Abstract: A computer-implemented method for aligning a sensor to a vehicle includes receiving a first frame of measurement from the sensor which includes a first point cloud. One or more clusters Ci representing one or more objects or the ground are segmented. A first set of feature vectors fi is computed for each cluster Ci. Based on the first set of feature vectors fi a second set of feature vectors fi? is predicted respectively using an initial transformation. A third set of feature vectors fj is computed for a second frame with a second point cloud with clusters Cj. A pair of matching clusters is identified from Ci and Cj. A feature distance between the matching clusters is computed. An alignment transformation is computed by updating the initial transformation based on the feature distance. The method further includes aligning the sensor and the vehicle based on the alignment transformation.

Abstract: Disclosed are a method, apparatus and system for exporting and recharging a digital currency, which mainly relate to the technical field of computers. The method includes: receiving a generation request of a digital currency sent by a digital currency terminal, wherein the generation request indicates a first denomination of the requested digital currency and user information corresponding to the digital currency terminal (S101); determining, according to the user information, an available currency corresponding to the user information and a second denomination of the available currency (S102); generating, according to the available currency and the second denomination of the available currency, the digital currency corresponding to the user information and the first denomination, and recording a status of the digital currency as an export status (S103), and notifying the digital currency terminal of the digital currency in the export status (S104).

Abstract: A computer-implemented method for aligning a sensor to reference coordinate system includes initiating a plurality of threads, each thread executes simultaneously and independent of each other. A first thread parses data received from the sensor and stores the parsed data in a data buffer. A second thread computes an alignment transformation using the parsed data to determine alignment between the sensor and the reference coordinate system. The computing includes checking that the data buffer contains at least predetermined amount of data. If at least the predetermined amount of data exists, an intermediate result is computed using the parsed data in the data buffer; otherwise, the second thread waits for the first thread to add more data to the data buffer. The second thread outputs the intermediate result into the data buffer. A third thread outputs the alignment transformation, in response to completion of alignment computations.

Abstract: A vehicle includes a system performing a method of operating a camera for the vehicle. The camera is configured to capture an image. The processor is configured to query the camera to obtain an intrinsic parameter of the camera, estimate a unified parameter based on the intrinsic parameter of the camera, and perform at least one application on the image using the unified parameter.

Abstract: An example method includes receiving a wakeup request from a device from a vehicle. The wakeup request indicates that the device desires to perform a task that consumes electrical power from a battery of the vehicle. The method further includes assigning a priority to the wakeup request. The method further includes queuing the wakeup request according to a wakeup schedule. The method further includes, responsive to a current time satisfying the wakeup schedule, performing the task based at least in part on the priority.

Abstract: A vehicle, arc fault protection device of the vehicle and a method for mitigating an arc fault in an electrical system of the vehicle. The arc fault protection device includes a sensor, a switch, and a processor. The sensor is used for measuring a current in an electrical system. The processor is configured to determine a precursor phase of an arc fault from the current, wherein the precursor phase indicates an onset of an arc flash phase of the arc fault. The processor opens the switch to mitigate the arc fault during the arc flash phase.

Abstract: A system includes an assessment module and a training module. The assessment module is configured to receive event data about an event associated with a subsystem of a vehicle. The assessment module is configured to determine deviations between reference data for the subsystem indicating normal operation of the subsystem and portions of the event data that precede and follow the event. The assessment module is configured to determine whether the event data indicates a fault associated with the subsystem by comparing the deviations to a threshold deviation. The training module is configured to update a model trained to identify faults in vehicles to identify the event as a fault associated with the subsystem of the vehicle based on the event data in response to the deviations indicating a fault associated with the subsystem.

Abstract: A method is provided that includes determining whether to perform a load shed operation for a vehicle. The method further includes responsive to determining to perform the load shed operation, issuing a command to a device to perform the load shed operation. The method further includes determining whether the device performed the load shed operation. The method further includes responsive to determining that the device failed to perform the load shed operation, opening a relay associated with the device to prevent power from being delivered to the device.

Abstract: A low voltage system of a vehicle having a battery, one of a generator and an accessory power module (APM), one or more low voltage load, and a smart energy center. The smart energy center having a first electronic fuse configured to selectively connect the smart energy center to the battery, a second electronic fuse configured to selectively connect the smart energy center to the one of the generator and the APM, and a third electronic fuse configured to selectively connect the smart energy center to the one or more low voltage load. The smart energy center further comprising a plurality of sensors and a controller configured to selectively activate and deactivate the first electronic fuse, the second electronic fuse, and the third electronic fuse based at least in part on data received from the plurality of sensors.

Abstract: A system for a vehicle includes a plurality of sensors onboard the vehicle and a controller. A first sensor of the plurality of sensors is configured to detect lane markings on a roadway. The controller is configured to store data from the plurality of sensors. In response to receiving an indication indicating a misdetection of lane markings on the roadway based on data received from the first sensor, the controller is configured to execute in parallel a plurality of procedures configured to detect a plurality of causes for the misdetection of lane markings, respectively, based on the stored data; isolate one of the causes as a root cause for the misdetection of lane markings; and provide a response for mitigating the misdetection of lane markings on the roadway based on the root cause for the misdetection of lane markings.

Abstract: A vehicle, and a system and method of navigating the vehicle. The system includes a camera and a processor. The camera obtains an image of a road upon which the vehicle is moving. The processor is configured to extract a feature of the road from the image, perform a lane detection algorithm to detect a set of lane markers in the road using the image and the feature, and move the vehicle along the road by tracking the set of lane markers.

Abstract: A first network device includes a transceiver, a memory and a control module. The transceiver receives an integrated model from a second network device that is separate from the first network device. The memory stores the integrated model and diagnostic trouble code data, most probable cause data, and least probable cause data, which have corresponding cause of issue indications for an issue of a vehicle. The control module while executing the integrated model: compares the cause of issue indications to determine whether the cause of issue indications are consistent such that a same cause of issue is indicated; in response to the cause of issue indications being consistent, displays the same cause of issue, and in response to the cause of issue indications being inconsistent and based on a set of conditions, displays a portion of health related information while refraining from displaying another portion of the health related information.

Abstract: A vehicle, a system and a method of navigating the vehicle. The system includes a camera conveyed by the vehicle and a processor. The camera is configured to obtain an original image data file of an environment. The processor is configured to reduce the original image data file to obtain a reduced image data file and determine an alignment between a camera-centered coordinate system and a ground coordinate system using the reduced image data file.

Abstract: A vehicle, electrical system of the vehicle, and a method of detecting a fault occurring in the electrical system. The electrical system includes an electronic control unit, a sensor configured to obtain a measurement of a parameter of the electronic control unit, and a processor. The processor is configured to determine a parasitic load at the electronic control unit from the measurement of the parameter, identify a type of fault occurring at the electronic control unit due to the parasitic load based on the measurement, and perform an action at the electrical system based on the type of fault.

Abstract: A LIDAR-to-vehicle alignment system includes a sensor data collection module configured to collect points of data provided based on outputs of one or more LIDAR sensors and an alignment module configured to identify lane markings based on the points of data, determine a lane marking direction based on the identified lane markings, calculate a yaw of a LIDAR coordinate system relative to a vehicle coordinate system based on the determined lane marking direction, identify a ground plane based on the points of data, calculate a roll and pitch of the LIDAR coordinate system relative to the vehicle coordinate system based on the identified ground plane, and update a transformation matrix based on the calculated yaw, roll, and pitch of the LIDAR coordinate system.

Abstract: Systems and methods for a vehicle are provided. In one embodiment, a method includes: receiving image data defining a plurality of images associated with an environment of the vehicle; determining, by a processor, feature points within at least one image of the plurality of images; selecting, by the processor, a subset of the feature points as ground points based on a fixed two dimensional image road mask and a three dimensional region; determining, by the processor, a ground plane based on the subset of feature points; determining, by the processor, a ground normal vector from the ground plane; determining, by the processor, a camera to ground alignment value based on the ground normal vector; and generating, by the processor, second image data based on the camera to ground alignment value.

Abstract: Systems and methods for a vehicle are provided. In one embodiment, a method includes: receiving image data defining a plurality of images associated with an environment of the vehicle; receiving vehicle data indicating a velocity of the vehicle, wherein the vehicle data is associated with the image data; determining, by a processor, feature points within at least one image based on the vehicle data and a three dimensional projection method; selecting, by the processor, a subset of the feature points as ground points; determining, by the processor, a ground plane based on the subset of feature points; determining, by the processor, a ground normal vector from the ground plane; determining, by the processor, a camera to ground alignment value based on the ground normal vector; and generating, by the processor, second image data based on the camera to ground alignment value.

Abstract: Methods and systems for a vehicle are provided. In one embodiment, the method includes: receiving image data defining a plurality of images associated with an environment of the vehicle; determining, by a processor, feature points within at least one image of the plurality of images; selecting, by the processor, a subset of the feature points as ground points; determining, by the processor, a ground plane based on the subset of feature points; determining, by the processor, a ground normal vector from the ground plane; determining, by the processor, the ground normal vector based on a sliding widow method; determining, by the processor, a camera to ground alignment value based on the ground normal vector; and generating, by the processor, second image data based on the camera to ground alignment value.

Abstract: A system for on-vehicle camera alignment monitoring includes an on-vehicle camera in communication with a controller. The controller monitors vehicle operating parameters and camera signal parameters, and captures an image file from the on-vehicle camera. A first level analysis of the image file, the vehicle operating parameters, and the camera signal parameters is executed to detect dynamic conditions and image feature parameters that affect camera alignment. An error with one of the dynamic conditions or the image feature parameters that affects the camera alignment is detected. A second level analysis of the camera signal parameters is executed to identify a root cause indicating one of the dynamic conditions or the image feature parameters that affects the camera alignment based upon the error. A camera alignment-related fault is detected based upon the root cause, and vehicle operation is controlled based upon the camera alignment-related fault.

Abstract: A LIDAR-to-vehicle alignment system includes a memory and alignment and autonomous driving modules. The memory stores points of data provided based on an output of one or more LIDAR sensors and localization data. The alignment module performs an alignment process including: based on the localization data; determining whether a host vehicle is turning; in response to the host vehicle turning; selecting a portion of the points of data; aggregating the selected portion to provide aggregated data; selecting targets based on the aggregated data; and based on the selected targets, iteratively reducing a loss value of a loss function to provide a resultant LIDAR-to-vehicle transformation matrix. The autonomous driving module: based on the resultant LIDAR-to-vehicle transformation matrix, converts at least the selected portion to at least one of vehicle coordinates or world coordinates to provide resultant data; and performs one or more autonomous driving operations based on the resultant data.