Synthetic training information/data of a second probe style is generated based on first probe information/data of a first probe style using a style transfer model. First probe information/data is defined. An instance of first probe information/data comprises labels and first probe style sensor information/data. A style transfer model generates training information/data based on at least a portion of the first probe information/data. An instance of training information/data corresponds to an instance of first probe information/data and comprises second probe style sensor information/data. The first and second probe styles are different. A second probe style model is trained using machine learning and the training information/data. The second probe style model is used to analyze second probe style second probe information/data to extract map information/data from the second probe information/data. Each instance of second probe data is captured by one or more second probe sensors of a second probe apparatus.

Machine learning a feature detector using synthetic training data

Systems and methods for managing speed thresholds for a fleet of vehicles are disclosed Input is used to provide associations between particular weather-relation conditions (such as rain) and arithmetic operations, that may be used to determine a current speed threshold as a function of a local posted speed limit at the current location of a vehicle The current speed threshold is subsequently used to detect whether vehicles are exceeding the current speed threshold

Systems and methods for managing speed thresholds for vehicles

Systems and methods for detecting a sitting duck scenario of a vehicle on or near a road are disclosed. The current location and speed of the vehicle are used for different comparisons and/or determinations, including a comparison to road-specific information to determine whether the vehicle is in a particular proximity of a highway, and a determination whether the vehicle has been stationary continuously for at least a specified duration. Additional comparisons and/or determinations may be used. If such an occurrence has been detected, one or more notifications are generated, and provided to one or more of the vehicle operator and/or a remote computing server.

Systems and methods for detecting a sitting duck scenario

A navigation system includes: a control unit configured to: determine an autonomous operation capability of an autonomous vehicle operation system to operate in an autonomous operation state; generate a planned travel route including an autonomous state prediction of the autonomous operation state for portions of the planned travel route; and a communication unit, coupled to the control unit, configured to provide operation instructions to the autonomous vehicle operation system for autonomous operation of a user vehicle along the planned travel route.

Navigation system with route prediction mechanism and method of operation thereof

Systems and methods for managing a fleet of vehicles are provided. In one example embodiment, a computer-implemented method includes obtaining data representing vehicle state information associated with one or more vehicles among a fleet of vehicles at one or more times. The method includes predicting a future vehicle state associated with the one or more vehicles at one or more future times. The method includes scheduling a vehicle assignment in a predetermined set of vehicle assignments for a selected vehicle among the one or more vehicles before a first future time among the one or more future times. The method includes transmitting a command signal to a computing system associated with the selected vehicle based at least in part on the scheduled vehicle assignment.

Systems and Methods for Implementing Vehicle Assignments using Vehicle State Information

In one embodiment, a method includes a computing system receiving information from an autonomous vehicle (AV). Based on the received information, the system may identify a target objective, with an associated target destination, for the AV and determine that the AV is able to transport passengers while furthering the target objective. The system may receive multiple ride requests from ride requestors, respectively. Each of the ride requests is associated with an origination location and a destination location. The system may match the AV with one of the ride requests based on a location of the AV, the target destination of the target objective, and the origination location and destination location associated with the ride request. The system may instruct the AV to perform a transportation task from the origination location to the destination location associated with the ride request, and drive to the target destination after completing the transportation task.

Autonomous-vehicle dispatch based on fleet-level target objectives

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Autonomous-vehicle dispatch based on fleet-level target objectives