Gm Global Technolgoy Operations, Inc.

Ron M Hecht

Ariel Telpaz

Omer Tsimhoni

Timothy Grost

Robert D Sims, Iii

Ke Liu

Ute Winter

Yael Shmueli Friedland

Michael Baltaxe

Asaf Degani

Yi G Glaser

Andrea Forgacs

Gaurav Talwar

Gila Kamhi

Dan Levi

Xufang Zhao

Peggy Wang

Jingyan Wan

Gershon Celniker

Tzvi Philipp

Eli Tzirkel-Hancock

Nadav Lavi

Claudia V Goldman-Shenhar

Jeremy A Salinger

Eilon Riess

Shaul Oron

Noa Garnett

Nadav Baron

Daniel S Glaser

Ronit Bustin

Ravid Erez

Daniel Urieli

Aharon Bar Hillel

Barak Hershkovitz

Wenjing Yang

Eric Chen

Refael Blanca

Ohad Rahamim

A method of training a disparity estimation network. The method includes obtaining an eye-gaze dataset having first images with at least one gaze direction associated with each of the first images. A gaze prediction neural network is trained based on the eye-gaze dataset to develop a model trained to provide a gaze prediction for an external image. A depth database is obtained that includes second images having depth information associated with each of the second images. A disparity estimation neural network for object detection is trained based on an output from the gaze prediction neural network and an output from the depth database.

System and method for eye-gaze direction-based pre-training of neural networks

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 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 and method for limited rate context-based eye gaze encoding includes generating, based on an outward looking camera situated in a vehicle, a first video stream of a surrounding environment to a controller. The controller generates a scene description based on the first video stream and selects a corresponding scene from a predefined list of known scenes. Further, the controller, based on the selected corresponding scene, selects a codebook of encoding and decoding parameters from a plurality of predefined codebooks. Based on an inward looking camera situated in the vehicle, a second video stream of a face of a driver to an eye tracker controller is generated, where the eye tracker controller estimates a gaze direction of the driver. Encoded data comprising the estimated gaze direction of the driver is transmitted over a bandwidth limited channel to a decoder, based on the selected codebook, the encoded data.

Limited rate context based eye gaze encoding

A method of retrieving a lost item from a compartment to an owner of the lost item is provided. The method comprises detecting the lost item in the compartment to define an item image of the lost item relative to the compartment, updating an inventory list of lost objects, and identifying at least one potential user of the lost item. The method further comprises providing notification to the at least one potential user and providing viewable access of the item image. The method further comprises verifying ownership of the lost item to define the owner of the lost item, determining a preferred pickup location for the owner, storing the lost item at the preferred location defining a stored item, and confirming identity of the owner for retrieval of the stored item at the preferred location. The method further comprises allowing access to the stored item from the preferred location.

System and method of retrieving a lost item

A method of managing operator take-over of autonomous vehicle. The method includes gathering information on an external surrounding of the autonomous vehicle; analyzing the gathered information on the external surrounding of the autonomous vehicle to determine an upcoming traffic pattern; gathering information on an operator of the autonomous vehicle; analyzing the gathered information on the operator of the autonomous vehicle to determine an operator behavior; predicting an operator action based on the determined upcoming traffic pattern and the determined operator behavior; and initiating a predetermined vehicle response based on the predicted operator action. The predicting the operator action includes comparing the determined upcoming traffic pattern with a similar historic traffic pattern and retrieving a historical operator action in response to the similar historical pattern.

System and method of managing a driver take-over from an autonomous vehicle based on monitored driver behavior

A method of notifying an owner of a lost item in a vehicle having a vehicle compartment is provided. The method comprises providing at least one sensor to sense the lost item in the vehicle compartment. The method further comprises sensing the lost item on a point relative to a spherical coordinate system to define a sensed item data and determining a position of the lost item in cartesian coordinates based on the sensed item data to define a position data. The method further comprises translating the sensed item data and the position data for visualization of the lost item to define an item image of the lost item relative to the vehicle compartment, and updating an inventory list of lost objects to include the lost item with the item image. The method further comprises identifying the owner of the lost item by way of a perception algorithm, providing notification to the owner of the lost item, and providing viewable access of the item image such that the image is viewed by the owner of the lost item.

System and method of notifying an owner of a lost item in a vehicle

A method of monitoring a driver in an autonomous vehicle includes monitoring, with a driver monitoring system, a driver of a vehicle, collecting, with a data processor, data from the driver monitoring system related to gaze behavior of the driver, classifying, with the data processor, the driver as one of a plurality of driver statuses based on the data from the driver monitoring system, and sending, with the data processor, instructions to at least one vehicle system based on the classification of the driver.

Predicting driver status using glance behavior

A system and method for error estimation in an eye gaze tracking system in a vehicle may include an operator monitoring system providing measured eye gaze information corresponding to an object outside the vehicle and an external object monitoring system providing theoretical eye gaze information and an error in the measured eye gaze information based upon the measured eye gaze information and the theoretical eye gaze information.

Eye gaze tracking calibration

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

An interactive driving system and method that improves a driving experience by allowing a passenger to interactively monitor and evaluate certain driving maneuvers in real-time with an augmented reality interface. The input provided by the passenger via the augmented reality interface is recorded for later processing and analysis. For instance, the passenger input may be used to generate driving reports for later review by the driver and passenger (e.g., when the passenger is teaching a new driver how to drive) or for the manufacturer of autonomous or semi-autonomous vehicles (e.g., when the manufacturer is seeking actionable feedback for improving various self-driving algorithms). In addition, the interactive driving system and method entertain the passenger with the augmented reality interface, which can be delivered in a game-like fashion via a computer program product installed on their own personal electronic device (e.g., a tablet, phone, laptop, etc.).

Interactive driving system and method

Examples of techniques for vehicle communication between a first vehicle and a second vehicle are disclosed. An example method includes displaying, on an external vehicle display of the first vehicle, vehicle data. The method further includes capturing, by a camera of the second vehicle, an image of the external vehicle display. The method further includes processing, by a processing device associated with the second vehicle, the image to extract the vehicle data about the first vehicle. The method further includes determining, by the processing device, a position of the first vehicle relative to a position of the second vehicle based at least in part on the vehicle data. The method further includes controlling, by the processing device associated with the second vehicle, the second vehicle based at least in part on the position of the first vehicle relative to the position of the second vehicle.

Using an external vehicle display for vehicle communication

Methods and systems are provided for a speech system of a vehicle. In particular, a method is taught for associating a speech utterance with a voice command in response to a failed voice control attempt followed by a successfully voice control attempt.

System and method for understanding standard language and dialects

A system and method of performing automatic speech recognition (ASR) includes: receiving speech at a vehicle microphone; communicating the received speech to an ASR system; measuring an amount of time that elapses while speech is received; selecting a point-of-interest (POI) context or an address context based on the measured amount of received time; and processing the received speech using a POI context-based grammar when a POI context is selected or an address-based grammar when an address context is selected.

Growing community of inventors

Raanana, Israel

Ron M Hecht