Abstract: A method of controlling a vehicle is provided. The method may include steps of: acquiring occupant pulse rate information and occupant motion information for an occupant of the vehicle; classifying, using the occupant motion information, a current activity level of the occupant into one of a plurality of predetermined activity levels; determining a range of safe pulse rates for the occupant at the current activity level; determining, using the range of safe pulse rates for the occupant, if a current pulse rate of the occupant indicates a cardiac health risk to the occupant; and, responsive to a determination that the current pulse rate of the occupant indicates a cardiac health risk to the occupant, controlling the vehicle.

Abstract: An AI system is provided and includes a memory and scoring, grading and response modules. The scoring module receives data describing aspects of an environment, determines an action performed, and scores the action to provide a score. The grading module generates score groups based on the score and determines which of the score groups the score belongs. The grading module, if the score belongs to one of the score groups, stores in the memory the score, a mean and standard normal distribution of scores, and a frequency of occurrences. Otherwise the grading module (i) reconfigures the score groups and redistributes the scores, other than the score of the action, into the score groups, and (ii) generates a new score group for the score of the action. The response module, based on an output of the grading module, selects a course of action and performs a corresponding action.

Abstract: An AI system is provided and includes a long short term memory and data processing, feature selection, behavior recognition, parameter, and driver assistance modules. The data processing module, at a host vehicle: receives training data associated with features; based on the training data, determines candidate features using a synchronous sliding window; and indexes and ranks the candidate features. The feature selection module selects ones of the candidate features having higher priority than other ones of the candidate features and filters the training data of the selected features to provide filtered data. The behavior recognition module determines a behavior of a remote vehicle based on the filtered data. The parameter module determines a parameter of the remote vehicle based on the determined behavior. The long short term memory predicts a value of the parameter. The driver assistance module assists in operation of the host vehicle based on the predicted value.

Abstract: Systems and methods for community-based transportation management, transportation funding and payment, transportation routing and scheduling, and transportation forecasting and marketplace trading are provided. The system may comprise a transportation planner module, a payment module, a scheduling and routing module, a forecasting module, and/or a marketplace module, each individually or in combination comprising one or more servers, databases, and instructions, in communication with a plurality of external user devices and one or more third-party transportation systems or vendors, for the purposes of transportation management, planning, routing, scheduling, and payment. The system can allow consumers and communities to plan and request trips and provide subsidies; invite contacts; allow vendors to forecast and bid on trips; communicate routing and schedule information; and provide payments.

Abstract: The disclosed embodiments illustrate methods and systems for formulating a policy for crowdsourcing of tasks. The method includes receiving a set of incoming tasks and a range associated with a task attribute corresponding to each task in the set of incoming tasks. Thereafter, an execution of a first policy is simulated over a period of time to determine one or more first performance metrics, associated with the execution of the first policy. The first policy is based on a first value selected from the range. Further, the first value is updated to generate a second value based on the one or more first performance metrics, wherein the second value is deterministic of the policy for crowdsourcing of the set of incoming tasks over the period of time.

Abstract: Embodiments of the present disclosure set forth methods for determining a probability of retransmitting a packet in a time slot for a source node in a wireless network. Some example methods include determining whether transmission of the packet in the time slot is successful, measuring a number of time slots accumulated since a most recent successful transmission of the packet by the source node, and determining a first value of the probability based on a second value associated with the number of time slots accumulated.

Abstract: Approaches for performing simulation optimization for solving a constrained optimization problem are generally disclosed. One embodiment according to the present disclosure is to formulate a Lagrange equation having incorporated a Lagrange parameter, a first long run average function for an objective associated with the constrained optimization problem, and a second long run average function for a constraint associated with the constrained optimization problem. Then, to identify a parameter value that may lead to an extreme value for the Lagrange equation, in an iterative manner, averages of the first long run average function and the second long run average function are calculated, a gradient of the Lagrange equation is estimated, and the Lagrange parameter is updated.

Abstract: Embodiment of the disclosure set forth resource allocation in a wireless network. Some example methods include selecting a node based on probability of having available data at the node, sending an inquiry to the node, deriving a first set of average cost estimates based on a first step-size function, network information measured at the node, and a predetermined value, calculating a second set of threshold values based on a second step-size function and the first set of average cost estimates, updating the second set of values to generate a third set of threshold values based on the predetermined value, and allocating resources for the node based on the third set of threshold values.

Abstract: Embodiments of the present disclosure set forth methods for determining a probability of retransmitting a packet in a time slot for a source node in a wireless network. Some example methods include determining whether transmission of the packet in the time slot is successful, measuring a number of time slots accumulated since a most recent successful transmission of the packet by the source node, and determining a first value of the probability based on a second value associated with the number of time slots accumulated.

Abstract: Embodiment of the disclosure set forth resource allocation in a wireless network. Some example methods include selecting a node based on probability of having available data at the node, sending an inquiry to the node, deriving a first set of average cost estimates based on a first step-size function, network information measured at the node, and a predetermined value, calculating a second set of threshold values based on a second step-size function and the first set of average cost estimates, updating the second set of values to generate a third set of threshold values based on the predetermined value, and allocating resources for the node based on the third set of threshold values.

Abstract: Approaches for performing simulation optimization for solving a constrained optimization problem are generally disclosed. One embodiment according to the present disclosure is to formulate a Lagrange equation having incorporated a Lagrange parameter, a first long run average function for an objective associated with the constrained optimization problem, and a second long run average function for a constraint associated with the constrained optimization problem. Then, to identify a parameter value that may lead to an extreme value for the Lagrange equation, in an iterative manner, averages of the first long run average function and the second long run average function are calculated, a gradient of the Lagrange equation is estimated, and the Lagrange parameter is updated.