Abstract: A sound absorber is configured as a rectangular hexahedral box and forms a porous double-layer Helmholtz resonance sound absorbing structure, at the same time, the sound absorber in the form of a box forms a structural resonance sound absorbing device itself, and the first-order natural mode frequency of the device is identical to that of a wheel air chamber. When the box-type sound absorbing structure is assembled in a wheel, double functions of absorbing acoustic resonance of the wheel air chamber under the organic combination of Helmholtz resonance sound absorption and structural resonance sound absorption can be realized.

Abstract: Methods and systems are provided for determining the category of a software application utilizing machine learning (ML) and knowledge graph techniques, and for controlling access to the application by a user based on the category and configured time restrictions for the user. The system includes a feature set extractor and a category predictor with a trained ML model. The trained ML model generates the category of the application based on a feature(s) of the application. The generated category is indicated in a data structure. An access request handler receives a request related to access to the application from a user device. A category determiner determines the category of the application from the data structure. A time usage manager determines an available time usage for the category and the specified user. The access arbiter responds to the request from the user device with the available time usage.

Abstract: Systems and methods for pessimistic offline reinforcement learning are described herein. In one example, a method for performing offline reinforcement learning determines when sampled states are out of distribution, assigns high probability weights to the sampled states that are out of distribution, generates a fitted Q-function by solving an optimization problem with a minimization term and a maximization term, estimates a Q-value using the fitted Q-function by estimating the overall expected reward assuming the agent is in the present state and performs a present action, and updates the policy according to an existing reinforcement learning algorithm. The minimization term penalizes an overall expected reward when a present state is out of distribution. The maximization term cancels the minimization term when the present state is an in-distribution state.

Abstract: A computerized method for instance segmentation using single-photon avalanche diode (SPAD) light detection and ranging (Lidar) includes obtaining sensor image data from photon detector of an SPAD Lidar sensor, supplying the obtained sensor image data to a two-dimensional convolutional neural network (CNN) to generate a background mask by identifying target objects in the obtained sensor image data and filtering out background pixels that do not belong to the identified target objects, and supplying point cloud data of the identified target objects to a PointNet model to generate a pixel level instance segmentation output of the identified target objects.

Abstract: A method includes obtaining steering data of the vehicle from one or more steering sensors, determining whether the vehicle is operating in one of a turning state and a non-turning state based on the steering data, performing a localization routine based on a first echo set of the 3D data points from among the plurality of 3D data points when the vehicle is operating in the turning state, and performing the localization routine based on a second echo set of the 3D data points from among the plurality of 3D data points when the vehicle is operating in the non-turning state, where a number of the plurality of 3D data points of the first echo set is greater than a number of the plurality of 3D data points of the second echo set.

Abstract: A method includes obtaining steering data of the vehicle from one or more steering sensors, determining whether the vehicle is operating in one of a turning state and a non-turning state based on the steering data, performing a localization routine based on a first echo set of the 3D data points from among the plurality of 3D data points when the vehicle is operating in the turning state, and performing the localization routine based on a second echo set of the 3D data points from among the plurality of 3D data points when the vehicle is operating in the non-turning state, where a number of the plurality of 3D data points of the first echo set is greater than a number of the plurality of 3D data points of the second echo set.

Abstract: Methods and systems are provided for determining the category of a software application utilizing machine learning (ML) and knowledge graph techniques, and for controlling access to the application by a user based on the category and configured time restrictions for the user. The system includes a feature set extractor and a category predictor with a trained ML model. The trained ML model generates the category of the application based on a feature(s) of the application. The generated category is indicated in a data structure. An access request handler receives a request related to access to the application from a user device. A category determiner determines the category of the application from the data structure. A time usage manager determines an available time usage for the category and the specified user. The access arbiter responds to the request from the user device with the available time usage.

Abstract: Methods and systems are provided for determining the category of a software application utilizing machine learning (ML) and knowledge graph techniques, and for controlling access to the application by a user based on the category and configured time restrictions for the user. The system includes a feature set extractor and a category predictor with a trained ML model. The trained ML model generates the category of the application based on a feature(s) of the application. The generated category is indicated in a data structure. An access request handler receives a request related to access to the application from a user device. A category determiner determines the category of the application from the data structure. A time usage manager determines an available time usage for the category and the specified user. The access arbiter responds to the request from the user device with the available time usage.

Abstract: A system for measuring accuracy of an electronic map or a localization of a vehicle may include one or more processors and a memory in communication with the one or more processors having an image capture module, a ground truth generating module, a curve function generating module, and an accuracy determining module. These modules contain instructions that when executed by the one or more processors causes the processors to obtain sensor data that includes images of one or more road lane markings of a road or trajectory data regarding the trajectory of the vehicle, determine ground truth curvature values, perform a curve fitting of map lane marking points or map trajectory points, and compare curvature values of a curve function generated by the curve fitting to the ground truth curvature values to determine the accuracy the electronic map or the localization of the vehicle.

Abstract: Methods and systems are provided for determining the category of a software application utilizing machine learning (ML) and knowledge graph techniques, and for controlling access to the application by a user based on the category and configured time restrictions for the user. The system includes a feature set extractor and a category predictor with a trained ML model. The trained ML model generates the category of the application based on a feature(s) of the application. The generated category is indicated in a data structure. An access request handler receives a request related to access to the application from a user device. A category determiner determines the category of the application from the data structure. A time usage manager determines an available time usage for the category and the specified user. The access arbiter responds to the request from the user device with the available time usage.

Abstract: The present disclosure is directed toward a lane detection method. The method includes: acquiring road information for a section of a road upon which a subject vehicle is traveling, determining whether an adjacent vehicle is traveling in vicinity of the subject vehicle, defining, in response to determining the presence of the adjacent vehicle, a vehicle trajectory based on movement of the adjacent vehicle and the road information, detecting one or more lane markings along the road upon which the subject vehicle is traveling, defining a lane marking trajectory in response to detecting the one or more lane markings, calculating a lane accuracy for each estimated lane trajectory that includes the vehicle trajectory, the lane marking trajectory, or a combination thereof, and selecting a drive lane from among the estimated lane trajectories based on the lane accuracy.

Abstract: A system for measuring accuracy of an electronic map or a localization of a vehicle may include one or more processors and a memory in communication with the one or more processors having an image capture module, a ground truth generating module, a curve function generating module, and an accuracy determining module. These modules contain instructions that when executed by the one or more processors causes the processors to obtain sensor data that includes images of one or more road lane markings of a road or trajectory data regarding the trajectory of the vehicle, determine ground truth curvature values, perform a curve fitting of map lane marking points or map trajectory points, and compare curvature values of a curve function generated by the curve fitting to the ground truth curvature values to determine the accuracy the electronic map or the localization of the vehicle.

Abstract: A lane trace control system for maintaining a vehicle in its intended lane of travel. The system includes a lane trace control module configured to: compare the road information obtained by a lane recognition camera with map data obtained from a map module; determine a confidence level of the lane recognition camera based on a magnitude of any differences identified between the road information obtained from the lane recognition camera and the obtained map data; generate a target wheel angle for keeping the vehicle in its intended lane based entirely on the road information obtained from the lane recognition camera when the confidence level is above a predetermined threshold; and generate the target wheel angle based on a combination of the road information obtained from the lane recognition camera and the obtained map data of the road when the confidence level is below the predetermined threshold.

Abstract: A vehicle control system includes a controller that is to be provided in a subject vehicle. The controller is configured to acquire data regarding an identified object ahead of the subject vehicle, determine whether the identified object is a stop-go object based on the acquired data, and output a stop-go control in response to the identified object being the stop-go object. The data includes information indicative of a classification of the identified object, and the stop-go control generates a dynamic profile that defines a deceleration and an acceleration of the subject vehicle for automatically performing a stop-go operation.

Abstract: An autonomous drive system for a vehicle. The system has an autonomous drive module configured to pilot the vehicle. A control module monitors operating performance of the autonomous drive system. A location module identifies the vehicle's location. A transmitter/receiver communicates with a high risk location monitoring module at a location remote to the vehicle. When the control module determines that performance of the autonomous drive system is below a predetermined performance threshold, the control module retrieves the vehicle's location from the location module and transmits the vehicle's location to the high risk location monitoring module, which records the vehicle's current location to notify other vehicles that autonomous drive issues may occur at the vehicle's location.

Abstract: A lane trace control system for maintaining a vehicle in its intended lane of travel. The system includes a lane trace control module configured to: compare the road information obtained by a lane recognition camera with map data obtained from a map module; determine a confidence level of the lane recognition camera based on a magnitude of any differences identified between the road information obtained from the lane recognition camera and the obtained map data; generate a target wheel angle for keeping the vehicle in its intended lane based entirely on the road information obtained from the lane recognition camera when the confidence level is above a predetermined threshold; and generate the target wheel angle based on a combination of the road information obtained from the lane recognition camera and the obtained map data of the road when the confidence level is below the predetermined threshold.

Abstract: The present disclosure is directed toward a lane detection method. The method includes: acquiring road information for a section of a road upon which a subject vehicle is traveling, determining whether an adjacent vehicle is traveling in vicinity of the subject vehicle, defining, in response to determining the presence of the adjacent vehicle, a vehicle trajectory based on movement of the adjacent vehicle and the road information, detecting one or more lane markings along the road upon which the subject vehicle is traveling, defining a lane marking trajectory in response to detecting the one or more lane markings, calculating a lane accuracy for each estimated lane trajectory that includes the vehicle trajectory, the lane marking trajectory, or a combination thereof, and selecting a drive lane from among the estimated lane trajectories based on the lane accuracy.

Abstract: An autonomous drive system for a vehicle. The system has an autonomous drive module configured to pilot the vehicle. A control module monitors operating performance of the autonomous drive system. A location module identifies the vehicle's location. A transmitter/receiver communicates with a high risk location monitoring module at a location remote to the vehicle. When the control module determines that performance of the autonomous drive system is below a predetermined performance threshold, the control module retrieves the vehicle's location from the location module and transmits the vehicle's location to the high risk location monitoring module, which records the vehicle's current location to notify other vehicles that autonomous drive issues may occur at the vehicle's location.

Abstract: A method for generating images of traffic objects using deep learning is disclosed. The method includes obtaining image data from an image. The method includes assigning a set of default parameters to the image data, and the set of default parameters include values associated with at least one of a weather condition of the image and a defect condition of an object of the image. The method includes generating a set of predicted parameters based on the image data. The method includes determining an error for each parameter of the set of predicted parameters, and the error is based on a value of the parameter of the set of predicted parameters and a value of a corresponding default parameter of the set of default parameters. The method includes adjusting a weight of a corresponding connection of the deep learning module.

Abstract: A driver visibility detection system for a vehicle includes an indicator, an eye position obtainer, and a visible range calculator. The indicator indicates the driver to look at, through a windshield of the vehicle, a furthest ground position of a road on which the vehicle is traveling. The eye position obtainer recognizes an eye position of the driver when the driver looks at the furthest ground position. The visible range calculator calculates a visible range of the driver based on the eye position detected by the eye position obtainer.