Abstract: The invention relates to a system and method for navigating an autonomous driving vehicle (ADV) that utilizes an-onboard computer and/or one or more ADV control system nodes in an ADV network platform. The on-board computer receives battery monitoring and management data concerning a battery stack. The on-board computer, utilizing a battery management system, determines the current state of charge (SOC) and other information concerning the battery stack and determines if the estimated total amount of electrical power required to navigate an ADV along a generated route to reach the predetermined destination is available. In response to determining that the ADV cannot reach the predetermined destination, the on-board computer automatically initiates a dynamic routing algorithm, which utilizes artificial intelligence, to generate alternative routes in an effort to find a route that the ADV can navigate to reach the destination utilizing the current state of charge (SOC) of the battery stack.

Abstract: A monitoring system and method are provided to monitor ground movement of aircraft driven with electric taxi drive systems and movement of ground service vehicles and equipment and personnel within airport ramp areas. Monitor and sensor devices, including those that are intelligent and employ scanning technology to generate image, positional, and other data may be mounted in locations on aircraft, ground service vehicles and equipment, passenger loading bridges, an airport terminal, and other ramp locations to generate a constant stream of data as the aircraft moves into, within, and out of an airport ramp area. The data stream is transmitted to an artificial intelligence-based processing system to identify and communicate possible safety hazards to multiple locations so that the aircraft's ground travel or a ground vehicle's travel may be altered to avoid identified safety hazards and to avoid collisions.

Abstract: A vehicle can include an on-board data processing system that receives sensor data captured by various sensors of the vehicle. As a vehicle travels along a route, the on-board data processing system can process the captured sensor data to identify a potential vehicle stop. The on-board data processing system can then identify geographical coordinates of the location at which the potential vehicle stop occurred, use artificial intelligence to classify a situation of the vehicle at the potential stop, and determine whether the stop was caused by a road obstacle, such as a speed bump, a gutter, an unmarked crosswalk, or any other obstacle not at an intersection. If the stop was caused by the road obstacle, the on-board data processing system can generate virtual stop or yield line data corresponding to the identified geographic coordinates and transmit this data to a server over a network for processing.

Abstract: A method, computer system, and a computer program product for activity-based mapping and navigation is provided. The present invention may include receiving a number of reports indicating a tethered device charging event in proximity of a particular location in a venue. The present invention may further include, in response to the received number of reports satisfying a threshold number of reports, determining that the particular location includes at least one power source.

Abstract: Systems and methods coordinate drone flights in an operating windfarm. A drone flight path that is removed from any location where the drone is conducting observations of a wind turbine and that extends through at least part of a windfarm to a destination location is determined. A respective wake pattern along at least one portion of the drone fight path is determined based on respective operating parameters for each of at least one wind turbine in the windfarm. Flight time commands to adjust at least one respective operating parameter of the respective wind turbine to reduce the effect of the respective wake pattern at a point ahead of the drone on the drone flight path are sent by a windfarm controller controlling the at least one wind turbine.

Abstract: Terrain characteristics of an off-road area are determined based on a map. The terrain characteristics include a terrain type, a terrain grade, and a presence or an absence of an obstacle. Vehicle characteristics are determined including a ground clearance and a breakover angle. Based on a user level, vehicle parameters for the off-road area are determined based on the terrain characteristics, the vehicle characteristics, and the user input. The vehicle parameters include a speed and a transmission gear. The vehicle parameters for the off-road area are output.

Abstract: The disclosure generally pertains to systems and methods for using vehicles to detect and remediate air pollution. In an example implementation, a server computer transmits a directive to a vehicle controller of a vehicle, to measure an air pollution level around the vehicle at a first location. The vehicle controller executes an air pollution measurement and transmits measurement data to the server computer. The server computer evaluates the measurement data and directs the vehicle controller to perform a remedial action for reducing the air pollution level at the first location. The remedial action can involve, for example, the vehicle controller moving the vehicle from the first location to a second location. The server computer may also determine whether the first location is a transient pollution location or a persistent pollution by directing the vehicle controller to carry out measurements at two different times and/or by employing two different sampling rates.

Abstract: A crane risk logic apparatus and system and method for use of the same are disclosed. In one embodiment of the crane risk logic apparatus, the crane risk logic apparatus is integral with a crane, such as a mobile or crawler crane or a tower crane, and located in communication with a load moment indicator. The crane risk logic apparatus receives crane data from the load moment indicator and determines various data analytics, such as, lift angle data, allowable capacity data, operator override data, anti-two-block activation data, operational time data, lift cycle count data, lift classification data, slewing speed data, wind speed data, warning message data, error message data, and winch direction and speed data for each crane lift cycle. The data analytics may be utilized to inform a crane operator evaluation or a crane maintenance schedule for the crane, for example.

Abstract: Methods and systems for a materials handling vehicle comprising a processor and a sensor to record warehouse features. The processor is configured to generate and extract features from sensor data, create a factor graph (FG) including a sensor extrinsics node (e0), and generate an initial vehicle node (v0), initial sensor frame node (c0), and initial sensor feature node (f0) that comprises a selected extracted feature associated with c0 and v0 in an initial data association. A subsequent vehicle node (v1) is generated based on an accumulated odometry amount, and a subsequent sensor frame node (c1) is generated and associated with e0, v1, and one of f0 or a subsequent sensor feature node (f1) in a subsequent data association. The FG is optimized to provide a sensor calibration output based on the data associations, and the vehicle is navigated based on the sensor calibration output.

Abstract: The invention provides designs and methods for a heavy vehicle operations and control system for heavy automated vehicles, which facilitates heavy vehicle operation and control for connected automated vehicle highway (CAVH) systems. The heavy vehicle management system provides heavy vehicles with individually customized information and real-time vehicle control instructions to fulfill the driving tasks such as car following, lane changing, route guidance. The heavy vehicle management system also realizes heavy vehicle related lane design, transportation operations, and management services for both dedicated and non-dedicated lanes. The heavy vehicle management system consists of one or more of the following physical subsystems: (1) Roadside unit (RSU) network, (2) Traffic Control Unit (TCU) and Traffic Control Center (TCC) network, (3) vehicles and onboard units (OBU), (4) traffic operations centers (TOCs), and (5) cloud platform.

Abstract: An excavator includes a rotatable house and a bucket operably coupled to the rotatable house. The excavator also includes one or more swing sensors configured to provide at least one rotation sensor signal indicative of rotation of the rotatable house and one or more controllers coupled to the sensor. The one or more controllers being configured to implement inertia determination logic that determines the inertia of a portion of the excavator and control signal generator logic that generates a control signal to control the excavator, based on the inertia of the portion of the excavator.

Abstract: A vehicular monitoring system (5) has a plurality of sensors (20, 30) that are used to sense the presence of objects (15) around a vehicle (10, 52) for detecting collision threats. At least one of the sensors is positioned such that a portion of the vehicle is at a predefined location relative to the sensor and is within the sensor's field of view. As an example, for an aircraft, a sensor may be positioned such that a portion of the aircraft's wing, aerospike, or other structure is within the sensor's field of view. The system is configured to automatically calibrate the sensor and, if desired, other sensors using the portion of the vehicle at the predefined location.

Abstract: Techniques for determining an output from a plurality of sensor modalities are discussed herein. Features from a radar sensor, a lidar sensor, and an image sensor may be input into respective models to determine respective intermediate outputs associated with a tracks associated with an object and associated confidence levels. The Intermediate outputs from a radar model, a lidar model, and an vision model may be input into a fused model to determine a fused confidence level and fused output associated with the track. The fused confidence level and the individual confidence levels are compared to a threshold to generate the track to transmit to a planning system or prediction system of an autonomous vehicle. Additionally, a vehicle controller can control the autonomous vehicle based on the track and/or on the confidence level(s).

Abstract: When having determined that an own-vehicle has become unable to proceed in a traveling direction corresponding to a travel path of the own-vehicle and has stopped halfway through a travel path, a parking support device transmits information to an operation terminal, the information indicating a travel path and a place at which the vehicle has stopped on the travel path. When having received, from the operation terminal, specification information specifying a place to which the own-vehicle is to be moved, the parking support device sets, as a travel path of the own-vehicle, a path through which the own-vehicle is to be moved to the specified place, and outputs, to a vehicle control unit, control information for causing the own-vehicle to move to the specified place.

Abstract: A control system in a delivery system performs a method to deliver a payload by an unmanned aerial vehicle, UAV, without requiring presence of the recipient even if the payload comprises sensitive or vulnerable goods. The method comprises obtaining a designated location for delivery of the payload, obtaining at least one payload vulnerability index for the payload, and obtaining a pick-up time indicative of a time point when the payload is expected to be retrieved by the recipient. The method further comprises selecting a final delivery area at or near the designated location as a function of the at least one payload vulnerability index and the pick-up time, and operating the UAV to deliver the payload to the final delivery area.

Abstract: In various examples, sensor data representative of a field of view of at least one sensor of a vehicle in an environment is received from the at least one sensor. Based at least in part on the sensor data, parameters of an object located in the environment are determined. Trajectories of the object are modeled toward target positions based at least in part on the parameters of the object. From the trajectories, safe time intervals (and/or safe arrival times) over which the vehicle occupying the plurality of target positions would not result in a collision with the object are computed. Based at least in part on the safe time intervals (and/or safe arrival times) and a position of the vehicle in the environment a trajectory for the vehicle may be generated and/or analyzed.

Abstract: An approach is provided for providing probe data accuracy while ensuring privacy. The approach includes receiving probe path consisting of multiple points from a vehicle, wherein each probe point includes a location of the vehicle and a timestamp indicating when the location was determined by a location sensor. The approach further includes determining a vehicle identifier from among a plurality of vehicle identifiers assigned to the vehicle based on the timestamp, wherein the plurality of vehicle identifiers are applicable for assigning at different respective time slots. The approach also involves generating a hashed vehicle identifier by using a hash function on the determined vehicle identifier and the timestamp. The approach also involves reporting the probe point using the hashed vehicle identifier to identify the probe point.

Abstract: The present disclosure discloses an engineering machinery equipment, and a method, system, and storage medium for safety control thereof, and relates to the field of artificial intelligence, automatic control, and engineering machinery technologies. A method can include: acquiring spatial sensing data of a work area; performing obstacle detection based on the spatial sensing data to determine a position of an obstacle within the work area; determining a safe working range of a mechanical structural component of the engineering machinery equipment based on the position of the obstacle within the work area; and controlling a working range of the mechanical structural component of the engineering machinery equipment based on the safe working range.

Abstract: A lifeform transmission system is used to locate a lifeform wanting to be identified as a lifeform for collision avoidance. The lifeform transmission system comprises a lifeform vitals detector, a GPS transmitter and various other electrical circuits. The lifeform transmitter is worn on a limb or neck of the lifeform. If the lifeform does not wear a transmitter no signal is emitted. The collision avoidance system is housed inside the vehicle and receives communication from invention. If any lifeform is positioned in the path or approaching the path that the ground vehicle is traveling, the vehicle's collision avoidance system is advised to avoid the collision to enhance the accuracy of the collision avoidance system. This invention complements the existing collision avoidance system and enhances the accuracy of vehicle systems by providing an input from the lifeform, making detection easier.

Abstract: A method for controlling traffic by a traffic control system includes transmitting, to a first traffic control vehicle, a first message requesting the first traffic control vehicle to autonomously navigate to a location corresponding to a traffic incident. The method also includes monitoring a location of the first traffic control vehicle based on receiving location information from the first traffic control vehicle. The method also includes transmitting, to the first traffic control vehicle, first information to be displayed via a first notification device integrated with the first traffic control vehicle based on the location information corresponding to the location of the traffic incident. The method still further includes transmitting, to the first traffic control vehicle after a period of time, a request to navigate away from the location of the traffic incident.