Abstract: A vehicle charging management method is disclosed. The method may include obtaining a first input associated with a vehicle. The first input may include a state of charge (SOC) level of a vehicle battery, a distance travelled by the vehicle since last charge, and time since the last charge. The method may further include obtaining a second input associated with historical vehicle battery charging information. Responsive to obtaining the first input and the second input, the method may include determining a vehicle user intent to charge the vehicle battery. The method may further include comparing the vehicle user intent with a threshold value, and transmitting a notification to a communication device when the vehicle user intent is greater than the threshold value.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for two-stage application testing using generative artificial intelligence. An example method includes receiving, by communications hardware, an application and a first context setting and testing, by preliminary testing circuitry, the application using a preliminary testing model. The example method further includes generating, by advanced testing circuitry and using a scenario generation model, one or more first context-based testing scenarios based on the first context setting and testing, by the advanced testing circuitry, the application using an advanced testing model, where the advanced testing model uses the one or more first context-based testing scenarios.

Abstract: A method can include training a machine learning algorithm to determine a duration of a new work order, based on (a) historical input data for the machine learning algorithm and (b) historical output data for the machine learning algorithm. The method can also include determining one or more work orders for a service provider comprising: determining, by the machine learning algorithm, as trained, one or more durations of the one or more work orders; and determining an optimized service route for the one or more work orders. The method can further include updating a work schedule for the service provider based on the optimized service route. Other embodiments are also provided.

Abstract: Embodiments herein describe assigning integrated circuits with defects as variants of the integrated circuit design. Each variant can deactivate different circuitry in the integrated circuit design. A location of the defect can be matched to a variant that has a deactivated region that covers the defect. The integrated circuit can then be assigned to that variant.

Abstract: Aspects of the present disclosure relate to improving runtime performance of equivalence checkers by a shift left process that uses an RTL lint tool to identify RTL code that can be a source of verification complexity for the downstream equivalence checker. The RTL code is further compared to a corresponding circuit model to determine a potential gate-level location of the potentially problematic RTL code. A weight can be calculated to represent a level of verification complexity that may be generated by the particular portion of the RTL code. Further, one or more recommendations can be generated by the RTL lint tool to prevent some of the verification complexity for the downstream equivalence checker.

Abstract: A vehicle charging management method is disclosed. The method may include obtaining a first input associated with a vehicle. The first input may include a state of charge (SOC) level of a vehicle battery, a distance travelled by the vehicle since last charge, and time since the last charge. The method may further include obtaining a second input associated with historical vehicle battery charging information. Responsive to obtaining the first input and the second input, the method may include determining a vehicle user intent to charge the vehicle battery. The method may further include comparing the vehicle user intent with a threshold value, and transmitting a notification to a communication device when the vehicle user intent is greater than the threshold value.

Abstract: In-vehicle advertisement presentation systems and methods are disclosed herein. An example method includes determining vehicle information for a trip, the vehicle information including any one or more of a current vehicle location, a vehicle speed, a drive mode, and/or traffic information, determining user information, the user information including any one or more of a route prediction for the trip, a speed prediction for the trip, and/or a destination, determining user preferences for advertisements from any one or more of audio signals within the vehicle and/or historical user data, selecting a number of the advertisements to present to the user during the trip, and providing the advertisements to the user during the trip through a human-machine interface (HMI) of the vehicle.

Abstract: Methods, systems, and computer readable media for utilizing machine learning for merging duplicate data records are disclosed. One method includes receiving duplicate data records, constructing collection level feature attributes from the duplicate data records, and extracting one or more samples from the collection level feature attributes. The method further includes receiving domain expert stimulus input corresponding to the one or more samples, training a merging machine learning model using the stimulus input, and utilizing the merging machine learning model to generate a master data record corresponding to the duplicate data records or a rule to derive the master data record.

Abstract: Systems and methods for the multifaceted analysis of a written work, such as an educational program admission essay, using machine learning, deep learning and natural language processing. Language, relevance, structure, and flows are evaluated for an overall impactful essay. Essay content is checked to evaluate whether the author has covered an essay's essential aspects. The essay is also analyzed for an effective structure for presenting details as per the essay type. The disclosure includes data preparation for the task, process of data tagging, feature engineering from the essay text, method for transfer learning and fine-tuning language model to adapt to the context of an essay. Finally, a process for building machine learning and deep learning models and technique for ensembling to use both models in combination is disclosed. The system may provide user-adapted feedback based on a persona created from the user profile.

Abstract: Systems and methods for personalized route prediction including receiving, at a first time, first input data associated with a first route; populating a first database with the input data; receiving, at a third time, second input data associated with a second route; comparing the second input data to the first input data included within the first database; determining, based on the comparison, a first cluster including the first data and the second input data or a second cluster including the second input data; populating a second database based on the first cluster or the second cluster; determining, using the first database and at a second time, at least one of: predicted departure data, predicted destination data, and/or predicted route data; and causing, based on the predicted departure data, predicted destination data, and/or predicted route data, to perform an action in association with a vehicle.

Abstract: A profile for an occupant is stored. The profile includes a plurality of clusters of sitting positions for the occupant in a seat and classifications of respective clusters as preferred or nonpreferred. Respective clusters are classified as preferred or nonpreferred based on sitting scores for the respective sitting positions in that cluster. A higher sitting score increases a likelihood of the classification being preferred. A series of pressure maps indicating a respective series of sitting positions of the occupant in the seat are received. The pressure maps include a current pressure map. One of the sitting positions is assigned to one of the clusters that is classified as preferred in response to the sitting score of that sitting position being greater than a threshold score. A physical configuration of the seat is adjusted in response to the current sitting position being in one of the clusters that is classified as nonpreferred.

Abstract: An identifier for first set of display content on a vehicle display is input to a statistical model that outputs a plurality of probabilities that a user input will select each of a plurality of second set of display contents for display after the first set of display content is displayed. A first probability is identified for a predicted set of display content that is a highest probability in the plurality of probabilities. The plurality of probabilities are provided to at least one of an optimization model and a neural network upon determining an accuracy of the statistical model is below a threshold. Upon receiving, from the at least one of the optimization model and the neural network, a second probability for the predicted set of display content, the display content is selected based on the first and second probabilities. The vehicle display is updated based on the selected set of display content.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to identify a first scenario in which a vehicle is operating from a plurality of scenarios, prompt an operator to activate an adaptive cruise control of the vehicle in response to a preference score for the first scenario being above a threshold, refrain from prompting the operator to activate the adaptive cruise control in response to the preference score for the first scenario being below the threshold, and activate the adaptive cruise control in response to receiving an input to activate the adaptive cruise control from the operator. The scenarios indicate at least one characteristic of a road on which the vehicle is traveling. The preference score indicates a preference of the operator for activating the adaptive cruise control in the first scenario.

Abstract: Electric vehicle battery monitoring systems and methods are disclosed herein. An example method includes displaying a state of charge for a battery of an electric vehicle on a display screen of the electric vehicle, determining a frequency of how often a user views the state of charge, inferring that the user has anxiety over a range for the electric vehicle based on the frequency, and presenting the user with options to extend the state of charge.

Abstract: Techniques for rendering two-dimensional vector graphics are described. The techniques include using a central processing unit to generate tessellate triangles along a vector path in which each of the tessellate triangles is represented by a set of vertices. From the tessellate triangles, an index buffer and a compressed vertex buffer are generated. The index buffer includes a vertex index for each vertex of each of the tessellate triangles. The compressed vertex buffer includes a vertex buffer entry for each unique vertex that maps to one or more vertex indices of the index buffer. The index buffer and the compressed vertex buffer are provided to a graphics processing unit to render the vector path with anti-aliasing.

Abstract: Recommendation of off-beat locations as destinations for vehicles is provided. Off-beat locations for a location of a vehicle are identified by a server. Each off-beat location is associated with a corresponding popularity score, the popularity score indicating a relative level of occupancy of the off-beat location. The off-beat locations are filtered by the server based on at least one threshold level of popularity score. The off-beat locations are recommended from the server to the vehicle as those that meet the at least one threshold level of popularity score.

Abstract: The disclosure generally pertains to recommending an alternative off-road track to a driver of a vehicle. In an example method, a processor receives a sensor signal that is generated in response to an operating condition of the vehicle on an off-road track. The operating condition can include, for example, an angular orientation of a chassis portion of the vehicle with respect to a ground surface (yaw, pitch, roll, etc.), steering characteristics, speed characteristics, and/or braking characteristics. The processor evaluates the sensor signal to identify an attribute of the off-road track, such as, for example, a difficulty factor and/or a challenge level. The processor may assign a track score to the off-road track based on the attribute and may provide to the driver, a recommendation for use of another off-road track based on comparing the first track score to a second track score associated with the other off-road track.

Abstract: The disclosure is generally directed to in-vehicle advertisement systems and methods. An example method executed by processor of a vehicle may include determining an attention availability index of a driver of a vehicle and determining a first presentation duration of a first advertisement based on the attention availability index. In one scenario, the attention availability index of the driver may be determined by applying an inverse relationship to a concentration index associated with driving the vehicle. The first advertisement is presented over the first presentation duration, via an advertisement presentation device in the vehicle. In another scenario, the attention availability index of the driver may be compared to a threshold value. Based on the comparison, a second presentation duration for a second advertisement may be determined and the second advertisement presented via the advertisement presentation device in the vehicle.

Abstract: A vehicle may determine that erratic vehicle behavior has been sensed, based on comparison of a sensed vehicle behavioral characteristic at a given location compared to a predefined expected value of the characteristic. The vehicle may further determine whether an environmental anomaly has been detected in association with the given location and classify the sensed erratic behavior based on whether the environmental anomaly was detected. Responsive to classifying the behavior as erratic based on determining no environmental anomaly was detected, the vehicle may report the erratic behavior to a remote server, along with the given location. The remote server may receive a plurality of such reports for a given location and update a classification of the behavior based on data indicated in the plurality of reports.

Abstract: A system, comprising a computer including a processor and a memory storing instructions executable by the processor to determine a first lateral boundary for movement of a vehicle. The first lateral boundary is parallel to a longitudinal axis of the vehicle and is based on at least a size of the vehicle. Based on at least the size of the vehicle and a detected yaw rate of the vehicle, the instructions include to determine a wind condition at a location. The instructions include to update a distance of the first lateral boundary from the longitudinal axis to obtain an updated first lateral boundary, based on the wind condition.