Example embodiments relate to identification of proxy calibration targets for a fleet of sensors. An example method includes collecting, using a sensor coupled to a vehicle, data about one or more objects within an environment of the vehicle. The sensor has been calibrated using a ground-truth calibration target. The method also includes identifying, based on the collected data, at least one candidate object, from among the one or more objects, to be used as a proxy calibration target for other sensors coupled to vehicles within a fleet of vehicles. Further, the method includes providing, by the vehicle, data about the candidate object for use by one or more vehicles within the fleet of vehicles.

A computer-implemented method for altering an appearance of an electrochromic coating of a vehicle is provided. The method includes determining that a vehicle is a boundary area of a testing zone based on a determined location of the vehicle. The method also includes changing a base appearance of the electrochromic coating of the vehicle to an altered appearance based on the determination that the vehicle is in the boundary area.

At least a method for determining risk behavior of a driver is described. While a vehicle is being driven, data is obtained related to the position and movement of a wireless communications device. The data may indicate the type of behavior exhibited by the driver while the vehicle is being driven.

A system for managing a set of fleet vehicles includes a central command unit having a processor and tangible, non-transitory memory on which instructions are recorded. The fleet vehicles have one or more respective electronic components and respective vehicle controllers adapted to perform self-diagnosis of their respective electronic components. The central command unit is adapted to receive notification of the self-diagnosis and classify the respective electronic components as working ones and non-working ones. The central command unit is adapted to perform component matching, including polling inventory across the fleet vehicles. Working ones of the respective electronic components are selectively redistributed for reuse across the fleet vehicles. In some embodiments, at least one of the fleet vehicles is designated as a master vehicle and the central command unit is stored in the master vehicle. In other embodiments, the central command unit is stored in a cloud computing service.

Described herein are techniques for optimizing operation of a fleet of electric vehicles. In some embodiments, a fleet management platform may maintain, in relation to a plurality of drivers, driving behavior patterns determined to be associated with the each of the plurality of drivers. Upon receiving a request for optimization of at least one operation related to a fleet of electric vehicles, such techniques may comprise determining one or more factors associated with the optimization of the at least one operation, identifying a set of driving behavior patterns correlated to the one or more factors, and customizing the at least one operation based on the identified set of behavior patterns and the driving behavior patterns.

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.

A method for determining an absolute position for an ego vehicle. The method includes a step of determining an other vehicle position of another vehicle and a piece of vehicle information of the other vehicle by using a vehicle signal that represents a vehicle-to-vehicle message of the other vehicle. The method furthermore comprises a step of determining a piece of vehicle information of the other vehicle and a relative position between the ego vehicle and the other vehicle by using a data signal provided by the other vehicle and a step of ascertaining the absolute position of the ego vehicle by using the other vehicle position and the relative position.

A vehicle coordination system is provided for coordinating the trajectories of vehicles on a road network. The vehicle coordination system comprises a plurality of vehicles each having respective vehicle position tracking assemblies that are in communication with respective vehicle communication systems for transmitting vehicle state messages including positions of the vehicles. A task assignment allocator is provided that is arranged to generate task assignments for each of the plurality of vehicles, including destinations in the road network for the vehicles. A vehicle coordination assembly is in communication with the vehicle communication systems via a data network for receiving the vehicle state messages. The vehicle coordination assembly is configured to determine respective paths for each vehicle to arrive at their respective destinations and determine trajectory control commands for each vehicle to traverse their respective paths whilst optimizing a predetermined objective and avoiding active interactions of two or more of the vehicles occurring in any shared areas of the paths. The vehicle coordination assembly is configured to transmit the trajectory control commands to each vehicle. The predetermined objective may be an aggregate traversal time for the vehicles.

This disclosure relates to a distributed data center that includes resources carried by a fleet of vehicles. The system includes sensors configured to generate output signals conveying information related to the vehicles and/or the surroundings of vehicles. The system includes a remote computing server configured to maintain map data and distribute it to the fleet, including local map data to individual vehicles pertaining to their surroundings. Individual vehicles may compare the local map data with the information related to their individual surroundings. Based on such comparisons, individual vehicles may detect discrepancies between the local map data and the information related to their individual surroundings. The remote computing server may modify and/or update the map data based on the detected discrepancies.

A method of determining optimal frequency to load haul trucks can include a central controller receiving an indication of the amount of paving material at a paving machine, and an indication of production rate of the paving machine. The central controller can then determine when more paving material should be provided to the paving machine. Based on this determination, the central controller can provide alerts or triggers to a paving material plant to indicate when haul trucks should be loaded to provide additional paving materials to the paving machine.