Abstract: A vehicular control system includes a plurality of sensors disposed at a vehicle and sensing exterior of the vehicle. An electronic control unit (ECU) includes a processor that processes sensor data captured by the sensors. The ECU, responsive at least in part to processing of captured sensor data as the vehicle travels in a traffic lane of a multi-lane road, determines a rearward approaching vehicle rearward of the equipped vehicle that is in an adjacent traffic lane. The ECU determines a leading vehicle ahead of the equipped vehicle and traveling in the same traffic lane as the equipped vehicle. The ECU controls the equipped vehicle to accelerate the vehicle to at least match the speed of the determined rearward approaching vehicle and to maneuver into the adjacent traffic lane to pass the determined leading vehicle ahead of the determined rearward approaching vehicle.

Abstract: The machine learning device includes a predicting part configured to use a machine learning model to predict predetermined information, an updating part configured to update the machine learning model, and a part information acquiring part configured to detect replacement of a vehicle part and acquire identification information of the vehicle part after replacement. The updating part is configured to receive a new machine learning model trained using training data sets corresponding to the vehicle part after replacement from a server and apply the new machine learning model to the vehicle, if a vehicle part relating to input data of the machine learning model is replaced with a vehicle part of a different configuration.

Abstract: A control unit of a server device as an information processing apparatus is configured to: provide first information on a service for each of a plurality of moving objects, each of which is configured to provide a different service and autonomous travel; acquire second information on time and location of at least one service that a user wants to use; and determine at least one moving object that matches with the user based on the first information and the second information, and generate a travel plan of the at least one moving object.

Abstract: In one embodiment, a computing system of a vehicle may receive vehicle driving data associated with a vehicle driving in an environment and detected environment data associated with the environment. The system may generate a reference trajectory of the vehicle driving in the environment based on the vehicle driving data. The system may determine driving constraints associated with the environment based on the detected environmental data. The system may generate a trajectory of the vehicle based on the driving constraints. The system may determine a difference in at least one parameter associated with the trajectory relative to at least one corresponding parameter associated with the reference trajectory. The system may adjust weight values associated with cost functions of the trajectory based on the difference between the at least one parameter associated with the trajectory and the corresponding parameter associated with the reference trajectory.

Abstract: A base module may be used to receive and house one or more unmanned aerial vehicles (UAVs) via one or more cavities. The base module receives commands from a manager device and identifies a flight plan that allows a UAV to execute the received commands. The base module transfers the flight plan to the UAV and frees the UAV. Once the UAV returns, the base module once again receives it. The base module then receives sensor data from the UAV from one or more sensors onboard the UAV, and optionally receives additional information describing its flight and identifying success or failure of the flight plan. The base module transmits the sensor data and optionally the additional information to a storage medium locally or remotely accessible by the manager device.

Abstract: Disclosed is a system and method for regenerating and distributing energy, such as employed on a vessel or floating offshore installation, the system including a first power distribution system, a second power distribution system electrically isolated from the first power distribution system, and a flywheel mechanically connected to both the first and second power distribution systems. Each of the first and second power distribution systems may include a motor/generator mechanically connected to the flywheel and a drive module electrically connected to its respective motor/generator. The flywheel drive modules may be connected to a common flywheel control module that includes a control algorithm for operating the system.

Abstract: Systems and methods are provided for controlling a vehicle. In one embodiment, a method includes: storing, in a data storage device, a decision model, wherein the decision model predicts when to automatically perform a motivational lane change; updating, by a processor, the decision model based on data obtained in response to user input; and automatically requesting, by the processor, a lane change based on the dynamically updated model.

Abstract: The machine learning device includes a predicting part configured to use a machine learning model to predict predetermined information, an updating part configured to update the machine learning model, and a part information acquiring part configured to detect replacement of a vehicle part and acquire identification information of the vehicle part after replacement. The updating part is configured to receive a new machine learning model trained using training data sets corresponding to the vehicle part after replacement from a server and apply the new machine learning model to the vehicle, if a vehicle part relating to input data of the machine learning model is replaced with a vehicle part of a different configuration.

Abstract: An electric travelling vehicle including: a motor controller configured to control an electric motor based on displacement of a steering operation part to a forward travel position, a neutral position, and a rearward travel position, a brake controller configured to bring an electromagnetic power-off brake into a released state or a braking state; and a travel state detector configured to detect a travelling state that is accompanied with the released state, a stopped state that is accompanied with the braking state, and a transit stopped state that is accompanied with the braking state.

Abstract: A method and device for operating at least two automated vehicles including picking up signals, which are transmitted in each case from the at least two automated vehicles; inputting a map as a function of the signals, determining a common driving strategy for the at least two automated vehicles in a way that optimizes a localization potential of the at least two automated vehicles on the basis of the map and as a function of the signals, and providing the common driving strategy for operating the at least two automated vehicles. A vehicle device for operating an automated vehicle, which includes a transmitter and receiver device, and a control unit is also provided.

Abstract: An automated vehicle assistance system is provided for supervised or unsupervised vehicle movement. The system includes a control system and a first sensor system. The first sensor system may receive first image data of a scene and may output a first disparity map and a first confidence map based on the first image data. The control system may output a video stream based on the first disparity map and the first confidence map. The vehicle assistance system also may include a second sensor system that receives second image data of at least a portion of the scene that outputs a second confidence map based on second image data. The video stream may include super-frames, with each super-frame including a 2D image of the scene, a depth map corresponding to the 2D image, and a certainty map corresponding to the depth map.

Abstract: Provided is a vehicle brake system equipped with an electric brake and which has high reliability and enables redundancy at low cost. This vehicle brake system 1 is equipped with a mutually connected master controller 30 and first and second sub-controllers 40, 41, and an output cut-off control unit 200. Each of the controllers includes: a braking force calculation section for calculating the braking force of the electric brake; a self-determination section for determining whether or not the controller itself is normal; and an other-determination section for comparing the braking force calculation results of the controllers to determine whether the other two are normal.

Abstract: A route may be recommended, and it may be determined if a vehicle detours from the recommended route. At least one recommend route may be obtained. A current route of a vehicle may be obtained. A variance value may be determined based on the current route and the at least one recommend route. The variance value may be compared to a threshold. It may be determined whether the variance value exceeds the threshold. If the variance value exceeds the threshold, it may be determined that the vehicle has detoured. If the variance value does not exceed the threshold, it may be determined that the vehicle has not detoured.

Abstract: A path generation device is configured to generate a path along which a mobile object is to travel. The device comprises a planner module configured to plan a steering angle and a steering angular velocity for the mobile object to travel from a start position to a goal position by generating first and second isosceles triangles connecting the start position and the goal position based on the start position of the mobile object, a start position traveling direction being a traveling direction of the mobile object at the start position, the goal position of the mobile object, and a goal position traveling direction being a traveling direction of the mobile object at the goal position. The device comprises a curved path applying module configured to apply a first curved path along legs of the first isosceles triangle and apply a second curved path along legs of the second isosceles triangle.

Abstract: A method of generating an aircraft display includes receiving data at a device. The data includes information associated with a first aircraft and one or more other aircraft in an airspace associated with the first aircraft. The method includes determining, at the device, estimated flightpaths for the first aircraft and the one or more other aircraft. The method includes generating, at the device, a detect and avoid gauge display based on a current flightpath of the first aircraft and the estimated flightpaths. The method also includes sending, from the device to a display device coupled to the device, the detect and avoid gauge for display. The detect and avoid gauge is configured to display a flightpath indicator to indicate a direction of travel of the first aircraft and is configured to display alert bands related to projected separation violations with respect to the one or more other aircraft.

Abstract: Systems and methods for mitigating damage caused by hail storms to self-driving motor-vehicles. When a self-driving motor vehicle determines or learns that a hail storm or other severe weather event may be approaching, it may seek cover under a protective structure or some other location where it might not be as exposed to the severe weather event. Depending upon the availability of suitable shelters, the self-driving motor vehicle might, for example, obtain a list of such possible shelters and apply certain factors to develop a priority list as to which shelters to seek. The self-driving motor vehicle may then drive itself to the location and remain there at least for the duration of the hail storm or other event. In some cases, the self-driving motor vehicle may evaluate the adequacy of the protective structure or other location, and may decide to seek a better protective structure or location.

Abstract: In one embodiment, a method includes accessing sensor data associated with an environment of the vehicle; identifying agents in the environment based on the sensor data; determining a probability distribution indicative of whether a preliminary trajectory of each of the agents intersects a potential travel space of the vehicle; generating a prioritization of the agents based on the probability distribution; allocating an amount of computing resources of the vehicle for analyzing each of one or more of the agents based on the prioritization and characteristics of the respective one or more agents; and predicting a trajectory of one or more of the agents according to the allocated computing resources. An accuracy of the prediction is proportional to the amount of allocated computing resources. The method also includes determining, based on the analysis of the agents, one or more operations for the vehicle to perform.

Abstract: A travel assistance method generates a first traveling path based on map information around a circumference of a host vehicle, generates a second traveling path based on the surroundings, determines that a switch from the first traveling path to the second traveling path is needed when detecting that the first traveling path being generated is to end ahead of the host vehicle while the host vehicle is traveling on the first traveling path, and controls the host vehicle to make a transition from the first traveling path to the second traveling path when the switch is determined to be needed.

Abstract: A system, method, infrastructure, and vehicle for performing automated valet parking enable an unmanned vehicle to autonomously move to and park at an empty parking space by communicating with a parking infrastructure. The system, method, infrastructure, and vehicle enable an unmanned vehicle to autonomously move from a parking space to a pickup zone by communicating with a parking infrastructure.

Abstract: In an information processing device, a first acquisition portion acquires a usage end time and a usage end point of an automatic driving vehicle. A second acquisition portion acquires information about a side trip point of the automatic driving vehicle. A derivation portion derives an expected time of arrival of the automatic driving vehicle at the usage end point by way of the side trip point. A permission portion permits a drop-in at the side trip point when the expected time of arrival at the usage end point is before the usage end time.