Gm Global Technolgoy Operations, Inc.

Ariel Telpaz

Ron M Hecht

Gila Kamhi

Yael Shmueli Friedland

Nadav Baron

Asaf Degani

Ravid Erez

Michael Baltaxe

Zahy Bnaya

Barak Hershkovitz

Refael Blanca

Omer Tsimhoni

Claudia V Goldman-Shenhar

Neeraj Roy Gautama

Laura R Chmielewski

Donald K Grimm

Tzvi Philipp

Eli Tzirkel-Hancock

Dan Levi

Yuval Gilboa

Eilon Riess

Mario Jodorkovsky

Peggy Wang

Michael R Colville

Ronit Bustin

Daniel Urieli

Christopher A Stanek

Ido Zelman

Andrea Forgacs

Gershon Celniker

Michael Baitaxe

Boris Kabisher

Michal Genussov

Michelle L Calloway

Shani Avnet

Benjamin Perahya

Oleg Karp

Omer Zerbib

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 computer-implemented method when executed by data processing hardware causes the data processing hardware to perform operations. The operations include generating, at an occupancy estimation network, an occupancy probability at one or more voxels, predicting, via a gaze prediction model, a gaze direction of a gaze prediction, and identifying, based on the predicted gaze direction, an object of interest. The operations also include generating, via an occupancy estimation application, a gaze saliency map based on the gaze direction and identified object of interest, and updating, based on the determined gaze direction and the gaze saliency map, the occupancy probability of the one or more voxels.

Occupancy estimation system for a vehicle

A system for managing a fleet of electric vehicles and respective fleet drivers includes a command unit having a processor and tangible, non-transitory memory on which instructions are recorded. The command unit is adapted to obtain input variables, including respective fleet tasks and their priority status. Route data for the respective fleet tasks is obtained. The command unit is adapted to obtain an objective function defined by a plurality of influence factors having respective weights. The command unit is adapted to obtain optimal charging schedules respectively for the electric vehicles and match the respective fleet tasks to the electric vehicles and the respective fleet drivers, based in part on the objective function, input variables and the route data. The influence factors may include energy cost optimization, timeliness of task completion and minimizing range anxiety. In some embodiments, the respective weights of the influence factors are designated by a fleet manager.

System and method for managing fleet of electric vehicles

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 vehicle trajectory planning. The system may include a polarized camera system configured to generate polarized images of a roadway to be driven over with a vehicle, a prior controller configured to generate a prior for the roadway based at least in part on the polarized images, and a driving assistance system configured to provide a driving assistance according to the prior.

Vehicle trajectory planning

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

Presented are intelligent vehicle systems for off-road driving incident prediction and assistance, methods for making/operating such systems, and vehicles networking with such systems. A method for operating a motor vehicle includes a system controller receiving geolocation data indicating the vehicle is in or entering off-road terrain. Responsive to the vehicle geolocation data, the controller receives, from vehicle-mounted cameras, camera-generated images each containing the vehicle's drive wheel(s) and/or the off-road terrain's surface. The controller receives, from a controller area network bus, vehicle operating characteristics data and vehicle dynamics data for the motor vehicle. The camera data, vehicle operating characteristics data, and vehicle dynamics data is processed via a convolutional neural network backbone to predict occurrence of a driving incident on the off-road terrain within a prediction time horizon. The system controller commands a resident vehicle system to execute a control operation responsive to the predicted occurrence of the driving incident.

Intelligent vehicle systems and control logic for incident prediction and assistance in off-road driving situations

A computer-implemented method for scheduling pre-departure charging for electric vehicles includes predicting a user-departure time based on a first machine learning prediction model. The method further includes determining a cabin temperature to be set for the user at the user-departure time based on a second machine learning prediction model. The method further includes determining a battery-temperature to be set at the user-departure time based on a third machine learning prediction model. The method further includes determining a present charge level of a battery of the electric vehicle. The method further includes computing a charging start-time to start charging the battery based on one or more attributes of a charging station to which the electric vehicle is coupled, and based on the user-departure time, the cabin temperature, and the battery-temperature. The method further includes initiating charging the battery at the charging start-time.

Scheduling pre-departure charging of electric vehicles

An operating system for a vehicle having an electric vehicle (EV) drivetrain and a plurality of electrically-powered accessories is described. A controller determines, via a navigation system, a target off-road trail segment, and characterizes the subject vehicle, ambient conditions, and the target off-road trail segment to determine an estimated consumption of electric energy for the vehicle to operate over the target off-road trail segment. The EV drivetrain and the electrically-powered accessories are controlled during operation of the vehicle on the off-road trail segment based upon the estimated consumption of electric energy for the subject vehicle. This is done to minimize a likelihood of a low SOC event for the DC power source for the trail segment and to avoid a low battery state at a location that is distal from a charging station.

Method and system for operating an electric vehicle in off-road conditions

Methods and systems involve obtaining information from one or more sources to determine a real-time context. A method includes determining a situational awareness score indicating a level of vigilance required based on the real-time context, obtaining images of eyes of a driver to detect a gaze pattern of the driver, determining a relevant attention score indicating a level of engagement of the driver in the real-time context based on a match between the gaze pattern of the driver and the real-time context, and obtaining images of the driver to detect behavior of the driver. A driver readiness score is determined and indicates a level of readiness of the driver to resume driving the vehicle based on the behavior of the driver. An off-road glance time is obtained based on using the situational awareness score, the relevant attention score, and the driver readiness score.

Context-sensitive adjustment of off-road glance time

Systems and methods are provided for controlling a vehicle. In one embodiment, a method includes: storing, in a data storage device, a decision model, wherein the decision model predicts when to automatically perform a motivational lane change; updating, by a processor, the decision model based on data obtained in response to user input; and automatically requesting, by the processor, a lane change based on the dynamically updated model.

Methods and systems for learning user preferences for lane changes

A method for controlling a vehicle includes collecting data about human driving styles; machine learning how the human driver reacts to different traffic scenarios based on the collected data to create a plurality of human driving styles profiles; selecting an optimal driving profile of the plurality of human driving styles profiles, wherein the optimal driving profile is selected based feedback provided by a passenger of the vehicle, the feedback is indicative of a pleasantness of each of the plurality of human driving styles profiles; creating a driving plan based on the optimal driving profile; commanding the vehicle to execute the driving plan in a controlled environment to test the pleasantness of the optimal driving profile; and receiving a pleasantness rating from the passenger of the vehicle while the vehicle executes the driving plan.

Control of autonomous vehicle based on pre-learned passenger and environment aware driving style profile

Systems and methods to mitigate an effect of an errant signal on an image sensor of a vehicle involve collecting light and separating the light to obtain signals at different wavelengths. A method includes determining an intensity of the light at each of the different wavelengths, and identifying the errant signal based on the intensity of the light exceeding a threshold value at an errant signal wavelength among the different wavelengths. The errant signal is mitigated using the controller.

Mitigation of errant signal effects on an image sensor of a vehicle

Systems and method are provided for generating an integrated interface in a vehicle. In one embodiment, a method includes: receiving data associated with an environment of the vehicle; encoding the environment data into a plurality of layers; extracting data from the plurality of layers into a point cloud; projecting the point cloud onto a meta-template defining a basic element and manipulable features of the basic element; and generating display data based on the projecting.

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Givat Haim Meuhad, Israel

Ariel Telpaz