Abstract: The present disclosure relates to a driving control apparatus, a driving control method, and a program that can safety update a personalization function that has been learned to match habits and customs of each driver on the basis of a driving operation of a driver and detection results of various sensors. A personalization function for each driver is found by learning on the basis of driving operation conducted by a driver through manual driving and detection results of various sensors provided on a vehicle body. Verification simulation is carried out by using the found personalization function, and when safety thereof is confirmed, the personalization function is updated as a new personalization function. Autonomous driving reflecting habits and customs of the driver can be realized by correcting an action command found during autonomous driving on the basis of the detection results with the personalization function, thereby realizing safe and comfortable autonomous driving.

Abstract: An autonomous delivery device can make unattended deliveries of customer orders to predetermined locations. The device includes a plurality of compartments containing portions of the order or all of the order or a number of separately deliverable orders. The device includes a compartment opening such that on arrival at the predetermined location the contents of the or each compartment may be automatically dispensed.

Abstract: Described are a system, method, and computer program product for machine-learning-based traffic prediction. The method includes receiving historic transaction data including a plurality of transactions. The method also includes generating, using a machine-learning classification model, a transportation categorization for at least one consumer. The method further includes receiving at least one message associated with at least one transaction, identifying at least one geographic node of activity in the region, and generating an estimate of traffic intensity for the at least one geographic node of activity. The method further includes comparing the estimate of traffic intensity to a threshold of traffic intensity and, in response to determining that the estimate of traffic intensity satisfies the threshold: generating a communication configured to cause at least one navigation device to modify a navigation route; and communicating the communication to the at least one navigation device.

Abstract: In some implementations, a computing device can proactively determine a destination and request traffic information for routes from a starting location to the destination. In some implementations, a computing device can identify some routes between a starting location and a destination as non-recommended routes and recommend other routes. In some implementations, a computing device can rank routes between a starting location and a destination based on automatically-determined user interest. In some implementations, a computing device can determine a user is familiar with a route and adjust the information presented to the user about the route accordingly.

Abstract: A moving body system for controlling operation of moving bodies comprises an accepter configured to accept utilization requests for requesting utilization of the moving bodies corresponding to respective users in order that the plurality of users gather by using the moving bodies corresponding to the respective users; an acquirer configured to acquire scheduled working times of the respective users in the moving bodies when the respective users move by the moving bodies corresponding thereto respectively; and a determiner configured to determine a meeting place at which the respective users gather, the determiner determining the meeting place so that the meeting place gets closer to a getting-on position of the user who has the scheduled working time shorter than that of the other user or those of the other users.

Abstract: A combined power take off and controller includes a power take off including a housing that is adapted to be supported on a housing of a source of rotational energy, an input mechanism that extends through an opening provided in the housing and is adapted to be rotatably driven by the source of rotational energy whenever the source of rotational energy is operated, and an output mechanism that is rotatably driven by the input mechanism and that is adapted to be connected to a rotatably driven accessory. The controller is responsive to one or more operating conditions of the power take off for monitoring and/or controlling the operation thereof.

Abstract: A computer implemented method of planning a drone flight path to increase safety of dynamic objects the drone is estimated to encounter along the flight path based on a mobility model constructed to predict movement of dynamic objects, comprising receiving sensory data captured over time by one or more sensors monitoring movement of a plurality of dynamic objects in a certain geographical area, analyzing the sensory data to identify one or more mobility patterns of one or more dynamic objects detected in the certain geographical area, updating a mobility model with the mobility pattern(s) and generating a planned flight path based on the mobility model for one or more drones planned to fly in the certain geographical area, the flight path is planned to maintain a predefined safety space between the drone(s) and the dynamic object(s).

Abstract: A parking brake control device is a control device for a vehicle used in a vehicle on which a shift by wire system and an electric parking brake system are mounted. A function of automatically operating an electric parking brake without an operation of the driver of the vehicle is referred to as an EPB automatic operation function, and a request by the driver for disabling the EPB automatic operation function is referred to as a disabling request. The parking brake control device includes a disabling determination part configured to determine a presence or absence of the disabling request, a vehicle stop determination part configured to determine whether the vehicle is stopped, a slope determination part configured to determine whether the vehicle is located on a slope, and an EPB automatic operation part. The EPB automatic operation part operates the electric parking brake when the vehicle is stopped on the slope even if the disabling request is made.

Abstract: A sensor of a vehicle receives an identification of a device coupled to an individual via a wireless communications protocol established between the device and the sensor. A computational device on the vehicle processes the identification to uniquely identify the individual. The computational device controls movements of the vehicle to maintain a computed distance at which the vehicle follows the individual.

Abstract: Systems and methods are provided for determining a road profile along a predicted path. In one implementation, a system includes at least one image capture device configured to acquire a plurality of images of an area in a vicinity of a user vehicle; a data interface; and at least one processing device configured to receive the plurality of images captured by the image capture device through the data interface; and compute a profile of a road along one or more predicted paths of the user vehicle. At least one of the one or more predicted paths is predicted based on image data.

Abstract: A navigation system for a host vehicle may include at least one processor programmed to receive images representative of an environment of the host vehicle. The processor may analyze the images to identify navigational state information associated with the host vehicle; determine a plurality of potential trajectories for the host vehicle based on the navigational state information; perform a preliminary analysis to select a subset of the plurality of potential trajectories; perform a secondary analysis to select one of the subset of the plurality of potential trajectories as a planned trajectory for the host vehicle; determine one or more navigational actions for the host vehicle based on the planned trajectory; and cause at least one adjustment of a navigational actuator of the host vehicle to implement the one or more navigational actions for the host vehicle.

Abstract: The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a machine-learned yield model. In particular, the machine-learned yield model can be trained or otherwise configured to receive and process feature data descriptive of objects perceived by the autonomous vehicle and/or the surrounding environment and, in response to receipt of the feature data, provide yield decisions for the autonomous vehicle relative to the objects. For example, a yield decision for a first object can describe a yield behavior for the autonomous vehicle relative to the first object (e.g., yield to the first object or do not yield to the first object). Example objects include traffic signals, additional vehicles, or other objects. The motion of the autonomous vehicle can be controlled in accordance with the yield decisions provided by the machine-learned yield model.

Abstract: A system for operating an automated-taxi includes an input-device and a controller-circuit. The input-device is operable by a client to indicate a desired-destination of the client. The display is viewable by the client. The controller-circuit is in communication with the input-device and the display. The controller-circuit is configured determine a preferred-route in accordance with the desired-destination and a plurality of other-destinations indicated by a plurality of other-clients of an automated-taxi. The preferred-route includes an alternate-destination for the client. The alternate-destination characterized as within a distance-threshold of the desired-destination of the client. The controller-circuit is further configured to operate the display to request a route-approval from the client for the alternate-destination, and, in response to receiving the route-approval, operate the automated-taxi in accordance with the preferred-route to transport the client to the alternate-destination.

Abstract: Provided is a steering assistance device 1, wherein and a demarcation line detection unit 100 detects left and right lane demarcation lines for a lane in which a vehicle is travelingtravels. An assistance unit 11 performs steering assistance for of the vehicle so that the vehicle travels parallel to the left and right lane demarcation lines. If the vehicle is between the left and right lane demarcation lines and the angle formed by the a direction of travel of the vehicle and a line along the center between the left and right lane demarcation lines is equal to or less than a prescribed angle, a steering control unit 102 stops the steering assistance for of the vehicle by the assistance unit 11.

Abstract: Examples provide a stackable smart item storage cart. The cart includes a top member with a lip along its outer edge. A set of wheels, a directional wheel locking mechanism, and a set of four side members is connected to a base member. A vertical cavity passes through a center of the main body enclosing a memory, a processor, and a data storage. The cart smart item storage carts are stackable two-carts high with the wheels locked. A set of sensor devices monitor contents of a plurality of item storage bins on the cart. A bin indicator associated with an item storage bin activates to identify a bin and/or indicate a quantity of items to be removed from the bin. A timer device monitors a cart dwell-time. A self-navigation system may return the cart to a temperature-controlled area if the dwell-time exceeds a threshold time.

Abstract: A control system an unmanned vehicle includes a first processing unit configured to execute a primary autopilot process for controlling the unmanned vehicle. The control system further includes a programmable logic array in operative communication with the first processing unit. The control system also includes a state machine configured in the programmable logic array. The state machine is configured to enable control of the unmanned vehicle according to a backup autopilot process in response to an invalid output of the first processing unit.

Abstract: A system comprising a processor configured to advise a vehicle occupant of an advisable route through a tollbooth determined based on tollbooth traffic, responsive to identifying a vehicle as being within a threshold distance of a tollbooth. The processor is also configured to offer an option for toll prepayment responsive to the advisable route corresponding to a prepaid toll lane.

Abstract: The present disclosure relates to a controller for controlling operation of at least first and second traction machines in a vehicle. The controller includes a processor configured to predict an operating temperature of each of said at least first and second traction machines for at least a portion of a current route. The processor determines at least first and second torque requests for said at least first and second traction machines. The at least first and second torque requests are determined in dependence on the predicted operating temperatures of the at least first and second traction machines. The processor generates at least first and second traction motor control signals in dependence on the determined at least first and second torque requests. The present disclosure also relates to method of controlling at least first and second traction machines in a vehicle.

Abstract: A control system for a tiltable vehicle may include a motor controller configured to respond to backward or reverse operation of the vehicle by hindering a responsiveness of the control system (e.g., proportionally) and/or eventually disengaging a drive motor of the vehicle. Accordingly, a user may intuitively and safely dismount the vehicle by selectively commanding reverse operation. In some examples, the backward direction may be user-defined.

Abstract: An example method involves detecting a sensor-testing trigger. Detecting the sensor-testing trigger may comprise determining that a vehicle is within a threshold distance to a target in an environment of the vehicle. The method also involves obtaining sensor data collected by a sensor of the vehicle after the detection of the sensor-testing trigger. The sensor data is indicative of a scan of a region of the environment that includes the target. The method also involves comparing the sensor data with previously-collected sensor data indicating detection of the target by one or more sensors during one or more previous scans of the environment. The method also involves generating performance metrics related to the sensor of the vehicle based on the comparison.