Fujitsu Corporation

Zoox, Inc.

Cisco Technology, Inc.

Samir Parikh

Kai Zhenyu Wang

Gowtham Garimella

William Warren Walker

Andres Guillermo Morales Morales

Kenneth Michael Siebert

Benjamin Isaac Mattinson

Wei-Peng Chen

Rajiv Asati

Nikola Nedovic

Daiki Masumoto

Lei Liu

Mehdi Bahrami

Linjun Zhang

Christopher Metz

Nestor Tzartzanis

Junhee Park

Chunlei Dai

Yousef Ali Emam

Xiaobo Ren

Woodrow Zhouyuan Wang

Wing-Yue Geoffrey Louie

Greg Thompson

A machine-learned architecture for determining whether an object is relevant to a vehicle's action planning may comprise a convolutional neural network, graph neural network, and/or multi-layer perceptron that may determine a relevance score associated with an object that indicates indicating whether an object is likely to impact operation(s) of a vehicle. In some examples, the machine-learned architecture may use scene information and/or an object track to determine the relevance score.

Machine-learned model for detecting object relevance to vehicle operation planning

Techniques for determining a response of a simulated vehicle to a simulated object in a simulation are discussed herein. Log data captured by a physical vehicle in an environment can be received. Object data representing an object in the log data can be used to instantiate a simulated object in a simulation to determine a response of a simulated vehicle to the simulated object. Additionally, one or more trajectory segments in a trajectory library representing the log data can be determined and instantiated as a trajectory of the simulated object in order to increase the accuracy and realism of the simulation.

Hybrid log simulated driving

System for simulating an autonomous vehicle using a trajectory library and log data

Techniques for determining a classification probability of an object in an environment are discussed herein. Techniques may include analyzing sensor data associated with an environment from a perspective, such as a top-down perspective, using multi-channel data. From this perspective, techniques may determine channels of multi-channel input data and additional feature data. Channels corresponding to spatial features may be included in the multi-channel input data and data corresponding to non-spatial features may be included in the additional feature data. The multi-channel input data may be input to a first portion of a machine-learned (ML) model, and the additional feature data may be concatenated with intermediate output data from the first portion of the ML model, and input into a second portion of the ML model for subsequent processing and to determine the classification probabilities. Additionally, techniques may be performed on a multi-resolution voxel space representing the environment.

Multi-resolution top-down prediction

Intermediate input for machine learned model

Techniques to predict object behavior in an environment are discussed herein. For example, such techniques may include determining a trajectory of the object, determining an intent of the trajectory, and sending the trajectory and the intent to a vehicle computing system to control an autonomous vehicle. The vehicle computing system may implement a machine learned model to process data such as sensor data and map data. The machine learned model can associate different intentions of an object in an environment with different trajectories. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on object's intentions and trajectories.

Trajectories with intent

Techniques to predict object behavior in an environment are discussed herein. For example, such techniques may include inputting data into a model and receiving an output from the model representing a discretized representation. The discretized representation may be associated with a probability of an object reaching a location in the environment at a future time. A vehicle computing system may determine a trajectory and a weight associated with the trajectory using the discretized representation and the probability. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the trajectory and the weight output by the vehicle computing system.

Trajectory classification

A method may include obtaining a task-specific state of a robotic device associated with performing a task by the robotic device. The method may also include mapping the task-specific state to a general state of the robotic device, where the general state may applicable to multiple tasks and may include a variable number of elements. The method may additionally include obtaining a set of generic actions represented by a set of parameters, where the set of generic actions may be used to perform the multiple tasks. The method may also include processing the general state of the robotic device to solve for the set of parameters and to predict a next action in performing the task, where the next action may be selected from the set of generic actions, and performing the next action using the set of parameters solved for.

Robotic device task learning

A bot network system may be provided. A system may include a plurality of bot agents, wherein at least one bot agent of the plurality of bot agents is configured to receive a request from a user in natural language. The system may further include a plurality of digital resources including one or more of a software program, a service, a web service and a dataset. Each digital resource of the plurality of digital resources may be configured to communicate with a dedicated bot agent of the plurality of bot agents. Also, each bot agent may be configured to interact with its associated digital resource via an application programming interface (API) of the associated digital resource and translate between the natural language and a language of the associated digital resource.

Bot networks

A method may include obtaining first images, each image including an object, and determining a set of one or more visual cues for each. The method may include selecting a common visual cue of the one or more visual cues that is common to each set of one or more visual cues determined for each corresponding image and determining a correlation between a location of the common visual cue in each image of the first images and a location of the object in each image of the first images. The method may include obtaining a second image of an environment and identifying the common visual cue in the second image. The method may include determining a placement location for the object in the environment based on the correlation and a location of the common visual cue in the second image.

Teaching a robot to place objects in dynamic environments

A method may include obtaining current sensor information that corresponds to one or more sensors of a wheelchair and that indicates a current state of the wheelchair and its surrounding environment. The method may further include obtaining current situation information that indicates a current situation of a wheelchair and its corresponding user. In addition, the method may include determining current solution information that corresponds to a current solution to help navigate the current situation. The current solution information may be determined based on one or more of the following: the current situation information and the current sensor information. The method may additionally include implementing the current solution based on the current solution information.

Wheelchair assistance system

According to one embodiment, a current-mode driver provides pre-emphasis for a transmitter. The current-mode driver includes a filtering circuit comprising a resistor, an inductor, and a capacitor. The filtering circuit is operable to receive a data signal and produce a filtered data signal. The filtering circuit may be tuned to produce a ringing frequency with an underdamped transient decay in the filtered data signal that compensates for signal degradation caused by the optical transmitter. The current-mode driver may also include a current source coupled to the filtering circuit. The current source may be operable to generate a compensation signal based on the filtered data signal that is capable of driving the transmitter.

Current-mode driver

According to one embodiment of the present disclosure, a current-mode driver with built-in CTLE provides signal correction for a transmitter. The current-mode driver includes a digital current source operable to use a data signal to produce a main current signal. The current-mode driver also includes a filtering circuit comprising a resistor and a capacitor. The filtering circuit is operable to receive a negative data signal and produce a filtered data signal. The filtering circuit may be tuned to produce a zero at a pole frequency in the filtered data signal that compensates for signal degradation caused by the optical transmitter. The current-mode driver also includes an analog current source coupled in parallel to the digital current source, wherein the analog current source is operable to generate a subtraction current signal proportional to the filtered data signal. The subtraction current signal combined with the main current signal are operable to drive the transmitter.

Current-mode driver with built-in continuous-time linear equalization

A circuit may include a delay element, a voltage adjust line, and a controllable capacitance. The delay element may have a delay and may include an input and an output. The input may be coupled to the output. The voltage adjust line may be configured to provide an adjusting voltage to the delay element to adjust the delay of the delay element. The controllable capacitance may be coupled to the output of the delay element and may be configured such that a change of the controllable capacitance adjusts the delay of the delay element.

Oscillator with adjustable frequency

A transmitter may include a first path configured to receive a signal, to attenuate the low frequency components of the signal, and to output the low frequency component attenuated signal. The transmitter may further include a second path configured to receive the signal, to amplify the signal, and to output the amplified signal. The transmitter may also include a node coupled to the first path and the second path and configured such that the low frequency component attenuated signal and the amplified signal combine at the node.

Growing community of inventors

San Jose, CA, United States of America

Samir Parikh