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As head of Cruise LLC, Ammann, 47, is looking to promote the idea that electric, self-driving vehicles purpose built for ride-sharing are the best cure for modern-day urban transportation woes. His old boss, GM CEO Mary Barra, has echoed those comments, but also stressed the need to make money on the company's current lineup to pay for that transformation to a more sustainable future.
https://www.sfchronicle.com/business/article/GM-s-former-president-calls-for-the-end-of-car-14901803.php

VOLKSWAGEN AG [DE]; AUDI AG [DE]; PORSCHE AG [DE] 2019-06-13
SYSTEM AND METHODOLOGIES FOR OCCUPANT MONITORING UTILIZING DIGITAL NEUROMORPHIC (NM) DATA AND FOVEA TRACKING
https://worldwide.espacenet.com/publicationDetails/biblio?CC=US&NR=2019180076A1&KC=A1&FT=D&ND=3&date=20190613&DB=&locale=en_EP#

A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking so as to detect and predict movement of a vehicle occupant.

[0011] Disclosed embodiments pertain to components, system and methodologies for using digital NM data and components and systems for vehicle occupant tracking. Disclosed embodiments may be implemented using the digital NM engine in combination with micro fovea and composite spikes to perform fovea tracking.

[0012] Disclosed embodiments may use intermediate generated digital NM data and/or generate relative velocity vector data to perform fine pattern recognition capabilities measured at close range including head tracking, eye tracking, biometrics, face detection, pupil dilation, eye gaze, color, blink and lid detection, coordinated head and eye tracking, unsupervised presponding, driver texting detection and alert, driver fatigue detection, occupant body gestures including hand gestures, seat belt gesture detection, driver emotion detection, lip reading, handwriting recognition.

[0013] In accordance with disclosed embodiments, eye tracking may be performed by tracking eye gaze using generation and analysis of three dimensional (3D) foveas. Likewise, 3D foveas may be used to detect and track eye-lid movement, determine eye color. In accordance with disclosed embodiments, eye blink and wink detection may be performed by generating and analyzing spatial temporal patterns. Such embodiments may also enable components of the system to be used to perform a methodology for detecting driver fatigue or driver emotion and generating an alert regarding such a detection.

VOLKSWAGEN GROUP OF AMERICA INC [US]; VOLKSWAGEN AG [DE]; AUDI AG [DE]; PORSCHE AG [DE] 2019-12-05

METHODOLOGIES, SYSTEMS, AND COMPONENTS FOR INCREMENTAL AND CONTINUAL LEARNING FOR SCALABLE IMPROVEMENT OF AUTONOMOUS SYSTEMS
https://worldwide.espacenet.com/publicationDetails/description?CC=WO&NR=2019232335A1&KC=A1&FT=D&ND=3&date=20191205&DB=&locale=en_EP#
Autonomous driving systems may be provided with one or more sensors configured to capture perception data, a model configured to be continually trained in the transportation vehicle and a scalable subset memory configured to store a subset of a dataset previously used to train a model. A processor may be provided for continually training the model in the transportation vehicle using captured perception data previously unseen and the subset and for generating a new subset of data to be stored so that the model avoids catastrophic forgetting.

[0015] The subset selection may be done in various ways. Formulating it as a submodular optimization function has the advantage of having a lower bound guarantee on the representativeness of the subset chosen. Along with representativeness, data points that are diverse enough may also be included. A way of ensuring diversity is by dividing the data into partitions based on some aspect (like environment type, or output range, etc.) and running the subset selection on each partition separately. Replaying such a subset along with the new data ensures that the model does not forget the teachings of the past. Such form of continual learning can be shown to work in tasks like object recognition, trajectory prediction, scene understanding etc. [0016] Currently, labeling done during data collection campaigns is done manually by a testing person sitting in the passenger seat trying to tag every situation as he sees while driving. With the disclosed subset replay based continuous learning, the disclosed embodiments provide a technical improvement in that labeling during data collection may be automated to a certain extent. By employing a model that does scenario recognition in a transportation vehicle and a computing resource, such as a processor, the model may be retrained while driving. Every time a new scenario is encountered, it may be labeled once by the human tester and provided as an input to the model, which continuously updates itself. When this type of scenario is encountered again, the model recognizes it and proposes it to the human tester. This substantially reduces the total number of manual clicks that a human tester has to do while driving.

May have posted this one previously but posted again to demonstrate how Camera based monitoring is being described as standard.

SYSTEM AND METHOD TO CONTROL A VEHICLE INTERFACE FOR HUMAN PERCEPTION OPTIMIZATION 2019-09-19
A computer-implemented method for controlling a vehicle interface system in vehicle includes receiving images of a driver from an image capture device and determining a population group based on the images. The method includes identifying a human perception condition of the driver that is characteristic of the population group. The human perception condition limits an ability of the driver to perceive a driving situation of a vehicle operation and a stimulus output that conveys information to the driver in the vehicle about the driving situation of the vehicle operation. The stimulus output is controlled by the vehicle interface system. The method includes modifying the stimulus output into an optimal stimulus output that can be perceived by the driver with the human perception condition, and controlling the vehicle interface system to provide the optimal stimulus output to the driver during the vehicle operation.

[0026] “Vehicle control system” and/or “vehicle system,” as used herein can include, but is not limited to, any automatic or manual systems that can be used to enhance the vehicle, driving, and/or safety. Exemplary vehicle systems include, but are not limited to: an electronic stability control system, an anti-lock brake system, a brake assist system, an automatic brake prefill system, a low speed follow system, a cruise control system, a collision warning system, a collision mitigation braking system, an auto cruise control system, a lane departure warning system, a blind spot indicator system, a lane keep assist system, a navigation system, a transmission system, brake pedal systems, an electronic power steering system, visual devices (e.g., camera systems, proximity sensor systems), a climate control system, an electronic pretensioning system, a monitoring system, a passenger detection system, a vehicle suspension system, a vehicle seat configuration system, a vehicle cabin lighting system, an audio system, a sensory system, an interior or exterior camera system among others.
https://worldwide.espacenet.com/publicationDetails/biblio?II=0&ND=3&adjacent=true&locale=en_EP&FT=D&date=20190919&CC=US&NR=2019283672A1&KC=A1#

Driver Monitoring/ Drowsiness Monitor as standard.

Multi-Level Hybrid Vehicle-to-Anything Communications for Cooperative Perception
The disclosure includes embodiments for providing cooperative perception among two or more connected vehicles. In some embodiments, a method includes receiving, by a vehicle-to-anything (V2X) radio of an ego vehicle, a basic cooperative perception message (CPM) broadcast by a remote vehicle. The basic CPM includes basic CPM data describing objects that a remote vehicle has perceived. The method includes determining that the remote vehicle has misperceived a select object and that the remote vehicle is on a collision course with the select object. The method includes unicasting to the remote vehicle, by the V2X radio of the ego vehicle using a different V2X protocol than was used for receiving the basic CPM, a supplemental CPM including supplemental CPM data describing the select object that the remote vehicle has misperceived so that the remote vehicle is alerted to a presence of the select object.

[0066] The set of ADAS systems 180 includes one or more of the following ADAS systems: an ACC system; an adaptive high beam system; an adaptive light control system; an automatic parking system; an automotive night vision system; a blind spot monitor; a collision avoidance system; a crosswind stabilization system; a driver drowsiness detection system; a driver monitoring system; an emergency driver assistance system; a forward collision warning system; an intersection assistance system; an intelligent speed adaption system; a lane departure warning system (also referred to as a LKA system); a pedestrian protection system; a traffic sign recognition system; a turning assistant; a wrong-way driving warning system; autopilot; sign recognition; and sign assist. Each of these example ADAS systems provide their own features and functionality that may be referred to herein as an “ADAS feature” or an “ADAS functionality,” respectively. The features and functionality provided by these example ADAS systems are also referred to herein as an “autonomous feature” or an “autonomous functionality,” respectively.
https://worldwide.espacenet.com/publicationDetails/description?CC=US&NR=2019325751A1&KC=A1&FT=D&ND=3&date=20191024&DB=&locale=en_EP#

On same theme .Closely Related

METHOD AND APPARATUS FOR ACQUIRING DEPTH INFORMATION USING CAMERAS FROM DIFFERENT VEHICLES
In various embodiments, methods, systems, and vehicles are provided for distance determinations using cameras from different vehicles. The system includes a first camera onboard a first vehicle, providing a first image, the first image having therein an object of interest (OOI) for which a distance from the first vehicle is desired; and a second camera, providing second camera data including a second image having therein the OOI. The system further includes a control module configured to process and time synchronize first camera data and second camera data to generate a distance determination for the OOI, using the first camera data, the second camera data, and a distance between the first and second camera.

[0036] In various embodiments, the cameras 118 capture images from around the vehicle 100, for example from various sides of the vehicle 100 (e.g., front side, rear side, passenger side, and driver side), and provide the images for processing, for example to assist the vehicle 100 and/or other nearby vehicles in travelling along a roadway (e.g., to avoid contact with other vehicles and/or other objects). In certain embodiments, cameras 118 may also be disposed on one or more other locations of the vehicle 100, non-limiting examples include: on a grille of a vehicle 100, on sides of a vehicle 100, in a rear-view mirror of a vehicle 100, on rear of the vehicle 100. Various combinations of these locations may be used to create a surround view for the vehicle 100. The cameras 118 may provide images for two-dimensional and/or three-dimensional viewing on the display system 108, which is disposed inside the vehicle 100 (i.e. inside the body 110 of the vehicle 100). The camera images may also be provided to other components of the vehicle control system (not shown) for use in vehicle control, and so on.

[0037] In the embodiment of FIG. 1, the cameras 118 are mounted on the vehicle 100 and located and configured to provide, with respect to the vehicle 100, a forward-looking field of view (camera 1181), a right-side field of view (camera 1183) a left-side field of view (camera 1184), and a backward-looking field of view (camera 1182). As alluded to above, in various embodiments, the number, locations, and/or placement of the cameras 1118 may vary (e.g., in certain embodiments, a single camera may be used, and so on). Regardless of the location and configuration of the one or more cameras 118, they are in operable communication with (via communications link 109), and under the control of, the control system 102, as depicted in FIG. 1.

https://worldwide.espacenet.com/publicationDetails/biblio?II=0&ND=3&adjacent=true&locale=en_EP&FT=D&date=20191128&CC=US&NR=2019361118A1&KC=A1#

GM patent that describes the Use of two or more vehicles collating camera objects data to cloud for detecting objects and other surroundings in proximity to the vehicle to obtain a global situation awareness graph for all vehicles.

METHOD AND APPARATUS FOR GENERATING SITUATION AWARENESS GRAPHS USING CAMERAS FROM DIFFERENT VEHICLES.
In various embodiments, methods, systems, and vehicles are provided that include obtaining first camera images from a first camera onboard a first vehicle; generating, via one or more computer processors, a first situation awareness graph with respect to objects near the first vehicle, using the first camera images; obtaining second camera images from a second camera of a second device that is in proximity to the first vehicle; generating, via one or more computer processors, a second situation awareness graph with the respect to the objects, using the second camera images; and generating, via one or more computer processor, a global situation awareness graph with respect to the objects, by merging the first situation awareness graph with the second situation awareness graph, using respective first and second weights for the first and second situation awareness graphs.

[0006] Also in one embodiment, the step generating the static global situation awareness graph includes generating the static global situation awareness graph using the respective first and second weights for the first and second static situation awareness graphs based on a principal components analysis of respective Mahalanobis Distances from the first and second static situation awareness graphs.

[0036] As depicted in FIG. 1, in certain embodiments, the cameras 102 include a rear vision camera that is mounted on a rear portion of the vehicle 100, a front vision camera that is mounted on a front portion of the vehicle 100, a driver side camera that is mounted on a driver side of the vehicle 100, and a passenger side camera that is mounted on a passenger side of the vehicle. In various embodiments, the cameras 102 provide images from around the vehicle, for example from various sides of the vehicle 100 (e.g., front side, rear side, passenger side, and driver side), for example to assist the vehicle 100 and/or other nearby vehicles in travelling along a roadway (e.g., to avoid contact with other vehicles and/or other objects). In certain embodiments, cameras 102 may also be disposed on one or more other locations of the vehicle 100, for example on top of the vehicle 100, for example to create a surround view and/or one or more other views for the vehicle 100. In various embodiments, the number, locations, and/or placement of the cameras 102 may vary (e.g., in certain embodiments, a single camera may be used, and so on).
https://worldwide.espacenet.com/publicationDetails/description?CC=US&NR=2019318041A1&KC=A1&FT=D&ND=3&date=20191017&DB=&locale=en_EP#

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