Abstract: A device and system using same that enables a non-native device to be a self-describing module or communicate in the same protocol as the system protocol for updating data in a medical device and system describing same. In one embodiment, the translation device described herein comprises a processor that converts the non-self-describing protocol (SDP) data into an SDP communication standard and connects to a rack, medical device, a multi-function medical device, or other components within a medical system.

Abstract: Systems and methods are provided for end-to-end learning of commands for controlling an autonomous vehicle. A pre-processor pre-processes image data acquired by sensors at a current time step (CTS) to generate pre-processed image data that is concatenated with additional input(s) (e.g., a segmentation map and/or optical flow map) to generate a dynamic scene output. A convolutional neural network (CNN) processes the dynamic scene output to generate a feature map that includes extracted spatial features that are concatenated with vehicle kinematics to generate a spatial context feature vector. An LSTM network processes, during the (CTS), the spatial context feature vector at the (CTS) and one or more previous LSTM outputs at corresponding previous time steps to generate an encoded temporal context vector at the (CTS). The fully connected layer processes the encoded temporal context vector to learn control command(s) (e.g., steering angle, acceleration rate and/or a brake rate control commands).

Abstract: Systems and methods are provided that employ spatial and temporal attention-based deep reinforcement learning of hierarchical lane-change policies for controlling an autonomous vehicle. An actor-critic network architecture includes an actor network that process image data received from an environment to learn the lane-change policies as a set of hierarchical actions, and a critic network that evaluates the lane-change policies to calculate loss and gradients to predict an action-value function (Q) that is used to drive learning and update parameters of the lane-change policies. The actor-critic network architecture implements a spatial attention module to select relevant regions in the image data that are of importance, and a temporal attention module to learn temporal attention weights to be applied to past frames of image data to indicate relative importance in deciding which lane-change policy to select.

Abstract: The present application generally relates to a method and apparatus for generating an action policy for controlling an autonomous vehicle. In particular, the system performs a deep learning algorithm in order to determine the action policy and an automatically generated curriculum system to determine a number of increasingly difficult tasks in order to refine the action policy.

Abstract: Systems and methods are provided for end-to-end learning of commands for controlling an autonomous vehicle. A pre-processor pre-processes image data acquired by sensors at a current time step (CTS) to generate pre-processed image data that is concatenated with additional input(s) (e.g., a segmentation map and/or optical flow map) to generate a dynamic scene output. A convolutional neural network (CNN) processes the dynamic scene output to generate a feature map that includes extracted spatial features that are concatenated with vehicle kinematics to generate a spatial context feature vector. An LSTM network processes, during the (CTS), the spatial context feature vector at the (CTS) and one or more previous LSTM outputs at corresponding previous time steps to generate an encoded temporal context vector at the (CTS). The fully connected layer processes the encoded temporal context vector to learn control command(s) (e.g., steering angle, acceleration rate and/or a brake rate control commands).

Abstract: Systems and methods are provided that employ spatial and temporal attention-based deep reinforcement learning of hierarchical lane-change policies for controlling an autonomous vehicle. An actor-critic network architecture includes an actor network that process image data received from an environment to learn the lane-change policies as a set of hierarchical actions, and a critic network that evaluates the lane-change policies to calculate loss and gradients to predict an action-value function (Q) that is used to drive learning and update parameters of the lane-change policies. The actor-critic network architecture implements a spatial attention module to select relevant regions in the image data that are of importance, and a temporal attention module to learn temporal attention weights to be applied to past frames of image data to indicate relative importance in deciding which lane-change policy to select.

Abstract: A method in an autonomous vehicle (AV) is provided. The method includes determining, from vehicle sensor data and road geometry data, a plurality of range measurements and obstacle velocity data; determining vehicle state data wherein the vehicle state data includes a velocity of the AV, a distance to a stop line, a distance to a midpoint of an intersection, and a distance to a goal; determining, based on the plurality of range measurements, the obstacle velocity data and the vehicle state data, a set of discrete behavior actions and a unique trajectory control action associated with each discrete behavior action; choosing a discrete behavior action and a unique trajectory control action to perform; and communicating a message to vehicle controls conveying the unique trajectory control action associated with the discrete behavior action.

Abstract: The present application generally relates to a method and apparatus for generating an action policy for controlling an autonomous vehicle. In particular, the system performs a deep learning algorithm in order to determine the action policy and an automatically generated curriculum system to determine a number of increasingly difficult tasks in order to refine the action policy.

Abstract: Systems and method are provided for controlling a first vehicle. In one embodiment, a method includes: determining, by a processor, that a second vehicle is going to merge into a current lane of the first vehicle at a merge point; and in response to the determining: obtaining, by the processor, a velocity history of the second vehicle, determining, by the processor, a first time value associated with the first vehicle and the merge point; determining, by the processor, a second time value associated with the second vehicle and the merge point; determining, by the processor, an intention of the second vehicle based on the velocity history, the first time value, and the second time value; and selectively generating, by the processor a command to at least one of control the first vehicle and provide notification based on the intention.

Abstract: Systems and method are provided for controlling a first vehicle. In one embodiment, a method includes: determining, by a processor, that a second vehicle is going to merge into a current lane of the first vehicle at a merge point; and in response to the determining: obtaining, by the processor, a velocity history of the second vehicle, determining, by the processor, a first time value associated with the first vehicle and the merge point; determining, by the processor, a second time value associated with the second vehicle and the merge point; determining, by the processor, an intention of the second vehicle based on the velocity history, the first time value, and the second time value; and selectively generating, by the processor a command to at least one of control the first vehicle and provide notification based on the intention.

Abstract: A method for automated lane centering and/or lane changing purposes for a vehicle traveling on a roadway that employs roadway points from a map database to determine a reference vehicle path and sensors on the vehicle for detecting static and moving objects to adjust the reference path. The method includes reducing the curvature of the reference path to generate a reduced curvature reference path that reduces the turning requirements of the vehicle and setting the speed of the vehicle from posted roadway speeds from the map database. The method also includes providing multiple candidate vehicle paths and vehicle speeds to avoid the static and moving objects in front of the vehicle.

Abstract: A technique for determining the position of a mobile device includes receiving messages from respective mobile reference devices. Each of the messages is broadcast beginning at one of several predetermined message start opportunity (MSO) times that have known timings relative to a reference time. Each of the messages contains a MSO value identifying the MSO time at which transmission of the message started. The MSO value is used to determine the time of transmission of each of the messages received at the mobile device, and the position of the mobile device is determined via multi-lateration. According to another approach, the mobile device receives a set of ADS-B messages from a respective set of mobile reference devices. The time of transmission of each of the ADS-B messages is supplied in the ADS-B message itself or in a subsequent message and used to determine the position of the mobile device.

Abstract: A method for automated lane centering and/or lane changing purposes for a vehicle traveling on a roadway that employs roadway points from a map database to determine a reference vehicle path and sensors on the vehicle for detecting static and moving objects to adjust the reference path. The method includes reducing the curvature of the reference path to generate a reduced curvature reference path that reduces the turning requirements of the vehicle and setting the speed of the vehicle from posted roadway speeds from the map database. The method also includes providing multiple candidate vehicle paths and vehicle speeds to avoid the static and moving objects in front of the vehicle.

Abstract: A technique for determining the position of a mobile device includes receiving at the mobile device a set of time-slotted messages from a respective set of reference devices. Each of the time-slotted messages contains a slot ID indicating the assigned time slot in which it was broadcast. The slot ID is used to determine the time of transmission of each of the time-slotted messages received at the mobile device, and the position of the mobile device is determined via multi-lateration based on the time of flight of the time-slotted messages and known positions of the reference devices. According to another approach, the mobile device receives a set of ADS-B messages from a respective set of SBS ground stations. The time of transmission of each of the ADS-B messages is supplied in the ADS-B message itself or in a subsequent message and used to determined the position of the mobile device.

Abstract: A technique for determining the position of a mobile device includes receiving messages from respective mobile reference devices. Each of the messages is broadcast beginning at one of several predetermined message start opportunity (MSO) times that have known timings relative to a reference time. Each of the messages contains a MSO value identifying the MSO time at which transmission of the message started. The MSO value is used to determine the time of transmission of each of the messages received at the mobile device, and the position of the mobile device is determined via multi-lateration. According to another approach, the mobile device receives a set of ADS-B messages from a respective set of mobile reference devices. The time of transmission of each of the ADS-B messages is supplied in the ADS-B message itself or in a subsequent message and used to determine the position of the mobile device.

Abstract: A technique for determining the position of a mobile device includes receiving messages from respective mobile reference devices. Each of the messages is broadcast beginning at one of several predetermined message start opportunity (MSO) times that have known timings relative to a reference time. Each of the messages contains a MSO value identifying the MSO time at which transmission of the message started. The MSO value is used to determine the time of transmission of each of the messages received at the mobile device, and the position of the mobile device is determined via multi-lateration. According to another approach, the mobile device receives a set of ADS-B messages from a respective set of mobile reference devices. The time of transmission of each of the ADS-B messages is supplied in the ADS-B message itself or in a subsequent message and used to determine the position of the mobile device.

Abstract: An autonomous driving merge management system includes an autonomous driving control device and an intention decision management system. The management system includes a candidate strategy subsystem generating a plurality of candidate driving strategies, a merging vehicle behavior recognition subsystem predicting a merging intention of a merging vehicle; an intention-based interactive prediction subsystem predicting future merging scenarios between the host vehicle and merging vehicle as a function of inputs by the merging vehicle behavior recognition subsystem and inputs by the candidate strategy subsystem, and a cost function-based evaluation subsystem determining a cost for each future merging scenario generated by the intention-based interactive prediction subsystem. A processor selects a merge strategy of the host vehicle based on intention-based prediction results and cost function-based evaluation results.

Abstract: An autonomous driving merge management system includes an autonomous driving control device and an intention decision management system. The management system includes a candidate strategy subsystem generating a plurality of candidate driving strategies, a merging vehicle behavior recognition subsystem predicting a merging intention of a merging vehicle; an intention-based interactive prediction subsystem predicting future merging scenarios between the host vehicle and merging vehicle as a function of inputs by the merging vehicle behavior recognition subsystem and inputs by the candidate strategy subsystem, and a cost function-based evaluation subsystem determining a cost for each future merging scenario generated by the intention-based interactive prediction subsystem. A processor selects a merge strategy of the host vehicle based on intention-based prediction results and cost function-based evaluation results.

Abstract: A technique for determining the position of a mobile device includes receiving at the mobile device a set of time-slotted messages from a respective set of reference devices. Each of the time-slotted messages contains a slot ID indicating the assigned time slot in which it was broadcast. The slot ID is used to determine the time of transmission of each of the time-slotted messages received at the mobile device, and the position of the mobile device is determined via multi-lateration based on the time of flight of the time-slotted messages and known positions of the reference devices. According to another approach, the mobile device receives a set of ADS-B messages from a respective set of SBS ground stations. The time of transmission of each of the ADS-B messages is supplied in the ADS-B message itself or in a subsequent message and used to determined the position of the mobile device.

Abstract: A technique for determining the position of a mobile device includes receiving messages from respective mobile reference devices. Each of the messages is broadcast beginning at one of several predetermined message start opportunity (MSO) times that have known timings relative to a reference time. Each of the messages contains a MSO value identifying the MSO time at which transmission of the message started. The MSO value is used to determine the time of transmission of each of the messages received at the mobile device, and the position of the mobile device is determined via multi-lateration. According to another approach, the mobile device receives a set of ADS-B messages from a respective set of mobile reference devices. The time of transmission of each of the ADS-B messages is supplied in the ADS-B message itself or in a subsequent message and used to determine the position of the mobile device.