Abstract: A device for non-invasive monitoring and measuring of intraocular pressure (IOP) of a subject includes a flexible lens that fits on the eye and changes curvature in response to a change in curvature of the eye. A microchannel disposed in or on the lens has one or more ends that are open to the atmosphere and an indicator solution is disposed in a portion of the 5 microchannel. The microchannel exhibits a change in volume in response to a change in curvature of the lens, which results in a change in position of the indicator solution in the microchannel. The change in position of the indicator solution in the microchannel is indicative of a change in IOP. The change in position of the indicator solution may be detected in digital images of the lens taken with a camera of a mobile electronic device such 0 as a smartphone or a camera worn by the subject.

Abstract: A system for transportation includes a vehicle configured to have a rider located therein or thereon, and an expert system to produce a recommendation for a configuration of the vehicle, wherein the recommendation includes at least one recommended parameter of configuration for the expert system that controls a parameter selected from the group consisting of a vehicle parameter, a rider experience parameter, and combinations thereof.

Abstract: Apparatuses, systems, and methods are provided for the utilization of vehicle control systems to cause a vehicle to take preventative action responsive to the detection of a near short term adverse driving scenario. A vehicle control system may receive information corresponding to vehicle operation data and ancillary data. Based on the received vehicle operation data and the received ancillary data, a multi-dimension risk score module may calculate risk scores associated with the received vehicle operation data and the received ancillary data. Subsequently, the vehicle control systems may cause the vehicle to perform at least one of a close call detection action and a close call detection alert to lessen the risk associated with the received vehicle operation data and the received ancillary data.

Abstract: A safe driving determination apparatus includes an angle value calculation part that calculates an angle value indicating a face direction angle of a driver with respect to the traveling direction, and a determination part that determines whether or not the driver is in a state of being inattentive to the road ahead on the basis of whether or not an integrated value of angle values during a past predetermined first determination period is equal to or greater than a threshold value, wherein the determination part does not determine that the driver is in a state of being inattentive to the road ahead if the angle value of the driver is less than a second threshold value at the present moment, even if an integrated value of angle values during a past predetermined first determination period is equal to or greater than a first threshold value.

Abstract: A system for operating a vehicle based on a state of a rider includes an artificial intelligence system, a vehicle control system, and a feedback loop. The artificial intelligence system processes a sensory input from a wearable device in a vehicle to determine a state of a rider and optimizes an operating parameter of the vehicle to improve the state of the rider. The artificial intelligence system includes a neural net with a perceptron to mimic human senses to facilitate determining a state of a rider based on an extent to which at least one of the senses of the rider is stimulated. The vehicle control system adjusts vehicle operating parameters and the feedback loop indicates the change in the state of the rider, where the vehicle control system adjusts at least one of the plurality of vehicle operating parameters responsive to the indication of the change.

Abstract: In an image processing apparatus, a foreground extraction unit extracts each foreground from input images, and generates a foreground extraction result. A movement trajectory feature extraction unit tracks each foreground based on the foreground extraction result, and extracts a movement trajectory feature of the foreground. The area variation feature extraction unit extracts an area variation feature representing a temporal area variation of each foreground. A foreground center estimation unit estimates a center of each foreground using the movement trajectory feature and the area variation feature.

Abstract: An apparatus for detecting a hand region includes a processor configured to: calculate a confidence score indicating a probability that a hand is represented for each pixel of a target image, determine, for each of predetermined points on an edge of the image, a probability that a hand extends outside the image at the predetermined point, set a lower hand region detection threshold at a predetermined point having a higher probability that a hand extends outside the image, set a hand region detection threshold of each pixel of the image at a value calculated by averaging hand region detection thresholds of the predetermined points respectively weighted by the distances from the pixel to the predetermined points, and detect a set of pixels in which the confidence score of each pixel is higher than the hand region detection threshold set for the pixel, as a hand region representing a hand.

Abstract: A driver monitor includes a processor configured to: detect the posture of a driver of a vehicle from an image of the interior of the vehicle generated by a camera provided on the vehicle, determine that the driver's condition is abnormal, when the detected posture satisfies an abnormality determining condition, detect an unusual sound made by the driver of the vehicle, based on a voice signal of the interior of the vehicle obtained by a microphone provided on the vehicle, and make the abnormality determining condition for the case where the unusual sound is detected less strict than an abnormality determining condition for the case where the unusual sound is not detected.

Abstract: An information processing apparatus that controls scenery display according to a current position of a mobile body is provided. The information processing apparatus includes a detection unit that detects a region where a predetermined object outside the mobile body appears within a window of the mobile body, a display unit that displays a transmissive region and a non-transmissive region within the window, and a control unit that controls the display unit based on the detected region. The detection unit detects an appearance place where the object appears in the window, based on a current position of the mobile body, a moving speed and a traveling direction of the mobile body, and position information associated with the object. Thereafter, a transmissive region is displayed in the region where the object appears, or a non-transmissive region that masks a region where the object is absent is displayed in the window.

Abstract: An image processing method includes acquiring an image frame; tracking a face region of a user based on first prior information obtained from at least one previous frame of the image frame; based on a determination that tracking of the face region based on the first prior information has failed, setting a scan region in the image frame based on second prior information obtained from the at least one previous frame; and detecting the face region in the image frame based on the scan region.

Abstract: Systems, methods, and computer-readable media are disclosed for determining obstacle data based on sensor data such as greyscale data and depth data. Based on the obstacle data navigation data may be determined and used by an autonomous vehicle to navigate an environment such as a warehouse or storage facility. The obstacle data may be determined by determining three-dimensional representations of the greyscale data and the depth data and segmented and combining or fusing the three-dimensional representations of the greyscale data and the depth data. The system used to determine the obstacle data may be trained to avoid false obstructions and omitted obstructions.

Abstract: A seat belt sensor assembly comprises a force sensor coupled to a seat belt webbing. The seat belt sensor assembly utilizes force readings from the force sensor indicating a force level above or below a predetermined threshold to determine if an action needs to be taken to control a vehicle system. Such vehicle systems could include, but are not limited to, an autonomous driving control system, an occupant health system, and a motorized seat belt retractor system. Controlling the vehicle systems could include, but is not limited to, sending audio, visual, and/or haptic warnings to vehicle occupants, including, in one embodiment, that the occupant's respiration rate is indicative of a dangerous health condition. The seat belt sensor assembly may be used in combination with a camera-based occupant monitoring system. In one embodiment, the occupant monitoring system can be used to selectively activate/deactivate discrete activation zones of the force sensor.

Abstract: A vehicular driver monitoring system includes a driver monitoring camera disposed at an interior rearview mirror assembly of a vehicle. The driver monitoring camera views a driver of the vehicle when the driver is driving the vehicle. The driver monitoring camera captures image data. An image processor is operable to process image data captured by the driver monitoring camera. The vehicular driver monitoring system determines driver performance based at least in part on (i) image processing by the image processor of image data captured by the driver monitoring camera, (ii) a vehicle characteristic and (iii) a condition exterior of the equipped vehicle. The vehicle characteristic includes at least one selected from the group consisting of (a) vehicle speed, (b) vehicle acceleration and (c) vehicle orientation.

Abstract: A method for determining driving parameters includes: acquiring driving data of a driver; extracting driving features of the driver based on the driving data, where the driving features include first operation frequency of a first component of a vehicle and second operation frequency of a second component of the vehicle; determining, based on the driving features, a first probability that the driver has a first driving style and a second probability that the driver has a second driving style; and determining the driving parameters based on the first probability and the second probability, where the driving parameters include at least longitudinal acceleration and longitudinal deceleration. The method can dynamically adjust automated driving parameters to meet the driving style of the driver, thereby effectively improving user experience.

Abstract: A system for man-machine interaction for a vehicle is disclosed. The proposed system includes an interactive reflected display on a windscreen of the vehicle; an eye tracking module including an eye gaze tracker for tracking eye gaze direction of a user, and a training unit to train the tracking module using a neural network to predict a real-time set of coordinates for eye gaze direction of the user based on a created reference data set of coordinates of the display; a finger tracking module to detect presence of a finger of the user and track movement of the finger; a cursor module configured to, on the basis of a received input, move a cursor on the display to an area of interest on the display; and a wireless switch module operatively coupled to the display for selecting a target in the area of interest on the display.

Abstract: A machine is configured to identify a media file that, when played to a user, is likely to modify an emotional or physical state of the user to or towards a target emotional or physical state. The machine accesses play counts that quantify playbacks of media files for the user. The playbacks may be locally performed or detected by the machine from ambient sound. The machine accesses arousal scores of the media files and determines a distribution of the play counts over the arousal scores. The machine uses one or more relative maxima in the distribution in selecting a target arousal score for the user based on contextual data that describes an activity of the user. The machine selects one or more media files based on the target arousal score. The machine may then cause the selected media file to be played to the user.

Abstract: A system for determining a blood alcohol level of a subject may include a single light-emitting diode (LED) or, in some embodiments, two LEDs that may be operated in a pulsed mode and may illuminate a target location on a subject (e.g., a portion of a distal phalanx of a finger) with multiple illuminated light pulses. The system may include a photosensor that may sense multiple light pulses received from (e.g., reflected from, or transmitted through) the target location and generate multiple output signals. The system may include a controller that may determine a blood alcohol level of the subject based on the multiple output signals. Utilization of a single LED/two LEDs and operation thereof in a pulsed mode may prevent (or significantly reduce) heating of the LED(s) and/or the photosensor, thus eliminating a need in cooling units.

Abstract: Embodiments provide probation officers with a recidivism probability score that is to be considered in conjunction with, or as a component of, compliance-based removal decisions. The recidivism score is computed based on a weighted combination of evidence-based factors that have been shown, through machine learning and other statistical analytics, to have a strong correlation with long-term recidivism rates. Examples of such evidence-based factors include the offender's Projected Date of IID Removal, Date of IID Install, Date of last Calibration, Violation Hour, Low BAC Non-Violation, BAC Violations, IID Violations, IID Service Lockout, IID Early Morning Violations, Violation Lockouts, and Circumvention Attempts Skipped Tests.

Abstract: A smart tolling system employs computer vision and machine learning techniques to automatically track and monitor vehicles across multiple video streams captured by a networked camera assembly. The system derives comprehensive vehicle trajectories by stitching together detections of each vehicle from the various video feeds using multi-object tracking algorithms. These trajectories enable correlating different events triggered by the vehicles at multiple roadside devices like cameras and radars. By mapping the trajectories in space and time, the system can associate seemingly fragmented events and synthesize complete vehicle profiles even when certain events may have missed capturing some vehicle information due to obstructions or other factors. This trajectory-based event correlation enhances tolling accuracy by minimizing revenue leakage from missed or incomplete vehicle data in congested traffic conditions.

Abstract: Provided are a control system and method using an in-vehicle gesture input, and more particularly, a system for receiving an occupant's gesture and controlling the execution of vehicle functions. The control system using an in-vehicle gesture input includes an input unit configured to receive a user's gesture, a memory configured to store a control program using an in-vehicle gesture input therein, and a processor configured to execute the control program. The processor transmits a command for executing a function corresponding to a gesture according to a usage pattern.

Abstract: A system and method for classifying a type of interaction between a human user and a mobile communication device within a defined volume, based on multiple sensors. The method may include: determining a position of the mobile communication device relative to a frame of reference of the defined volume, based on: angle of arrival, time of flight, or received intensity of radio frequency (RF) signals transmitted by the mobile communication device and received by a phone location unit located within the defined volume configured to wirelessly communicate with the mobile communication device; obtaining at least one sensor measurement related to the mobile communication device from various non-RF sensors; repeating the obtaining, to yield a time series of sensor readings; and using a computer processor to classify the type of interaction into one of many predefined types of interactions, based on the position and the time series of sensor readings.

Abstract: A vehicle control apparatus includes a circuitry capable of controlling a vehicle. The circuitry is configured to estimate a traffic risk score, a travel risk score, and a driving ability score. The circuitry is configured to change a first threshold, with which automated vehicle attitude stability control is initiated, so as to promptly execute the automated vehicle attitude stability control when a travel risk is not avoided by the driving ability, change a second threshold, with which automatic entry avoidance control is initiated, so as to promptly execute the automatic entry avoidance control when the traffic risk is not avoided by the driving ability, and control the vehicle to travel on a target travel route when the traffic risk and the travel risk are not avoided by the driving ability or when the driving ability is lower than a specified level.

Abstract: A device, a computer implemented method, and a computer program product, the device including a capture device configured for capturing an image, and a processor configured to: receiving at least a part of an image captured by the capture device, analyzing the at least part of the image to determine a looking direction of a subject at an object, based on the looking direction, determining an engagement level of a subject with the object, and outputting the engagement level or whether the engagement level is in compliance with a required engagement level, while disabling transmission of information that enables reconstruction of the at least part of the image.

Abstract: This document describes methods and systems for vehicle localization based on radar detections. Radar localization starts with building a radar reference map. The radar reference map may be generated and updated using different techniques as described herein. Once a radar reference map is available, real-time localization may be achieved with inexpensive radar sensors and navigation systems. Using the techniques described in this document, the data from the radar sensors and the navigation systems may be processed to identify stationary localization objects, or landmarks, in the vicinity of the vehicle. Comparing the landmark data originating from the onboard sensors and systems of the vehicle with landmark data detailed in the radar reference map may generate an accurate pose of the vehicle in its environment. By using inexpensive radar systems and lower quality navigation systems, a highly accurate vehicle pose may be obtained in a cost-effective manner.

Abstract: An adjustment device is configured to collect estimation results of the estimated state of the occupant; determine whether or not it is necessary to check the state of the occupant on the basis of the estimation results; determine a behavior of the occupant on the basis of behavior determination information and set the state of the occupant when it is determined that it is necessary to check the state of the occupant; and output adjustment information for causing an occupant state estimating device to adjust a state estimation condition used for estimating the state of the occupant so that the estimation result of the state of the occupant becomes the set state of the occupant.

Abstract: A vehicle device may execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. The vehicle device may further pass the input to a backend server for further analysis and the backend server can detect safety events based on the input. The vehicle device may analyze the output of the vehicle device and the output of the backend server to determine whether the output of the vehicle device is correct. If the output of the vehicle device is incorrect, the vehicle device can adjust how the vehicle device identifies safety events.

Abstract: A system includes an inertial navigation system module (INS module) that detects vehicle yaw rates and vehicle lateral speeds, a controller circuit communicatively coupled with the INS module. The controller circuit determines a tire cornering stiffness (Cf, Cr) based on vehicle physical parameters and vehicle dynamic parameters. The controller circuit determines a vehicle moment of inertia (Iz) based on the vehicle physical parameters, the vehicle dynamic parameters, and the tire cornering stiffness (Cf, Cr).

Abstract: An in-flight risk management system includes an eye sensor, a seat sensor, and one or more in-flight risk management computers that perform operations including: obtaining history information for a flight crew member, obtaining flight information associated with a flight on which the flight crew member will be present, determining, based on the history information and the flight information, a predicted fatigue profile for the flight crew member, determining, based on the predicted fatigue profile for the crew member, one or more fatigue indicator profiles for the flight crew member, obtaining, from at least one of the eye sensor or seat sensor, one or more measured fatigue indicator values associated with the flight crew member, generating, based on the one or more measured fatigue indicator values, one or more updated fatigue indicator profiles, and generating, based on the one or more updated fatigue indicator profiles, an updated predicted fatigue profile.

Abstract: The invention is a vehicular safety system that can detect the presence or absence of a child and a driver. Where it detects a child present/driver absent status, it concludes a child is unattended and initiates an escalating series of alarms.

Abstract: A display controller converts a detection target region contained within a detection range of an obstruction sensor group and extending from a vehicle to the periphery thereof into a virtual marker image and causes a virtual image to be displayed by superimposing the image over a scene in front of the vehicle. When the relative position of an obstruction is within the detection target region, the display controller causes an arc-shaped ripple spreading in a direction based on the relative position of the obstruction to be displayed in a region, of the virtual marker image, located in a direction based on the relative position of the obstruction. The display controller converts the important information into a notification image and causes the notification image to be displayed in a display medium. The display controller changes the display format of at least one of the virtual marker image or the notification image.

Abstract: A server includes memory and a processor that obtains a log created by a first node monitored on a V2X network, selects predetermined information therefrom, and performs supervised machine learning using the selected predetermined information as training data to create a model for a monitoring node that monitors the first node. The log associatively includes a time, and at the time: first node status information as indicated by at least one of sensor or control data on the first node; first anomaly information indicating whether an anomaly occurred on the first node; and second anomaly information indicating whether an anomaly occurred on a second, surrounding node of the first node that communicates with the first node. When the first anomaly information indicates an anomaly, or when the first and second anomaly information indicate no anomaly and an anomaly, respectively, the associated status information is selected as the predetermined information.

Abstract: The safety process covering a vehicle having driving apparatus and a station designed for a person and a person fitted with a medical device, in which: the medical device is designed to work in response to the physiological data of the person with a device and trigger an alarm signal in case of malfunctioning of the medical device per se and alarm signal for any actual or inferred problem regarding the medical condition of the person fitted with a device.

Abstract: Systems, methods, models, and training data for models are discussed, for determining vehicle positioning, and in particular identifying tailgating. Simulated training images showing vehicles following other vehicles, under various conditions, are generated using a virtual environment. Models are trained to determine following distance between two vehicles. Trained models are used in detection of tailgating, based on determined distance between two vehicles. Results of tailgating are output to warn a driver, or to provide a report on driver behavior. Following distance over time is determined, and simplified following distance data is generated for use at a management device.

Abstract: An approach for assisting users with disabilities in an emergency situation relating to a vehicle is disclosed. The approach determines the profile of the passenger in a vehicle by noting preferences and disabilities associated with the passenger. After a vehicle accident, the approach determines the condition of the vehicle and the condition of the passenger. Based on various information received, the approach creates an action list of solutions for the passenger, wherein the action list has assigned dynamic risk scores. The approach determines the best solution based on the risk scores and selects the best solution from the action list.

Abstract: A system and method of deploying a portable monitoring device. The method comprises inferring a rate of motion associated with the portable monitoring device; initiating, at a display screen of the portable monitoring device when the rate of motion is above a predetermined threshold, an image capture request; determining, responsive to the image capture request, a violation; and reporting the violation to a remote server communicatively coupled to the portable monitoring device.

Abstract: This application relates to a vehicle and method for generating a map corresponding to a three-dimensional space. In one aspect, the vehicle includes a sensor unit configured to sense a three-dimensional space by using at least one sensor and output spatial information in a plurality of frames and a memory storing computer executable instructions. The vehicle may also include a processor configured to execute the computer-executable instructions to predict a position of an object corresponding to a current frame by using an object movement model based on the plurality of frames, remove the object from the current frame based on the predicted position of the object, update a map database, which includes a predetermined number of previous frames, with the current frame from which the object is removed, and generate a map corresponding to the three-dimensional space based on the updated map database.

Abstract: A monitoring device has a processor configured to detect surrounding objects of specified types present within a predetermined region around a vehicle, decide a level of monitoring of surroundings by a driver, set an inattention threshold to a first value as a reference for the level of monitoring of the surroundings by the driver when the number of surrounding objects is less than an object number threshold, and set the inattention threshold to a second value that is smaller than the first value when the number of surrounding objects is equal to or greater than the object number threshold, determine whether or not the level of monitoring of the surroundings has exceeded the inattention threshold, and give the driver a warning to direct the driver's attention to the vehicle surroundings when the level of monitoring of the surroundings has exceeded the inattention threshold.

Abstract: The systems and methods disclosed herein provide a risk prediction system that uses trained machine learning models to make predictions that a VRU will take a particular action. The system first receives, in a video stream, an image depicting a VRU operating a micro-mobility vehicle and extract the depictions from the image. The extraction process may be determined by bounding box classifiers trained to identify various VRUs and micro-mobility vehicles. The system feeds the extracted depictions to machine learning models and receives, as an output, risk profiles for the VRU and the micro-mobility vehicle. The risk profile may include data associated with the VRU/micro-mobility vehicle determined based on classifications of the VRU and the micro-mobility vehicles. The system may then generate a prediction that the VRU operating the micro-mobility vehicle will take a particular action based on the risk profile.

Abstract: A method for improving a state of a rider through optimization of operation of a vehicle includes capturing vehicle operation-related data with at least one Internet-of-things device, and analyzing the captured data with a first neural network that determines a state of the vehicle based at least in part on a portion of the captured vehicle operation-related data. The method further includes receiving data descriptive of a state of a rider occupying the operating vehicle, and using a neural network to determine at least one vehicle operating parameter that affects a state of a rider occupying the operating vehicle. The method still further includes using an artificial intelligence-based system to optimize the at least one vehicle operating parameter so that a result of the optimizing includes an improvement in the state of the rider.

Abstract: A system is disclosed herein, including a cushion including a plurality of tactors and a plurality of air bladders disposed adjacent the plurality of tactors, a pump assembly operably connected to the plurality of bladders, a processor, and a memory operatively coupled to the processor. The memory includes instructions stored thereon that, when executed by the processor, cause the processor to determine an inflation sequence for the plurality of air bladders, determine a deflation sequence for the plurality of air bladders, output commands to the pump assembly corresponding to the inflation sequence and the deflation sequence, and output vibrate commands to the plurality of tactors.

Abstract: A rider state modification system for improving a state of a rider in a vehicle includes a first neural network that operates to classify a state of the vehicle through analysis of information about the vehicle captured by an Internet-of-things device during operation of the vehicle. The rider state modification system further includes a second neural network that operates to optimize at least one operating parameter of the vehicle based on the classified state of the vehicle, information about a state of a rider occupying the vehicle, and information that correlates vehicle operation with an effect on rider state.

Abstract: A method of determining a fused sensor measurement is disclosed including: obtaining sensor measurements from sensors detecting a same type of physiological measurement; determining a signal quality index (SQI) of each sensor including determining an extent to which a sensor measurement differs from others among the sensor measurements obtained from each sensor; determining a weightage of each sensor based on the SQI of each sensor; and determining a fused sensor measurement from the plurality of sensors based on the weightage of each sensor and filtered sensor measurements of each sensor obtained from a Kalman filter operation. A vehicle safety system includes: a vehicle electronic control unit configured to: determine the sensor measurement extent, to determine the SQI of each sensor, determine the weightage of each sensor, determine the fused sensor measurement, determine the occupant's physiological condition, and if the physiological condition is abnormal, perform at least one vehicle operation.

Abstract: Systems and methods are disclosed for detecting both the occurrence and type of driver distraction experienced by a driver, determined through evaluation of sensed vehicle conditions or activities, such as steering, braking, lane changing, etc. as detected by one or more sensors on or associated with the vehicle. That detection of the occurrence and type of driver distraction may then be used to initiate another action, including initiating an audible or visual alert to the driver, taking measures to interfere with or stop operation of the device that is causing the distraction, log the occurrence and type of distraction that occurred, and report the occurrence and type of distraction to an outside monitoring computer (such as one associated with a guardian of the driver, an insurer of the vehicle, a law enforcement authority, or the like).

Abstract: The presently disclosed embodiments may include a non-transitory computer readable medium containing instruction that when executed by at least one processor cause the at least one processor to perform thresholding operations for interpretation of facial skin micromovements. The operations may include detecting facial micromovements in an absence of perceptible vocalization associated with the facial micromovements, determining an intensity level of the facial micromovements and comparing the determined intensity level with a threshold. When the intensity level is above the threshold, the operations may include interpreting the facial micromovements and when the intensity level falls beneath the threshold, the operations may include disregarding the facial micromovements.

Abstract: A method is provided to monitor a resident of a dwelling comprising: using a sensor located at the dwelling to sense for resident activity at a location at the dwelling; using a machine learning trained model, trained based at least in part upon resident activity at the location at the dwelling sensed by the sensor to learn an anticipated time of resident activity at the location of the dwelling, to identify the anticipated time of occurrence of resident activity at the location at the dwelling; determining whether the sensor information indicates an occurrence of the anticipated resident activity; and sending an alert indicating a failed anticipated activity event, on a condition that the sensor data indicates none occurrence of the anticipated resident activity.

Abstract: A vehicular driver monitoring system includes a camera disposed at an interior rearview mirror assembly within a cabin of a vehicle and viewing a driver operating of the vehicle. Image data captured by the camera is transferred to and is processed at an electronic control unit (ECU). The vehicular driver monitoring system, via processing at the ECU of image data captured by the camera, determines posture of the driver operating the vehicle. The vehicular driver monitoring system, based at least in part on a comparison between the determined posture of the driver and a proper posture stored in memory, determines improper posture of the driver. The proper posture stored in memory is determined based at least in part on a determined head position of the driver relative to the interior rearview mirror assembly of the vehicle.

Abstract: An occupant detection system and method may include a user interface and at least one sensor to determine if there is an occupant in the vehicle. In one embodiment, the system and method use capacitive sensing sensors embedded into a cushion and/or back of seats in a vehicle. The capacitive sensor sends a sense signal to a controller and the controller would recognize various conditions such as an empty seat or a seat occupied by a person or animal. It could also determine if there was a child seat in a position on a vehicle seat and if there is a child in the child seat.

Abstract: A system and method for detecting a perceived level of driver discomfort in an automated vehicle that include receiving image data associated with a driving scene of an ego vehicle, dynamic data associated with an operation of the ego vehicle, and driver data associated with a driver of the ego vehicle during autonomous operation of the ego vehicle. The system and method also include analyzing the image data, the dynamic data, and the driver data and extracting features associated with a plurality of modalities. The system and method additionally include analyzing the extracted features and detecting the perceived level of driver discomfort. The system and method further include analyzing the perceived level of driver discomfort and detecting a probable driver takeover intent of the driver of the ego vehicle to takeover manual operation of the ego vehicle.

Abstract: A camera wing system, a vehicle comprising such camera wing system, and a method to operate such a camera wing system are provided, comprising a camera to record a field of view of the camera in a scenery at least around a rear part of the vehicle, where a presented view is provided to a driver of the vehicle as a part of the field of view, wherein the camera wing system is adapted to automatically panning the presented view of the camera to another presented view different from the previous presented view in order to keep a point of interest of the rear part of the vehicle within the presented view provided to the driver regardless of a driving situation of the vehicle.

Abstract: This disclosure relates to a system and method for determining responsiveness of a driver of a vehicle to feedback regarding driving behaviors. The system may include a sensor configured to generate output signals conveying first driving behavior information, which may characterize operation of the vehicle by the driver. The system may include one or more processors configured to obtain the first driving behavior information. The one or more processors may effectuate provision of feedback defined by feedback information based on the first driving behavior. The sensor may be configured to output signals conveying second driving behavior information, which may characterize operation of the vehicle by the driver during and/or subsequent to the provision of the feedback. The one or more processors may be configured to obtain the second driving behavior information and assess responsiveness of the driver to the feedback based on the second driving behavior information.