Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive a series of pressure maps indicating a respective series of sitting positions of an occupant in a seat, wherein the pressure maps include a current pressure map; update a profile of the occupant based on the pressure maps, wherein the profile includes a plurality of clusters of sitting positions and classifications of the clusters as preferred or nonpreferred, and updating the profile includes sorting one of the sitting positions into one of the clusters that is classified as preferred in response to the occupant remaining in that sitting position for greater than a threshold time; and adjust a physical configuration of the seat in response to the current sitting position being in one of the clusters that is classified as nonpreferred.

Abstract: Example embodiments described herein are generally directed to a navigation system that generates a pedestrian travel path based on various types of data stored in a database, such as, for example, a map, a blueprint, a historical record, a public record, and/or a personal record. In an example embodiment, the data may be obtained by executing a crowdsourcing procedure, where multiple individuals provide various types of information such as, information about an underground walkway, an elevated walkway, an unpaved pedestrian path, weather conditions, handicap-friendly pathways, and handicap-friendly structures. In an example application, a navigation device receives a navigation request that includes a start spot and an end spot for a pedestrian trip. The navigation device evaluates data stored in a database and uses the data to generate a pedestrian travel path that may include an underground walkway, an elevated walkway, an unpaved pedestrian path, and/or an enclosed pedestrian path.

Abstract: The disclosure is generally directed to systems and methods for facilitating travel over a last leg of a journey. In one example embodiment, an unmanned aerial vehicle (UAV) captures an image of an area to be traversed by an individual during the final leg of the journey. The image is evaluated to identify a hindrance that may be encountered by the individual in the area. A pre-emptive action may be taken to address the hindrance before reaching the area. The pre-emptive action, may, for example, include using the UAV and/or a terrestrial robot to assist the individual traverse the area. The assistance may be provided in various ways such as by use of the terrestrial robot to transport the individual and/or a personal item of the individual through the area, and/or by use of the UAV to provide instructions to guide the individual through the area.

Abstract: Opportunistic fueling for car hailing autonomous vehicles are disclosed herein. An example method includes evaluating a trip schedule having one or more destinations for a vehicle, calculating a plurality of potential routes based on the trip schedule with fueling options along a proposed path of travel for the vehicle, applying one or more vehicle fueling constraints, selecting an optimized route from the plurality of potential routes for the vehicle using the one or more vehicle fueling constraints, and completing the trip schedule using the optimized route.

Abstract: A system is disclosed that may comprise: one or more processors of a center node, wherein the center node is a vehicle computer; and memory of the center node, wherein the memory stores instructions executable by the one or more processors, the instructions comprising to: determine a first network comprising the center node and a plurality of member nodes based on a mesh network of the center node and the plurality of member nodes; add at least one virtual node to the first network; and using the first network, exchange cryptographic data between the at least one virtual node, the center node, and the plurality of member nodes.

Abstract: An example method of crowd-sourcing energy consumption data for vehicles includes controlling a vehicle in response to a predicted energy consumption that is continually updated based on a difference between a previous predicted energy consumption and a previous actual base energy consumption. Another example method of routing a vehicle includes changing a route for a vehicle in response to a predicted energy consumption for the vehicle when travelling the route, the predicted energy consumption based on a difference between a previous predicted energy consumption and a previous actual base energy consumption.

Abstract: A computer determines that a rate of travel value for a route segment being traversed by a first vehicle exceeds a predetermined threshold. A maneuver is planned for the first vehicle to depart from the route segment based on collected vehicle operating data from one or more second vehicles previously traversing the route segment. The first vehicle is instructed to execute the maneuver to depart the route segment.

Abstract: Opportunistic fueling for car hailing autonomous vehicles are disclosed herein. An example method includes evaluating a trip schedule having one or more destinations for a vehicle, calculating a plurality of potential routes based on the trip schedule with fueling options along a proposed path of travel for the vehicle, applying one or more vehicle fueling constraints, selecting an optimized route from the plurality of potential routes for the vehicle using the one or more vehicle fueling constraints, and completing the trip schedule using the optimized route.

Abstract: A system comprises a processor that is programmed to define a plurality of vehicle groups based on vehicle specification data and define a plurality of sub-groups for each of the vehicle groups based on environmental data and sensor data received from each of a plurality of vehicles. The processor is programmed to adjust fuel tank leak detection classifiers for the sub-groups based on ground truth data. The ground truth data include, for each of the plurality of vehicles, a leak detection status and a leak test result.

Abstract: A method for identifying vehicle vibration includes receiving signals indicative of a vehicle vibration from one or more vehicle sensors while the vehicle is in operation. The method also includes determining a magnitude of the vehicle vibration at one or more target frequency bands. The method additionally includes determining whether the vehicle vibration is associated with one or more wheels of the vehicle based on the magnitude of the vehicle vibration at the one or more target frequency bands. The method further includes generating a diagnostic recommendation when the vehicle vibration is associated with the one or more wheels of the vehicle.

Abstract: Methods and apparatus for on-demand fuel delivery are disclosed herein. An example method includes predicting, by executing an instruction with a processor, a vehicle usage event for a vehicle. The predicted vehicle usage event is associated with a fuel amount. The example method includes comparing a fuel level of the vehicle and the fuel amount. The example method includes automatically generating, via the processor, a refuel request for the vehicle based on the comparison and transmitting the refuel request to a mobile device. The refuel request is to be viewable via a user interface at the mobile device.

Abstract: A method is provided, comprising terminating a pressure rise portion of an engine-off natural vacuum test based on an initial rate of change of a fuel system pressure upon sealing a fuel system; and initiating a vacuum portion of the engine-off natural vacuum test responsive to suspending the pressure rise portion. The initial rate of change may indicate a likelihood of the pressure rise portion reaching a pressure rise threshold. In this way, the vacuum portion of the test may be initiated earlier, increasing the likelihood of a conclusive result being obtained during a test time limit.

Abstract: Methods and apparatus are disclosed for infrastructure-centric vehicle mode selection. An example vehicle includes a GPS receiver to provide a location of the vehicle, memory storing instructions, and a processor. The example processor, when executing the instructions, causes the vehicle to (a) when the vehicle is in a vicinity of an infrastructure of interest, determine a low traction anomaly value, and (b) in response to the a low traction anomaly value satisfying a threshold, change a driving mode of the vehicle.

Abstract: A computer determines that a rate of travel value for a route segment being traversed by a first vehicle exceeds a predetermined threshold. A maneuver is planned for the first vehicle to depart from the route segment based on collected vehicle operating data from one or more second vehicles previously traversing the route segment. The first vehicle is instructed to execute the maneuver to depart the route segment.

Abstract: A vehicle system includes a processor having a memory. The processor is programmed to predict a vehicle key-on time based on a next destination of a vehicle. The processor is further programmed to request, at a predetermined amount of time before the key-on time, optimization data from a remote server.

Abstract: Described herein is a route and model based energy estimation system and methods therein for predicting an energy consumption of a vehicle for a selected road segment. The predicted energy consumption may be generated for the vehicle based on information specifically related to the vehicle, such as historical energy consumption information for the vehicle, external information that may affect energy consumption by the vehicle, vehicle information, and/or selected road segment information.

Abstract: A pressure of a spare tire of a vehicle is determined. Whether the spare tire is installed is determined based at least in part on the pressure. A vehicle subsystem is actuated based at least in part on whether the spare tire is installed.

Abstract: Methods and apparatus are disclosed for infrastructure-centric vehicle mode selection. An example vehicle includes a GPS receiver to provide a location of the vehicle, memory storing instructions, and a processor. The example processor, when executing the instructions, causes the vehicle to (a) when the vehicle is in a vicinity of an infrastructure of interest, determine a low traction anomaly value, and (b) in response to the a low traction anomaly value satisfying a threshold, change a driving mode of the vehicle.

Abstract: Methods and systems are provided for conducting an engine-off natural vacuum test and filtering the output of the engine-off natural vacuum (EONV) test based on a variable weighting factor. In one example, one or more EONV test entry conditions are evaluated with one or more membership functions corresponding to the indicated result of the EONV test to obtain an overall confidence value that is used to modify the weighting factor. In this way, the filtered EONV output reflects the confidence in the test results, and as such a malfunction indicator light may be more appropriately set as compared to conditions wherein filtered EONV output is not based on confidence in the test results.

Abstract: A method for identifying vehicle vibration includes receiving signals indicative of a vehicle vibration from one or more vehicle sensors while the vehicle is in operation. The method also includes determining a magnitude of the vehicle vibration at one or more target frequency bands. The method additionally includes determining whether the vehicle vibration is associated with one or more wheels of the vehicle based on the magnitude of the vehicle vibration at the one or more target frequency bands. The method further includes generating a diagnostic recommendation when the vehicle vibration is associated with the one or more wheels of the vehicle.