Abstract: Disclosed herein is method and congestion management system for reducing road congestion. Traffic data related to plurality of vehicles is analyzed by a trained traffic model for predicting speed of each vehicle and signal time associated with intersection points. Optimal speed for each vehicle and an optimal signal time for each of the intersection points is determined based on analysis of the previous values and historic traffic data. The determined optimal speed and the optimal signal time are respectively provided to a vehicle control system associated with each vehicle and a traffic controller associated with each intersection point. In an embodiment, the method of present disclosure reduces traffic congestion on any selected portion of road. Further, the method of present disclosure eliminates and/or minimizes number of instances that a vehicle has to stop/start at the traffic signals, thereby enhancing fuel economy and reducing waiting time for the vehicles.

Abstract: The present disclosure is related to Just-In-Time (JIT) services, that discloses a method and system for providing JIT services to automotive users. A Point of Interest (PoI) service aggregator system may receive a service request from an automotive user, including one of: information related to a preferred PoI service provider, request to recommend PoI service providers, or request to list PoI service providers based on user query. Based on service request, the PoI service aggregator system may perform one of: dynamically on-boarding the preferred PoI service provider, recommending PoI service providers, or providing PoI service providers based on user query. Thereafter, a real-time synchronization may be established between a candidate PoI service provider and the automotive user to enable the candidate PoI service provider to determine arrival events including an estimated time of arrival and non-arrival events of the automotive user, to provide JIT service to the automotive user.

Abstract: This disclosure relates to method and system for managing Internet of Things (IoT) devices in heterogeneous communication networks. The method includes receiving metadata corresponding to each of a set of IoT devices including a plurality of IoT sensors; validating each of the set of IoT devices based on the received metadata; establishing communication with each of the set of IoT devices through an associated predefined data communication protocol; receiving real-time data from each of the plurality of sensors associated with each of the set of IoT devices through an IoT protocol; monitoring the real-time data received from each of the plurality of sensors at predefined time intervals through a Graphical User Interface (GUI); and managing one or more device parameters corresponding to each of the set of IoT devices in the heterogeneous communication network through the GUI based on the real-time data.

Abstract: The present disclosure is related to Just-In-Time (JIT) services, that discloses a method and system for providing JIT services to automotive users. A Point of Interest (PoI) service aggregator system may receive a service request from an automotive user, including one of: information related to a preferred PoI service provider, request to recommend PoI service providers, or request to list PoI service providers based on user query. Based on service request, the PoI service aggregator system may perform one of: dynamically on-boarding the preferred PoI service provider, recommending PoI service providers, or providing PoI service providers based on user query. Thereafter, a real-time synchronization may be established between a candidate PoI service provider and the automotive user to enable the candidate PoI service provider to determine arrival events including an estimated time of arrival and non-arrival events of the automotive user, to provide JIT service to the automotive user.

Abstract: A method and system for navigating vehicles based on road conditions determined in real-time is disclosed. The method includes the steps of receiving a first dataset including an image of a section of a road within a Field of View (FOV) of a camera attached to a vehicle and a second dataset associated with the road. The method further includes detecting edges and a vanishing point in the image, correcting road perspectivity in the image, and determining surface anomalies in the road based on a set of parameters, the second dataset and a Time of Flight technique (ToF), creating a digital elevation model for the image, and assigning a value, in real-time, from a predefined value range to each of a plurality of grids in the image based on a digital elevation model to generate a digital elevation image. The set of parameters includes volume associated with the surface anomalies.

Abstract: The present invention relates to a method of identifying a brand and a model of a vehicle. The method comprises obtaining one or more video frames. Further, the method comprises detecting one of presence or absence of text in the at least one vehicle. Upon detecting the presence of the text, the method comprises determining at least one of the brand and the model of the at least one vehicle by accumulating the text recognized in the one or more video frames. Further, identifying at least one of the brand and the model of the at least one vehicle based on an accumulated text. Upon detecting the absence of the text, the method comprises determining the brand of the at least one vehicle based on logo associated with the vehicle.

Abstract: A method and system for navigating vehicles based on road conditions determined in real-time is disclosed. The method includes the steps of receiving a first dataset including an image of a section of a road within a Field of View (FOV) of a camera attached to a vehicle and a second dataset associated with the road. The method further includes detecting edges and a vanishing point in the image, correcting road perspectivity in the image, and determining surface anomalies in the road based on a set of parameters, the second dataset and a Time of Flight technique (ToF), creating a digital elevation model for the image, and assigning a value, in real-time, from a predefined value range to each of a plurality of grids in the image based on a digital elevation model to generate a digital elevation image. The set of parameters includes volume associated with the surface anomalies.

Abstract: A method and a system for implementing neural network models on edge devices in an Internet of Things (IoT) network are disclosed. In an embodiment, the method may include receiving a neural network model trained and configured to detect objects from images, and iteratively assigning a new value to each of a plurality of parameters associated with the neural network model to generate a re-configured neural network model in each iteration. The method may further include deploying for a current iteration the re-configured neural network on the edge device. The method may further include computing for the current iteration, a trade-off value based on a detection accuracy associated with the at least one object detected in the image and resource utilization data associated with the edge device, and selecting the re-configured neural network model, based on the trade-off value calculated for the current iteration.

Abstract: This disclosure relates to method and system for providing a light weight secure communication for computing devices. In one example, the method includes generating a new encryption key based on a selected encryption key from among a plurality of encrypted keys and a current synchronized hash based on a set of pre-defined rules, generating an updated synchronized hash based on a message to be transmitted and the current synchronized hash using a pre-defined hash algorithm, encrypting the message to be transmitted using the new encryption key to generate an encrypted message, transmitting the encrypted message, and replacing the current synchronized hash with the updated synchronized hash. The set of pre-defined rules and the pre-defined hash algorithm are retrieved from a pre-installed library. Further, the current synchronized hash, the plurality of encryption keys, and the pre-installed library are synchronized between the first computing device and the second computing device.

Abstract: The present invention relates to a method of identifying a brand and a model of a vehicle. The method comprises obtaining one or more video frames. Further, the method comprises detecting one of presence or absence of text in the at least one vehicle. Upon detecting the presence of the text, the method comprises determining at least one of the brand and the model of the at least one vehicle by accumulating the text recognized in the one or more video frames. Further, identifying at least one of the brand and the model of the at least one vehicle based on an accumulated text. Upon detecting the absence of the text, the method comprises determining the brand of the at least one vehicle based on logo associated with the vehicle.

Abstract: This disclosure relates generally to a human machine interface (HMI) application, and more particularly to modular testing kits and testing framework for testing the HMI application. In one embodiment, a method for developing a modular testing kit for testing a modular component of the HMI application is disclosed. The method may include generating a plurality of test cases for testing the modular component based on a definition of each of a set of interfaces configured to implement the modular component on a target platform, developing the modular testing kit to execute the plurality of test cases by simulating each of the set of interfaces, and creating a test script for each of the plurality of test cases using the modular testing kit.

Abstract: A method and a system are described for counting objects in an image. The method includes receiving at least one image comprising one or more objects. The method includes determining contours of the one or more objects using one or more morphological operations on the at least one image. The method further includes identifying shapes of the one or more objects based on counting a number of contours associated with each of the one or more objects. The method includes comparing the shapes of the one or more objects with one or more predefined training images to identify one or more objects of interest. The method includes counting the one or more objects of interest based on the shapes of the one or more objects of interest.

Abstract: The present disclosure discloses a method and system for integrating a dynamic Business Logic (BL) Database (DB) with a Human Machine Interface (HMI) application. The method comprises receiving a Business Logic (BL) specification. The BL specification comprises BL data and one or more parameters associated with the BL data. Further, one or more state charts of the BL data are generated from the BL specification. Thereafter, a dynamic BL DB is developed based on the one or more parameters and the one or more state charts. Furthermore, the BL data in the dynamic BL DB is updated when the BL data in the BL specification is updated. Lastly, the dynamic BL DB is integrated with a HMI application utilizing the BL data to implement the BL. The HMI application retrieves the updated BL data from the dynamic BL DB for implementing updated BL.

Abstract: Disclosed herein is method and congestion management system for reducing road congestion. Traffic data related to plurality of vehicles is analyzed by a trained traffic model for predicting speed of each vehicle and signal time associated with intersection points. Thereafter, optimal speed for each vehicle and an optimal signal time for each of the intersection points is determined based on analysis of the previous values and historic traffic data. Finally, the determined optimal speed and the optimal signal time are respectively provided to a vehicle control system associated with each vehicle and a traffic controller associated with each intersection point. In an embodiment, the method of present disclosure reduces traffic congestion on any selected portion of road. Further, the method of present disclosure eliminates and/or minimizes number of instances that a vehicle has to stop/start at the traffic signals, thereby enhancing fuel economy and reducing waiting time for the vehicles.

Abstract: This disclosure relates generally to a human machine interface (HMI) application, and more particularly to method, system, and framework for testing the HMI application on a target device. In one embodiment, the method may include accessing a test script corresponding to a test case for testing the HMI application on the target device. The test script may include a set of mutually independent primitive executables. Each primitive executable may be configured to simulate at least one of a trigger or a condition corresponding to a business logic (BL) component of the HMI application and to verify an output using at least one of a view component or a platform component of the HMI application. The method may further include transmitting each of the set of primitive executables to the target device for execution, and receiving a result corresponding to the execution of the each of the set of primitive executables.

Abstract: A method and a system for implementing neural network models on edge devices in an Internet of Things (IoT) network are disclosed. In an embodiment, the method may include receiving a neural network model trained and configured to detect objects from images, and iteratively assigning a new value to each of a plurality of parameters associated with the neural network model to generate a re-configured neural network model in each iteration. The method may further include deploying for a current iteration the re-configured neural network on the edge device. The method may further include computing for the current iteration, a trade-off value based on a detection accuracy associated with the at least one object detected in the image and resource utilization data associated with the edge device, and selecting the re-configured neural network model, based on the trade-off value calculated for the current iteration.

Abstract: The present disclosure relates to integrating a plurality of applications with an infotainment system of a vehicle (101). A plurality of integration properties (206) is received from a plurality of applications. Each of the plurality of applications is associated with a projection technology and each of the plurality of application is developed in a respective development language and respective development platform. A plurality of generic behaviour of the plurality of integration properties (206) of the plurality of applications is identified. A projection model is created based on the plurality of generic behaviour of the plurality of integration properties (206) of the plurality of applications. A unified projection application is generated based on the projection model, to integrate the plurality of applications developed using respective development language and development platform with the infotainment system of the vehicle (101).

Abstract: This disclosure relates to method and system for providing a light weight secure communication for computing devices. In one example, the method includes generating a new encryption key based on a selected encryption key from among a plurality of encrypted keys and a current synchronized hash based on a set of pre-defined rules, generating an updated synchronized hash based on a message to be transmitted and the current synchronized hash using a pre-defined hash algorithm, encrypting the message to be transmitted using the new encryption key to generate an encrypted message, transmitting the encrypted message, and replacing the current synchronized hash with the updated synchronized hash. The set of pre-defined rules and the pre-defined hash algorithm are retrieved from a pre-installed library. Further, the current synchronized hash, the plurality of encryption keys, and the pre-installed library are synchronized between the first computing device and the second computing device.

Abstract: The present disclosure relates to a recommendation engine and a method for providing recommendations during navigation of the vehicle on a road. The recommendation engine provides recommendations in real-time to one or more occupants of the vehicle. The recommendation engine receives data related to the vehicle and environment surrounding the vehicle in real-time. The date along with a historical data set regarding the road is used to generate a training set corresponding to a plurality of conditions of the road. Thereafter, the training set is used to create a multi-variate regression model. The multi-variate regression model is applied on the received data for detecting a condition of the road during navigation of the vehicle. The detected condition of the road is used for providing one or more recommendations to the one or more occupants of the vehicle.

Abstract: The present disclosure relates to the field of device testing. In one embodiment, a method is provided for testing a resource constrained device. The method includes determining a test case for testing the resource constrained device, and accessing a test script corresponding to the test case. The test script includes a set of mutually independent primitive executables. The method further includes transmitting each of the set of primitive executables to the resource constrained device for execution, and receiving a result corresponding to the execution of the each of the set of primitive executables.