Abstract: Driver behavior recognition or driver behavior prediction are described herein. A first image sequence including image frames associated with a forward-facing image capture device of a vehicle and a corresponding vehicle data signal sequence may be received. A second image sequence including image frames associated with a rear or driver facing image capture device of the vehicle may be received. Feature vectors may be generated for respective sequences using neural networks, such as a convolutional neural network (CNN), a depth CNN, a recurrent neural network (RNN), a fully connected layer, a long short term memory (LSTM) layer, etc. A fusion feature may be generated by performing data fusion on any combination of the feature vectors. A predicted driver behavior may be generated based on the LSTM layer and n image frames on an image sequence and include x number of prediction frames.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for jointly training an image embedding model and a text embedding model. In one aspect, a method comprises: processing data from a historical query log of a search system to generate a candidate set of training examples, wherein each training example comprises: (i) a search query comprising a sequence of one or more words, (ii) an image, and (iii) selection data characterizing how often users selected the image in response to the image being identified by a search result for the search query; selecting a plurality of training examples from the candidate set of training examples; and using the training data to jointly train the image embedding model and the text embedding model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an image embedding model. In one aspect, a method comprises: obtaining training data comprising a plurality of training examples, wherein each training example comprises: an image pair comprising a first image and a second image; and selection data indicating one or more of: (i) a co-click rate of the image pair, and (ii) a similar-image click rate of the image pair; and using the training data to train an image embedding model having a plurality of image embedding model parameters.

Abstract: Methods, systems, and computer-readable mediums storing computer executable code for visual recognition implementing a triplet loss function are provided. The method include receiving an image generated from an image source associated with a vehicle. The method may also include analyzing the image based on a convolutional neural network. The convolutional neural network may apply both a triplet loss function and a softmax loss function to the image to determine classification logits. The method may also include classifying the image into a predetermined class distribution based upon the determined classification logits. The method may also include instructing the vehicle to perform a specific task based upon the classified image.

Abstract: Multi-modal data representing driving events and corresponding actions related to the driving events can be obtained and used to train a neural network at least in part by using a triplet loss computed for the driving events as a regression loss to determine an embedding of driving event data. In some cases, using the trained neural network, a retrieval request for an input driving event and corresponding action can be processed by determining, from the neural network, one or more similar driving events or corresponding actions in the multi-modal data.

Abstract: A hydrogen generation method including steps as follows: adding a nitrogen-substituted derivative of an alkyldithiolate ruthenium complex as a biomimetic hydrogenase photocatalyst into a solution, adding an organic acid into the solution, adding a P-ligand into the solution, adding an electron donor into the solution, and irradiating the solution with light in order to generate hydrogen.

Abstract: A method of forming a layout of a semiconductor device includes the following steps. First line patterns extend along a first direction in a first area and a second area, but the first line patterns extend along a second direction in a boundary area. Second line patterns extend along a third direction in the first area and the second area, but the second line patterns extend along a fourth direction in the boundary area, so that minimum distances between overlapping areas of the first line patterns and the second line patterns in the boundary area are larger than minimum distances between overlapping areas of the first line patterns and the second line patterns in the first area and the second area. A trimming process is performed to shade the first line patterns and the second line patterns in the boundary area and the second area.

Abstract: A system, computer-readable medium, and method for improving semantic mapping and traffic participant detection for an autonomous vehicle are provided. The methods and systems may include obtain a two-dimensional image, obtain a three-dimensional point cloud comprising a plurality of points, perform semantic segmentation on the image to map objects with a discrete pixel color, and overlaying the semantic segmentation on the image to generate a updated image, generate superpixel clusters from the semantic segmentation to group like pixels together, project the point cloud onto the updated image comprising the superpixel clusters, and remove points determined to be noise/errors from the point cloud based on determining noisy points within each superpixel cluster.

Abstract: Performing semantic segmentation of an image can include processing the image using a plurality of convolutional layers to generate one or more feature maps, providing at least one of the one or more feature maps to multiple segmentation branches, and generating segmentations of the image based on the multiple segmentation branches, including providing feedback to, or generating feedback from, at least one of the multiple segmentation branches in performing segmentation in another of the segmentation branches.

Abstract: A system and method for utilizing a temporal recurrent network for online action detection that include receiving image data that is based on at least one image captured by a vehicle camera system. The system and method also include analyzing the image data to determine a plurality of image frames and outputting at least one goal-oriented action as determined during a current image frame. The system and method further include controlling a vehicle to be autonomously driven based on a naturalistic driving behavior data set that includes the at least one goal-oriented action.

Abstract: A method of forming a layout of a semiconductor device includes the following steps. First line patterns extend along a first direction in a first area and a second area, but the first line patterns extend along a second direction in a boundary area. Second line patterns extend along a third direction in the first area and the second area, but the second line patterns extend along a fourth direction in the boundary area, so that minimum distances between overlapping areas of the first line patterns and the second line patterns in the boundary area are larger than minimum distances between overlapping areas of the first line patterns and the second line patterns in the first area and the second area. A trimming process is performed to shade the first line patterns and the second line patterns in the boundary area and the second area.

Abstract: An antenna structure of reduced size but able to cover first, second, and third LTE-A bands together with WI-FI and BLUETOOTH frequencies includes a metal frame defining at least two gaps. The gaps extend and pass completely through the metal frame, and divide the metal frame into radiating portions. At least one feeding portion is electrically coupled to each radiating portion. Each radiating portion can simultaneously activate first, second, and third operating modes for the radiation of signals in first, second, and third frequency bands.

Abstract: A reduced graphene oxide-based biosensor includes a nano-structure field-effect transistor including a channel region which includes a reduced graphene oxide having a linking moiety to be bonded to a receptor specific to an analyte, and which is represented by a formula of —(C?O)—X—COOH, wherein X represents a C1-C3 alkenylene group or a C1-C3 alkylene group.

Abstract: Driver behavior recognition or driver behavior prediction are described herein. A first image sequence including image frames associated with a forward-facing image capture device of a vehicle and a corresponding vehicle data signal sequence may be received. A second image sequence including image frames associated with a rear or driver facing image capture device of the vehicle may be received. Feature vectors may be generated for respective sequences using neural networks, such as a convolutional neural network (CNN), a depth CNN, a recurrent neural network (RNN), a fully connected layer, a long short term memory (LSTM) layer, etc. A fusion feature may be generated by performing data fusion on any combination of the feature vectors. A predicted driver behavior may be generated based on the LSTM layer and n image frames on an image sequence and include x number of prediction frames.

Abstract: An antenna structure includes a housing, four feed sources, a first radiator, a second radiator, and a third radiator. The housing includes a first radiating portion and a second radiating portion. The first to third radiators are positioned in the housing. The first radiator is spaced apart from the second radiator. The four feed sources respectively connect to the first radiating portion, the second radiating portion, the first radiator, and the third radiator. The first radiating portion activates a first operation mode and a second operation mode. The second radiating portion activates a third operation mode. The first to third radiators activate a fourth operation mode, a fifth operation mode, and a sixth operation mode.

Abstract: A system and method for learning naturalistic driving behavior based on vehicle dynamic data that include receiving vehicle dynamic data and image data and analyzing the vehicle dynamic data and the image data to detect a plurality of behavioral events. The system and method also include classifying at least one behavioral event as a stimulus-driven action and predicting at least one behavioral event as a goal-oriented action based on the stimulus-driven action. The system and method additionally include building a naturalistic driving behavior data set that includes annotations that are based on the at least one behavioral event that is classified as the stimulus-driven action. The system and method further include controlling a vehicle to be autonomously driven based on the naturalistic driving behavior data set.

Abstract: A system and method for learning naturalistic driving behavior based on vehicle dynamic data that include receiving vehicle dynamic data and image data and analyzing the vehicle dynamic data and the image data to detect a plurality of behavioral events. The system and method also include classifying at least one behavioral event as a stimulus-driven action and building a naturalistic driving behavior data set that includes annotations that are based on the at least one behavioral event that is classified as the stimulus-driven action. The system and method further include controlling a vehicle to be autonomously driven based on the naturalistic driving behavior data set.

Abstract: A method of forming an overlay mark structure includes the following steps. An insulation layer is formed on a substrate. A first overlay mark is formed in the insulation layer. A metal layer is formed on the substrate. The metal layer covers the insulation layer and the first overlay mark. The metal layer on the first overlay mark is removed. A top surface of the first overlay mark is lower than a top surface of the insulation layer after the step of removing the metal layer on the first overlay mark. A second overlay mark is formed on the metal layer. In the method of forming the overlay mark structure, the first overlay mark may not be covered by the metal layer for avoiding influences on related measurements, and the second overlay mark may be formed on the metal layer for avoiding related defects generated by the height difference.

Abstract: A vehicular synchronization system includes an image sensor of a vehicle. The image sensor captures image data for an image sensor field of view. The vehicular synchronization system also includes a light sensor having a radial view. The light sensor captures light data from around the vehicle in a full rotation. The vehicular synchronization system includes a synchronization control unit having a data receiving module, a trigger time determination module, a timer module, and a sensor trigger module. The data receiving module receives a data packet from the light sensor. The trigger time determination module determines a trigger time when a desired light sensor orientation overlaps with the image sensor field of view. The timer module sets a timer to elapse at the trigger time. The sensor trigger module controls the image sensor to capture image data at the trigger time when the timer elapses.

Abstract: A method of forming an overlay mark structure includes the following steps. An insulation layer is formed on a substrate. A first overlay mark is formed in the insulation layer. A metal layer is formed on the substrate. The metal layer covers the insulation layer and the first overlay mark. The metal layer on the first overlay mark is removed. A top surface of the first overlay mark is lower than a top surface of the insulation layer after the step of removing the metal layer on the first overlay mark. A second overlay mark is formed on the metal layer. In the method of forming the overlay mark structure, the first overlay mark may not be covered by the metal layer for avoiding influences on related measurements, and the second overlay mark may be formed on the metal layer for avoiding related defects generated by the height difference.