Abstract: Provided is an object detection device or the like which efficiently generates good-quality training data. This object detection device is provided with: a detection unit which uses a dictionary to detect objects from an input image; a reception unit which displays, on a display device, the input image accompanied by a display emphasizing partial areas of detected objects, and receives, from one operation of an input device, a selection of a partial area and an input of the class of the selected partial area; a generation unit which generates training data from the image of the selected partial area and the inputted class; and a learning unit which uses the training data to learn the dictionary.

Abstract: Methods, systems, and techniques for utilizing image processing systems to measure damage to vehicles include utilizing an image processing system to generate a heat map of an image of a damaged vehicle, where the heat map is indicative of a damaged area of the vehicle, and determining at least one measurement of the damaged area based on the heat map and a depth of field indicator corresponding to the image. In some embodiments, the image processing system also determines one or more types of damage of the damaged area, and/or also generates a segmentation map of the depicted vehicle and utilizes the segmentation map in conjunction with the heat map to measure damaged areas and locations thereof on the vehicle depicted within the image. In some embodiments, the techniques include determining the depth of field indicator of the image or portions thereof.

Abstract: Techniques are presented for the detection and management of collision warning (CW) events. A training dataset comprising videos of vehicle collisions and non-collisions, sensor readings, environmental conditions, and more is utilized to train a CW classification model for detecting potential collision events in vehicles. A backend CW classification model, with greater computational resources, employs a more complex neural network to review CW events received by the Behavioral Monitoring System (BMS) based on video data, achieving higher precision and reducing false positives. The CW model is installed in vehicles for real-time detection, while the backend model is deployed at the BMS. The BMS validates detected CW events, filters out false positives, and streamlines the review process for fleet administrators and customers. Additional BMS filtering operations include assessing non-proximity-related CW events and camera impairments, with the filtered CW events presented for review in the safety inbox.

Abstract: In one aspect, an agricultural method for monitoring surface conditions for an agricultural field includes receiving, with a computing system, an image of an imaged portion of an agricultural field, with the imaged portion of the agricultural field being represented by a plurality of pixels within the image. The method also includes identifying, with the computing system, at least one pixel-related parameter associated with the plurality of pixels within the image, determining, with the computing system, whether at least one image quality metric for the image is satisfied based at least in part on the at least one pixel-related parameter, and estimating, with the computing system, a surface condition associated with the agricultural field based at least in part on the image when it is determined that the at least one image quality metric is satisfied.

Abstract: Methods, systems, and computer programs are presented for the assignment of courses based on driver behavior. One method includes monitoring driver behavior and analyzing reported events to generate course recommendations. These recommendations are based on configurable rules set by a fleet manager. A safety inbox user interface (UI) allows for the review of events and the assignment of one or more courses to a driver. The method further includes recording the assigned course as pending, notifying the driver, updating the driver application to reflect the course requirement, detecting course completion by the driver, and updating the Safety Behavior Management System (BMS) with the course completion record. This systematic approach ensures that drivers are aware of and complete necessary educational courses to improve driving safety and adherence to fleet standards.

Abstract: An apparatus for controlling an in-vehicle lighting environment includes: a passenger state determination unit that determines a state of a passenger using a gaze of the passenger photographed by a camera of a vehicle; a driving state determination unit that determines a driving state of the vehicle using an acceleration value measured by an acceleration sensor of the vehicle; an external environment state determination unit that determines an external environment state of the vehicle using an external illuminance value measured by an external illuminance sensor of the vehicle; and a lighting environment control unit that controls an illuminance and a color of a first light disposed inside the vehicle based on data determined by at least one determination unit among the passenger state, driving state, and external environment state determination units.

Abstract: Embodiments are a method for controlling a lens module, an aerial vehicle, and an aircraft system, wherein the method for controlling a lens module is applied to the aerial vehicle, the aerial vehicle is provided with a tripod head to mount the lens module thereon, and the method includes: acquiring model data of the lens module if the lens module is adapted to the aerial vehicle; determining a lens model of the lens module according to the model data; identifying the lens module corresponding to the lens model to control the lens module. Following the method, the model data of the lens module is acquired, the lens module adapted to the corresponding aerial vehicle is identified, and a control program is called to control the lens module. Therefore, the aerial vehicle can identify and enable drive control over a new lens module.

Abstract: Techniques for improving operational decisions of an autonomous vehicle are discussed herein. In some cases, a system may generate reference graphs associated with a route of the autonomous vehicle. Such reference graphs can comprise precomputed feature vectors based on grid regions and/or lane segments. The feature vectors are usable to determine scene context data associated with static objects to reduce computational expenses and compute time.

Abstract: A vehicular driver monitoring system includes an event camera disposed in a cabin of a vehicle and viewing the head of a driver of the vehicle. The dynamic range of the event camera is at least 100 dB. The response time of the event camera is less than 10,000 microseconds. The event camera has a quantum efficiency of at least 15% for near-infrared light having a wavelength of 940 nm. The system, responsive to processing of data output by the event camera, monitors the driver of the vehicle. Responsive at least in part (a) to monitoring of the driver of the vehicle and (b) to vehicle kinematic data provided to the ECU, at least one selected from the group consisting of (i) a driver attentiveness alert is generated, (ii) a driver drowsiness alert is generated, (iii) steering of the vehicle is controlled and (iv) braking of the vehicle is controlled.

Abstract: Systems, methods, and computer program products of intelligent image analysis using object detection models to identify objects and locate and detect features in an image are disclosed. The systems, methods, and computer program products include automated learning to identify the location of an object to enable continuous identification and location of an object in an image during periods when the object may be difficult to recognize or during low visibility conditions.

Abstract: In a method for determining a source of danger on a roadway in a detection area in front of or behind a vehicle with the aid of a camera of the vehicle, an image of the detection area is captured with the aid of the camera, image data corresponding to the image are generated, a first image area of the image is determined with the aid of the image data using a neural network, which first image area corresponds to the roadway in the detection area, and a second image area of the image is determined with the aid of the first image area using the neural network, which second image area corresponds to the source of danger on the roadway in the detection area.

Abstract: Computing devices, such as mobile computing devices, have access to one or more image sensors that can capture images and video with multiple subjects. Some of these subjects may vary in priority for various tasks. It may be desired to increase or decrease the compression on each subject in order to more efficiently store the image data. Low-power, fast-response machine learning logic can be configured to allow for the generation of a plurality of inference data. Inference data can be associated with the type, motion and/or priority of the subjects as desired. This inference data can be utilized along with other subject data to generate one or more variable compression regions within the image data. The image data can be subsequently processed to compress different areas of the image based on a desired application. The variably compressed image can reduce file sizes and allow for more efficient storage and processing.

Abstract: A parking slot detection method and system includes receiving a plurality of images taken from a plurality of cameras mounted on a vehicle in a parking environment; generating a top view image comprising a surrounding view of the vehicle based on the plurality of images; processing the top view image using a parking line detection model that has been trained using an annotated dataset to detect parking lines for a parking slot in the parking environment, estimate a bounding box for the parking slot and identify an occupancy state of the parking slot; and converting pixel coordinate information of the bounding box to vehicle information.

Abstract: A system for capturing location-based data includes an interface and a processor. The interface is configured to receive a location-based data description for capturing the location-based data. The processor is configured to determine a location based at least in part on the location-based data description, create a location-based data identification job based at least in part on the location, cause execution of the location-based data identification job to a set of vehicle event recorder systems, wherein the location-based data identification job is executed by a vehicle event recorder system of the set of vehicle event recorder systems to acquire sensor data in response to a vehicle being able to acquire the sensor data related to the location of the location-based data identification job, receive the sensor data from the vehicle event recorder system, and determine the location-based data based at least in part on the sensor data.

Abstract: A 3D time-of-flight camera for detecting three-dimensional image data from a detection zone is provided, the 3D time-of-flight camera comprising an illumination unit for transmitting transmission light that is modulated with a first modulation frequency; an image sensor having a plurality of reception elements for generating a respective reception signal; a plurality of demodulation units for demodulating the reception signals with the first modulation frequency in order to obtain sampled values; and a control and evaluation unit that is configured to control the illumination unit and/or the demodulation units for a number of measurement repetitions, in each case with a different phase shift between the first modulation frequency for the transmission light and the first modulation frequency for the demodulation, and that is configured to determine a distance value from the sampled values obtained per light reception element by the measurement repetitions.

Abstract: A method for classifying a roadway condition on the basis of image data from a camera system includes providing image data which is configured to image at least one portion of the surroundings of the vehicle, at least part of the portion containing the roadway on which the vehicle is driving. The method includes distinguishing diffuse reflection and specular reflection of the roadway by evaluating differences in the appearances of at least one point of the roadway in at least two images. The method also includes determining whether, in at least one image, there are disturbances that have been caused by at least one wheel of a vehicle whirling up a substance covering a roadway as said wheel travels thereover. The method further includes classifying the roadway condition into one of several roadway condition classes, taking account of the results with regard to the reflection type and the disturbance intensity.

Abstract: A test setup for testing a stereo camera for a vehicle includes: a computing unit; and an autostereoscopic screen. The computing unit is configured to generate synthetic image data and to output the synthetic image data on the autostereoscopic screen in such a way that the image data is detectable by the stereo camera as a stereo image.

Abstract: Disclosed is a street light controller and concealed video surveillance system having an optical zoom lens mounted in a lamp head for securement to a modern street light having a NEMA socket. The video surveillance device is a single, compact unit consisting of a 10× zoom camera with an internal recorder, cellular modem, and a video compressor. The video compressor with a modem and recorder allows for live video from an optical zoom lens, which can be transferred across a cellular network at bitrates down to as low as 10 kbps.

Abstract: A headlight control system (10) for a motor vehicle including, a controllable headlight (24) adapted to generate variable illumination of the vehicle environment, an imaging apparatus (11) adapted to capture images (100) from a region in front of the motor vehicle, and a data processing device (14) adapted to perform image processing of images (100) captured by the imaging apparatus (11) and to vary the light characteristics of the controllable headlight (24) depending on the image processing. A machine learning model (27) is implemented in the data processing device (14) which is trained to estimate and output an output signal (29) representing a desired illumination of the vehicle environment from one or more images (28) received as input from the imaging apparatus (11).

Abstract: An information processing device includes a memory and a processor coupled to the memory and being configured to: acquire a rotation angle by which a head of a user has rotated and a maintained time during which the rotation angle has been maintained, and in a case in which the rotation angle is less than a preset angle, or in a case in which the rotation angle is equal to or greater than the preset angle and the maintained time is less than a preset time, cause a notification unit to execute notification corresponding to the rotation angle and the maintained time.

Abstract: There is provided methods, systems, and computer-readable media for determining intention of bicycles and other person-wide vehicles. A method comprises: receiving, from one or more sensors of an autonomous vehicle, sensor data relating to an external environment of the autonomous vehicle; detecting, based at least in part on the sensor data, a person-wide vehicle in the external environment proximate to the autonomous vehicle; determining, based at least in part on the sensor data and the person-wide vehicle, image data including the person-wide vehicle; inputting the image data to a machine-learned model; receiving, from the machine-learned model based on the image data, a future intention of the person-wide vehicle; and controlling the autonomous vehicle based at least in part on the future intention of the person-wide vehicle.

Abstract: A vehicular vision system includes first and second cameras disposed at a vehicle and having respective overlapping fields of view that include a road surface of a road along which the vehicle is traveling. Image data captured by the cameras is provided to an image processor and is processed to determine relative movement of a road feature present in the captured image data. The determined relative movement of the road feature relative to the vehicle in first image data captured by the first camera is compared to the determined relative movement of the road feature relative to the vehicle in second image data captured by the second camera, and at least a rotational offset of the second camera relative to the first camera is determined and the image data are remapped to at least partially accommodate misalignment of the second camera relative to the first camera.

Abstract: A method for determining a semantic free space in an environment of a vehicle comprises capturing a two dimensional visual image from the environment of the vehicle via a camera and determining a limitation of a free space within the visual image. Via a sensor, distance data of objects are captured and assigned to the visual image, and the limitation of the free space is transferred to a bird's-eye view based on the assigned distance data. For objects identified in the visual image a respective bounding box and a respective classification are determined. Objects limiting the free space are selected, and their bounding box is assigned to the limitation of the free space in the bird's-eye view. Finally, segments of the limitation of the free space are classified according to the classification of each bounding box of the selected objects.

Abstract: A radar detector (10) comprises a radar receiver (12) for receiving and characterizing the signal characteristics of a radar signal and providing one or more of radar frequency, radar intensity and radar direction to a control system (13) which comprises a neural network (42) structured in multiple layers, each layer processing signal characteristics delivered thereto to develop neural pathways associated with the distinguishing signatures of the signal characteristics provided to the neural network. The network thus distinguishes law enforcement-originated and non-law enforcement-originated radar signals in an adaptable manner that does not rely upon traditional logic programming of the detector system. Training, deployment and further learning and retraining of the neural network are described, as is the use of multiple and disparate vehicle environment and operation signals.

Abstract: Disclosed are an apparatus and method for controlling driving of a vehicle. The apparatus for controlling driving of the vehicle includes a controller that recognizes a double traffic line on a road on which the vehicle travels, generates a virtual center line between an inside line and an outside line of the double traffic line, and controls driving of the vehicle based on the virtual center line, and an output device that outputs the virtual center line.

Abstract: A vehicle control device includes: a recognition part recognizing a first road marking line on a first side of a lane traveled by a vehicle; an obtaining part obtaining information on a second road marking line on the first side of the lane from map information; and a support part that executes a support processing for preventing the vehicle from deviating from a road marking line when a probability that the vehicle deviates from the road marking line is greater than or equal to a predetermined degree. When a degree of matching between a first position of the first road marking line and a second position of the second road marking line is less than or equal to a first threshold value, the support part suppresses execution of the support processing for preventing the vehicle from deviating from a road marking line on the first side.

Abstract: A calibration device for calibrating line-of-sight detection processing based on an image of a face of a user includes an instruction unit configured to instruct the user to look at each of a plurality of positions, and an acquisition unit configured to acquire, for each of the plurality of positions, the image of the face of the user looking at each position. The instruction unit is configured to, when giving an instruction to look at a position that is not in front of the user of the plurality of positions without moving the face, give an instruction to look in front of the user each time a position at which the user is instructed to look switches from one position that is not in front of the user to another position that is not in front of the user.

Abstract: Measuring speed of a vehicle in a road environment. During calibration, multiple images are captured of a calibration vehicle traveling at a known ground speed. A calibration image feature is located in the image of the calibration vehicle. An optical flow of the calibration image feature is computed to determine a model between an image speed of the calibration image feature and the known ground speed of the calibration vehicle. During speed measurement, multiple images are captured of a target vehicle traveling along a road surface at unknown ground speed. A target image feature may be located in an image of the target vehicle. An image speed may be computed of the target image feature. The model may be applied to determine the ground speed of the target vehicle from the image speed of the target image feature.

Abstract: Techniques for detecting an obstruction associated with a lidar sensor are discussed herein. For example, a computing device can implement an obstruction detection component to detect rain, mud, dirt, dust, snow, ice, animal droppings, etc., on and/or near an outer surface of the lidar sensor. The obstruction detection component can apply one or more heuristics and/or models to the lidar data and/or compare pulse information associated with the lidar data to a threshold to determine a size, a type, or a location of an obstruction blocking a lidar beam.

Abstract: In various examples, a hazard detection system fuses outputs from multiple sensors over time to determine a probability that a stationary object or hazard exists at a location. The system may then use sensor data to calculate a detection bounding shape for detected objects and, using the bounding shape, may generate a set of particles, each including a confidence value that an object exists at a corresponding location. The system may then capture additional sensor data by one or more sensors of the ego-machine that are different from those used to capture the first sensor data. To improve the accuracy of the confidences of the particles, the system may determine a correspondence between the first sensor data and the additional sensor data (e.g., depth sensor data), which may be used to filter out a portion of the particles and improve the depth predictions corresponding to the object.

Abstract: In various examples, perception-based parking assistance systems and methods for an ego-machine are presented. Example embodiments may determine a location of a real-world parking strip relative to an ego-machine and an associated parking rule for the parking strip. A virtual parking strip and one or more virtual parking signs may be generated based at least in part on one or more detected features in an environment of the ego-machine and a tracked motion of the ego machine, and the virtual parking strip may be used to track parking strip locations and associated parking rules. The virtual parking strips and associated rules may be relied upon by an ego-machine to determine parking locations and/or to navigate into a suitable parking spot.

Abstract: An unmanned vehicle belongs to an unmanned vehicle group that includes a plurality of unmanned vehicles to control the unmanned vehicle in accordance with the condition of the entire unmanned vehicle group The unmanned device includes a reception means configured to receive a restriction condition pertaining to an amount of activity of the unmanned vehicle group; and a control means configured to control the unmanned vehicle on the basis of the restriction condition received by the reception means and the activity condition of the unmanned vehicle.

Abstract: Provided is an aircraft docking guidance system using a three-dimensional (3D) laser scanner, including a laser scanner configured to acquire data related to aircraft docking guidance and docking control; a database configured to store information related to specifications and characteristics of each aircraft type that is a target of the aircraft docking guidance and docking control using the laser scanner; a communicator configured to transmit and receive information between the laser scanner and the database; and a data analysis decision algorithm processing unit configured to determine information of an object by comparing image information acquired through the laser scanner and information stored in the database.

Abstract: Methods for performing operations for improving driver safety across a fleet of vehicles are disclosed. A plurality of safety events pertaining to a driving of a fleet of vehicles by a plurality of drivers are detected. A subset of the events is identified. The subset corresponds to one or more safety events of the plurality of safety events involving one or more vehicles of the fleet of vehicles to which drivers have not been assigned. A user interface is generated for presentation on a client device, the user interface including an interactive user interface element for accessing the subset of the events. One or more user interface elements are provided for accepting or rejecting recommendations for assignments of one of the plurality of drivers to each of the vehicles. The recommendations are generated based on an application of a machine-learned model to images of faces captured.

Abstract: A method of creating a high-definition (HD) map of a roadway includes receiving a multi-layer probability density bitmap. The multi-layer probability density bitmap represents a plurality of lane lines of the roadway sensed by a plurality of sensors of a plurality of vehicles. The multi-layer probability density bitmap includes a plurality of points. The method further includes recursively conducting a hill climbing search using the multi-layer probability density bitmap to create a plurality of lines. In addition, the method includes creating the HD map of the roadway using the plurality of lines determined by the hill climbing search.

Abstract: A camera pose information detection method, includes collecting a plurality of images by means of a multi-lens camera, and determining a road surface region image based on the plurality of images; and projecting points in the road surface region image onto a camera coordinate system, so as to obtain camera pose information.

Abstract: A method of instance segmentation in an image and a system for instance segmentation of images. The method includes identifying, with a processor, a starting pixel associated with an object in an image, the image having a plurality of rows of pixels, the starting pixel located in a row of the plurality of rows; identifying, with the processor, at least one pixel located in an adjacent row to the row in which the starting pixel is located, the at least one pixel being part of the same object as the starting pixel; iterating the previous two identification steps using the at least one identified adjacent row pixel as a start pixel for the next iteration; and connecting, with the processor, the at least one identified adjacent row pixels to form polylines representing the object.

Abstract: A driving assistance apparatus is provided in which the detection range of a left-front-corner sonar (12a) located at the vehicle's left front corner is included in the field of view of a second imaging means (14) located at the vehicle's left front corner. When the left-front-corner sonar (12a) detects a three-dimensional object at the vehicle's left front corner, an image processing means (3) synthesizes an image of the image created using a second imaging means (14) and the images created with four cameras (7a-7d) for imaging the complete periphery of the vehicle, and creates a bird's-eye-view image (40b). The detection range of the left-front-corner sonar (12a) is included within a region of the bird's-eye image (40b) on the basis of the image created with the second imaging means (14).

Abstract: The disclosure relates to airport stand arrangement (100,200,300) comprising: a display (130); a radar-based system (110R); and one or more additional systems selected from laser-based systems (110L) and imaging systems (110C), wherein said radar-based system (110R) and said one or more additional systems together form a combined system (110,210,310), wherein the airport stand arrangement (100) is configured, based on output data from said combined system (110), to detect and track (S108,S110) an aircraft (10) within a stand area (20) when said aircraft (10) is approaching a stand within the stand area (20) for parking at a parking position (160) therein, and configured, based on said detection and tracking of the approaching aircraft (10), to provide (S114,S116) pilot maneuvering guidance information on said display (130) for aiding a pilot of the approaching aircraft (10) in maneuvering the aircraft (10) towards said parking position (160).

Abstract: Provided herein is a system and method that acquires data and determines a driving action based on the data. The system comprises a sensor, one or more processors, and a memory storing instructions that, when executed by the one or more processors, causes the system to perform, determining data of interest comprising an object, feature, or region of interest, determining whether a sharpness of the data of interest exceeds a threshold, in response to determining that the sharpness does not exceed a threshold, operating the sensor to increase the sharpness of the data of interest until the sharpness exceeds the threshold, in response to the sharpness exceeding the threshold, determining a driving action of a vehicle based on the data of interest, and performing the driving action.

Abstract: A driver assistance method that is applied to a vehicle includes: reading a speed limit at a position at which the vehicle is traveling, from map information in which the speed limit is registered; displaying the speed limit read from the map information on a display device; and disabling updating of the speed limit to be displayed on the display device based on the map information in a first section in which a temporary speed limit is set, even when the speed limit registered in the map information changes.

Abstract: An apparatus for detecting a lane boundary includes one or more processors configured to: input an image representing a region around a vehicle into a classifier to identify the types of objects represented in respective pixels of the image, and detect a boundary of the travel lane by determining, for each of pixel lines in a direction crossing the travel lane in the image, whether the position corresponding to a pixel group including a predetermined number of contiguous pixels is inside the travel lane in order along a scanning direction from one end to the other end of the pixel line, depending on the order of the types of objects represented in the pixel group and the result of determination whether the position corresponding to the immediately preceding pixel group with respect to the scanning direction is inside the travel lane.

Abstract: A device for controlling a vehicle and a method for outputting platooning information are provided. The device includes a display, a communication device, and a processor connected to the display and the communication device, and the processor is configured to receive information on an image of a region ahead and platooning state information from a leading vehicle through the communication device during platooning, synchronize the information on the image of the region ahead with a vehicle location when a platooning state is a stable state based on the platooning state information, and output the synchronized information on the image of the region ahead on the display.

Abstract: A method for acquiring data, in an electronic acquisition module, detected by a sensor embedded on a vehicle with a predefined trajectory, including the following operations implemented in real time by an electronic acquisition module including a database defining, for each of a plurality of points of the predefined trajectory, a set of subsections of interest indicating at least one subsection of interest in the acquisition zone of the sensor: determining a next position of the moving vehicle on the predefined trajectory of the moving vehicle, and determining, as a function of at least the subsection of interest defined by the database for the point corresponding to the determined position, the value of a parameter included among acquisition of the data by the embedded sensor in the next position, or a processing parameter of the data captured by the embedded sensor in the next position.

Abstract: A method for unregulated pedestrian step down (PSD) alert, the method may include (i) receiving, by a vehicle computerized system, a unregulated PSD indicators that are indicative of unregulated PSD situations; (ii) obtaining sensed information regarding an environment of the vehicle; (iii) processing the sensed information, wherein the processing comprises searching for at least one of the unregulated PSD indicators; and (iv) autonomously determining, when finding the at least one of the unregulated PSD identifiers, that the vehicle is approaching a situation in which a pedestrian is expected to step down into a drivable space within the environment of the vehicle. Wherein the autonomously determining triggering a response to the finding, the response is an immediate response and is executed by at least the vehicle computerized system.

Abstract: A localization device for visually determining the location of a vehicle includes a camera unit for capturing image data and a control unit. The control unit is configured to recognize at least one landmark in the captured image data and to determine a quality value for each landmark. The determination of the quality value involves multiple recognitions of the particular landmark. In addition, multiple quality values for one landmark can be determined for different observation positions or observation directions. The control unit is further configured to adjust the quality value of a landmark already contained in a map, based on a new recognition by the localization device. The control unit is configured to determine the position of the localization device based on the recognized landmarks in conjunction with the map. The control unit is further configured to determine the position of landmarks in relation to a global coordinate system.

Abstract: A system for processing bank transactions and a method for operating the system are disclosed. The system can include a plurality of article exchanging units. Each of said plurality of article exchanging units can be configured to receive items from a bank customer and present items to the bank customer. The system can also include a communications controller configured to communicate according to the internet protocol with said plurality of article exchanging units.

Abstract: The present disclosure discloses a method for positioning an auxiliary transportation vehicle in a coal mine and a positioning system thereof. The method comprises: acquiring a rotation velocity of each wheels and a rotation angle of a steering wheel, constructing a kinematics model based on wheels not for steering of the vehicle and a kinematics model based on wheels for steering of the vehicle respectively, and constructing a kinematics model based on a geometric center of the vehicle according to the above two kinematics models; and according to a travelling condition of the vehicle, integrating the kinematics model based on the geometric center with a strap-down inertial navigation system for positioning, when the vehicle is in a normal travelling state; and integrating, the kinematics model based on the wheels not for steering with the strap-down inertial navigation system for positioning, when the vehicle is in an abnormal travelling state.

Abstract: A method includes mounting a vehicle component to a base member of a vehicle, capturing a baseline image of a fiducial marker, capturing a subsequent image of the fiducial marker, comparing the subsequent image to the baseline image, and adjusting operation of the vehicle component in response to the identification of differences between the baseline image and the subsequent image.

Abstract: A method for enabling vehicle connected services for a hearing-impaired vehicle occupant includes receiving sensor data from a plurality of sensors of a vehicle. The sensor data includes scene data indicative of a scene inside the vehicle and outside the vehicle. The method further includes determining that the hearing-impaired vehicle occupant is articulating sign language using the sensor data. Also, the method includes determining a vehicle-occupant message corresponding to the sign language articulated by the hearing-impaired vehicle occupant to generate vehicle occupant-message data in response to determining that the hearing-impaired vehicle occupant is articulating sign language. The method further includes transmitting the vehicle occupant-message data and the scene data to a remote system in response to generating the vehicle occupant-message data. Further, the method includes receiving a vehicle connected service from the remote system.