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.

Abstract: A vehicle computing device has a data storage medium and a processing device programmed to execute instructions stored on the data storage medium. The instructions include detecting an emergency vehicle near an autonomous host vehicle, receiving operational data from nearby vehicles, and transmitting the operational data to the emergency vehicle. The operational data indicates whether one or more of the nearby vehicles is operating in an autonomous mode.

Abstract: Systems and methods for vehicle mode scheduling with learned user preferences include monitoring vehicle data when a vehicle changes from a first mode to a second mode. The method also includes analyzing the vehicle data to identify a preference for the second mode during a driving context. Additionally, the method includes generating a recommendation on whether to switch to the second mode while traversing a route based on the vehicle data, and the driving context, and presenting the recommendation to the driver.

Abstract: A vehicle system includes a processor and a memory accessible to the processor and storing computer-executable instructions. The instructions include receiving data from a plurality of vehicles, generating at least one cluster from the received data, and determining a life cycle profile for a vehicle component based on the at least one cluster. The data includes state of health information associated with the vehicle component.

Abstract: A vehicle system includes a processor and a memory accessible to the processor and storing computer-executable instructions. The instructions include receiving data from a plurality of vehicles, generating at least one cluster from the received data, and determining a life cycle profile for a vehicle component based on the at least one cluster. The data includes state of health information associated with the vehicle component.

Abstract: Vehicle trajectory data is obtained. A variance of the trajectory data is recursively determined. A speed-dependent weighting function is computed to obtain a speed-dependent weight. An anomaly index is determined based at least on part on application of the speed dependent weight to the recursively determined variance.

Abstract: A system comprising a processor is programmed to receive, from a plurality of vehicles, sets of diagnostic test data relating to a performed diagnostic test. Each set of diagnostic test data includes a test output value and one or more corresponding test condition values. The processor is further programmed to select some of the test output values based on selecting a function to relate the test output value to test output values from different sets of diagnostic data. The processor is further programmed to provide the test output value and the corresponding test conditions values as input to the selected function to obtain a plurality of scaled test output values; and generate an adjustment to the diagnostic test based at least in part on the scaled test output values.

Abstract: A vehicle system includes at least one environmental sensor and a processing device. The environmental sensor is programmed to collect traffic information. The processing device is programmed to receive the traffic information, generate a traffic complexity index based at least in part on the traffic information, compare the traffic complexity index to a predetermined threshold, and generate a traffic alert signal if the traffic complexity index exceeds the predetermined threshold.

Abstract: Example systems and methods for vehicle mode scheduling with learned user preferences are disclosed. The example disclosed method includes monitoring vehicle data when a vehicle changes from a first mode to a second mode. The example method also includes analyzing the vehicle data to identify a preference for the second mode during a driving context. Additionally, the example method includes generating a recommendation on whether to switch to the second mode while traversing a route based on the vehicle data, and the driving context, and presenting the recommendation to the driver.

Abstract: The present disclosure relates to a connectivity system with: (a) a vehicle including an accelerator, brakes, steering, a cellular network antenna, a processor, and memory; (b) a connectivity program operatively coupled with the vehicle antenna and configured to: receive a connectivity map, sample connectivity, transmit the samples, anticipate connectivity based on the connectivity map, and generate an instruction for another program based on the anticipated connectivity.

Abstract: This disclosure generally relates to a system, apparatus, method, and process for generating a route based energy consumption estimation based on physical models. More particularly, the disclosure describes the generation of the route based energy consumption estimation based on physical models for a vehicle traveling on a specific road segment. The energy consumption estimation may be based on information related to historical energy consumption information for the vehicle, external information that may affect energy consumption by the vehicle, vehicle systems information that may affect energy consumption by the vehicle, and/or selected road segment information that may be used to predict energy consumption by the vehicle.