Abstract: A system selecting a parking spot using reinforcement learning includes a first monitor evaluating available parking spots in a parking lot. Reinforcement learning (RL) is used to pick a parking spot based on user preferences. A computer computes a probability of a future parking spot availability from the available parking spots. An information source wherein the computer retrieves information including time of day, weather and location from the information source to assist in computing the probability of the future parking spot availability. An update module receives the future parking spot availability from the computer and updates a belief probability, the belief probability including a belief defined as a data structure in which a probability of the available individual parking spots within the parking lot is stored. An RL model is updated for the parking lot after a park maneuver is completed for a host vehicle.

Abstract: A method of guidance for an aircraft includes obtaining relative position data indicative of a direction from a location of the aircraft to a location of a moving target point. The method also includes determining, based on the relative position data, a location of a virtual leader based on projecting the location of the aircraft onto a virtual leader path, where the virtual leader path corresponds to a straight approach path to the location of the moving target point along a desired approach direction. The method further includes determining, based on the location of the virtual leader, a guidance input to cause the aircraft to follow the virtual leader.

Abstract: A system for cleaning sensors of a vehicle includes at least one cleaning apparatus which is configured to clean a number of sensors of a vehicle and a control apparatus which is configured to control the at least one cleaning apparatus. The control apparatus is also configured so as, on the basis of at least one situation parameter, to select a plurality of sensors which are intended to be cleaned from the number of sensors and to determine the order in which the selected plurality of sensors are intended to be cleaned.

Abstract: A method for controlling brake assemblies of a vehicle is provided. Responsive to identifying a locked wheel protection event for a first wheel of a paired set of wheels on the vehicle, where the paired set of wheels is the first wheel on one side of the vehicle and a second wheel on an opposite side of the vehicle, a first brake pressure to a first brake assembly associated with the first wheel is released to zero pounds per square inch (PSI) and a second brake pressure to a second brake assembly associated with the second wheel of the paired set of wheels is reduced by a predetermined amount thereby reducing a yaw effect to the vehicle.

Abstract: A control device is provided for controlling an adjusting device of a human-powered vehicle. The adjusting device adjusts a front initial sag amount of a front suspension of the human-powered vehicle in a state in where a rider is riding the human-powered vehicle and a rear initial sag amount of a rear suspension of the human-powered vehicle in a state where the rider is riding the human-powered vehicle. The control device comprises an electronic controller configured to output a front control signal that adjusts the front initial sag amount of the front suspension of the human-powered vehicle, and output a rear control signal that adjusts the rear initial sag amount of the rear suspension of the human-powered vehicle to the adjusting device based on traveling information related to traveling of the human-powered vehicle.

Abstract: Techniques for determining a response of a simulated vehicle to a simulated object in a simulation are discussed herein. Log data captured by a physical vehicle in an environment can be received. Object data representing an object in the log data can be used to instantiate a simulated object in a simulation to determine a response of a simulated vehicle to the simulated object. Additionally, one or more trajectory segments in a trajectory library representing the log data can be determined and instantiated as a trajectory of the simulated object in order to increase the accuracy and realism of the simulation.

Abstract: One embodiment is a system including a component for installation on a vehicle comprising a central maintenance computer (“CMC”); a configuration/maintenance module (“CMM”) associated with the component and including memory for storing component information, a sensor for detecting a condition and generating data indicative thereof; a microprocessor for processing the sensor data and updating the component information with the processing results; and a communications interface between the CMM and the CMC. The system further includes a remaining useful life (“RUL”) module associated with the component that periodically updates an RUL, the RUL module periodically updating an RUL value for the component and communicating the updated RUL value to the CMM for storage in the memory. The CMC communicates with the CMM to update the component information included in the memory based on information input to the CMC by a user or changes in a condition of the vehicle.

Abstract: A vehicle controller includes a processor configured to determine whether there is fogging on a window of a vehicle or a sign of fogging thereof, based on a fogginess sensor signal indicating a physical index related to the degree of fogging of the window of the vehicle or operation of a vehicle-mounted device of the vehicle capable of defogging operation. The fogginess sensor signal is obtained by a fogginess sensor provided on the vehicle. The processor is further configured to change a driving control level applicable to the vehicle so as to increase or prevent decreasing the degree of a driver's involvement in driving the vehicle, depending on the driving control level currently applied to the vehicle, upon determination that there is fogging on the window of the vehicle or a sign of fogging thereof.

Abstract: Systems and methods of model or prediction algorithm selection are provided. An autonomous control system may include a perception component that, based on environmental inputs regarding an object(s), a vehicle's operating characteristics, etc., outputs a current state of the vehicle's surrounding environment. This in turn, is used as input to a prediction component comprising a plurality of prediction algorithms. The prediction component outputs a set of predictions regarding the trajectory of the object(s). Accordingly, for each object, a set of trajectories at specific timesteps may be generated by the different prediction algorithms which are input to a planner component. These trajectories may then be analyzed, compared, or otherwise processed to determine which trajectory regarding the object is most accurate. The prediction algorithm or model that produced the most accurate predicted trajectory may then be used for subsequent predictions/timesteps.

Abstract: Techniques are described for uniquely identifying a quadrant of a symmetrical vehicle, such as a symmetrical autonomous vehicle. The techniques include using service indicator lights to convey information regarding the status and operational mode of the vehicle to an observer and/or service technician. The service indicator lights may uniquely identify quadrants of the vehicle for orienting the vehicle with a representation of the vehicle to guide a technician to a particular location and component of the vehicle located within the identified quadrant.

Abstract: An electric vehicle control method including using a first motor and a second motor as travel drive sources, performing drive control on the first motor by transmitting a first torque command value to a first inverter, and performing drive control on the second motor by transmitting a second torque command value to a second inverter, and performing switching control of switching, based on a required drive force, the drive control on the second motor by the second inverter between an ON state in which the drive control is performed and an OFF state in which the drive control is stopped, wherein a torque fluctuation amount generated in the second motor during the switching control is calculated based on a rotation speed of the second motor, and the first torque command value is corrected based on the torque fluctuation amount.

Abstract: An apparatus and method of calibrating a vehicle optical sensor includes positioning a vehicle in a test station and proximate a projection surface, the vehicle having an optical sensor, the projection surface in view of the optical sensor, determining a position of the optical sensor with respect to the projection surface using a control unit that is external to the vehicle, and selecting an image to be displayed. The method further includes displaying the image from the projection surface, adapting the image displayed from the projection surface to dimensions of the projection surface, using the control unit, based on a size of the projection surface and the position of the optical sensor, spatially adjusting the projection surface with respect to the position of the optical sensor, and calibrating the optical sensor by adjusting a position of an optical axis of the optical sensor based on the image.

Abstract: Provided is an electronic control device that estimates a presence of a vehicle incapable of performing vehicle-to-vehicle communication ahead of a communication partner vehicle is estimated. An electronic control device 30 detects, on map data, an intersection 60 located ahead in a traveling direction of a host vehicle 10 based on location data and the map data of the host vehicle 10, calculates a stop position (first position) 71 where a communication partner vehicle 11 is supposed to pass through or stop when the communication partner vehicle 11 stops at the intersection 60, based on vehicle information of the communication partner vehicle 11 that is traveling toward the intersection 60, and determines whether or not there is another vehicle 13 ahead in the traveling direction of the communication partner vehicle 11, based on the stop position (first position) 71 and an intersection area 20 (second position 72) set before the intersection 60.

Abstract: Provided is a driver assistance system that may acquire more accurate position information of a vehicle without extracting landmark information or feature point information around the vehicle, the driver assistance system including: a global positioning system (GPS) module configured to acquire GPS information of the vehicle; a lidar configured to acquire point cloud data for an outside field of view of the vehicle; a communicator configured to receive a high definition map; and a controller comprising at least one processor configured to process the point cloud data, the high definition map, speed data and steering angle data, the speed data and the steering angle data being received through a vehicle communication network, wherein the controller is configured to generate dead reckoning information in response to processing the speed data and the steering angle data, generate position information of the vehicle based on the dead reckoning information and the GPS information, acquire intensity data around the

Abstract: A method of landing an aircraft in which the aircraft receives information from a second aircraft via a direct aircraft-to-aircraft communication from the second aircraft to the aircraft; determines a landing plan of the aircraft based on the information; and lands the aircraft based on the landing plan. The use of a direct aircraft-to-aircraft communication (i.e. a communication from the second aircraft to the first aircraft which does not travel via an intermediary such as a land station, air traffic controller or satellite) makes the method reliable because such a communication is inherently secure and difficult to hack.

Abstract: The subject disclosure relates to estimating a grade of a road and calibrating sensors of an autonomous vehicle based on the grade. A process of the disclosed technology can include effectuating an autonomous vehicle to perform one or more maneuvers that cause the autonomous vehicle to face different directions along a road, obtaining a first set of sensor measurements of an autonomous vehicle in a first pose, wherein the first set of sensor measurements includes a first measured specific force, obtaining a second set of sensor measurements of the autonomous vehicle in a second pose, wherein the second set of sensor measurements includes a second measured specific force, determining a direction of gravity based on the first measured specific force and the second measured specific force, and calibrating at least one sensor of the autonomous vehicle based on the determined direction of gravity.

Abstract: An apparatus may be configured to determine whether to adjust an acceleration limit level associated with an acceleration limit operation for a corresponding vehicle speed range. The adjustment may be performed by using driver information acquired based on biometric information of a driver. The apparatus may be configured to control an adjustment of the acceleration limit level. The apparatus may be configured to change a limit acceleration of the vehicle that is accelerated based on a degree of accelerator pedal manipulation by the driver and the adjusted acceleration limit level.

Abstract: A mobile object control device acquires, as learning history data, driving history data obtained when a learning mobile object is operated in a risk-free environment; performs learning for imitating driving of the learning mobile object in the risk-free environment using the learning history data as training data and generates an imitation learning model; acquires, as training history data, driving history data obtained when a mobile object for generating training data is operated in the same environment as the environment in which the learning history data has been acquired; estimates whether the training history data matches the learning history data using the training history data as input to the imitation learning model and assigns a label related to risks; and infers a result for controlling a mobile object to be controlled using at least the label related to risks as training data, based on sensor information of the mobile object.

Abstract: A method and a system analyze the control of a vehicle having an autonomous driving unit. A change in the driving mode from autonomous driving to manual driving is detected, and at least one driving parameter before and/or after detecting the change is monitored. Based on driving values obtained by the monitoring with respect to the detected change in driving mode, at least one driving quantity quantifying the quality of interplay between the autonomous driving unit and a human driver is determined.

Abstract: Systems, devices, and methods for tracking and/or predicting motion of a wing of an aircraft are disclosed herein. The systems, devices, and methods track wing motion (e.g., in real-time). In some embodiments, the systems and devices include stereo binocular vision (SBV) cameras and/or light detection and ranging (LIDAR) emitters and receivers mounted on the aircraft. In these and other embodiments, the systems and devices include a network of contact sensors (e.g., accelerometers or strain gauges) mounted on a wing and corresponding receivers mounted on the aircraft. In these and other embodiments, based at least in part on the captured wing motion data, machine learning is employed to predict wing motion (e.g., normal, turbulent, and/or chaotic wing motion) of the aircraft.