Abstract: Navigation based on internal state inference and interactivity estimation may include training a policy for autonomous navigation by extracting spatio-temporal features from one or more historical observations of one or more agents within a simulation environment including an ego-agent, analyzing the spatio-temporal features to infer one or more internal states of one or more of the agents, predicting one or more future behaviors for one or more of the one or more of the agents in a first scenario including an existence of the ego-agent within the simulation environment and in a second scenario excluding the existence of the ego-agent within the simulation environment, and calculating one or more interactivity scores for one or more of the agents based on a difference between the first scenario and the second scenario. The trained policy may be implemented to control an autonomous vehicle.

Abstract: Disclosed are a homogeneous silicon carbide material and an electrode plate and a battery that include the homogeneous silicon carbide material. A negative electrode plate in the battery of the present disclosure includes a homogeneous silicon carbide material. The homogeneous silicon carbide material includes an element Si and an element C. The homogeneous silicon carbide material is a homogeneous material and has a bi-continuous phase structure. The homogeneous silicon carbide material in the present disclosure has better cycling performance and higher specific capacity.

Abstract: A computer system obtains the image including one or more text areas, and generates a sequence of feature maps from the image based on a downsampling rate. Each feature map has a first dimension and a second dimension, and the feature maps include a first feature map and a second feature map. Each of the first and second dimensions of the first feature map has a respective size that is reduced to that of a respective dimension of the second feature map by the downsampling rate. The second feature map is upsampled by an upsampling rate using a local context-aware upsampling network. The upsampled second feature map is aggregated with the first feature map to generate an aggregated first feature map. The one or more text areas are identified in the image based on the aggregated first feature map.

Abstract: Systems and methods for path planning with latent state inference and spatial-temporal relationships are provided. A system includes an inference module, a policy module, a graphical representation module, and a planning module. The inference module receives sensor data associated with a plurality of agents. The inference module also maps the sensor data to a latent state distribution to identify latent states of the plurality of agents. The latent states identify agents of the plurality of agents as cooperative or aggressive. The policy module predicts future trajectories of the plurality of agents at a given time based on sensor data and the latent states of the plurality of agents. The graphical representation module generates a graphical representation based on the sensor data and a graphical representation neural network. The planning module generates a motion plan for the ego agent based on the predicted future trajectories and the graphical representation.

Abstract: Systems and methods for path planning with latent state inference and spatial-temporal relationships are provided. In one embodiment, a system includes an inference module, a policy module, a graphical representation module, and a planning module. The inference module receives sensor data associated with a plurality of agents. The inference module maps the sensor data to a latent state distribution to identify latent states of the plurality of agents. The latent states identify agents as cooperative or aggressive. The policy module predicts future trajectories of the plurality of agents at a given time based on sensor data and the latent states of the plurality of agents. The graphical representation module generates a graphical representation based on the sensor data and a graphical representation neural network. The planning module generates a motion plan for the ego agent based on the predicted future trajectories and the graphical representation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for agent behavior prediction using keypoint data. One of the methods includes obtaining data characterizing a scene in an environment, the data comprising: (i) context data comprising data characterizing historical trajectories of a plurality of agents up to the current time point; and (ii) keypoint data for a target agent; processing the context data using a context data encoder neural network to generate a context embedding for the target agent; processing the keypoint data using a keypoint encoder neural network to generate a keypoint embedding for the target agent; generating a combined embedding for the target agent from the context embedding and the keypoint embedding; and processing the combined embedding using a decoder neural network to generate a behavior prediction output for the target agent that characterizes predicted behavior of the target agent after the current time point.

Abstract: Aspects related to accuracy prior and diversity prior based future prediction may include a diversity prior, a concatenator, a decoder, and a processor. The diversity prior may receive a feature extracted history portion of a time series of information and generate a diversity latent representation. The concatenator may concatenate the diversity latent representation and the feature extracted history portion to generate a second decoder input. The decoder may receive a first decoder input and a second decoder input, generate a first output based on the first decoder input, and generate a second output based on the diversity decoder input. The processor may generate an accuracy prior and diversity prior based future prediction based on the first output and the second output. The diversity prior may be trained during a training stage utilizing an accuracy prior distinct from the diversity prior.

Abstract: Systems and methods are provided for implementing hybrid prediction. Hybrid prediction integrates two deep learning based trajectory prediction approaches: grid-based approaches and graph-based approaches. Hybrid prediction techniques can achieve enhanced performance by combining the grid and graph approaches in a manner that incorporates appropriate inductive biases for different elements of a high-dimensional space. A hybrid prediction framework processor can generate trajectory predictions relating to movement of agents in a surrounding environment based on a prediction model generating using hybrid prediction. Trajectory predictions output from the hybrid prediction framework processor can be used to control an autonomous vehicle. For example, the autonomous vehicle can perform safety-aware and autonomous operations to avoid oncoming objects, based on the trajectory predictions.

Abstract: Object identification may be provided herein. A feature extractor may extract a first set of visual features, extract a second set of visual features, concatenate the first set of visual features, the second set of visual features, and a set of bounding box information, determine a number of object features and a global feature for a scene, and receive ego-vehicle feature information associated with an ego-vehicle. An object classifier may receive the number of object features, the global feature, and the ego-vehicle feature information, generate relational features with respect to relationships between each of the number of objects from the scene, and classify each of the number of objects from the scene based on the number of object features, the relational features, the global feature, the ego-vehicle feature information, and an intention of the ego-vehicle.

Abstract: This application relates to a systems and methods for trending issue identification in text streams. In one embodiment, a method for improving resolution of a trending issue identified in a set of text streams includes presenting a user interface of an application that is being executed by a computing device. The method also includes receiving a notification including the trending issue that has been identified in the set of text streams based at least in part on textual analysis performed on the set of text streams, and presenting the trending issue on the user interface of the application to enable an action to be performed to resolve the trending issue.

Abstract: A system and method for providing long term and key intentions for trajectory prediction that include receiving image data and LiDAR data associated with RGB images and LiDAR point clouds that are associated with a surrounding environment of an ego agent and processing a long term and key intentions for trajectory prediction dataset (LOKI dataset) that is utilized to complete joint trajectory and intention prediction for heterogeneous traffic agents. The system and method also include encoding a past observation history of each of the heterogeneous traffic agents and sampling a respective goal. The system and method further include decoding and predicting future trajectories associated with each of the heterogeneous traffic agents based on data included within the LOKI dataset, the encoded past observation history, and the respective goal.

Abstract: A system and method for completing continual multi-agent trajectory forecasting with a graph-based conditional generative memory system that include receiving data associated with a surrounding location of an ego agent and inputting the data associated with the surrounding location of the ego agent to at least one episodic memory buffer and processing scene graphs associated with the surrounding location of the ego agent that are associated with the plurality of time steps. The system and method additionally include aggregating the data associated with the surrounding location of the ego agent associated with the plurality of time steps into mixed data and training a generative memory and a predictor with the mixed data. The system and method further include predicting future trajectories associated with traffic agents that are located within the surrounding location of the ego agent based on the training of the generative memory and the predictor.

Abstract: Aspects of the disclosure are directed to a training method and apparatus of an adversarial attack model, a generating method and apparatus of an adversarial image, an electronic device, and a storage medium. The adversarial attack model can include a generator network, and the training method can include using the generator network to generate an adversarial attack image based on a training digital image, and performing an adversarial attack on a target model based on the adversarial attack image, to obtain an adversarial attack result. The training method can further include obtaining a physical image corresponding to the training digital image, and training the generator network based on the training digital image, the adversarial attack image, the adversarial attack result, and the physical image.

Abstract: Systems and methods for reinforced hybrid attention for motion forecasting are provided. According to one embodiment, a system for reinforced hybrid attention for motion forecasting is provided. The system includes a sensor module, a hard attention module, a soft attention module, and a motion module. The sensor module receives patio-temporal historical observations associated at least one element in an environment. The hard attention module selects information from the spatio-temporal historical observations associated with the at least one element based on a reinforcement learning model. The soft attention generates ranked information by applying attention weights to the selected information. The motion module generates motion predictions based on the ranked information.

Abstract: Systems and methods for path planning with latent state inference and spatial-temporal relationships are provided. In one embodiment, a system includes an inference module, a policy module, a graphical representation module, and a planning module. The inference module receives sensor data associated with a plurality of agents. The inference module also maps the sensor data to a latent state distribution to identify latent states of the plurality of agents. The latent states identify agents of the plurality of agents as cooperative or aggressive. The policy module predicts future trajectories of the plurality of agents at a given time based on sensor data and the latent states of the plurality of agents. The graphical representation module generates a graphical representation based on the sensor data and a graphical representation neural network. The planning module generates a motion plan for the ego agent based on the predicted future trajectories and the graphical representation.

Abstract: Systems and methods are provided for implementing hybrid prediction. Hybrid prediction integrates two deep learning based trajectory prediction approaches: grid-based approaches and graph-based approaches. Hybrid prediction techniques can achieve enhanced performance by combining the grid and graph approaches in a manner that incorporates appropriate inductive biases for different elements of a high-dimensional space. A hybrid prediction framework processor can generate trajectory predictions relating to movement of agents in a surrounding environment based on a prediction model generating using hybrid prediction. Trajectory predictions output from the hybrid prediction framework processor can be used to control an autonomous vehicle. For example, the autonomous vehicle can perform safety-aware and autonomous operations to avoid oncoming objects, based on the trajectory predictions.

Abstract: Systems and methods for utilizing interactive Gaussian processes for crowd navigation are provided. In one embodiment, a system for a crowd navigation of a host is provided. The system includes a processor, a statistical module, and a model module. The encoder receives the sensor data and context information. The encoder also extracts interaction patterns from observed trajectories from the sensor data and context information. The encoder further generates a static latent interaction graph for a first time step based on the interaction patterns. The recurrent generates a distribution of time dependent static latent interaction graphs iteratively from the first time step for a series of time steps based on the static latent interaction graph. The series of time steps are separated by a re-encoding gap. The decoder generates multi-modal distribution of future states based on the distribution of time dependent static latent interaction graphs.

Abstract: This application relates to a systems and methods for trending issue identification in text streams. In one embodiment, a method for improving resolution of a trending issue identified in a set of text streams includes presenting a user interface of an application that is being executed by a computing device. The method also includes receiving a notification including the trending issue that has been identified in the set of text streams based at least in part on textual analysis performed on the set of text streams, and presenting the trending issue on the user interface of the application to enable an action to be performed to resolve the trending issue.

Abstract: Embodiments of the present disclosure include a mobility management entity (MME) receives a downlink data notification (DDN) message sent by a serving gateway (SGW), where the DDN message carries an identity of user equipment, and the DDN message is used to instruct the MME to send a paging message to a base station in a tracking area list (TA list) of the user equipment. After enabling an aggregate paging function, the MME determines, according to the DDN message, whether a priority of the user equipment is higher than a preset level. If the priority of the user equipment is higher than the preset level, the MME directly sends the paging message to the base station. The present disclosure is applicable to a paging message sending process.

Abstract: Embodiments of the present disclosure include a mobility management entity (MME) receives a downlink data notification (DDN) message sent by a serving gateway (SGW), where the DDN message carries an identity of user equipment, and the DDN message is used to instruct the MME to send a paging message to a base station in a tracking area list (TA list) of the user equipment. After enabling an aggregate paging function, the MME determines, according to the DDN message, whether a priority of the user equipment is higher than a preset level. If the priority of the user equipment is higher than the preset level, the MME directly sends the paging message to the base station. The present disclosure is applicable to a paging message sending process.