Honda Motor Co., Ltd.

Taiwan Semiconductor Manufacturing Comp. Ltd.

E Ink Holdings Inc.

TSMC Nanjing Company, Limited

Other

University of California

Tsinghua University

Yi-Ting Chen

Teruhisa Misu

Behzad Dariush

Athmanarayanan Lakshmi Narayanan

Nakul Agarwal

Ahmed Taha

Chengxi Li

Ming-Hsuan Yang

Qingchao Meng

Jing Ding

Zhang-Ying Yan

Larry Davis

Chih-Hua Cheng

Chien-Yi Wang

Yung-Ta Huang

Yu-Sheng Lin

Ching-Hung Kao

Yu-Lin Wang

Goksel Dedeoglu

Bernd Heisele

Ashish Tawari

John Canny

Jiawei Huang

Yi-Sin Wang

Xitong Yang

Haiming Gang

Yan Lu

Mingfei Gao

Jinkyu Kim

Shan-Yun Cheng

Jing-Jyu Chou

Shin-Yi Hsieh

Mingze Xu

Vasili Ramanishka

Yue Meng

Yuchen Xiao

Zihao Xiao

Revathi Sukesan

Wan-Ten Chen

A system and method for completing risk object identification that include receiving image data associated with a monocular image of a surrounding environment of an ego vehicle and analyzing the image data and completing semantic waypoint labeling of at least one region of the surrounding environment of the ego vehicle. The system and method also include completing counterfactual scenario augmentation with respect to the at least one region. The system and method further include determining at least one driver intention and at least one driver response associated with the at least one region.

System and method for completing risk object identification

Driver behavior risk assessment and pedestrian awareness may include an receiving an input stream of images of an environment including one or more objects within the environment, estimating an intention of an ego vehicle based on the input stream of images and a temporal recurrent network (TRN), generating a scene representation based on the input stream of images and a graph neural network (GNN), generating a prediction of a situation based on the scene representation and the intention of the ego vehicle, and generating an influenced or non-influenced action determination based on the prediction of the situation and the scene representation.

Driver behavior risk assessment and pedestrian awareness

A method includes: forming first, second, and third NWs; forming form first to fourth transistors in corresponding first to fourth groups of active regions, connecting selected transistors amongst the first and second transistors to form first and second input circuits respectively receiving a first input signal in a first domain and a second input signal in the first domain; connecting selected transistors amongst the first and third transistors and amongst the first and fourth transistors to respectively form a first single bit level shifter (SBLS) and a second SBLS; each SBLS operates in the second domain and receives correspondingly versions of the first and second input signals; and connecting selected transistors amongst the first and third transistors to form a control circuit for toggling the first and second SBLSs between a normal and a standby state, a portion of the control circuit and the first SBLS sharing the second NW.

Method of method of forming a multi-bit level shifter

A system and method for performing intersection scenario retrieval that includes receiving a video stream of a surrounding environment of an ego vehicle. The system and method also include analyzing the video stream to trim the video stream into video clips of an intersection scene associated with the travel of the ego vehicle. The system and method additionally include annotating the ego vehicle, dynamic objects, and their motion paths that are included within the intersection scene with action units that describe an intersection scenario. The system and method further include retrieving at least one intersection scenario based on a query of an electronic dataset that stores a combination of action units to operably control a presentation of at least one intersection scenario video clip that includes the at least one intersection scenario.

System for performing intersection scenario retrieval and method thereof

Systems and techniques for target object retrieval may include or utilize an image capture device, and a task planner. The image capture device may receive an image of an environment including identified objects. The task planner may determine potential actions, calculate a probability of success of achieving a desired goal for each of the potential actions based on an action prediction model, the corresponding potential action, a current state of the environment, any previously taken action, and the desired goal, select a potential action associated with the highest calculated probability of success, and simulate a subsequent state based on the selected potential action and a dynamic prediction model. The potential actions may be associated with an identified object of the identified objects and an operation to be performed on the identified object.

Target object retrieval

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.

System and method for learning naturalistic driving behavior based on vehicle dynamic data

A system and method for providing unsupervised domain adaption for spatio-temporal action localization that includes receiving video data associated with a source domain and a target domain that are associated with a surrounding environment of a vehicle. The system and method also include analyzing the video data associated with the source domain and the target domain and determining a key frame of the source domain and a key frame of the target domain. The system and method additionally include completing an action localization model to model a temporal context of actions occurring within the key frame of the source domain and the key frame of the target domain and completing an action adaption model to localize individuals and their actions and to classify the actions based on the video data. The system and method further include combining losses to complete spatio-temporal action localization of individuals and actions.

System and method for providing unsupervised domain adaptation for spatio-temporal action localization

A system and method for risk object identification via causal inference that includes receiving at least one image of a driving scene of an ego vehicle and analyzing the at least one image to detect and track dynamic objects within the driving scene of the ego vehicle. The system and method also include implementing a mask to remove each of the dynamic objects captured within the at least one image. The system and method further include analyzing a level of change associated with a driving behavior with respect to a removal of each of the dynamic objects. At least one dynamic object is identified as a risk object that has a highest level of influence with respect to the driving behavior.

System for risk object identification via causal inference and method thereof

A system and method for predicting driving actions based on intent-aware driving models that include receiving at least one image of a driving scene of an ego vehicle. The system and method also include analyzing the at least one image to detect and track dynamic objects located within the driving scene and to detect and identify driving scene characteristics associated with the driving scene and processing an ego-thing graph associated with the dynamic objects and an ego-stuff graph associated with the driving scene characteristics. The system and method further include predicting a driver stimulus action based on a fusion of representations of the ego-thing graph and the ego-stuff graph and a driver intention action based on an intention representation associated with driving intentions of a driver of the ego vehicle.

Driver-centric risk assessment: risk object identification via causal inference with intent-aware driving models

Systems and methods for driver behavior recognition is provided. In one embodiment a computer implemented method includes receiving image data associated with a general objects. The method also includes identifying a reactive object and an inert object from the general objects based on the image data. An ego reactive graph is generated for the reactive object based on a reactive feature of the reactive object and a reactive position vector. An ego inert graph is generated for the inert object based on an inert feature of the inert object and an inert distance. The method further includes performing interaction modeling based on the ego reactive graphs and the ego inert graphs to generate updated features. The method also includes performing temporal modeling on the updated features. The method further includes determining an egocentric representation of a tactical driver behavior based at least in part on the updated features.

System and method for tactical behavior recognition

A multi-bit level-shifter (MBLS) includes two or more input circuits correspondingly configured to operate in a first voltage domain. The MBLS also includes two or more single bit level shifters (SBLSs) electrically coupled correspondingly to the two or more input circuits, and correspondingly configured to operate in a second voltage domain. The MBLS also includes a control circuit configured to toggle each of the two or more SBLSs between a normal mode and a standby mode according to a toggle-control signal received from the control circuit.

Multi-bit level shifter and method of operating same

Aspects of the present disclosure may include methods, apparatuses, and computer readable media for receiving one or more images having a plurality of objects, receiving a notification from an occupant of the self-driving vehicle, generating an attention map highlighting the plurality of objects based on at least one of the one or more images and the notification, and providing at least one of a steering control or a velocity control to operate the self-driving vehicle based on the attention map and the notification.

Methods and apparatuses for operating a self-driving vehicle

A system and method for providing unsupervised domain adaption for spatio-temporal action localization that includes receiving video data associated with a surrounding environment of a vehicle. The system and method also include completing an action localization model to model a temporal context of actions occurring within the surrounding environment of the vehicle based on the video data and completing an action adaption model to localize individuals and their actions and to classify the actions based on the video data. The system and method further include combining losses from the action localization model and the action adaption model to complete spatio-temporal action localization of individuals and actions that occur within the surrounding environment of the vehicle.

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.

System and method for learning and predicting naturalistic driving behavior

A method and apparatus for minimizing silicon germanium facets in planar metal oxide semiconductor structures is disclosed. For example, a device fabricated according to the method may include a semiconductor substrate, a plurality of gate stacks formed on the substrate, a plurality of source/drain regions formed from silicon germanium, and a shallow trench isolation region positioned between two source/drain regions of the plurality of source/drain regions. Each source/drain region of the plurality of source/drain regions is positioned adjacent to at least one gate stack of the plurality of gate stacks. Moreover, the shallow trench isolation region forms a trench in the substrate without intersecting the two source/drain regions.

Minimization of silicon germanium facets in planar metal oxide semiconductor structures

Growing community of inventors

Sunnyvale, CA, United States of America

Yi-Ting Chen