements.

Any publications, including patents, patent applications and articles, referenced or mentioned in this specification are herein incorporated in their entirety into the specification, to the same extent as if each individual publication was specifically and individually indicated to be incorporated herein. In addition, citation, or identification of any reference in the description of some embodiments of the invention shall not be construed as an admission that such reference is available as prior art to the present invention.