Abstract: An autonomous vehicle (AV) computing device including at least one processor may be provided. The at least processor may be programmed to (i) receive a proposed trip including a destination location and a departure time, (ii) determine environmental conditions data based on the destination location and the departure time, (iii) retrieve current software ecosystem data for the AV, (iv) retrieve aggregated data for a plurality of AVs, the aggregated data including a plurality of correlations, each correlation including a) an interaction between at least one software application and at least one environmental condition and b) an adverse performance outcome associated with the interaction, (v) compare the environmental conditions data for the proposed trip and the current software ecosystem data for the AV to the plurality of correlations to identify an adverse performance outcome, and (vi) execute a remedial action to avoid the adverse performance outcome.

Abstract: Systems, methods, and media for safety reinforcement for a personal protective equipment (PPE) device for a material handling vehicle are provided. The safety reinforcement system can include a control unit co-located with the material handling vehicle and configured to receive an identification from the PPE device, communicate the identification to a server, receive an operating routine for the identified PPE device from the server in response to the identification, receive a first operational data from the PPE device, and transmit a limiting instruction to a subsystem of the material handling vehicle according to the operating routine. The operating routine may comprise at least one of a translation routine and a governing rule. The limiting instruction may limit a function of the material handling vehicle.

Abstract: This application discloses use of moving and stationary IoT objects to enhance deep learning algorithms used for autonomous vehicles. The autonomous vehicle acts as an IoT device and exchange information data with moving or stationary IoT devices in its vicinity. The moving and stationary objects share their information data using their broadcast, Ethernet, or proprietary packets with the autonomous vehicle through its IoT device. The shared information data is used by autonomous vehicle navigation and protection system (NPS) where the deep learning algorithm resides. The shared information data includes specification, video and images of the stationary device and moving object. When only selected stationary devices are active, then the active stationary device broadcast the information data that belongs to stationary devices in its vicinity along the road and freeway that are not active.

Abstract: Described herein are systems for roof scan using an unmanned aerial vehicle. For example, some methods include capturing, using an unmanned aerial vehicle, an overview image of a roof of a building from above the roof; presenting a suggested bounding polygon overlaid on the overview image to a user; determining a bounding polygon based on the suggested bounding polygon and user edits; based on the bounding polygon, determining a flight path including a sequence of poses of the unmanned aerial vehicle with respective fields of view at a fixed height that collectively cover the bounding polygon; fly the unmanned aerial vehicle to a sequence of scan poses with horizontal positions matching respective poses of the flight path and vertical positions determined to maintain a consistent distance above the roof; and scanning the roof from the sequence of scan poses to generate a three-dimensional map of the roof.

Abstract: A method and a device for driving a hybrid electric vehicle, and a vehicle is provided. The method includes: driving, if the vehicle demand torque is greater than a minimum value of the engine economic zone and less than a maximum value of the power assistance reserve zone, the hybrid electric vehicle in a parallel drive mode; or maintaining, if the vehicle demand torque continues to increase to the maximum value of the power assistance reserve zone, the hybrid electric vehicle being driven in the parallel drive mode when the hybrid electric vehicle is in the parallel drive mode and a torque provided by the parallel drive mode under a current drive mode is greater than a maximum torque provided by the drive motor.

Abstract: A control apparatus for a plurality of outboard motors 4, 5 spaced apart at predetermined intervals in a width direction of a hull 2, comprises a first steering unit 26 that adjusts a steering angle of the first outboard motor 4, a second steering unit 26 that adjusts a steering angle of the second outboard motor 5, and a control unit 30, 40 that controls the first steering unit and the second steering unit such that the hull moves or turns in a direction in accordance with a steering instruction of an operator by differentiating the control of the steering angle of the first outboard motor from the control of the steering angle of the second outboard motor when propulsion of either the first outboard motor or the second outboard motor cannot be obtained.

Abstract: Transporting loads is a fundamental component of intralogistics. Autonomous industrial trucks can be used for this. The autonomous industrial truck claimed according to the invention is able, by means of the implemented navigation, load receiving and load placing methods, to ensure efficient transporting of loads. The invention relates to an autonomous industrial truck having a rearwardly-directed sensor unit for detecting the load and the loading environment and implemented process sequences for approaching the load, receiving the load and placing the load.

Abstract: Methods, systems, and apparatus for a power hood system. The power hood system includes a powered hood rotatable between a closed position and an open position, at least one hood latch (e.g., a primary hood latch and/or a secondary hood latch) configured to secure the powered hood in the closed position (or a half latch position). The power hood system further includes an electronic control unit electrically coupled to the power hood. The electronic control unit is configured to automatically control the powered hood to move to the closed position in response to (1) receiving a drive mode signal indicating that the vehicle has been shifted into drive and (2) receiving a latch position signal indicating that a hood latch (e.g., the secondary hood latch) is not in the latched state.

Abstract: In variants, a method for generating synthetic data can include: determining an initial dataset, determining characteristics of the initial dataset, generating a set of prompts based on the characteristics, prompting a model to generate the synthetic data using the set of prompts, and training an AV model using the synthetic data.

Abstract: An embodiment provides unmanned aerial vehicles (UAVs) for infrastructure surveillance and monitoring. One example includes monitoring power grid components such as high voltage power lines. The UAVs may coordinate, for example using swarm behavior, and be controlled via a platform system. Other embodiments are described and claimed.

Abstract: A method of managing patrols includes one or more processors receiving GPS data from multiple patrol mobile devices. The processors receive GPS data for the patrols and compare the same to GPS coordinates for patrol points. Patrol points are displayed on a digital map using patrol status visual indicators. The patrol status is based upon the length of time between the last patrol of the patrol point and the time remaining until its next scheduled patrol frequency time expires. The map is continuously automatically updated in real time to display the patrol status of each patrol point, for example, as patrolled or unpatrolled.

Abstract: A mobile robot is configured to navigate on a sidewalk and deliver a delivery to a predetermined location. The robot has a body and an enclosed space within the body for storing the delivery during transit. At least two cameras are mounted on the robot body and are adapted to take visual images of an operating area. A processing component is adapted to extract straight lines from the visual images taken by the cameras and generate map data based at least partially on the images. A communication component is adapted to send and receive image and/or map data. A mapping system includes at least two such mobile robots, with the communication component of each robot adapted to send and receive image data and/or map data to the other robot. A method involves operating such a mobile robot in an area of interest in which deliveries are to be made.

Abstract: A method and an apparatus are provided for a displaying a route in a first electronic device. A first message is received from a second electronic device. First position information included in the first message is identified. The first position information includes position information of the second electronic device. A user input for selecting the first message from a plurality of messages is received. Position information of the first electronic device is calculated. Route information between a start position and a destination position is received from a navigation server. The start position corresponds to the position information of the first electronic device and the destination position corresponds to the position information of the second electronic device. Based at least on receiving the route information from the navigation server, a route representing the received route information between the start position and the destination position is displayed on the first electronic device.

Abstract: A system and method for AI-based dynamic pricing, mapping, and emissions control to optimize inner-city mobility. The system acquires real-time nitrogen dioxide data across a geographic area from sources including satellites, feeds this data into a decision support system along with road-specific weight matrices from authorities, and updates a dynamic penalty matrix for roads in the area. Automatic number plate recognition tracks vehicle movement, queries vehicle databases for emissions profiles, and updates the platform. Users input journey parameters and the system, using AI tools including a genetic algorithm economic and environmental dispatch (GA-EED) optimizer, generates an optimized route based on user-selected criteria such as time, emissions, penalty, and cost. The system enables dynamic, environmentally-aware route optimization for inner-city mobility.

Abstract: A method for location-dependent verification of a teleoperator for remote control of a vehicle. At its current location, the vehicle repeatedly and automatedly performs the following: receiving, from the teleoperator, during remote control by the teleoperator, a response message requested via a network, ascertaining a current value of at least one network-dependent property of the received response message, determining a target value of the at least one network-dependent property, verifying the teleoperator by means of a verification of the current value of the at least one network-dependent property and of the determined target value based on a test condition, wherein in the case of a positive test result of the check, the teleoperator is authorized to perform the remote control of the vehicle, and in the case of a negative test result of the check, the vehicle terminates the remote control and/or sends a safety warning.

Abstract: A vehicle control system includes a setting unit to sense a plurality of forward vehicles positioned ahead of a subject vehicle in a driving direction, to classify each of the forward vehicles as a far-away vehicle or a near-by vehicle, and to set each of the far-away vehicles as an interest vehicle, a receiving unit to receive braking information of each of the interest vehicles from the respective interest vehicles, and a control unit to control braking of the subject vehicle based on the braking information of each of the interest vehicles received by the receiving unit.

Abstract: In an embodiment an apparatus includes a path generator provided in a vehicle, wherein the path generator is configured to generate, based on a minimum turning radius of the vehicle, a default U-turn path and to generate a final U-turn path based on a path following control reference point of the vehicle which is based on the generated default U-turn path, and a controller configured to control autonomous driving of the vehicle to follow the generated final U-turn path.

Abstract: A vehicle may include a movable roof, a sensor supported by the roof, and an actuator for selectively raising and lowering the roof.

Abstract: An abnormality detection device includes: a time window process part configured to divide, by time windows, each of a plurality of vehicle signals each including a time-series data and to extract the data included in the time window; a learning part configured to create learned information via learning using the time-series data in the vehicle signal divided by the time windows; and an abnormality degree calculation part configured to reconstruct the time-series data in the vehicle signal divided by the time windows, based on the learned information, thereby calculate an error between before and after the reconstruction, and further calculate an abnormality degree of the vehicle signal based on the calculated error and the learned information.

Abstract: A system for determining a pose of a vehicle and building maps from vehicle priors processes received ground intensity LIDAR data including intensity data for points believed to be on the ground and height information to form ground intensity LIDAR (GIL) images including pixels in 2D coordinates where each pixel contains an intensity value, a height value, and x- and y-gradients of intensity and height. The GIL images are formed by filtering aggregated ground intensity LIDAR data falling into a same spatial bin on the ground and using a registration algorithm to align two GIL images relative to one another by estimating a 6-degree-of-freedom pose with associated uncertainty that minimizes error between the two GIL images. The aligned GIL images are provided as a pose estimate to a localizer. The system may provide online localization and pose estimation, prior building, and prior to prior alignment pose estimation using image-based techniques.