Abstract:
A system and method for identifying a vehicle driver based on driver behaviors. The system and method include analyzing a sequence of vehicle start-up behaviors for rapid identification of the driver. The start-up analysis includes detecting and evaluating the sequence and timing of events including door opening, door closing, seat belt fastening, ignition switch usage and shift/drive, among others. The technique further includes analyzing a set of longitudinal (or long-term) behaviors for more robust verification of driver identification. The longitudinal behaviors include acceleration and braking patterns, speed pattern (compared to road type and speed limit), stop sign behavior, cruise control usage and many others. Statistical clustering techniques are employed for both the start-up and longitudinal behavior analyses to identify the driver.
Abstract:
An in-vehicle display system includes a selection tool, a memory for storing a number of style templates, and a rendering module. The rendering module is configured to select, in response to user input received via the selection tool, first content from a first data source associated with a mobile device within a vehicle, second content from a second data source, and a first style template from the set of style templates. The rendering module is further configured to render the first content and the second content on a first in-vehicle display based on the first style template.
Abstract:
Methods and system of alerting a driver of a vehicle is provided. In one embodiment, the method includes: receiving conditions data from one or more collision avoidance systems; selectively coordinating an alert pattern for at least one of a haptic alert device, a visual alert device, and an auditory alert device based on the conditions data; receiving at least one of interior vehicle conditions data indicating conditions within the vehicle and exterior conditions data indicating conditions outside of the vehicle; and modifying the alert pattern based on the at least one of interior vehicle conditions data and exterior vehicle conditions data.
Abstract:
A method of alerting a driver of a vehicle is provided. The method includes: receiving conditions data from one or more collision avoidance systems; determining an alert mode based on the conditions data; receiving a fault status indicating a fault of at least one of a haptic alert device, a visual alert device, and an auditory alert device; resetting the alert mode to an override mode based on the fault status; and selectively generating an alert pattern for at least one of a haptic alert device, a visual alert device, and an auditory alert device that does not have a fault based on the override mode of the alert mode.
Abstract:
A method for acquiring road data onboard a vehicle, the road data associated with a segment of road is provided. The method obtains, via vehicle onboard sensors, sensor data associated with current weather conditions, current road conditions, and a physical road state; determines whether the current weather conditions indicate existence of severe weather, whether the current road conditions indicate potential slip, and whether the physical road state indicates one or more road anomalies; generates a road data result, based on existence of severe weather, potential slip, and one or more road anomalies; and transmits the road data result, via a vehicle onboard telematics unit.
Abstract:
Methods and systems are disclosed for participative sensing of events and conditions by road vehicles, collection of this data from a large number of road vehicles by a central server, processing the data to identify events and conditions which may be of interest to other vehicles in a particular location, and sending notifications of the events and conditions to vehicles. A large number of vehicles use participative sensing systems to identify a safety-related event or condition which should be reported to the central server—such as a large pothole, an obstacle in the roadway, an icy road surface, a traffic accident, etc. The central server stores and aggregates the data, filters it and ages it. Vehicles requesting advisories from the central server will receive notices of safety-related events and conditions based on their location and heading. Driver warnings can be issued, and vehicle systems may respond to the notices.
Abstract:
A method of determining ride share compatibility. Vehicle data acquisition devices are employed to collect user attribute information relating to a travel route and locations traveled by the operator. The attribute information includes regularity data, frequency data, and duration data. A regression analysis is applied by a processor for using the regularity data, the frequency data, and the duration data, for identifying an importance probability of each of the locations visited by the operator. A match is determined between the operator and a potential travel partner traveling to locations in proximity to the locations traveled by the operator.
Abstract:
An antenna assembly for a vehicle that includes an AM/FM mast antenna element for AM/FM signals and a WiFi or DSRC antenna element positioned at a tip of the mast, where the antenna assembly is mounted to a vehicle roof and where the WiFi or DSRC antenna element extends above a roof line of the vehicle.
Abstract:
A method of alerting a driver of a vehicle is provided. The method includes: receiving alert settings configured by a user through a user interface; and selectively generating an alert pattern for at least one of a haptic alert device, an auditory alert device, and a visual alert device based on the alert settings.
Abstract:
A system and method for providing navigation routing options to a vehicle driver, including estimated fuel consumption and fuel cost. A server collects data from a large number of road vehicles driving different routes, where the data includes road grade, average speed, stop/start and acceleration/deceleration info and vehicle specifications, and the data is collected via a telematics or other wireless system. The server also receives map data, point of interest data and real-time traffic data from their respective providers. When a driver of a road vehicle requests navigation routing from a start point to a destination, the server provides multiple routing options including not only distance and time for each routing option, but also fuel consumption and cost. The estimated fuel consumption is computed using models based on the crowd-sensed data from the other vehicles driving the routes, where the models include a physics-based model and a machine learning model.