Gm Global Technolgoy Operations, Inc.

Alaa M Khamis

Gaurav Talwar

Wei Tong

Yun Qian Miao

Xu Fang Zhao

Upali Priyantha Mudalige

Jarvis Chau

Neeraj Roy Gautama

Vyacheslav Berezin

Kenneth R Booker

Roddi L MacInnes

Ali Rafiei

Arief Barkah Koesdwiady

Romeo D Garcia, Jr

Joseph F Szczerba

Donald K Grimm

Shuqing Zeng

Mohannad F Murad

Chin-Hsu Lin

Sai Vishnu Aluru

Alok Warey

Ralph D Schlottke

Todd M Tumas

Michael D Alarcon

Steffen P Lindenthal

Lei Li

Ute Winter

Hojjat Izadi

Terry W Ostan

Dhruv Patel

Sravan Daruri

Mark T Gaylord

Scott E Jackson

Amin Abdossalami

Karl Bo Albert Mikkelsen

Abbas Mohammed

Robert D Burns

Mohamed A Naiel

Ramzi Abdelmoula

Kelley J Kettenbeil

Maria Alejandra Gonzalez

Sachin Raviram

Jomo Hanley

Nathan Robinson

Ian Gibbs

Nicholis Festa

Gina E Griffith

Cristinel Padurariu

Pedro E Villanueva Pena

Carmine F D'agostino

Paul H K Hui

Mutaz Keldani

An accident severity estimation system for estimating the severity of an accident for a vehicle includes one or more microphones that capture a plurality of audio-based inputs indicative of verbal and non-verbal sounds emitted by one or more occupants of the vehicle. The accident severity estimation system also includes a vision system that captures a plurality of vision-based inputs representing image data indicative of the occupants, a motion-based input system that collects a plurality of motion-based inputs indicative of the motion of the vehicle during the accident, a thermal incident system that collects a plurality of thermal inputs indicative of thermal events within the vehicle, a propulsion system that provides a status-based input of the propulsion system of the vehicle, and one or more controllers.

Accident severity estimation system for a vehicle

A smart parking recommendation system for energy conservation in a vehicle includes a smart parking recommendation application (SPRA) that determines that enabling conditions have been met and obtains from sensors and actuators of the vehicle, information about the vehicle and the vehicle's environment. The SPRA monitors information from the sensors and actuators and obtained from remote computing resources, a vehicle operator's current vehicle usage and determines a typical parking duration for a current parking location, and categorizes the current parking location. The SPRA utilizes an artificial intelligence (AI) or machine learning (ML) model to suggest a parking spot within the parking location that satisfies user and energy consumption passenger compartment thermal quality preferences. The SPRA selectively parks or guides the vehicle to be parked within a parking spot that satisfies the constraints and preferences, and minimizes energy consumption for maintaining passenger compartment and battery thermal qualities.

Smart parking recommendation system for energy conservation

A system for determining opportunity and capability for identified adversarial behavior within an autonomous vehicle, comprising a pedestrian detection system, a vehicle controller and an adversarial intent algorithm adapted to determine a risk level of adversarial behavior from at least one pedestrian within proximity of the autonomous vehicle, and, when the adversarial intent algorithm determines that there is a risk of adversarial behavior, to determine a risk level of the adversarial behavior based on opportunity and capability.

Vulnerable road user's adversarial behavior opportunity and capability

Methods and systems are provided for a vehicle. In one embodiment, a method includes: storing, in a datastore device, intrinsic and extrinsic parameter data associated with an image sensor coupled to a trailer and configured to capture an image that includes at least a portion of the vehicle; receiving, by a processor, image data from the image sensor of the trailer; determining, by the processor, a region of interest within the image data based on a feature of the vehicle; computing, by the processor, a hitch articulation angle from the region of interest and the parameter data; and generating, by the processor, hitch articulation angle data for use in controlling at least one of the vehicle and the trailer.

Methods and systems for estimating hitch articulation angle

Methods and systems for controlling movement of e-pallets using multi-modal interaction with a user are provided that include: one or more first sensors configured to obtain first sensor data of a first modality pertaining to a command from the user for movement of the e-pallets; one or more second sensors configured to obtain second sensor data of a second modality, different from the first modality, pertaining to the command; and a processor that is coupled to the one or more first sensors and the one or more second sensors, the processor configured to at least facilitate: determining an intended movement of the e-pallets based on both the first sensor data and the second sensor data; and moving the e-pallets in executing the intended movement, in accordance with instructions provided by the processor to actuators of the e-pallets.

Fiadaptive multi-modal e-pallet control

A method of occupant profiling for seamless experience and actionable insights. The method includes receiving occupant data about an occupant of a host vehicle, generating a knowledge graph based on the occupant data using automotive ontology, querying the knowledge graph to generate an occupant profile comprising the wants and the needs of the occupant of the host vehicle, communicating the occupant profile to a target vehicle, comparing features of a target vehicle to features of the host vehicle to determine a compatible feature, and configuring the at least one compatible feature of the target vehicle based on the occupant profile. The compatible feature is a vehicle feature present in both the target vehicle and the host vehicle. The occupant data includes meta-data about the occupant of the host vehicle, customer-facing features of the host vehicle, and interaction data between the occupant and the customer-facing features of the host vehicle.

Method of occupant digital profiling for seamless experience and actionable insights

A system for responding to adversarial behavior within an autonomous vehicle includes a pedestrian detection system in communication with a vehicle controller and an adversarial intent algorithm adapted to determine a risk level of adversarial behavior from at least one pedestrian within proximity of the autonomous vehicle, the vehicle controller adapted to provide proactive and reactive actions to be performed by the autonomous vehicle in response to the adversarial behavior based on the risk level of the adversarial behavior.

Responses to vulnerable road user's adversarial behavior

Methods and systems are provided for data processing that include cameras and processors. The cameras are configured to obtain data including frames. The processors are coupled to the cameras, and are configured to at least facilitate identifying, for each frame, regions of interest for privacy protection; extracting, for each region of interest, high level properties of the region of interest for the frame; generating a replacement image for each of the regions of interest, thereby protecting privacy while maintaining the high level properties of the region of interest; and updating the frame utilizing the replacement image for each of the one or more regions of interest, generating an updated frame that protects individual privacy.

Lossless privacy preserving data anonymation for ADAS/AD model development

A system for identifying adversarial behavior directed to an autonomous vehicle includes a pedestrian detection system in communication with a vehicle controller adapted to identify a pedestrian within proximity of the autonomous vehicle, track the pedestrian, determine if the pedestrian is intending to cross in front of the autonomous vehicle, and if the pedestrian tracking system determines that the pedestrian is not intending to cross in front of the autonomous vehicle, the pedestrian tracking system is further adapted to continue tracking the pedestrian, and if the pedestrian tracking system determines that the pedestrian is intending to cross in front of the autonomous vehicle, the pedestrian tracking system is further adapted to determine, with an adversarial intent algorithm, if the pedestrian is exhibiting any adversarial behavior.

Vulnerable road user's adversarial behavior recognition

The concepts include a user support system in the form of a microphone, a visual display arranged in a vehicle cabin, and a controller. The controller includes an instruction set that is executable to receive, via the microphone, a voice-based request from a user, and employ a virtual assistant to determine a user interaction in real time and to determine a specific intent for the voice-based request, including capturing metadata. A user interaction routine determines a user interaction parameter associated with the virtual assistant based upon the voice-based request from the user. A user sentiment analysis routine determines a user satisfaction parameter associated with the virtual assistant based upon the voice-based request from the user. The controller responds in real-time to the voice-based request based upon the user satisfaction parameter and the user interaction parameter that are associated with the virtual assistant.

System and method for a voice-activated user support system

A system in a vehicle includes a personal thermal device. The personal thermal device provides heating or cooling to an individual occupant of the vehicle. The system also includes a controller implementing reinforcement learning to control the personal thermal device. The controller obtains states, from one or more sensors, indicating current conditions, to obtain a score that is determined according to the states and that represents a reward used in the reinforcement learning. The controller provides a stochastic policy indicating a probability of taking a particular action to control the personal thermal device based on the score acting as a feedback for feedback control of the personal thermal device using the reinforcement learning.

Adaptive personal thermal control in a vehicle

A method includes virtually dividing a cargo area of a vehicle into a plurality of virtual cells. The method also includes creating, by implementing machine learning, a load profile for the cargo area. The load profile indicates a weight estimate at each of the plurality of virtual cells based on a load in the cargo area. Guidance on moving the load within the cargo area is provided based on the load profile to balance the load in the cargo area.

Vehicle load weight determination and distribution guidance

A method for dynamically matching energy demand of a population of electric vehicles (EVs) with energy supply of a population of charging stations within a geofenced perimeter includes receiving, via a cloud-based server, EV information from each respective EV, and receiving charging station information from each respective charging station. The method includes generating an SoC map and a charging station power map from the EV information and the charging station information, respectively, and predicting the energy supply and demand using the maps. The server dynamically matches the EVs to at least one of the charging stations or vice versa using a reward function, the predicted energy supply, and the predicted energy demand, including generating a rank-ordered listing for each of the EVs and/or each of the charging stations in a manner that maximizes an expected discounted future reward.

Dynamic multiple bi-directional supply and demand matching for EV charging

An in-vehicle violence detection system includes a speech and non-speech audio recognition module capturing occupant threat words and non-speech audio events. In-vehicle accelerometers generate data analyzed in a shaking movement recognition module. A heart rate and breathing rate detection module measures physiological changes in occupant heart rates and breathing rhythms. An in-vehicle semantic scene recognition module analyzes occupant non-verbal interactions. Occupant threat indicators including an audible threat indicator are generated by the speech and non-speech audio event recognition module. Visual threat indicators are generated by the in-vehicle semantic scene recognition module. Physiological threat indicators are generated by the heart rate and breathing rate detection module. Vibration-based threat indicators are generated by the shaking movement recognition module. The four threat indicators are fused to estimate a threat level, consolidated by incorporating contextual information including a vehicle location and time. Different actions are taken based on the consolidated threat level.

In-vehicle multimodal violence detection for shared vehicles and self-driving shuttles

Systems and methods for measuring how level a trailer is and providing trailer levelling guidance. The systems and methods include a vehicle comprising a vehicle levelness estimator, a trailer connectable to the vehicle and a rear facing camera mounted on the vehicle. The methods and systems include capturing an image of the trailer using the rear facing camera, receiving a vehicle levelness parameter from the vehicle levelness estimator of the vehicle, determining a rotation angle of the trailer relative to the vehicle based on the image, determining an estimated trailer levelness parameter based on the rotation angle and the vehicle levelness parameter, and outputting the estimated trailer levelness parameter.

Systems and methods for estimating levelness of a trailer and providing levelling guidance

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Alaa M Khamis