Abstract: The technology relates to adjusting a trajectory of an autonomous vehicle. In one example, one or more processors are configured to determine a current trajectory and an alternate trajectory of the autonomous vehicle. The one or more processors select a segment of the current trajectory and a segment of the alternate trajectory and determine, for each of the segments, a total expected value of an estimated length of time to traverse the respective segment. The one or more processors may then select one of the segments having the lowest expected value and maneuver the autonomous vehicle along at least a portion of the selected segment.

Abstract: A system includes a processor and a memory. The memory stores instructions executable by the processor to, upon determining that a first fluid level in a vehicle container is below a threshold based on fluid level sensor data, determine a second fluid level based on a fluid consumption signal and the fluid level sensor, and to navigate the vehicle based on the determined second fluid level.

Abstract: A method and system for providing a companion autonomous vehicle are described. In one embodiment, a method includes linking a companion autonomous vehicle to at least one vehicle, device, or user. The companion autonomous vehicle is tethered to the at least one vehicle, device, or user such that the companion autonomous vehicle is configured to stay within a predetermined range of the at least one vehicle, device, or user. The method further includes operating the companion autonomous vehicle to travel along with the tethered at least one vehicle, device, or user within the predetermined range.

Abstract: A method that recommends capturing a stream of images of the landing zone, using a first imaging system to calculate position information for the landing zone on the basis of at least one real image of the stream, using an autopilot system to determine a current position of the landing zone at the current calculation instant, using a second imaging system to verify the presence of a landing zone at the current position in the real image, and generating an alarm if the presence is not found.

Abstract: Disclosed herein are techniques for guiding a vehicle over a flight path. The techniques include receiving guideway data, such as information corresponding to a track segment, generated by one or more guideway sensors associated with a metallic track, and receiving flight path data, such as a set of 3-D space coordinates for the vehicle. The method further includes determining an amount of deviation between one or more coordinates of the flight path data and a position of the vehicle based on the guideway data, and adjusting the position of the vehicle relative to the track segment to minimize the amount of deviation in at least one dimension in the 3-D space.

Abstract: A scanning and perception system for a vehicle camera having an instantaneous field of view (iFOV) and configured to capture an image of an object at a saturation level in the camera's iFOV. The system also includes a light source configured to illuminate the camera's iFOV. The system additionally includes a radar configured to determine the object's velocity and the object's distance from the vehicle, and a mechanism configured to steer the radar and/or the camera. The system also includes an electronic controller programmed to regulate the mechanism and adjust illumination intensity of the light source in response to the saturation level in the image or the determined distance to the object. The controller is also programmed to merge data indicative of the captured image and data indicative of the determined position of the object and classify the object and identify the object's position in response to the merged data.

Abstract: A mobile vehicle includes one or more wheels, a sensor configured to detect the position of a follow target, and a controlling section configured to control the one or more wheels based on a detection result of the sensor such that the mobile vehicle follows the follow target. The controlling section is configured to determine a change point at which the traveling direction of the follow target has changed based on the detection result of the sensor, and to set a target point based on the change point.

Abstract: Disclosed are probabilistic prediction for a motion of a lane-based surrounding vehicle and a longitudinal control method and apparatus using the same. The method includes obtaining surrounding vehicle information using a sensor, predicting a target lane of the surrounding vehicle based on the obtained surrounding vehicle information, performing future driving trajectory prediction for each target lane based on the surrounding vehicle information, and computing a probability of a collision likelihood based on a target lane and trajectory predictions of the surrounding vehicle in which future uncertainty has been taken into consideration and performing longitudinal control for collision avoidance.

Abstract: A system, a method, and a computer program product for determining a sign type of a road sign are disclosed herein. The system comprises a memory configured to store computer-executable instructions and one or more processors configured to execute the instructions to obtain sensor data associated with the road sign, wherein the sensor data comprises data associated with counts of road sign observations, determine one or more features associated with the road sign, based on the obtained sensor data, and determine the sign type of the road sign, based on the one or more features.

Abstract: In some implementations, a method performed by data processing apparatuses includes receiving stored map data corresponding to a digital map, identifying a discrepancy between the digital map and a physical space which the digital map represents, updating the digital map to correct the identified discrepancy, storing the updated digital map, and optionally providing the updated digital may for presentation on a device display.

Abstract: A map information system includes: an in-vehicle device that executes driving support control based on map information; an external device having external map information used for the driving support control; and an update determination device. The in-vehicle device further executes external update processing that updates first map information being the map information of a first area by using first external map information being the external map information of the first area. The update determination device calculates a change degree being a difference between the first map information and the first external map information, for each point or each area in the first area. The external update processing is prohibited with respect to a section in which the change degree is equal to or less than a threshold. The external update processing is permitted with respect to a section in which the change degree exceeds the threshold.

Abstract: An action recommendation system uses reinforcement learning that provides a next action recommendation to a traffic controller to give to a vehicle pilot such as an aircraft pilot. The action recommendation system uses data of past human actions to create a reinforcement learning model and then uses the reinforcement learning model with current ABS-B data to provide the next action recommendation to the traffic controller. The action recommendation system may use an anisotropic reward function and may also include an expanding state space module that uses a non-uniform granularity of the state space.

Abstract: A driving torque command generating apparatus and method of operating a hybrid electric vehicle can obtain torsional state observation values using an engine speed, a motor speed, and a wheel speed detected by an engine speed detector, a motor speed detector, and a wheel speed detector, respectively, together with a motor torque command generated in a previous period, and generate an engine torque command and a motor torque command of a driving torque command based on a driving input value input by a driving input detector and the torsional state observation values.

Abstract: A vehicle control system that sets a preliminary destination or a preliminary destination area toward which a vehicle heads in an autonomously driven state; that causes the vehicle to travel in the autonomously driven state to the preliminary destination or the preliminary destination area set by the preliminary destination/area setting section; that acquires position information for the vehicle; and that switches a driving state of the vehicle from the autonomously driven state to a manually driven state or a remotely operated driven state when the vehicle has been detected to have arrived at the preliminary destination or in the preliminary destination area based on the position information.

Abstract: A vehicle and a method for controlling the same are disclosed. The vehicle may include a plurality of charging ports electrically coupled to a first connector and a second connector, each of the first and second connectors configured to receive power from at least one charger; and a controller configured to receive a charging start command and, upon receiving the charging start command, to control operation of the vehicle to be sequentially paired with the first connector and the second connector when the first connector and the second connector are electrically coupled to the plurality of charging ports.

Abstract: Permissible level information indicating the highest permissible level of driving assistance control for each of sections on a target route is stored in a storage device. In a first section of the target route, the highest permissible level is a first level. In a second section that follows the first section, the highest permissible level is a second level higher than the first level. In a third section that follows the second section, the highest permissible level is a third level different from the second level. In a case where the length of the second section or a passage time taken for a vehicle to pass through the second section is smaller than a threshold value, the processor performs a level maintenance process of maintaining the selection level in the second section at a level equal to the selection level in the first section.

Abstract: Provided an information processing device that is capable of switching between a state in which a device is carried and a state in which the device is not carried in a more suitable form in accordance with a state or situation in which the device is used. The information processing device includes an acquisition unit that acquires information on a user and a controller that executes a control process for moving a device that can be carried by the user so that the device changes between a carry state in which the device is carried by the user and a non-carry state in which the device is not carried by the user on the basis of the information on the user.

Abstract: Provided is a vehicle operation management system having a server device and one or more vehicles. The vehicle includes equipment configured to provide a predetermined service inside the vehicle and a running control unit configured to control autonomous running of the vehicle. The server device includes a service request information acquisition unit. At least one of the server device and the vehicle includes a position information acquisition unit configured to acquire position information on the autonomous vehicle and a running route setting unit configured to set a running route of the vehicle. The running control unit controls running of the vehicle such that the vehicle runs along the running route set by the running route setting unit.

Abstract: Generating control effort to control an autonomous driving vehicle (ADV) includes determining a direction (forward or reverse) in which the ADV is driving and selecting a driving model and a predictive model based upon the direction. In a forward direction, the driving model is a dynamic model, such as a “bicycle model,” and the predictive model is a look-ahead model. In a reverse direction, the driving model is a hybrid dynamic and kinematic model and the predictive model is a look-back model. Current and predicted lateral error and heading error are determined using the driving model and predictive model, respectively. A linear quadratic regulator (LQR) uses the current and predicted lateral error and heading errors to determine a first control effort An augmented control logic determines a second, additional, control effort, to determine a final control effort that is output to a control module to drive the ADV.

Abstract: The present disclosure provides a patient transporting device having a docking member at one end of the patient transporting device that allows to attach to and detach from a receiving portion of the patient loading systems in an emergency vehicle. The device includes a moveable base member that a patient lies down on, a pathway member for the base member to slidably move along tracks of the pathway member, and a docking member at one end of the pathway member that fits and mates with the receiving portion of the vehicle. In operation, the docking member docks with the receiving portion and establishes an extended bridge formed of the pathway member and the docking member of the device and the receiving portion of the vehicle. The extended bridge has a substantially coplanar surface so that the patient lying down on the base member may be easily loaded to the vehicle.