Abstract: A system and method to provide a trailer-considerate route recommendation involve obtaining logged information from vehicles, the logged information from each of the vehicles corresponding with a location of the vehicle, a time, a type of the vehicle, and a type of trailer pulled by the vehicle. A method includes obtaining potential routes under consideration for an on-going navigation by a trailering vehicle towing a towed trailer and obtaining road characteristics for the potential routes. A risk score is computed for each of the potential routes, the risk score being a weighted sum of risk factors. Each of the risk factors results from quantifying the logged information or the road characteristics. The trailer-considerate route recommendation is provided based on the risk score associated with each of the potential routes to affect the on-going navigation by the trailering vehicle.

Abstract: Methods and apparatus are provided for controlling a vehicle. In one embodiment, a method includes: receiving, by a processor, vehicle position data from the vehicle; processing, by the processor, vehicle position data with vehicle position data from other vehicles to determine a lane topology; processing, by the processor, the vehicle position data to determine traffic conditions within a lane of the lane topology; generating, by the processor, a map for controlling the vehicle based on the lane topology and the traffic conditions.

Abstract: Presented are systems and methods for deriving speed limits of designated road segments by mining large-scale vehicle data traces. A method for controlling operation of a motor vehicle includes: determining a current location of the vehicle; determining a designated road segment corresponding to the vehicle's location; receiving host speed data indicative of the vehicle's speed while travelling on the road segment for a calibrated timeframe; receiving crowd-sourced speed data indicative of the speeds of participatory vehicles while travelling on the road segment for the calibrated timeframe; accumulating a speed distribution function for the road segment based on the host and crowd-sourced speed data; generating a finite mixture model from the speed distribution function to estimate a speed limit range; selecting a speed limit candidate from the estimated speed limit range; and transmitting command signals to a vehicle subsystem to execute a control operation based on the selected speed limit candidate.

Abstract: Presented are systems and methods for extracting lane-level information of designated road segments by mining vehicle dynamics data traces.

Abstract: Methods and apparatus are provided for controlling a vehicle. In one embodiment, a method includes: receiving, by a processor, vehicle position data from the vehicle; processing, by the processor, vehicle position data with vehicle position data from other vehicles to determine a lane topology; processing, by the processor, the vehicle position data to determine traffic conditions within a lane of the lane topology; generating, by the processor, a map for controlling the vehicle based on the lane topology and the traffic conditions.

Abstract: Presented are systems and methods for extracting lane-level information of designated road segments by mining vehicle dynamics data traces.

Abstract: Presented are systems and methods for deriving speed limits of designated road segments by mining large-scale vehicle data traces. A method for controlling operation of a motor vehicle includes: determining a current location of the vehicle; determining a designated road segment corresponding to the vehicle's location; receiving host speed data indicative of the vehicle's speed while travelling on the road segment for a calibrated timeframe; receiving crowd-sourced speed data indicative of the speeds of participatory vehicles while travelling on the road segment for the calibrated timeframe; accumulating a speed distribution function for the road segment based on the host and crowd-sourced speed data; generating a finite mixture model from the speed distribution function to estimate a speed limit range; selecting a speed limit candidate from the estimated speed limit range; and transmitting command signals to a vehicle subsystem to execute a control operation based on the selected speed limit candidate.

Abstract: Disclosed are vehicle communications networks for adapting user information using crowd-sensed contextual data, computer-executable instructions for provisioning such content/information, and vehicles equipped with a telematics system for adapting driver information using contextual data from vehicle participatory sensing systems. A disclosed method for provisioning information to occupants of a motor vehicle includes determining trip characteristics, including vehicle and driver data, for a current trip of the vehicle, and determining trip characteristics for previous trips that correspond to the current trip. The vehicle's communications system receives, over a distributed computer network from a vehicle participatory sensing system aggregating data from participative vehicles, operational data for the current trip as sensed by the participative vehicles.

Abstract: A system configured to receive baseline map data from a remote map-data source, and determine whether the baseline map data is sufficient for present mobile navigation-unit function, yielding a map-sufficiency determination. If the map-sufficiency determination is affirmative, using the baseline map data for the present mobile navigation-unit function. If the map-sufficiency determination is negative, the system predicts navigation-unit movement, obtains based on the navigation-unit movement predicted, supplemental map data, and uses the baseline map data and the supplemental map data for the present mobile navigation-unit function.

Abstract: Systems and method are provided for participative map anomaly detection and correction. In one embodiment, a processor-implemented method for map anomaly detection is provided. The method includes receiving, by a processor in a vehicle, pre-planned trajectory data from a navigation module in the vehicle, retrieving, by the processor, sensor data from one or more vehicle sensing systems, analyzing, by the processor, the sensor data and the pre-planned trajectory data, identifying, by the processor, an anomaly from the analysis, and transmitting information regarding the anomaly to a central repository external to the vehicle wherein the central repository is configured to analyze the information regarding the anomaly to determine if a navigation map attribute is incorrect.

Abstract: Disclosed are vehicle communications networks for adapting user information using crowd-sensed contextual data, computer-executable instructions for provisioning such content/information, and vehicles equipped with a telematics system for adapting driver information using contextual data from vehicle participatory sensing systems. A disclosed method for provisioning information to occupants of a motor vehicle includes determining trip characteristics, including vehicle and driver data, for a current trip of the vehicle, and determining trip characteristics for previous trips that correspond to the current trip. The vehicle's communications system receives, over a distributed computer network from a vehicle participatory sensing system aggregating data from participative vehicles, operational data for the current trip as sensed by the participative vehicles.

Abstract: Systems and method are provided for implementing selective crowd sourcing. In one embodiment, a processor-implemented method for obtaining data from vehicles includes calculating a greedy parameter value for each of a plurality of vehicles in a geographical area; selecting no more than a predetermined number of the plurality of vehicles having a greedy parameter value in a greedy parameter threshold range; instructing the selected vehicles to transmit data while in the geographical area; and receiving the data from the selected vehicles. In another embodiment, a vehicle including a crowd sourcing selection module is provided. The crowd sourcing selection module is configured to retrieve consensus information from a central repository; calculate greedy parameter information regarding the vehicle using the consensus information; and determine whether to transmit an event observation to the central repository based on the greedy parameter information.

Abstract: A system having a resource-availability module configured to cause the processing unit to determine whether a resource scarcity condition exists in connection with providing map data to a mobile navigation unit. The system also includes a tuning-parameter module configured to cause the processing unit to, in response to determining that the resource scarcity condition exists, determine a tuning parameter to use in preparing map data to provide to the mobile navigation unit. The system further includes an encoding module configured to cause the processing unit to encode map data based on the tuning parameter, and a map-data-delivery module configured to cause the processing unit to provide or make available the encoded map data to the mobile navigation unit.

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.

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: A system having a resource-availability module configured to cause the processing unit to determine whether a resource scarcity condition exists in connection with providing map data to a mobile navigation unit. The system also includes a tuning-parameter module configured to cause the processing unit to, in response to determining that the resource scarcity condition exists, determine a tuning parameter to use in preparing map data to provide to the mobile navigation unit. The system further includes an encoding module configured to cause the processing unit to encode map data based on the tuning parameter, and a map-data-delivery module configured to cause the processing unit to provide or make available the encoded map data to the mobile navigation unit.

Abstract: A system configured to receive baseline map data from a remote map-data source, and determine whether the baseline map data is sufficient for present mobile navigation-unit function, yielding a map-sufficiency determination. If the map-sufficiency determination is affirmative, using the baseline map data for the present mobile navigation-unit function. If the map-sufficiency determination is negative, the system predicts navigation-unit movement, obtains based on the navigation-unit movement predicted, supplemental map data, and uses the baseline map data and the supplemental map data for the present mobile navigation-unit function.

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.

Abstract: An assisted learning (AL) system for a vehicle including vehicle systems with input devices is provided. The AL system includes: memory that stores usage instructions for the input devices; an AL input device; an audio output device; and a control module that retrieves a respective one of the usage instructions associated with a selected one of the input devices when the selected one of the input devices is actuated after the AL input device is actuated and that outputs the usage instructions to the audio output device.

Abstract: A vehicular navigation system includes a display, a memory configured to store data related to a plurality of preset map view, and a user input configured to permit the selection of a map view from the plurality of preset map views. A controller is coupled to the user input, the display, and the memory. The controller is configured to generate the selected map view on the display.