Abstract: The present disclosure provides a method comprising, subsequent to arrival of a vehicle at a charging station, establishing communications between a vehicle side autonomous charging system associated with the vehicle and a charging station side autonomous charging system associated with the charging station; receiving by the charging station side autonomous charging system at least one signal from the vehicle side autonomous charging system, wherein the at least one signal is indicative of a location of a charging port of the vehicle; and using the received at least one signal to guide a robotic extension arm of the charging station to a location proximate the vehicle charging port.

Abstract: A computer-implemented method for training a neural network, which, in particular, is configured to classify physical measuring variables. The neural network is trained with the aid of a training data set. Pairs including an input signal and an associated desired output signal are drawn from the training data set for training. An adaptation of parameters of the neural network occurs as a function of an output signal of the neural network, when the input signal is supplied, and as a function of the desired output signal. The drawing of pairs always takes place from the entire training data set.

Abstract: Predictive maintenance and diagnostics for an electronic module of an autonomous vehicle using modular condition monitoring is described herein. A computing system receives a signal from a data logger which monitors a condition of the electronic module of the autonomous vehicle, wherein the signal is indicative of damage accumulation information thereof. The computing system identifies a type of the electronic module and a damage accumulation threshold for the type of the electronic module to generate a predicted maintenance schedule for the electronic module of the autonomous vehicle. The damage accumulation information can be stored in a data store to define the damage accumulation threshold for the type of the electronic module.

Abstract: A fleet management system dispatches autonomous electric vehicles (AEVs) as on-demand power sources. The fleet management system receives a request for a power source including a location and data describing the amount of power requested. The fleet management system selects an AEV of the fleet to service the request based on the relative locations of the AEVs to the requested location, and based on the amount of power requested. The fleet management system instructs the selected AEV to drive to the location and supply power. The fleet management system instructs the selected AEV to disconnect and return to the charging station, and may instruct another AEV to continue fulfilling the request if additional power is needed.

Abstract: A computer-implemented method for obtaining an adversarial input signal to a classifier for classifying input signals obtained from a sensor. The adversarial input signal is obtained from an original input signal. The adversarial input signal and the original input signal cause the classifier to classify the original input signal as belonging to a first class and the adversarial input signal as belonging to a second class different from said first class. The method includes: modifying said original input signal to yield a modified input signal; projecting said modified input signal onto a metric ball around said original input signal to yield a projected input signal; and obtaining said adversarial input signal depending on the projected input signal, characterized in that the metric is an at least approximate Wasserstein distance.

Abstract: An equipment inspection system receives data captured by a sensor of an autonomous vehicle (AV). The captured data describes a current state of equipment for servicing the AV. The equipment inspection system compares the captured data to a model describing an expected state of the equipment. The equipment inspection system determines, based on the comparison, that the equipment differs from the expected state. The equipment inspection system may transmit data describing the current state of the equipment to an equipment manager. The equipment manager may schedule maintenance for the equipment based on the current state of the equipment.

Abstract: A computer-implemented method for assessing a robustness of a smoothed classifier for classifying sensor signals received from a sensor. The method includes: providing an input signal depending on the sensor signal, determining, by the smoothed classifier, a first value which characterizes a probability that the input signal, when subjected to noise will be classified as belonging to a first class, wherein the first class is a most probable class, determining, by the smoothed classifier, a second value which characterizes a probability that the input signal, when subjected to the noise, will be classified as belonging to a second class, wherein the second class is a second-most probable class, determining a robustness value on a first inverse value of a standard Gaussian cumulative distribution function at the first value and/or depending on a second inverse value of the standard Gaussian cumulative distribution function at the second value.

Abstract: The present disclosure provides a method comprising, subsequent to arrival of a vehicle at a charging station, establishing communications between a vehicle side autonomous charging system associated with the vehicle and a charging station side autonomous charging system associated with the charging station; receiving by the charging station side autonomous charging system at least one signal from the vehicle side autonomous charging system, wherein the at least one signal is indicative of a location of a charging port of the vehicle; and using the received at least one signal to guide a robotic extension arm of the charging station to a location proximate the vehicle charging port.

Abstract: Predictive maintenance and diagnostics for an electronic module of an autonomous vehicle using modular condition monitoring is described herein. A computing system receives a signal from a data logger which monitors a condition of the electronic module of the autonomous vehicle, wherein the signal is indicative of damage accumulation information thereof. The computing system identifies a type of the electronic module and a damage accumulation threshold for the type of the electronic module to generate a predicted maintenance schedule for the electronic module of the autonomous vehicle. The damage accumulation information can be stored in a data store to define the damage accumulation threshold for the type of the electronic module.

Abstract: A method for communication between autonomous vehicles and personnel can include receiving a signal from a remote computing system, the signal based upon a position of an autonomous vehicle and a first position of a mobile device, wherein the signal identifies that the mobile device is located in a path of the autonomous vehicle, responsive to receipt of the signal from the remote computing system, stopping the autonomous vehicle, receiving an updated signal from the remote computing system, the updated signal based upon a second position of the mobile device, the second position effective to facilitate a determination that the mobile device is no longer within the path of the autonomous vehicle, and responsive to receiving the updated position of the mobile device, resuming operation of the autonomous vehicle along the path. An autonomous vehicle and non-transitory computer-readable medium is also provided.

Abstract: The use of powder-form, crosslinked, water-insoluble, low-swelling polyacrylates as disintegrants for solid pharmaceutical dosage forms.

Abstract: A light inspection system positions an autonomous vehicle (AV) in a field of view of a camera such that the camera captures an image of a light of the AV. The light inspection system instructs a camera to capture an image of the light while the light is switched on. The light inspection system receives the captured image, determines a luminance of the light based on the image, and determines to service the light in response to the luminance of the light being below a threshold luminance.

Abstract: Sensors coupled to a first vehicle are used to measure sensor measurements. Based on the sensor measurements, the first vehicle identifies a risk to the first vehicle and/or to a second vehicle near the first vehicle, for example based on a distance between the first and second vehicle being unsafely low. The first vehicle generates an alert based on the risk and outputs the alert toward the second vehicle, for example visually through a screen and/or audibly using speakers, to warn an operator of the second vehicle about the risk.

Abstract: A computer-implemented method for training a classifier, particularly a binary classifier, for classifying input signals to optimize performance according to a non-decomposable metric that measures an alignment between classifications corresponding to input signals of a set of training data and corresponding predicted classifications of the input signals obtained from the classifier. The method includes providing weighting factors that characterize how the non-decomposable metric depends on a plurality of terms from a confusion matrix of the classifications and the predicted classifications, and training the classifier depending on the provided weighting factors.

Abstract: A mobile device having a communication system, one or more processors, and one or more memories. The communication system may be configured to communicate with a positional system. The one or more memories may store stores computer-executable instructions that, when executed by the processor, cause the processor to receive a signal from the positional system and generate a notification in response to the signal. The signal may be based upon a position of an autonomous vehicle and a position of the mobile device. The notification may be based upon the position of the autonomous vehicle and the position of the mobile device and identify a direction from which the autonomous vehicle approaches.

Abstract: A monitoring device (118) for monitoring the leak-tightness of a vacuum-insulated system has a corrugated bellows (108) which is connected in terms of flow to an evacuated space (104) of the vacuum-insulated system in such a way that, in the event of an increase in pressure in the evacuated space, the length of the corrugated bellows (108) is adjusted beyond a threshold value. A position detector (113) connected to an energy store (115) responds to the change in length of the corrugated bellows and outputs a signal. The position detector outputs a signal to a display device (116), which provides an indication if a leak in the vacuum-insulated system occurs.

Abstract: A mobile device having a communication system, one or more processors, and one or more memories. The communication system may be configured to communicate with a positional system. The one or more memories may store stores computer-executable instructions that, when executed by the processor, cause the processor to receive a signal from the positional system and generate a notification in response to the signal. The signal may be based upon a position of an autonomous vehicle and a position of the mobile device. The notification may be based upon the position of the autonomous vehicle and the position of the mobile device and identify a direction from which the autonomous vehicle approaches.

Abstract: A method to classify sensor data with improved robustness against label noise. A predicted label may be computed for a novel input with improved robustness against label noise by estimating a label which is most likely under repeated application of a base training function to the training labels incorporating noise according to a noise level and subsequent application of a base classifier configured according to the base prediction function to the novel input.

Abstract: Terpolymer, wherein 20 to 35% by weight of the structural units are derived from acrylic acid, 45 to 60% by weight of the structural units from a hydrophobic methacrylate selected from a group consisting of isopropyl methacrylate, tert-butyl methacrylate and cyclohexyl methacrylate and 15 to 40% by weight of the structural units from a third olefinic monomer selected from the group consisting of N-vinyl lactam, hydroxy ethyl methacrylate and phenoxyethyl acrylate with the proviso that the total amount of structural units derived from the three monomer groups adds up to 100% by weight, and the use of the terpolymers as crystallization inhibitors in pharmaceutical dosage forms for inhibiting the recrystallization in an aqueous environment of a human or animal body of an active ingredient.

Abstract: A system for training a classification model to be robust against perturbations of multiple perturbation types. A perturbation type defines a set of allowed perturbations. The classification model is trained by, in an outer iteration, selecting a set of training instances of a training dataset; selecting, among perturbations allowed by the multiple perturbation types, one or more perturbations for perturbing the selected training instances to maximize a loss function; and updating the set of parameters of the classification model to decrease the loss for the perturbed instances. A perturbation is determined by, in an inner iteration, determining updated perturbations allowed by respective perturbation types of the multiple perturbation types and selecting an updated perturbation that most increases the loss of the classification model.