Abstract: Aspects of the disclosure relate to determining and responding to an internal state of a self-driving vehicle. For instance, an image of an interior of the vehicle captured by a camera mounted in the vehicle is received. The image is processed in order to identify one or more visible markers at predetermined locations within the vehicle. The internal state of the vehicle is determined based on the identified one or more visible markers. A responsive action is identified action using the determined internal state, and the vehicle is controlled in order to perform the responsive action.

Abstract: Technologies are disclosed herein for selecting one or more content instances of a plurality of content instances for display on a head unit of a vehicle. The content instances correspond to a location of a vehicle and are received by the head unit. The head unit obtains vehicle specific information from memory of the head unit. Based on the vehicle specific information, the head unit selects the one or more content instances using a set of criteria for determining which, if any, content instances to display. Selection of the one or more content instances using the set of criteria may involve consideration of information associated with individual instances of the plurality of content instances. The selected one or more content instances are displayed on a display of the head unit.

Abstract: A method including generating a curvilinear coordinate system from a current position of the vehicle. The method further includes generating a probability distribution from the curvilinear coordinate system and predicting a future vehicle behavior of the vehicle based on the probability distribution.

Abstract: Systems and methods that compare a validation image to one or more confirmation data sets, wherein each of the one or more confirmation sets is formed based on matching a document image and a confirmation image. In at least one example, one or more vehicle operability actions may be performed responsive to the validation image matching a confirmation data set of the one or more confirmation data sets. A refusal action may further be performed responsive to the validation image not matching the confirmation data, for example.

Abstract: The present invention relates to automated guided vehicles, hereinafter referred to as AGVs, and specifically to AGVs used for entertainment purposes. More specifically, the present invention relates to using in a passenger carrying AGV a capacitor or a plurality of capacitors as a power source.

Abstract: A system for identifying data includes an interface and a processor. The interface is configured to receive training data samples associated with interesting events of an interesting event type. The processor is configured to train a model to recognize the interesting events; determine embedded vectors associated with the interesting events; and determine a representative embedded vector associated with the interesting event type.

Abstract: Systems and methods are provided for selecting points of interest (POIs) to include in navigation maneuvers presented by a navigation application of an electronic device. Navigation directions, including several maneuvers, are generated by a server device. Candidate POIs near locations corresponding to each of the maneuvers are identified and the server device receives requests from third parties that own each of the candidate POIs. The server device selects one of the candidate POIs for each maneuver based on the requests from the third parties. The server device provides the navigation directions, including an indication of the selected POI for a corresponding maneuver, to the client device of the user who requested the navigation directions.

Abstract: Some radar sensors may provide a Doppler measurement indicating a relative velocity of an object to a velocity of the radar sensor. Techniques for determining a two-or-more-dimensional velocity from one or more radar measurements associated with an object may comprise determining a data structure that comprises a yaw assumption and a set of weights to tune the influence of the yaw assumption. Determining the two-or-more-dimensional velocity may further comprise using the data structure as part of regression algorithm to determine a velocity and/or yaw rate associated with the object.

Abstract: A vehicle apparatus onboard a vehicle identifies a human operator-free trip trigger corresponding to a human operator-free trip the vehicle is to take. The vehicle is located at an origin location of the human operator-free trip. Responsive to the vehicle apparatus identifying the human operator-free trip trigger, a candidate destination location for the human operator-free trip is identified. A candidate route from the origin location to the candidate destination location is generated. The vehicle apparatus provides a trip request including the candidate route to an approval apparatus. The vehicle apparatus receives a message from the approval apparatus. Responsive to determining that the message comprises an approval of the candidate route, the vehicle apparatus controls one or more systems of the vehicle to cause the vehicle to traverse the candidate route from the origin location to the candidate destination location.

Abstract: A route from a current location of a portable device to a destination is determined, where the route includes a sequence of directed sections. Navigation instructions to guide a user of the portable device along the route to the destination are generated. To this end, candidate navigation landmarks perceptible within a 360-degree range of the current location of the portable device are identified, a navigation landmark disposed in a direction substantially opposite to the direction of the first one in the sequence of directed sections is selected from among the candidate navigation landmarks, and an initial instruction in the navigation instructions is generated and provided via a user interface of the portable device. The initial instruction references the selected navigation landmark.

Abstract: Example implementations may relate to determining a strategy for a drop process associated with a light detection and ranging (LIDAR) device. In particular, the LIDAR device could emit light pulses and detect return light pulses, and could generate a set of data points representative of the detected return light pulses. The drop process could involve a computing system discarding data point(s) of the set and/or preventing emission of light pulse(s) by the LIDAR device. Accordingly, the computing system could detect a trigger to engage in the drop process, and may responsively (i) use information associated with the environment around the vehicle, operation of the vehicle, and/or operation of the LIDAR device as a basis to determine the strategy for the drop process, and (ii) engage in the drop process in accordance with the determined strategy.

Abstract: The present application discloses an improved transportation matching system, and corresponding methods and computer-readable media. According to the disclosed embodiments, the transportation matching system utilizes an image-based transportation request interface and environmental digital image stream to efficiently generate transportation requests with accurate pickup locations. For instance, the disclosed system can utilize one or more environmental digital images provided from a requestor computing device (e.g., a mobile device or an augmented reality wearable device) to determine information such as the location of the requestor computing device and a transportation pickup location within the environmental digital images. Furthermore, the disclosed system can provide, for display on the requestor computing device, one or more augmented reality elements at the transportation pickup location within an environmental scene that includes the transportation pickup location.

Abstract: A crash sensor system for a vehicle capable of operating in an autonomous mode includes an electronic control unit (ECU) having a processor circuit. The ECU triggers an occupant restraint system of the vehicle in the event of a severe impact event with the vehicle. Satellite sensors are electrically connected to the ECU and are mounted at the front end, the rear end, the right side and the left side of the vehicle near or on an outer surface thereof to detect low severity impact event with the vehicle that does not cause activation of the occupant restraint system. The processor circuit executes an algorithm to confirm, via data from the plurality of satellite sensors, whether the low severity impact event with the vehicle occurred and if so, the ECU triggers a brake controller and a steering controller to cause the vehicle, while in the autonomous mode, to pull over and stop.

Abstract: When a required download time is equal to or shorter than a communication-enabled time on a running route, an update program is received from an external device via wireless communication, so a vehicular program is appropriately updated. Besides, when the required download time is longer than the communication-enabled time on the running route, a driving method ensuring the communication-enabled time that is equal to or longer than the required download time is suggested to a driver, so the update program is easy to receive via wireless communication. In consequence, the opportunities to update the vehicular program can be ensured. That is, the vehicular program can be restrained or prevented from being updated with a delay.

Abstract: In an aspect, a system for monitoring a battery system in-flight. The system includes at least a battery pack and a pack monitoring unit (PMU) communicatively connected with a battery pack. The battery pack includes a plurality of battery packs. The PMU may include at least a sensor and a controller. At least a sensor is configured to detect battery datum. A controller is communicatively connected with at least a sensor. A controller is configured to receive battery datum, detect a significant event as a function of battery datum, and transmit the significant event to a remote device.

Abstract: A power-steering system for a motor vehicle includes a steering wheel and an assistance motor controlled by a closed-loop regulation system, the regulation system determining a motor torque of the assistance motor as a function of a measured steering-wheel torque, using at least one “setpoint monitoring” arm calculating a component of the motor torque, referred to as “variant motor torque”, by subtracting a set steering-wheel torque from the RFe corresponding to the sum of the measured steering-wheel torque and the motor torque, wherein the variant motor torque is multiplied by a gain determined by a three-dimensional map as a function of a vehicle speed and the measured steering-wheel torque.

Abstract: Provided is an electronic apparatus, including: a sensor; a camera; a storage configured to store first and second artificial intelligence models; a first processor; and a second processor. The second processor is configured to: input an image obtained through the camera to the first artificial intelligence model and identify the object of the first type in the image, and input the image obtained through the camera to the second artificial intelligence model and identify the object of the second type, at least a part of which is not viewable in the image. The first processor is configured to: determine a distance between the object of the first type and the object of the second type based on sensing data received from the sensor; and control the electronic apparatus to travel based on the determined distance.

Abstract: According to an aspect of an embodiment, operations may comprise receiving a plurality of frame sets generated while navigating a local environment, receiving an occupancy map (OMap) representation of the local environment, for each of the plurality of frame sets, generating, using the OMap representation, one or more instances each comprising a spatial cluster of neighborhood 3D points generated from a 3D sensor scan of the local environment, and classifying each of the instances as dynamic or static, tracking instances classified as dynamic across the plurality of frame sets using a tracking algorithm, assigning a single instance ID to tracked instances classified as dynamic across the plurality of frame sets, estimating a bounding box for each of the instances in each of the plurality of frame sets, and employing the instances as ground truth data in a training of one or more deep learning classifiers.

Abstract: The present application relates to an accelerator control method and device, a power system and an unmanned aerial vehicle (UAV). The method includes: receiving, by an electronic speed control (ESC), an accelerator signal through a serial communication interface; extracting accelerator control data from the accelerator signal; generating, according to the accelerator control data, a motor control signal for controlling operation of a motor; and transmitting the motor control signal to the motor. The accelerator signal received through the serial communication interface is a digital signal subjected to small interference during transmission, unlike an analog signal that is susceptible to interference such as impedance and capacitive reactance, which causes data inaccuracy. In addition, in the manner of serial communication, a higher baud rate may be adopted to shorten an accelerator control cycle, thereby achieving high-speed control of an accelerator and increasing the control frequency.

Abstract: An apparatus for safety collaboration in computer-assisted or autonomous driving (CA/AD) vehicles includes an input interface to obtain sensor data from one or more sensors of a CA/AD vehicle, an output interface, and an analyzer coupled to the input and output interfaces to process the sensor data to identify an emergency condition of the CA/AD vehicle, and in response to the identified emergency condition, cause a communication interface of the CA/AD vehicle, via the output interface, to broadcast a request for assistance to be received by one or more nearby CA/AD vehicles. The apparatus may be disposed in the CA/AD vehicle.