Abstract: A controller receives probe data from a vehicle traveling on a path segment. The probe data may be collected by a mobile device. The path segment may be a multilane roadway. The controller identifies a first lane of the path segment from the probe data. The controller associated a forked route with the first lane of the path segment. The controller calculates different traffic values for the lanes of the path segment. One traffic value may be calculated directly from speeds derived from the path data. Another traffic value may be calculated by the probe data and a historical relationship.

Abstract: An autonomous vehicle may be partially controlled or monitored by a remote endpoint assigned to the autonomous vehicle. The autonomous vehicle sends one or more assisted driving messages directly to the autonomous vehicle or through a server. The server may identify multiple potential remote driving endpoints and assign one or more of the potential remote driving endpoints to the autonomous vehicle. The one or more potential remote driving endpoints return a command to the autonomous vehicle directly or through the server.

Abstract: Methods for communicating a ranking characterizing a portion of a roadway include: (a) ranking at least one segment of a roadway based on an amount of deviation between a true driving behavior on the at least one segment of the roadway and an expected driving behavior predefined for the at least one segment of the roadway; and (b) communicating the ranking to a client. Apparatuses for communicating a ranking characterizing a portion of a roadway are described.

Abstract: An autonomous vehicle may be operable in an autonomous mode and a manual mode. A confidence threshold is accessed from a database. The confidence threshold may be associated with a particular geographic area containing the autonomous vehicle. The confidence threshold may be constant for the geographic area accessible by the autonomous vehicle. A computing device calculates a vehicle confidence level based on at least one confidence factor and compares the confidence threshold to the vehicle confidence level. The computing device generates a driving mode command for a vehicle based on the comparison. In one example, the driving mode command transitions the autonomous vehicle to the autonomous mode, if applicable, when the vehicle confidence score exceeds the confidence threshold. In one example, the driving mode command transitions the autonomous vehicle to the manual mode, if applicable, when the vehicle confidence score does not exceed the confidence threshold.

Abstract: Methods for providing a lane-level road view include: (a) accessing lane-specific data comprising information characterizing a first segment of a first lane of a road in a direction of travel; and (b) communicating at least a portion of the lane-specific data to a client. Apparatuses for providing lane-level road views are described.

Abstract: A controller receives probe data from a vehicle traveling on a path segment. The probe data may be collected by a mobile device. The path segment may be a multilane roadway. The controller identifies a first lane of the path segment from the probe data. The controller associated a forked route with the first lane of the path segment. The controller calculates different traffic values for the lanes of the path segment. One traffic value may be calculated directly from speeds derived from the path data. Another traffic value may be calculated by the probe data and a historical relationship.

Abstract: Probabilistic road system reporting may involve determining a probability that a section of road is congested, calculating the congestion levels for sections of road having a high probability of congestion, and providing calculated congestion levels. The high probability congestion road sections may also be subject to more frequent congestion level calculation and updating than road sections having lesser probabilities of congestion.

Abstract: Methods for differentiating high quality probe reports in a plurality of probe reports include: (a) receiving, by a processor, a probe report from a probe source, wherein the probe report includes data indicative of a travel condition; (b) determining, by the processor, whether the probe report satisfies a criterion indicative of high quality data; and (c) identifying, by the processor, the probe report as being (i) suitable for use in modeling the travel condition if the probe report satisfies the criterion, or (ii) unsuitable for use in modeling the travel condition if the probe report does not satisfy the criterion. Systems for differentiating high quality probe reports in a plurality of probe reports are described.

Abstract: Precision traffic flow indication may involve receiving device data over a period of time representing a plurality traffic flow readings associated with a road involving a plurality of subsections. Calculating traffic flows and determining road subsections having similar traffic flows may also be involved. Also, indicating a different traffic flow level for a first subsection and a second subsection of road may be involved.