Gm Global Technolgoy Operations, Inc.

Verint Systems, Inc.

Zahy Bnaya

Ariel Telpaz

Refael Blanca

Yuval Elovici

Claudia V Goldman-Shenhar

Neeraj Roy Gautama

Asaf Degani

Yael Shmueli Friedland

Rami Puzis

Laura R Chmielewski

Lior Rokach

Nadav Baron

Roni Stern

Tal Beja

Ariel Felner

Liron Samama

Omer Tsimhoni

Gila Kamhi

Roi Reshef

Ronit Bustin

Ravid Erez

Vlad Goldner

Jigar Kapadia

Michelle L Calloway

A system and method for resource sharing among vehicle fleets using a bidding mechanism is presented. A fleet scheduler generates a bidding proposal for one or more unassigned tasks associated with a first fleet of vehicles, where the bidding proposal is associated with a second fleet of vehicles associated with an excess quantity of resources. An online platform receives the bidding proposal for the one or more unassigned tasks and determines, using the bidding proposal and based on an auctioning scheme, one or more winning bids. The one or more winning bids includes assigning the one or more unassigned tasks from the first fleet of vehicles to the second fleet of vehicles with the excess quantity of resources.

Resource sharing among vehicle fleets using bidding mechanism

A system and method for determining driver assignments based on fleet trips history logs is presented. The system and method include receiving, at a server or online platform, a set of fleet driver requirements from a fleet owner. The server receives fleet trip data from a vehicle with an integrated communication device where a weighted trip score for a driver based on a particular fleet owner is determined. The server also ranks one or more drivers, based on the weighted trip score, that best matches the fleet driver requirements. The weighted trip score is determined based on the fleet trip data received from the integrated communication device in the vehicle where the fleet trip data includes at least a start trip information, a trip log, an event trigger, and an end of trip data.

Fleet telemetry data sourcing and evaluation tool

A system for optimizing ownership cost of a fleet having electric vehicles includes a command unit adapted to selectively execute a simulation module, a sampling module and an optimization module. The command unit is configured to construct a multi-agent model based at least partially on historical fleet trip data and mobility pattern data of the fleet. Route data for a set of fleet tasks is obtained, including charging infrastructure data. The command unit is configured to simulate different configurations of the electric vehicles carrying out the set of fleet tasks over a predefined period, via the simulation module, based in part on the multi-agent model and the route data. The command unit is configured to determine an optimal configuration from the different configurations of the electric vehicles, via the optimization module. The optimal configuration minimizes investment and operational costs of the fleet.

Ownership cost optimization for fleet with electric vehicles

A system for control of an automated device includes a control system configured to operate a device during an operating mode corresponding to a first state in which the control system automatically controls operation, the operating mode prescribing that a user monitor the device operation during automated control, and a scheduling module configured to, during the operating mode, receive a request for the user to temporarily stop monitoring in order to perform a task unrelated to the device operation, allocate a time period during which automated control is maintained and the user stops monitoring, the time period including a non-monitoring period having a duration based on a minimum amount of time for the task, and put the device into a temporary state at initiation of the allocated time period during which the user stops monitoring and automated control is maintained.

Allocation of non-monitoring periods during automated control of a device

Methods and systems are provided for controlling an autonomous vehicle. In one embodiment, a method includes: identifying, by a processor, at least one constraint on a longitudinal dimension of an upcoming road; defining, by the processor, constraint activation logic based on a type of the at least one constraint; performing, by the processor, the constraint activation logic to determine a state of the constraint to be at least one of active and inactive; when the state of the constraint is active, validating, by the processor, a motion plan of the autonomous vehicle based on the constraint; and selectively controlling the autonomous vehicle based on the validating of the motion plan.

Autonomous look ahead methods and systems

A method for controlling an automated driving operation includes setting up respective independent models for executing planning and learning in the automated driving operation, including a vehicle planning model and a cognitive learning model. A semantic layer is generated to act as a bridge between the cognitive learning model and the vehicle planning model, the semantic layer including a first data adaptor and a second data adaptor. The method includes transforming real traffic data to a respective equivalent abstract representation such that it can be used by the cognitive learning model to generate a humanized reward model, via the first data adaptor. The method includes determining a trajectory plan, via the vehicle planning model, based in part on the humanized reward model. The vehicle has a controller that executes the automated driving operation based in part on the trajectory plan.

Control of vehicle automated driving operation with independent planning model and cognitive learning model

A system for facilitating transfers of control includes an input module configured to acquire a set of user preferences relating control states to contexts, the control states including at least a manual control state, and an autonomous and/or semi-autonomous control state. The system includes a processing device configured to automatically perform, during operation of a semiautonomous system, generating a transfer of control (TOC) policy based on the set of user preferences and a current context state, the TOC policy prescribing transitions between control states in response to actions. The processing device is also configured to perform, based on an action performed by the user or a control system, determining whether to perform a transition from a current control state to a second control state, and in response to the TOC policy prescribing the transition, transitioning from the current control state to the second control state.

Facilitating transfers of control between a user and a vehicle control system

Systems and methods to augment audio output in an electric vehicle (EV) include obtaining inputs from one or more sensors. The inputs include information about the EV and about one or more persons outside the EV. A current scenario is defined based on the inputs. Whether the current scenario matches a predefined scenario among a set of predefined scenarios is determined, and augmented audio output is produced according to the predefined scenario.

Augmented audio output by an electric vehicle

When using Web intelligence ('Webint') to collect information regarding a target social network user, one of the most valuable pieces of information is the target user's List-Of-Friends (LOF). In some cases, however, the LOF of the target user is not accessible in his profile. Herein are described methods and systems for identifying the LOF of a target user. An analysis system crawls the profiles of social network users, other than the target user, and reconstructs the LOF of the target user from the crawled profiles.

Growing community of inventors

Tel Aviv, Israel

Zahy Bnaya