Abstract: A parking verification system and method, a movement verification system and method, and a monitoring system and method for a train, or other transit vehicle.

Abstract: An example includes obtaining first sensor data from a first sensor and second sensor data from a second sensor, the first sensor of a first sensor type different than a second sensor type of the second sensor; generating first encoded sensor data based on the first sensor data and second encoded sensor data based on the second sensor data; generating a contextual fused sensor data representation of the first and second sensor data based on the first and second encoded sensor data; generating first and second reconstructed sensor data based on the contextual fused sensor data representation; determining a deviation estimation based on the first and second reconstructed sensor data, the deviation estimation representative of a deviation between: (a) the first reconstructed sensor data, and (b) the first sensor data; and detecting an anomaly in the deviation estimation, the anomaly indicative of an error associated with the first sensor.

Abstract: The method for autonomous vehicle control preferably includes sampling measurements, determining refined sensor poses based on the measurements, determining an updated vehicle pose based on the measurements and operation matrix, optionally determining evaluation sensor poses based on the refined sensor poses, optionally updating the operation matrix(es) based on the evaluation sensor poses, and/or any other suitable elements. The system for autonomous vehicle control can include one or more vehicles, one or more sensors, one or more processing systems, and/or any other suitable components.

Abstract: An example of a mobile computing device for managing medical equipment includes a display and a processor, a memory, and associated circuitry, the processor configured to receive patient data gathering device location information for a plurality of patient data gathering devices, the patient data gathering device location information being indicative of one or more patient data gathering device locations in an assigned monitoring area corresponding to the mobile computing device, control the display to provide the patient data gathering device location information, capture a user selection of at least one patient data gathering device of the plurality of patient data gathering devices based on the provided patient data gathering device location information, and provide patient data gathering device information based on the user selection.

Abstract: This disclosure provides techniques for the creation of maps of indoor spaces. In these techniques, an individual or a team with no mapping or cartography expertise can contribute to the creation of maps of buildings, campuses or cities. An indoor location system can track the location of contributors in the building. As they walk through indoor spaces, an application may automatically create a map based on data from motion sensors by both tracking the location of the contributors and also inferring building features such as hallways, stairways, and elevators based on the tracked contributors' motions as they move through a structure. With these techniques, the process of mapping buildings can be crowd sourced to a large number of contributors, making the indoor mapping process efficient and easy to scale up.

Abstract: A platooning control apparatus may include: a navigation unit configured to guide an ego vehicle to a destination set by a driver; a driving module configured to drive the ego vehicle; and a control unit configured to primarily select platooning groups based on the destination set in the navigation unit, analyze platooning information of the primarily selected platooning groups, finally decide any one of the primarily selected platooning groups, and then control the driving module to join the finally decided platooning group.

Abstract: The systems, methods, and apparatuses provided herein disclose interpreting a performance criteria for a vehicle, wherein the performance criteria is indicative of a desired operating parameter for the vehicle; interpreting a good driver definition value indicative of a good driver profile for the interpreted performance criteria; determining a performance value indicative of how an operator of the vehicle is performing with respect to the good driver definition value; and in response to the performance value indicating that the vehicle is not satisfying the performance criteria, managing an actuator output response value for at least one actuator in the vehicle to facilitate achievement of the good driver definition value.

Abstract: A method of prompting an operator of an electric vehicle for pre-conditioning the electric vehicle comprises monitoring a location of the electric vehicle, monitoring the temperature of an electric propulsion system within the electric vehicle, accessing historical data of driving patterns for the electric vehicle, monitoring the location of the operator of the electric vehicle, identifying a condition that indicates imminent usage of the electric vehicle based on the location of the vehicle, the location of the operator of the electric vehicle, and the historical data of driving patterns for the electric vehicle, comparing the temperature of the electric propulsion system of the electric vehicle to a pre-determined preferred operating temperature, and sending a prompt to the operator of the electric vehicle suggesting that pre-conditioning of the electric vehicle may be appropriate.

Abstract: A wayfinding system comprising: a first device comprising at least one sensor for sensing a spatial environment to generate a first map, said a first device positioned in a first position in said spatial environment; a second device for receiving signals from said first device to determine a relative position of said second device within said spatial environment; and wherein on said second device said relative position is used in conjunction with said first map to provide turn-by-turn navigation within said spatial environment.

Abstract: Techniques are discussed for determining prediction probabilities of an object based on a top-down representation of an environment. Data representing objects in an environment can be captured. Aspects of the environment can be represented as map data. A multi-channel image representing a top-down view of object(s) in the environment can be generated based on the data representing the objects and map data. The multi-channel image can be used to train a machine learned model by minimizing an error between predictions from the machine learned model and a captured trajectory associated with the object. Once trained, the machine learned model can be used to generate prediction probabilities of objects in an environment, and the vehicle can be controlled based on such prediction probabilities.

Abstract: Systems and methods for vehicle behavior prediction include an imaging device that captures images of a vehicle in traffic. A processing device including policy stored in a memory of the processing device in communication with the imaging device stochastically models future behavior of the vehicle based on the captured images. A policy simulator in communication with the processing device simulates the policy as a reparameterized pushforward policy of a base distribution. An evaluator receives the simulated policy from the policy simulator and performs cross-entropy optimization on the future behavior of the vehicle by analyzing the simulated policy and updating the policy according to cross-entropy error. An alert system retrieves the future behavior of the vehicle and recognizes hazardous trajectories of the future trajectories and generates an audible alert using a speaker.

Abstract: An autonomous driving system mounted on a vehicle determines a target path based on necessary information and performs vehicle travel control such that the vehicle follows the target path. A first coordinate system is a vehicle coordinate system at a first timing when the necessary information is acquired. A second coordinate system is a vehicle coordinate system at a second timing later than the first timing. The autonomous driving system calculates, based on the necessary information acquired at the first timing, a first target path defined in the first coordinate system. Then, the autonomous driving system corrects the first target path to a second target path defined in the second coordinate system by performing coordinate transformation from the first coordinate system to the second coordinate system. The autonomous driving system uses the second target path as the target path to perform the vehicle travel control.

Abstract: The present invention contributes to safe driving by facilitating recognition of the timing at which driving assistance is switched. In the present invention, an advance notice display unit obtains information relating to vehicle driving assistance to be disabled and displays a advance notice image for giving advance notice of disabling of the driving assistance on the basis of said information, and a completion display unit obtains information relating to the detection of a disabling operation required for disabling the driving assistance and/or the completion of disabling of the driving assistance and displays a completion image for giving notice of the completion of disabling of the driving assistance or the completion of the disabling operation on the basis of said information.

Abstract: Techniques are discussed for determining predicted trajectories based on a top-down representation of an environment. Sensors of a first vehicle can capture sensor data of an environment, which may include agent(s) separate from the first vehicle, such as a second vehicle or a pedestrian. A multi-channel image representing a top-down view of the agent(s) and the environment and comprising semantic information can be generated based on the sensor data. Semantic information may include a bounding box and velocity information associated with the agent, map data, and other semantic information. Multiple images can be generated representing the environment over time. The image(s) can be input into a prediction system configured to output a heat map comprising prediction probabilities associated with possible locations of the agent in the future. A predicted trajectory can be generated based on the prediction probabilities and output to control an operation of the first vehicle.

Abstract: A computer system can receive pre-requests for transport from computing devices of users while the users are utilizing a transit service. Each pre-request can specify a start location and a destination for the user. The system can remotely monitor location data from the computing device of the user to determine a current position of the user as the user utilizes the transit service to travel towards the start location, and repeatedly compare (i) a first estimated time of arrival (ETA) of the user to arrive at the start location based, at least in part, on the current position of the user, to (ii) a second ETA, associated with an available vehicle, to arrive at the start location based at least in part on a vehicle position of the available vehicle. Based on this information, the computer system may then automatically select the available vehicle to transport the user.

Abstract: A system and a method for collision avoidance within a monitoring zone includes a first transmitter and receiver apparatus disposed on an industrial truck, a second transmitter and receiver apparatus disposed on a movable object, and at least two stationary positioning apparatuses that are set up for transmitting and receiving electromagnetic signals. A position of the movable object within the monitoring zone is determined. It is determined whether a collision risk exists between the industrial truck and the movable object, and safety measures are initiated if such a collision risk exists. The monitoring zone may include at least one separation zone, wherein no collision risk between the industrial truck and the movable object is determined to exist if the object position is located within the separation zone.

Abstract: A system for providing integrated detection and countermeasures against unmanned aerial vehicles include a detecting element, a location determining element and an interdiction element. The detecting element detects an unmanned aerial vehicle in flight in the region of, or approaching, a property, place, event or very important person. The location determining element determines the exact location of the unmanned aerial vehicle. The interdiction element can either direct the unmanned aerial vehicle away from the property, place, event or very important person in a non-destructive manner, or can cause disable the unmanned aerial vehicle in a destructive manner.

Abstract: A travel-lane determination unit determines whether a vehicle is going to enter a traffic lane on which a toll booth is provided. After it is determined that the vehicle is going to enter the traffic lane, a travel control unit stops the vehicle at a position before the toll booth in a travel direction of the vehicle.

Abstract: An inertial navigation system (INS) device includes three or more atomic interferometer inertial sensors, three or more atomic interferometer gravity gradiometers, and a processor. Three or more atomic interferometer inertial sensors obtain raw inertial measurements for three or more components of linear acceleration and three or more components of rotation. Three or more atomic interferometer gravity gradiometers obtain raw measurements for three or more components of the gravity gradient tensor. The processor is configured to determine position using the raw inertial measurements and the raw gravity gradient measurements.

Abstract: An abnormality detection device includes a sensor configured to detect an external environment of a vehicle and an electronic control unit configured to: detect a traveling state of the vehicle; acquire a control command value for the vehicle in automatic driving control, the vehicle being caused to travel automatically in the automatic driving control; determine, based on a difference between the traveling state and the control command value, whether the traveling state is abnormal; determine, based on a detection result from the sensor, whether the external environment is suitable for execution of the automatic driving control; and determine that an abnormality has occurred in an automatic driving system when it is determined that the traveling state is abnormal and it is determined that the external environment is suitable for the execution of the automatic driving control, the automatic driving system performing the automatic driving control.