Intel Corporation

Mobileye Vision Technologies Ltd.

Other

Ignacio Javier Alvarez

David Israel Gonzalez Aguirre

Javier Felip Leon

Javier Sebastian Turek

Justin E Gottschlich

Kathiravetpillai Sivanesan

Sridhar G Sharma

S M Iftekharul Alam

Satish Chandra Jha

Javier Perez-Ramirez

David Arditti Ilitzky

Cornelius Buerkle

Adam Jordan

Alexander F Heinecke

Liuyang L Yang

Paulo Lopez Meyer

Julio Cesar Zamora Esquivel

Kshitij Arun Doshi

Giuseppe Beppe Raffa

Deepak Shashidhar Vembar

Daniel Pohl

Jason Martin

Rafael Misoczki

Jose Rodrigo Camacho Perez

Hector A Cordourier Maruri

Francesc Guim Bernat

Jill MacDonald Boyce

Glen J Anderson

Rajesh Poornachandran

Markus Dominik Mueck

Leonardo Gomes Baltar

Nageen Himayat

John Charles Weast

Christian Maciocco

Sara S Baghsorkhi

Dawei Wang

Omesh Tickoo

Fei Fei Li

Jennifer Anne Healey

Yang Liu

Xiruo Liu

Marcos Emanuel Carranza

Fei Li

Willem M Beltman

Vesh Raj Sharma Banjade

Maria Soledad Elli

Luis Carlos Maria Remis

Florian Geissler

Patrick Mead

Fabian Oboril

Rafael Rosales

Ranganath Krishnan

Victor Palacios Rivera

Neslihan Kose Cihangir

Shengtian Zhou

Richard Dorrance

Deepak Dasalukunte

Carlos Montesinos

Evangelos Georganas

Michael Paulitsch

Xiaolong Liu

Xiangbin Wu

Bradley Alan Jackson

Mariano Tepper

Moreno Ambrosin

Daniel S Lake

Nilesh Amar Ahuja

Yuqing Hou

Maik Sven Fox

Vy Vo

Ravikumar Balakrishnan

Rafael De La Guardia Gonzalez

Kay-Ulrich Scholl

Paul H Dormitzer

Mahesh M Subedar

Ralf Graefe

Li Chen

Zhigang Wang

Mic Bowman

Ankur Agrawal

Yiwen Guo

Yaser M Fouad

Ibrahima J Ndiour

Mateo Guzman

Cesar Martinez-Spessot

Alejandro Ibarra Von Borstel

Mohammad Mejbah Ul Alam

Arvind Merwaday

Qianying Zhu

Todd A Anderson

Sindhu Pandian

Jesus Cruz Vargas

Dario Oliver

Joshua Ekandem

Jeffrey Ota

Frederik Pasch

Yang Peng

Avinash Kumar

Carlos Ornelas

Techniques are disclosed to detect, inform, and automatically correct typical awareness-related human driver mistakes. This may include those that are caused by a misunderstanding of the current situation, a lack of focus or attention, and/or overconfidence in any currently-engaged assistance features. The disclosure is directed to the prediction of vehicle maneuvers using driver and external environment modeling. The consequence of executing a predicted maneuver is categorized based upon its risk or danger posed to the driving environment, and the vehicle may execute various actions based upon the categorization of a predicted riving maneuver to mitigate or eliminate that risk.

Driver and environment monitoring to predict human driving maneuvers and reduce human driving errors

A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

Safety system for a vehicle

The present disclosure is related to vehicle-to-everything (V2X) and Intelligent Transport System (ITS) communications technologies, and in particular, to misbehavior detection and misbehavior reporting services for Collective Perception Messages (CPMs). The misbehavior detection mechanisms include one or more data consistency checks, including a multi-step systematic data consistency check within individual CPMs, across multiple CPMs from the same transmitter, and across multiple CPMs from different transmitters. Potential misbehaviors are reported to a misbehavior authority in one or more misbehavior reports.

Misbehavior detection using data consistency checks for collective perception messages

Maneuver Coordination Services (MCS) may be used in various implementations of vehicular networks. Emergency Group Maneuver Coordination (EGMC) may be used for detected unexpected safety-critical situations (USCS) on the road. EGMC provides cooperative maneuver coordination among a group of vehicles to ensure safer collective actions in the case of USCS. An MCS and EGMC may utilize a Maneuver Coordination Message (MCM) based on procedures for the Generation and Transmission of MCM with reduced communication overhead.

Maneuver coordination service in vehicular networks

Disclosed herein are systems and methods for peer-assisted safety models for autonomous and assisted-driving vehicles. In an embodiment, a safety-model service receives a safety-model request for a safety model from a target vehicle. The safety-model service identifies, responsive to receiving the safety-model request, one or more source vehicles as safety-model input sources. The safety-model service receives safety-model data associated with the identified one or more source vehicles. The safety-model service generates, based on the safety-model request and the received safety-model data, a target-vehicle safety model for the target vehicle. The safety-model service transmits the target-vehicle safety model to the target vehicle for use by the target vehicle.

Systems and methods for brokering peer-assisted safety models for autonomous and assisted-driving vehicles

The automated driving perception systems described herein provide technical solutions for technical problems facing navigation sensors for autonomous vehicle navigation. These systems may be used to combine inputs from multiple navigation sensors to provide a multimodal perception system. These multimodal perception systems may augment raw data within a development framework to improve performance of object detection, classification, tracking, and sensor fusion under varying external conditions, such as adverse weather and light, as well as possible sensor errors or malfunctions like miss-calibration, noise, and dirty or faulty sensors. This augmentation may include injection of noise, occlusions, and misalignments from raw sensor data, and may include ground-truth labeling to match the augmented data. This augmentation provides improved robustness of the trained perception algorithms against calibration, noise, occlusion, and faults that may exist in real-world scenarios.

Autonomous vehicle perception multimodal sensor data management

Disclosed herein are systems and methods for privacy-preserving facemask-compliance-level measurement. In an embodiment, a mask-compliance measurement system includes a processor that is configured to generate, from an image of a person, a facial depth image of a region of a face of the person, and to generate facial wavelet descriptors from the facial depth image. The processor is also configured to determine spectral-density values of the wavelet descriptors, and to analyze the spectral-density values to generate a mask-wearing-compliance result for the person. In an embodiment, the analyzing includes using a classification model that is trained to classify sets of spectral-density values with respect to facemask wearing in images from which the spectral-density values were derived.

Systems and methods for privacy-preserving facemask-compliance-level measurement

Disclosed herein is a vehicle handover system that monitors an environment of a vehicle. The vehicle handover system receives a transition request to change control of the vehicle from an automated driving mode to a passenger of the vehicle. The vehicle handover system detects a key event that may be relevant to the transition request and the detection of the key event is based on the monitored environment. The vehicle handover system may generate a handover scene that includes images associated with the key event, and the images include an image sequence over a time-period of the key event. Before the vehicle handover system changes control of the vehicle from the automated driving mode to the passenger, the handover scene is displayed to the passenger.

Handover assistant for machine to driver transitions

Devices and methods for an occupant of a vehicle are provided in this disclosure. A device for identifying an occupant of a vehicle may include a processor. The processor may be configured to determine an identity for the occupant based on a first sensor data including information indicating a detection of a facial attribute of the occupant. The processor may further be configured to estimate a behavior for the occupant based on a second sensor data including information indicating a detection of a body attribute of the occupant. The processor may further be configured to determine a spoofing attempt result indicating whether the occupant attempts a spoofing based on the determined identity and the estimated behavior.

Methods and devices for an occupant of a vehicle

Disclosed herein are systems and methods for vehicle-occupancy-based and user-preference-based smart routing, and autonomous volumetric-occupancy measurement. In an embodiment, a system is configured to receive from a user device associated with a user, a routing-options request for routing options between two locations, and to responsively identify one or more routing options between the two locations based at least in part on occupancy data for a vehicle that would be utilized for at least a portion of at least one of the identified routing options. The occupancy data is based on an output of an automated occupancy-measurement system onboard the vehicle. The system is also configured to provide the one or more identified routing options to the user device. In some embodiments, the occupancy data is obtained using volumetric-occupancy measurement. Some embodiments relate to volumetric-occupancy measurement conducted by autonomous mesh nodes.

Systems and methods for vehicle-occupancy-based and user-preference-based smart routing and autonomous volumetric-occupancy measurement

An apparatus, including: an interface operable to receive real-time information related to one or more actors in a real environment; and processing circuitry operable to: generate a digital twin of the real environment, wherein the digital twin is a virtual representation of the real environment; spawn a model that mirrors the one or more actors based on the real-time information; and generate a spatial sound signal based on the digital twin for transmission to an ego actor of the one or more actors in the real environment, wherein the generation of the spatial sound signal comprises generating the spatial sound signal to be individualized from an auditory perspective of the ego actor in the real environment to originate from a source actor of the one or more actors.

Generation of spatial sound signal from auditory perspective of individual

A system is described to derive local driving behaviors from naturalistic data, which are then incorporated as guidance into the behavioral layer of an Automated Vehicle (AV) for adaptation to local traffic. Moreover, the local driving behaviors are implemented in the AV performance validation process. The techniques facilitate scaling of this process via automatic crowdsourced behavioral data aggregation from human-driven vehicles, as well as ADAS vehicles and autonomous vehicles, and map information. The described techniques also enable the creation of a spatial-behavioral relational database that provides interfaces for efficiently querying geo-bounded local driving information, enabling customization of automated vehicle driving policies to local norms, and enabling traffic behavior analysis.

Safe and scalable model for culturally sensitive driving by automated vehicles

A robotic manipulation planning system, including at least one processor; and a non-transitory computer-readable storage medium including instructions that, when executed by the at least one processor, cause the at least one processor to: process perception data to detect known, familiar, and unknown objects to generate manipulation candidates; filter manipulation candidates against constraints to reduce the manipulation candidates; and determine quality metrics for the reduced manipulation candidates using a soft-body simulation technique.

Robotic manipulation planning based on probalistic elastoplastic deformation material point method

Systems, apparatuses and methods may provide for infrastructure node technology that conducts a mutual authentication with a vehicle and verifies, if the mutual authentication is successful, location information received from the vehicle. The infrastructure node technology may also send a token to the vehicle if the location information is verified, wherein the token includes an attestation that the vehicle was present in a location associated with the location information at a specified moment in time. Additionally, vehicle technology may conduct a mutual authentication with an infrastructure node and send, if the mutual authentication is successful, location information to the infrastructure node. The vehicle technology may also receive a token from the infrastructure node.

Automatically verifying vehicle identity and validating vehicle presence

Methods, systems, and computer programs are presented for implementing Personalized Mobility as a Service (PMaaS) to improve transportation services delivery. One storage medium includes instructions for detecting, by a mobility as a service (MaaS) system, a request for a trip from a user device of a user. The storage medium further includes instructions for mapping, using a model executing on the machine, the user to a persona from a plurality of persona models. Each persona model has one or more characteristics associated with users of the MaaS system. Further yet, the storage medium includes instructions for determining trip parameters for the trip based on the persona mapped to the user, the trip parameters defining one or more trip segments for the trip, and instructions for providing trip parameters to the user device.

Personalized mobility as a service

Growing community of inventors

Portland, OR, United States of America

Ignacio Javier Alvarez