Abstract: Proposed is a vehicle embedded or mobile-phone-based, automated dynamic routing unit associated with a vehicle and/or a user along a route traveled and method thereof. The dynamic routing unit provides an optimized route between a departure location and a destination location, wherein the automated dynamic routing unit comprises a routing interface for receiving destination input parameters of a destination location and/or departure location input parameters of a departure location, and wherein the automated dynamic routing unit comprises a routing generator for generating one or more routes between the departure location and the destination location.

Abstract: An automated automobile claims fraud detector and method for automatically evaluating validity and extent of at least one damaged object from image data and detect possible fraud, the automated automobile claims fraud detector comprising the processing steps of: (a) receiving image data comprising one or more images of at least one damaged object; (b) processing said one or more images for existing image alteration using unusual pattern identification and providing a first fraud detection; (c) processing said one or more images for fraud detection using RGB image input, wherein the RGB values are used for (i) CNN-based pre-existing damage detection, (ii) parallel CNN-based color matching, and (iii) double JPEG compression (DJCD) detection using custom CNN; (d) processing output of (i) CNN-based pre-existing damage detection, (ii) parallel CNN-based color matching, and (iii) double JPEG compression detection using custom CNN as input for ML-based fraud identifier providing a second fraud detection; and (e) gen

Abstract: A method and system for electronic trip familiarity detection based on sensory data measured by a plurality of sensors of a mobile telematics device associated with a user and/or a vehicle, the plurality of sensors at least comprising a GPS sensor and/or an accelerometer, the mobile device comprising one or more wireless connections, wherein by at least one of the wireless connection the mobile device acts as a wireless node within a cellular data transmission network by means of antenna connections of the mobile device to the cellular data transmission network, and the plurality of sensors being connected to a monitoring mobile node application of the mobile device, wherein the monitoring mobile node application captures usage-based and/or user-based sensory data of the plurality of sensors of mobile device.

Abstract: A method and system for automated transportation mode recognition based on sensory data measured by a plurality of sensors of a cellular mobile device of a user, the plurality of sensors at least comprising an accelerometer and a gyroscope, the plurality of sensors being connected to a monitoring mobile node application of the mobile device, wherein the mobile device measures time series of sensory parameter values based on measuring parameters obtained from the sensors, the measuring parameters comprise time series of sensory parameter values of a 3-axis accelerometer as sensor and time series of sensory parameter values of GPS-based speed measurements of a GPS receiver as sensor, and wherein the measured time series of sensory parameter values trigger the automated transportation mode recognition as input feature values to a gradient boosting machine-learning classifier, the transportation modes at least comprising the modes public transportation and/or motorcycle and/or cycling and/or train and/or tram and

Abstract: An automated system, phone distraction detector and method for automatically detecting phone distraction events in a moving vehicle, caused by a driver of the moving vehicle by using a smartphone. An Internet of Things (IoT) server is communicatively coupled to the smartphone. The smartphone (comprises one or more sensors built into the smartphone, wherein sensory data of the one or more sensors, are captured by the sensors of the smartphone during a vehicle trip and are transmitted to the Internet of Things (IoT) server, and wherein the sensor data comprises one or more chunks of data streams associated with an IoT service from the smartphone. The sensory data are processed and analyzed by the server to identify one or more phone handling events associated with the smartphone during the vehicle trip. One or more phone handling events associated with the smartphone are aggregated into one or more macro events by the processing means of the server.

Abstract: A method and system for identifying and/or classifying a user of a vehicle based on sensory data measured by sensors of a cellular mobile device, the sensors at least comprising an accelerometer and a gyroscope. The mobile device measures gravitational acceleration movement sensory data by the accelerometer based on measuring parameters obtained from the accelerometer, vehicle entering or exiting movement patterns of the user being detected from the acceleration movement sensory data at least comprising pattern for base axis and degree of rotation associated with a vehicle entrance or exit of the user. The detected vehicle entering or exiting movement patterns of the user trigger as input features the recognition of a vehicle entering or exiting movement of the user by performing a decision-tree classification on the input features to rule out whether the user entered or exited from a left or right side of the vehicle.