Abstract: The present invention provides specific systems, methods and algorithms based on artificial intelligence expert system technology for determination of preferred routes of travel for electric vehicles (EVs). The systems, methods and algorithms provide such route guidance for battery-operated EVs in-route to a desired destination, but lacking sufficient battery energy to reach the destination from the current location of the EV. The systems and methods of the present invention disclose use of one or more specifically programmed computer machines with artificial intelligence expert system battery energy management and navigation route control. Such specifically programmed computer machines may be located in the EV and/or cloud-based or remote computer/data processing systems for the determination of preferred routes of travel, including intermediate stops at designated battery charging or replenishing stations.

Abstract: A vehicular parking system includes a plurality of exterior viewing cameras, at least one receiver and a control. The control, when the vehicle is located at an entrance of a parking structure, controls the vehicle to autonomously drive the vehicle from the entrance of the parking structure toward a parking location in the parking structure. The parking structure includes a positioning system having a plurality of short range communication devices at known locations at the parking structure. Responsive to communication signals generated by the devices, the control determines the location of the vehicle relative to the known locations and drives the vehicle from the entrance of the parking structure toward the parking location in the parking structure. With the vehicle positioned at the parking location, and responsive at least to image processing by the image processor of captured image data, the control parks the vehicle in the parking location.

Abstract: Various aspects of a device and method to manage interaction with one or more control circuits in a vehicle and one or more wearable devices are disclosed herein. The device comprises one or more circuits configured to receive a first set of input values from the one or more wearable devices associated with a first user. The one or more wearable devices are communicatively coupled to the device used in the vehicle. A second set of input values is received from one or more vehicle sensors embedded in the vehicle. An operating mode of the device is determined based on the received first set of input values and the second set of input values. One or more functions of the vehicle are controlled based on the determined operating mode of the device.

Abstract: A method for utilizing a trip history of a vehicle during a trip from an original position to a destination includes: (a) determining the original position; (b) comparing the original position to a mapping database covering the trip; (c) determining a road segment of the mapping database associated to the original position; (d) determining a current position during the trip; (e) comparing the current position to the mapping database; (f) determining a road segment of the mapping database associated to the current position; (g) setting the road segment as a link of the trip; (h) repeating (e)-(g) until the destination is reached; (i) determining the destination; (j) comparing the destination to the mapping database; (k) determining a road segment of the mapping database associated to the destination; and (l) representing the trip as connected links between the original position and destination, each link corresponding to a road segment.

Abstract: A data map is modeled to generate a modeled data map, the data map including a calibration to control vehicle operation over various weather conditions encountered by a vehicle, the modeled data map characterizing data elements of the data map according to a mathematical model. The modeled data map is sent to the vehicle to expand into at least a portion of the data map to control the vehicle operation.

Abstract: A rear wheel regenerative braking control system for vehicle, may include a brake controller; a vehicle controller; a hydraulic controller; and a motor controller, wherein the system and the method may maximize an amount of rear wheel regenerative braking while easily securing braking stability of a vehicle.

Abstract: A method, a device and a computer-readable storage medium with instructions for determining the lateral position of a vehicle relative to the lanes on a roadway. The device has an image processing unit for detecting roadway markings as well as a positioning unit for determining an initial position for the vehicle. An evaluation unit is set up to determine that the detected roadway markings are insufficient for detecting a lateral position of the vehicle by comparing the detected roadway markings with roadway marking information from a lane geometry map for the initial position determined for the vehicle. In this case, the evaluation unit identifies an approximate lateral position of the vehicle while taking into consideration information from the lane geometric map.

Abstract: The disclosed computer-implemented method may include (i) receiving a first transport request and a second transport request, (ii) evaluating a fitness of matching the first and second transport requests to be fulfilled by a transport provider, based at least partly on a transportation overlap between the first and second transport requests, (iii) generating a simulated future transport request, (iv) evaluating a fitness of matching the first transport request with the simulated future transport request, based at least in part on a transportation overlap between the first transport request and the simulated future transport request, and (v) matching the first and second transport requests based at least in part on the fitness of matching the first and second transport requests and based at least in part on the fitness of matching the first transport request with the simulated future transport request. Various other methods, systems, and computer-readable media are disclosed.

Abstract: A method for maintaining a marine vessel at a global position and/or heading includes receiving measurements related to vessel attitude and estimating water roughness conditions based on the measurements. A difference between the vessel's actual global position and the target global position and/or a difference between the vessel's actual heading and the target heading are determined. The method includes calculating a desired linear velocity based on the position difference and/or a desired rotational velocity based on the heading difference. The vessel's actual linear velocity and/or actual rotational velocity are filtered based on the roughness conditions. The method includes determining a difference between the desired linear velocity and the filtered actual linear velocity and/or a difference between the desired rotational velocity and the filtered actual rotational velocity.

Abstract: A message conveying system comprising a set of sensors that can detect features such as a camera, LADAR, ranging sensors, or acoustic sensors in which the lead autonomous vehicle conveys messages to the other autonomous vehicles that follow behind of rendezvous locations for the entire convoy to meet when they get separated from the lead autonomous vehicle. The rendezvous locations are derived from paths entered by the operator, by using the general direction of travel, or by using the old route. In addition, different rendezvous points can be chosen based on exactly where the loss of communication occurs. These rendezvous points may or may not lead to the final destination dictated by the lead autonomous vehicle.

Abstract: An approach is provided for shared vehicle availability detection based on vehicle trajectory information. A vehicle sharing platform determines that a vehicle is engaged in a parking search behavior, wherein the vehicle is in use by a current user. The vehicle sharing platform further computes a probability that the vehicle will become available for use by another user based on the parking search behavior. The vehicle sharing platform further designates the availability of the vehicle for use by the another user based on the probability.

Abstract: Example implementations relate to a method of dynamically updating a transport task of a UAV. The method includes receiving, at a transport-provider computing system, an item provider request for transportation of a plurality of packages from a loading location at a given future time. The method also includes assigning, by the transport-provider computing system, a respective transport task to each of a plurality of UAVs, where the respective transport task comprises an instruction to deploy to the loading location to pick up one or more of the plurality of packages. Further, the method includes identifying, by the transport-provider system, a first package while or after a first UAV picks up the first package. Yet further, the method includes based on the identifying of the first package, providing, by the transport-provider system, a task update to the first UAV to update the respective transport task of the first UAV.

Abstract: A subject vehicle includes: a communication device communicating with an external device; a detection unit detecting a state of the surroundings of a subject vehicle; a driving support control unit executing driving support of automatically executing at least a part of speed control or steering control of the subject vehicle on the basis of the state of the surroundings of the subject vehicle detected by the detection unit; an extraction unit extracting environment information causing a change in control in the driving support from information of the state of the surroundings of the subject vehicle detected by the detection unit; and a communication control unit transmitting the environment information extracted by the extraction unit to an external device using the communication unit.

Abstract: The present disclosure provides a method and system for training an unmanned aerial vehicle control model based on artificial intelligence. The method comprises: obtaining training data by using sensor data and target state information of the unmanned aerial vehicle and state information of the unmanned aerial vehicle under action of control information output by a deep neural network; training the deep neural network with the training data to obtain an unmanned aerial vehicle control model, the unmanned aerial vehicle control model being used to obtain the control information of the unmanned aerial vehicle according to the senor data and target state information of the unmanned aerial vehicle.

Abstract: A system provides feedback to a user to guide the user to point a part of the body at a target of interest. An angle sensor senses the angle in which the part of the user's body is pointing, such as the head or the hand. The system computes the angle to a target and compares to the angle in which the part of the user's body is pointing and the feedback indicates to the user how to point more closely to the direction of the target. Additional sensors allow the system to update the angle to the target as the position of the user changes. A walking sensor is disclosed to accurately measure the position of the user.

Abstract: A control unit is provided for controlling the dumping of a material entity from an implement of a working machine into a receiver. The control unit is adapted to determine a loading operation information including a current loading condition indicative of the distribution of the material that is currently accommodated by the receiver, and material characteristic information comprising one or more properties of the material of the material entity that is present in the implement. The control unit is adapted to, on the basis of the loading operation information, determine a dumping position within the receiver at which dumping position the material entity is to be dumped.

Abstract: A vehicle system includes an electronic control unit. The electronic control unit is configured to execute a first program, a second program, and a third program. The first program is configured to recognize an object present around a vehicle, the second program is configured to store information related to the recognized object as time-series map data, and the third program is configured to predict a future position of the object based on the stored time-series map data. The first program and the third program are configured to be (i) first, individually optimized based on first training data corresponding to output of the first program and second training data corresponding to output of the third program, and (ii) then, collectively optimized based on the second training data corresponding to the output of the third program.

Abstract: Disclosed herein are methods and systems that can help an aerial transport service provider (ATSP) determine how to distribute and redistribute unmanned aerial vehicles (UAVs) amongst a plurality of UAV deployment stations located throughout a geographic area. In accordance with example embodiments, the ATSP system can take one or more performance metrics for item providers into account when determining how much UAV transport capacity to allocate to different item providers for a given time period. The ATSP can then determine how to distribute UAVs amongst different UAV nests in advance of and/or during the given time period, such that each item provider's allocated UAV transport capacity is available from the UAV nest or nest(s) that serve each item provider during the given time period.

Abstract: A computer-implemented robot pickup method including receiving a request from a user to pick up an article at a first location. A robot having a closeable transport container is navigated to the first location. The robot opens the closeable transport container and moves the article inside the closeable transport container at the first location, and navigates over an outdoor transportation network from the first location to the second location to deliver the article to the second location.

Abstract: The present invention provides specific systems, methods and algorithms based on artificial intelligence expert system technology for determination of preferred routes of travel for electric vehicles (EVs). The systems, methods and algorithms provide such route guidance for battery-operated EVs in-route to a desired destination, but lacking sufficient battery energy to reach the destination from the current location of the EV. The systems and methods of the present invention disclose use of one or more specifically programmed computer machines with artificial intelligence expert system battery energy management and navigation route control. Such specifically programmed computer machines may be located in the EV and/or cloud-based or remote computer/data processing systems for the determination of preferred routes of travel, including intermediate stops at designated battery charging or replenishing stations.