Abstract: A method for an end-to-end boundary lane detection system is described. The method includes gridding a red-green-blue (RGB) image captured by a camera sensor mounted on an ego vehicle into a plurality of image patches. The method also includes generating different image patch embeddings to provide correlations between the plurality of image patches and the RGB image. The method further includes encoding the different image patch embeddings into predetermined categories, grid offsets, and instance identifications. The method also includes generating lane boundary keypoints of the RGB image based on the encoding of the different image patch embeddings.

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 forming a semiconductor device includes receiving a substrate having a first opening and a second opening formed thereon, wherein the first opening has a first width, and the second opening has a second width less than the first width; forming a protecting layer to cover the first opening and expose the second opening; performing a wet etching to widen the second opening with an etchant, wherein the second opening has a third width after the performing of the wet etching, and the third width of the second opening is substantially equal to the first width of the first opening; and performing a photolithography to transfer the first opening and the second opening to a target layer.

Abstract: A photolithographic apparatus includes a particle removing cassette, a pump and a compressor. The particle removing cassette includes a first slit that includes an array of parallel wind blade nozzles arranged along a length of the first slit, protruding from the first slit, and configured to eject and direct pressurized cleaning material to a patterning surface of a mask to remove debris particles on the patterning surface. The pump and the compressor are controlled by a controller to adjust a flow rate and a pressure of the pressurized cleaning material based on an amount of debris particles on the patterning surface of the mask.

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: System, methods, and other embodiments described herein relate to accurately distinguishing a traffic light from other illuminated objects in the traffic scene and detecting states using hierarchical modeling. In one embodiment, a method includes detecting, using a machine learning (ML) model, two-dimensional (2D) coordinates of illuminated objects identified from a monocular image of a traffic scene for control adaptation by a control model. The method also includes assigning, using the ML model, computed probabilities to the illuminated objects for categories within a hierarchical ontology of environmental lights associated with the traffic scene, wherein one of the probabilities indicates existence of a traffic light instead of a brake light in the traffic scene. The method also includes executing a task by the control model for a vehicle according to the 2D coordinates and the computed probabilities.

Abstract: An electronic device adapted to connect to an external device is provided. The electronic device includes an input/output module, a control module, and a touch screen. The control module is electrically connected to the input/output module, and the control module is configured to send a request signal through the input/output module and receive set information corresponding to the request signal through the input/output module. The touch screen is electrically connected to the control module. When the touch screen generates a touch signal in response to a touch behavior, the control module converts the touch signal to a control signal based on the set information, and sends the control signal through the input/output module.

Abstract: An electronic device adapted to connect to an external device is provided. The electronic device includes an input/output module, a control module, and a touch screen. The control module is electrically connected to the input/output module, and the control module is configured to send a request signal through the input/output module and receive set information corresponding to the request signal through the input/output module. The touch screen is electrically connected to the control module. When the touch screen generates a touch signal in response to a touch behavior, the control module converts the touch signal to a control signal based on the set information, and sends the control signal through the input/output module.