Abstract: In some implementations, a UAV flight system can dynamically adjust UAV flight operations based on thermal sensor data. For example, the flight system can determine an initial flight plan for inspecting a flare stack and configure a UAV to perform an aerial inspection of the flare stack. Once airborne, the UAV can collect thermal sensor data and the flight system can automatically adjust the flight plan to avoid thermal damage to the UAV based on the thermal sensor data.

Abstract: To provide an enhanced method to determine from a driving characteristics of the vehicle a long term mechanical stress of a vehicle, it is proposed that during operation of the vehicle an acceleration of the vehicle is detected by an acceleration sensor in the vehicle and evaluated by a vehicle analyzing system in the vehicle, wherein a driving parameter occurring during an acceleration event in which the vehicle acceleration is above a predetermined threshold is used to determine a driving characteristics value of the vehicle.

Abstract: Vehicles, systems, and methods for navigating or tracking the navigation of a materials handling vehicle along a surface that may include a camera and vehicle functions to match two-dimensional image information from camera data associated with the input image of overhead features with a plurality of global target locations of a warehouse map to generate a plurality of candidate optical targets, an optical target associated with each global target location and a code; filter the targets to determine a candidate optical target; decode the target to identify the associated code; identify an optical target associated with the identified code; determine a camera metric relative to the identified optical target and the position and orientation of the identified optical target in the warehouse map; calculate a vehicle pose based on the camera metric; and navigate the materials handling vehicle utilizing the vehicle pose.

Abstract: A method, system, and computer readable storage medium are provided for predicting behavior of a vehicle. More specifically, image data of the vehicle is obtained by generating class prediction data for the vehicle and then identifying one or more related points of interest. The method, system, and computer readable storage medium then predict the behavior of the vehicle based upon the one or more points of interest and the class prediction data.

Abstract: A method performed by an autonomous vehicle includes identifying a condition preventing the autonomous vehicle from proceeding along an intended route. The method also includes prompting a passenger of the autonomous vehicle to interact with a driver of a first vehicle in response to identifying the condition. The method further includes receiving, from the passenger, an input at an interface of the autonomous vehicle indicating a successful interaction or an unsuccessful interaction with the driver. The method also includes controlling the autonomous vehicle to proceed along the intended route based on the input indicating the successful interaction with the driver.

Abstract: Techniques are discussed for controlling a vehicle, such as an autonomous vehicle, based on occluded areas in an environment. An occluded area can represent areas where sensors of the vehicle are unable to sense portions of the environment due to obstruction by another object. An occlusion grid representing the occluded area can be stored as map data or can be dynamically generated. An occlusion grid can include occlusion fields, which represent discrete two- or three-dimensional areas of driveable environment. An occlusion field can indicate an occlusion state and an occupancy state, determined using LIDAR data and/or image data captured by the vehicle. An occupancy state of an occlusion field can be determined by ray casting LIDAR data or by projecting an occlusion field into segmented image data. The vehicle can be controlled to traverse the environment when a sufficient portion of the occlusion grid is visible and unoccupied.

Abstract: Aspects relate to methods and systems for reversionary flight control for an electrical vertical take-off and landing (eVTOL) aircraft. An exemplary system includes a pilot control, a sensor configured to sense and transmit analog control data associated with a pilot interaction with the pilot control, a pilot interface module configured to receive the analog control data, convert the analog control data to digital control data, and transmit digital control, an actuator, and a flight controller. The flight controller may be configured to receive the digital control data, determine a primary command datum as a function of the digital control data, transmit the primary command datum to the actuator, determine that the digital control signal is non-functional, receive the analog control data, determine a reversionary command datum as a function of the analog control data, and transmit the reversionary command datum to the actuator.

Abstract: A vehicle management system can include self-driving vehicles. Before entering a self-driving vehicle, a rider can use a remote computing device to select a pick-up location at which a self-driving vehicle will later pick up the rider. Detecting that the remote computing device is unable to communicate with the vehicle management system can trigger several responses configured to minimize the risk of a self-driving vehicle failing to pick up the rider.

Abstract: A control stand is disclosed for controlling a crane, an excavator, a crawler, or a similar construction machine having a seat, control elements actuable from the seat for inputting control commands, a display apparatus for displaying information, and a control unit for generating adjustment signals in dependence on input control commands and/or for providing information to the display apparatus. A crane is disclosed having such a control stand that can be configured in the form of a crane operator's cabin. The control elements and/or the display apparatus of the control stand and various further auxiliary units can be individually configurable. Personal pre-settings can be saved and can be reinvoked when the control stand or the crane is to be controlled by a certain machine operator.

Abstract: In various examples, at least partial control of a vehicle may be transferred to a control system remote from the vehicle. Sensor data may be received from a sensor(s) of the vehicle and the sensor data may be encoded to generate encoded sensor data. The encoded sensor data may be transmitted to the control system for display on a virtual reality headset of the control system. Control data may be received by the vehicle and from the control system that may be representative of a control input(s) from the control system, and actuation by an actuation component(s) of the vehicle may be caused based on the control input.

Abstract: A vehicle control device includes an electronic control unit configured to: enlarge the detection range, when the electronic control unit determines that a current deceleration support control is control for passing the object; set a new target deceleration of the host vehicle when a new object with a possibility of collision with the host vehicle has been detected in the enlarged detection range; determine whether an interval from an ending time of the current deceleration support control to a starting time of the next deceleration support control is less than a threshold value, when the electronic control unit determines that the next deceleration support control is control for passing the new object; and perform one of the inter-vehicle distance control and acceleration support control from the ending time to the starting time, when the electronic control unit determines that the interval is less than the threshold value.

Abstract: In various examples, at least partial control of a vehicle may be transferred to a control system remote from the vehicle. Sensor data may be received from a sensor(s) of the vehicle and the sensor data may be encoded to generate encoded sensor data. The encoded sensor data may be transmitted to the control system for display on a virtual reality headset of the control system. Control data may be received by the vehicle and from the control system that may be representative of a control input(s) from the control system, and actuation by an actuation component(s) of the vehicle may be caused based on the control input.

Abstract: Systems and Methods for controlling an autonomous vehicle, may include: receiving sensor data, the sensor data comprising vehicle parameter information for the autonomous vehicle; using the sensor data to determine a vehicle state for the autonomous vehicle, wherein the vehicle state comprises information regarding a magnitude of an actual or predicted effective understeer gradient for the vehicle; computing a yaw moment required to correct the effective understeer gradient based on the magnitude of the effective understeer gradient; and determining a combination of one or more vehicle control inputs, including applying a brake torque, to correct the effective understeer gradient; applying the brake torque to a single wheel of the vehicle, wherein an amount of brake torque applied is sufficient to lock up the single wheel to create a yaw moment on the vehicle to achieve the computed yaw moment required to correct the effective understeer gradient.

Abstract: An automated driving system includes perception, intervention, and vehicle control modules. The perception module detects an upcoming situation to be experienced by a vehicle. The intervention module: determines that a low confidence level exists for a driving decision to be made for the upcoming situation; enables intervention of an autonomous driving mode and indicate information pertaining to the upcoming situation to a driver of the vehicle via an interface; based on the upcoming situation, requests assistance from the driver by (i) indicating available options for the situation via the interface, or (ii) requesting information from the driver to aid in making the driving decision; and determine whether an input has been received from the driver via the interface indicating (i) a selected one of the available options, or (ii) the requested information. The vehicle control module autonomously drives the vehicle based on whether the input has been received.

Abstract: A method of providing traffic related information is provided. The method is performed in a device and comprises obtaining data relating to a first vehicle and data relating to a second vehicle, establishing a relative position between the first vehicle and the second vehicle based on the obtained data, and providing, to an entity in the first vehicle, information based on the established relative position. A corresponding device, computer program and computer program product are also provided.

Abstract: A technique is introduced for touchdown detection during autonomous landing by an aerial vehicle. In some embodiments, the introduced technique includes processing perception inputs with a dynamics model of the aerial vehicle to estimate the external forces and/or torques acting on the aerial vehicle. The estimated external forces and/or torques are continually monitored while the aerial vehicle is landing to determine when the aerial vehicle is sufficiently supported by a landing surface. In some embodiments, semantic information associated with objects in the environment is utilized to configure parameters associated with the touchdown detection process.

Abstract: Systems and methods for forecasting trajectories of objects. The method includes obtaining a prediction model trained to predict future trajectories of objects. The prediction model is trained over a first prediction horizon selected to encode inertial constraints in a predicted trajectory and over a second prediction horizon selected to encode behavioral constraints in the predicted trajectory. The method also include generating a planned trajectory of an autonomous vehicle by receiving state data corresponding to the autonomous vehicle, receiving perception data corresponding to an object, predicting a future trajectory of the object based on the perception data and the prediction model, and generating the planned trajectory of the autonomous vehicle based on the future trajectory of the object and the state data.

Abstract: A system comprises a computer including a processor and a memory. The memory storing instructions executable by the processor to cause the processor to detect a roadside device via at least one vehicle sensor of a plurality of vehicle sensors; determine a location of a vehicle based on a fixed location of the roadside device; determine a location correction adjustment, wherein the location correction adjustment comprises a difference between an assumed location of the vehicle and the determined location of the vehicle, wherein the assumed location is obtained from a navigation system of the vehicle; and adjust the assumed location based on the location correction adjustment.

Abstract: Techniques are disclosed for collaborative map construction using multiple vehicles. Such a system may include a ground vehicle including a first computing device and a first scanning sensor, and an aerial vehicle including a second computing device and a second scanning sensor. The ground vehicle can obtain a first real-time map based on first scanning data using the first scanning sensor, and transmit a first real-time map and position information to the aerial vehicle. The aerial vehicle can receive the first real-time map and the position information from the first computing device, obtain a second real-time map based on second scanning data collected using the second scanning sensor, and obtain a third real-time map based on the first real-time map and the second real-time map.

Abstract: An accommodation area management device which manages an accommodation area for accommodating a moving body and stops the moving body at a predetermined accommodation position in the accommodation area includes an acquisition unit configured to acquire autonomous movement level information related to autonomous movement that the moving body can perform and a processing unit configured to determine an accommodation position for stopping the moving body based on the autonomous movement level information when the moving body enters the accommodation area.