Abstract: Systems and methods are provided for depth estimation from monocular images using a depth model with sparse range sensor data and uncertainty in the range sensor as inputs thereto. According to some embodiments, the methods and systems comprise receiving an image captured by an image sensor, where the image represents a scene of an environment. The method and systems also comprise deriving a point cloud representative of the scene of the environment from range sensor data, and deriving range sensor uncertainty from the range sensor data. Then a depth map can be derived for the image based on the point cloud and the range sensor uncertainty as one or more inputs into a depth model.

Abstract: Systems, methods, and other embodiments described herein relate to a multi-task model that integrates recurrent models to improve handling of multi-sweep inputs. In one embodiment, a method includes acquiring sensor data from multiple modalities. The method includes separately encoding respective segments of the sensor data according to an associated one of the different modalities to form encoded features using separate encoders of a network. The method includes accumulating, in a detector, sparse features associated with sparse sensor inputs of the multiple modalities to densify the sparse features into dense features. The method includes providing observations according to the encoded features and the sparse features using the network.

Abstract: A humidification system comprises a first sensor and a second sensor. The first and second sensors are adapted to sense flow characteristics within the system. The first and second sensors are isolated from the flow by barriers formed by respective first and second sealing members. The sealing members extend through apertures formed in the system and have a portion that contacts the sensing elements of the respective first and second sensors. A cartridge can hold the sensors and provide repeatable penetration depths into a flow passage of the system. A medical tube has a composite structure made of two or more distinct components that are spirally wound to form an elongate tube. One component can be a spirally wound elongate hollow body; the other component can be an elongate structural component spirally wound between turns of the spirally wound hollow body.

Abstract: Systems and methods are provided for training a depth model to recover scale factor for self-supervised depth estimation in monocular images. Examples include deriving a depth map for an image based on a depth model. The depth map comprises depth values for pixels of the image. A first scale for the image can be estimated based on the depth values, and depth data captured by a range sensor can be received. The depth data comprises a point cloud comprising depth measures. A second scale for the point cloud can be determined based on the depth measures and a scale factor can be determined based the second scale and the first scale. The depth model can be updated based on the scale factor, wherein the depth model generates metrically accurate depth estimates based on the scale factor.

Abstract: The disclosure relates to medical tubes and methods of manufacturing medical tubes. The tube may be a composite structure made of two or more distinct components that are spirally wound to form an elongate tube. For example, one of the components may be a spirally wound elongate hollow body, and the other component may be an elongate structural component also spirally wound between turns of the spirally wound hollow body. The tube need not be made from distinct components, however. For instance, an elongate hollow body formed (e.g., extruded) from a single material may be spirally wound to form an elongate tube. The elongate hollow body itself may in transverse cross-section have a thin wall portion and a relatively thicker or more rigid reinforcement portion. The tubes can be incorporated into a variety of medical circuits or may be employed for other medical uses.

Abstract: A method for controlling an ego vehicle in an environment includes detecting one or more changes in a position of an agent vehicle over time in accordance with capturing at least a first representation of the environment and a second representation of the environment via one or more sensors associated with the ego vehicle. The method also includes determining a velocity of the object based on detecting the one or more changes. The method further includes classifying the agent vehicle as parked based on the velocity and contextual data associated with the agent vehicle and/or the environment. The method still further includes planning a trajectory for the ego vehicle based on classifying the agent vehicle as parked. The method also includes controlling the ego vehicle to navigate along the trajectory.

Abstract: The disclosure relates to medical tubes and methods of manufacturing medical tubes. The tube may be a composite structure made of two or more distinct components that are spirally wound to form an elongate tube. For example, one of the components may be a spirally wound elongate hollow body, and the other component may be an elongate structural component also spirally wound between turns of the spirally wound hollow body. The tube need not be made from distinct components, however. For instance, an elongate hollow body formed (e.g., extruded) from a single material may be spirally wound to form an elongate tube. The elongate hollow body itself may in transverse cross-section have a thin wall portion and a relatively thicker or more rigid reinforcement portion. The tubes can be incorporated into a variety of medical circuits or may be employed for other medical uses.

Abstract: Provided herein are fusion proteins comprising a CD8-binding site (e.g., a silent CD8-binding site) and an IL-12 protein. Also provided are pharmaceutical compositions comprising such fusion proteins, expression vectors and host cells for making such fusion proteins, and methods of using such fusion proteins in treating cancers.

Abstract: A method for controlling an ego vehicle in an environment includes associating, by a velocity model, one or more objects within the environment with a respective velocity instance label. The method also includes selectively, by a recurrent network of the taillight recognition system, focusing on a selected region of the sequence of images according to a spatial attention model for a vehicle taillight recognition task. The method further includes concatenating the selected region with the respective velocity instance label of each object of the one or more objects within the environment to generate a concatenated region label. The method still further planning a trajectory of the ego vehicle based on inferring, at a classifier of the taillight recognition system, an intent of each object of the one or more objects according to a respective taillight state of each object, as determined based on the concatenated region label.

Abstract: A method controlling an ego vehicle in an environment includes determining, via a flow model of a parked vehicle recognition system, a flow between a first representation of the environment and a second representation of the environment. The method also includes determining, via a velocity model of the parked vehicle recognition system, a velocity of a vehicle in the environment based on the flow. The method further includes determining, via a parked vehicle classification model of the parked vehicle recognition system, the vehicle is parked based on the velocity of the vehicle and one or more of features associated with the vehicle and/or the environment. The method still further includes planning a trajectory of the ego vehicle based on determining the vehicle is parked.

Abstract: A method for generating a dense light detection and ranging (LiDAR) representation by a vision system of a vehicle includes generating, at a depth estimation network, a depth estimate of an environment depicted in an image captured by an image capturing sensor integrated with the vehicle. The method also includes generating, via a sparse depth network, one or more sparse depth estimates of the environment, each sparse depth estimate associated with a respective sparse representation of one or more sparse representations. The method further includes generating the dense LiDAR representation based on a dense depth estimate that is generated based on the depth estimate and the one or more sparse depth estimates. The method still further includes controlling an action of the vehicle based on the dense LiDAR representation.

Abstract: A material handling system has a vertical lift two vertically aligned carriages that are independently positionable on a vertical support structure by respective drive systems. A processor subsystem selects one of the two carriages to move an article from at least one in-feed conveyor to a selected level of an automated storage and retrieval system (AS/RS). The processor subsystem concurrently positions the other carriage to avoid a collision.

Abstract: A plasma generating device comprising: a cylindrical rotational electrode situated lengthwise on a rotating shaft connected to a motor, the rotational electrode disposed over a reservoir and having a contact portion extending into the reservoir; one or more static electrodes held in proximity to the rotational electrode to generate a plasma therebetween when a sufficient voltage difference exists between the static electrodes and the rotational electrode, the sufficient voltage difference created by a high voltage generator connected directly or indirectly to the static electrodes, the rotational electrode, or both; and a dielectric material situated between the rotational electrode and the static electrodes, the dielectric material having a sufficient thickness to prevent a short-circuit between the rotational electrode and the static electrodes yet a minimal thickness to allow the plasma to be generated between the rotational electrode and the static electrodes.

Abstract: Some embodiments provide for an inspiratory limb for a breathing circuit that includes a first segment that comprises a first heater wire circuit and a second segment that comprises a second heater wire circuit. The inspiratory limb can include an intermediate connector that includes a connection circuit that electrically couples the first heater wire circuit to the second heater wire circuit. The inspiratory limb can be configured to operate in two modes wherein, in a first mode, electrical power passes through the first electrical connection to provide power to the first heater wire circuit without providing power to the second heater wire circuit, and in a second mode, electrical power pass through the first electrical connection to provide power to both the first heater wire circuit and the second heater wire circuit.

Abstract: A method for generating a dense light detection and ranging (LiDAR) representation by a vision system includes receiving, at a sparse depth network, one or more sparse representations of an environment. The method also includes generating a depth estimate of the environment depicted in an image captured by an image capturing sensor. The method further includes generating, via the sparse depth network, one or more sparse depth estimates based on receiving the one or more sparse representations. The method also includes fusing the depth estimate and the one or more sparse depth estimates to generate a dense depth estimate. The method further includes generating the dense LiDAR representation based on the dense depth estimate and controlling an action of the vehicle based on identifying a three-dimensional object in the dense LiDAR representation.

Abstract: A method for vehicle prediction, planning, and control is described. The method includes separately encoding traffic state information at an intersection into corresponding traffic state latent spaces. The method also includes aggregating the corresponding traffic state latent spaces to form a generalized traffic geometry latent space. The method further includes interpreting the generalized traffic geometry latent space to form a traffic flow map including current and future vehicle trajectories. The method also includes decoding the generalized traffic geometry latent space to predict a vehicle behavior according to the traffic flow map based on the current and future vehicle trajectories.

Abstract: System, methods, and other embodiments described herein relate to detection of traffic lights corresponding to a driving lane from views captured by multiple cameras. In one embodiment, a method includes estimating, by a first model using images from multiple cameras, positions and state confidences of traffic lights corresponding to a driving lane of a vehicle. The method also includes aggregating, by a second model, the state confidences and a multi-view stereo composition from geometric representations associated with the positions of the traffic lights. The method also includes assigning, by the second model according to the aggregating, a relevancy score computed for a candidate traffic light of the traffic lights to the driving lane. The method also includes executing a task by the vehicle according to the relevancy score.

Abstract: A method for vehicle prediction, planning, and control is described. The method includes separately encoding traffic state information at an intersection into corresponding traffic state latent spaces. The method also includes aggregating the corresponding traffic state latent spaces to form a generalized traffic geometry latent space. The method further includes interpreting the generalized traffic geometry latent space to form a traffic flow map including current and future vehicle trajectories. The method also includes decoding the generalized traffic geometry latent space to predict a vehicle behavior according to the traffic flow map based on the current and future vehicle trajectories.

Abstract: System, methods, and other embodiments described herein relate to improving the detection of traffic lights associated with a driving lane using a camera instead of map data. In one embodiment, a method includes estimating, from an image using a first model, depth and orientation information of traffic lights relative to a driving lane of a vehicle. The method also includes computing, using a second model, relevancy scores for the traffic lights according to geometric inferences between the depth and the orientation information. The method also includes assigning, using the second model, a primary relevancy score for a light of the traffic lights associated with the driving lane according to the depth and the orientation information. The method also includes executing a control task by the vehicle according to the primary relevancy score and a state confidence, computed by the first model, for the light.

Abstract: Some embodiments provide for an inspiratory limb for a breathing circuit that includes a first segment that comprises a first heater wire circuit and a second segment that comprises a second heater wire circuit. The inspiratory limb can include an intermediate connector that includes a connection circuit that electrically couples the first heater wire circuit to the second heater wire circuit. The inspiratory limb can be configured to operate in two modes wherein, in a first mode, electrical power passes through the first electrical connection to provide power to the first heater wire circuit without providing power to the second heater wire circuit, and in a second mode, electrical power pass through the first electrical connection to provide power to both the first heater wire circuit and the second heater wire circuit.