Abstract: A wireless communication device according to the present disclosure is set to be mounted on a vehicle including at least one wheel. The wireless communication device includes an input circuit, and an output circuit. The input circuit is set to connect to a lean angle detection circuit capable of detecting a lean angle of the vehicle with a traveling direction of the vehicle as a rotation axis. The output circuit is configured to repeatedly transmit a wireless signal including at least a position of the vehicle, a speed of the vehicle, and the lean angle of the vehicle at predetermined time intervals.

Abstract: The present disclosure is directed to deviating from a planned path for an autonomous vehicle. In particular, a computing system comprising one or more computing devices physically located onboard an autonomous vehicle can identify one or more boundaries at least in part defining a lane in which the autonomous vehicle is traveling along a path of a planned route. Responsive to identifying one or more obstructions ahead of the autonomous vehicle along the path, the computing system can: determine one or more deviations from the path that would result in the autonomous vehicle avoiding the obstruction(s) and at least partially crossing at least one of the one or more boundaries; and generate, based at least in part on the deviation(s), a motion plan instructing the autonomous vehicle to deviate from the path such that it avoids the obstruction(s) and continues traveling along the planned route.

Abstract: A apparatus for controlling displaying of a cluster for a vehicle and a method for the same includes a communication apparatus to support communication with the vehicle cluster, and at least one processor operatively connected to the communication apparatus. The at least one processor is configured to generate screen data associated with a first event occurring during a first driving cycle of the vehicle, transmits the screen data to the cluster through the communication apparatus, displays, on a first area of a display provided in the cluster, an object corresponding to the first event, based on the screen data. The object corresponding to the first event is displayed at any one of a plurality of positions specified in the first area, in sequence in which the first event occurs.

Abstract: Systems and techniques are described for improving the accuracy of autonomous vehicle simulations by dynamically assigning friction coefficients to road features based on sensor data. A method of the disclosed technology can include steps for associating a predetermined friction coefficient to road features; receiving sensor data from a sensor on an autonomous vehicle; identifying, from the sensor data, the presence of a road feature; assigning in a simulation, a friction coefficient corresponding to the road feature based on the predetermined friction coefficient associated with road feature; and generating, based on the assigned friction coefficient, a simulation of a trip by the autonomous vehicle as the autonomous vehicle traverses the road feature.

Abstract: A method for operating an autonomous wheeled device, including: obtaining first data indicative of objects within an environment; generating, with a processor of the autonomous wheeled device, a map of the environment using the first data; transmitting, with the processor, first information to an application of a smartphone; proposing, with the application, a suggested schedule for the autonomous wheeled device; receiving, with the processor, second information from the application of the smartphone; and actuating, with the processor, the autonomous wheeled device to operate according to the new schedule or the adjustment to the existing schedule and the suggested schedule, wherein the processor only actuates the autonomous wheeled device to operate according to the suggested schedule after the application receives approval of the suggested schedule.

Abstract: A collision risk calculation unit (12) calculates a collision risk between a vehicle B and each of a plurality of objects C to G present in a traveling direction of the vehicle B. An object selection unit (13) determines a transmission order of pieces of information on the individual objects C to G based on the collision risk, and transmits the pieces of object information to the vehicle B based on the transmission order.

Abstract: Systems and methods for robotic disinfection and sanitation of environments are disclosed herein. The robotic devices disclosed herein may detect and map locations of pathogens within environments. The robotic devices disclosed herein may utilize data from sensor units to sanitize objects using desired methods specified by operators. The robotic devices disclosed herein may sanitize environments comprising people.

Abstract: To provide a route arbitration apparatus, an automatic driving controller, and an automatic driving and route arbitration system which can avoid an overlapping between travel routes and make vehicles carry out a traveling operation smoothly while considering priority between vehicles, in a broad perspective, about a multiple of vehicles existing in a periphery. A route arbitration apparatus receives a target travel route from each vehicle; when it is determined that a mutual overlapping of the target travel routes occurs, determines a priority travel area which is an area where the highest priority vehicle can travel in accordance with the target travel route, transmits the priority travel area to the highest priority vehicle, determines an avoidance travel area which is an area where the low priority vehicle can travel while avoiding the priority travel area, and transmits the avoidance travel area to the low priority vehicle.

Abstract: A system and method for providing multiple agents for decision making, trajectory planning, and control for autonomous vehicles are disclosed.

Abstract: The present disclosure relates to a method for determining a state of a vehicle on a road portion having two or more lanes. The method comprises obtaining map data associated with the road portion and positioning data indicating a position of the vehicle on the road and a sensor data from a sensor system of the vehicle. The method further comprises initializing a filter per lane of the road portion based on the obtained map data, the obtained positioning data, and the obtained sensor data, wherein each filter indicates an estimated state of the vehicle on the road portion. Then, selecting one of the initialized filters using a trained machine-learning algorithm, configured to use the obtained map data, the positioning data, the sensor data, and each estimated state as indicated by each filter as input and to output a current state of the vehicle on the road portion.

Abstract: Methods and systems for operating implements of a vehicle are described. Operation of the implements may be adjusted in response to input to a human/machine interface, such as a joystick. In one example, one or more implement requests are scaled according to a human/machine allowance factor, where the human/machine allowance factor is based on an implements power limit and an implements requested power.

Abstract: A computing device, method and computer program product are provided for facilitating modification of the route of an autonomous surface vehicle. In the context of a method, a stylized image of an input image, such as a photographic image, is constructed. The stylized image distinguishes between surfaces having different texture that are present in the input image. Based upon the stylized image, the method also includes determining a frictional coefficient associated with at least some of the surfaces having different texture. The method further includes determining whether the route of the autonomous surface vehicle is to be altered from a predefined route based upon the frictional coefficient that has been determined to be associated with a surface over which the predefined route of the autonomous surface vehicle extends.

Abstract: Systems and methods for using risk profiles for creating and deploying new vehicle event definitions to a fleet of vehicles are disclosed. Exemplary implementations may: obtain a first risk profile, a second risk profile, and vehicle event characterization information; select individual ones of the previously detected vehicle events that have one or more characteristics in common; determine circumstances for at least a predefined period prior to occurrences of the selected vehicle events; create a new vehicle event definition based on the determined set of circumstances; distribute the new vehicle event definition to individual vehicles in the fleet of vehicles; and receive additional vehicle event information from the individual vehicles in the fleet of vehicles.

Abstract: A display device includes a transparent display screen element, which has a front side and a rear side opposite to the front side, and a polarizing element arranged flatly on the rear side of the transparent display screen element. The polarizing element is to attenuate light output by at least one lighting element of the transparent display screen element. A motor vehicle may have such a display device, in particular where the display device may be arranged freestanding at or on an interior trim element of the motor vehicle.

Abstract: An apparatus for alerting an operator to the presence of obstacles during the towing or push-back of an aircraft while it is on the ground, including: a self-propelled platform; at least one sensor attached to said platform, configured to sense potential obstacles; and a communication system attached to said platform for transmitting data relating to said sensed obstacles, the communication system being operable to communicate with at least one of: a same said apparatus; an operator control panel; a command centre; the aircraft being towed or pushed-back; and a vehicle towing or pushing-back the aircraft. An aircraft collision avoidance system is used during towing or push-back of an aircraft while it is on the ground, the system includes: at least one apparatus as described; and a carrier configured to carry the at least one apparatus.

Abstract: An automatic driving system includes a control device that controls automatic driving of a vehicle, and a storage device that contains external environment information indicating an external environment of the vehicle. A driving transition zone is a zone in which a driver of the vehicle takes over at least a part of driving of the vehicle, from the control device. A termination target velocity is a target velocity of the vehicle at an end point of the driving transition zone. The control device variably sets the termination target velocity, depending on the external environment at the end point or the external environment surrounding the end point. Then, the control device controls the velocity of the vehicle in the driving transition zone, such that the velocity of the vehicle at the end point is the termination target velocity.

Abstract: A method for supervising an air traffic tracking system that includes determining performance indicator values and an operating feature of the tracking system for the current situation on the basis of the performance indicator values; if the operating feature is representative of an operating abnormality of the tracking system, determining at least one performance indicator corresponding to the operating abnormality, and determining values of the at least one performance indicator corresponding to normal operation; determining at least one quality of service measure of the tracking system based on the performance indicator values; if the at least one quality of service measure is representative of a degradation in the quality of service, determining at least one performance indicator corresponding to the degradation in the quality of service; executing at least one evaluation process associated with the at least one performance indicator and/or to the degradation in the quality of service.

Abstract: A method (200) and device (500) for managing data and a computer program product, wherein the method (200) comprises: acquiring data related to a schedule of a user (101) at a mobile terminal device (102). The method (200) further comprises: identifying the data to obtain a travel time related to the user (101) and location information corresponding to the travel time. The method (200) further comprises: sending a transmission request related to the travel time and the location information to a server (103), so that the server (103) sends the travel time and the location information to a vehicle (104) related to an identification of user of the user (101), wherein the transmission request at least comprises the identification of user, the travel time and the location information.

Abstract: System is configured to receive article information corresponding to articles to be transported by computer-controlled vehicles, the articles comprising a first article and a second article, each having a maximum article dimension. System is also configured to assign travel routes about a grid comprising grid cells for the vehicles to travel thereon. The travel route of a first vehicle carrying the first article includes a turning maneuver at a first grid cell. System is configured to control the turning maneuver of the first vehicle in the first grid cell such that there is no contact between the first article carried on the first vehicle with a second article carried on a second vehicle present in a second grid cell that is adjacent to the first grid cell when the first vehicle is undertaking the turning maneuver.

Abstract: Disclosed herein are system, method, and computer program product embodiments for automated autonomous vehicle pose validation. An embodiment operates by generating a range image from a point cloud solution comprising a pose estimate for an autonomous vehicle. The embodiment queries the range image for predicted ranges and predicted class labels corresponding to lidar beams projected into the range image. The embodiment generates a vector of features from the range image. The embodiment compares a plurality of values to the vector of features using a binary classifier. The embodiment validates the autonomous vehicle pose based on the comparison of the plurality of values to the vector of features using the binary classifier.