Transportation systems have artificial intelligence including neural networks for recognition and classification of objects and behavior including natural language processing and computer vision systems. The transportation systems involve sets of complex chemical processes, mechanical systems, and interactions with behaviors of operators. System-level interactions and behaviors are classified, predicted and optimized using neural networks and other artificial intelligence systems through selective deployment, as well as hybrids and combinations of the artificial intelligence systems, neural networks, expert systems, cognitive systems, genetic algorithms and deep learning.

A building intelligence gathering, assessment and decision-support system including a GPS system deployed about the Earth and supporting a plurality of GPS satellites for transmitting GPS signals to the surface of the Earth. A building data sensor network, including a plurality of GPS-tracked rooftop-mounted sensors, are mounted on the rooftop surface of the building, and adapted for collecting GPS-indexed data specifying conditions on the rooftop surface at particular dates and times of the year. The building rooftop conditions includes one or more conditions selected from the group consisting of snow load conditions, windspeed and direction, and temperature. The system also includes one or more hand-held mobile augmented-reality (AR) based rooftop navigation and inspection devices, each configured for communication with communication servers within a data center over a wireless data communication network. Each hand-held mobile AR-based inspection device is capable displaying digital images of objects and scenery captured in its field of view (FOV) while the user is moving about the rooftop surface, along with graphical indications of GPS-tracked rooftop-mounted sensors collecting data regarding conditions on the building rooftop surface. By virtue of the present invention, users can now navigate building rooftops, inspect rooftop situations, identify where GPS-tracked rooftop-mounted sensors have been installed, and quickly determine where particular conditions have been automatically detected during rooftop inspections.

In one embodiment, a method includes determining a plurality of available locations for a vehicle to pick up or drop off a user in an area based on sensor data that is captured by the vehicle and is associated with physical characteristics of the area. The method includes calculating a viability value for each of the plurality of available locations. The viability value is calculated based on the physical characteristics of the area as determined from the sensor data. The method includes ranking the plurality of available locations according to the viability values of the plurality of available locations. An available location with a higher viability value is ranked above an available location with a lower viability value. The method includes sending instructions to present, on a computing device, one or more selectable locations of the plurality of available locations based on the ranking.

Apparatuses, systems, methods, and computer-readable media are provided for the preemptive logical configuration of vehicle control systems. A vehicle control computer may determine a location of a vehicle. The vehicle control computer may query a historical data source server for historical incident data corresponding to a first vicinity around the first location of the vehicle. Based on the historical incident data, the vehicle control computer may identify one or more driving danger areas in the first vicinity around the first location of the vehicle, wherein each of the one or more driving danger areas are associated with one or more driving hazards. The vehicle control computer may generate a configuration for vehicle operation in the first vicinity around the first location of the vehicle based on the one or more driving danger areas and may update driving logic of the vehicle with the configuration.

Systems and techniques are provided for expanding a scope of coverage of test scenarios for training an autonomous vehicle (AV). An example method can include identifying a maneuver of an AV; receiving, from a test repository, a plurality of tests that includes the maneuver; identifying one or more segments on a map of an operational design domain (ODD) that include a driving environment for the maneuver; determining a similarity between a driving scene of each of the plurality of tests and the one or more segments on the map of the ODD; and determining a degree of test coverage for each of the one or more segments for the maneuver based on the determined similarity.

Herein, a perception statistical performance model (PSPM) for modelling a perception slice of a runtime stack for an autonomous vehicle or other robotic system may be used e.g. for safety/performance testing. A PSPM is configured to: receive a computed perception ground truth; determine from the perception ground truth, based on a set of learned parameters, a probabilistic perception uncertainty distribution, the parameters learned from a set of actual perception outputs generated using the perception slice to be modelled. The PSPM comprises a time-dependent model such that the perception output sampled at the current time instant depends on at least one of: an earlier one of the perception outputs sampled at a previous time instant, and an earlier one of the perception ground truths computed for a previous time instant.

A transportation system and method for managing rewards in the transportation system includes using a merchant interface to a cognitive system for managing the offering or fulfillment of a reward to a rider of a vehicle, where a merchant may specify parameters of the reward that can be earned by the rider as a result of performing an action while in the vehicle.

Example implementations include a method, apparatus, system, and computer-readable medium of using a robot to validate a license plate recognition check at a parking facility, comprising identifying license plate information of a license plate. The implementations further include associating the license plate information with a parking spot. Additionally, the implementations further include storing the license plate information in association with the parking spot. Additionally, the implementations further include checking the license plate information in association with the parking spot against a parking facility monitoring system.

A method and system of determining whether a stationary vehicle is a blocking vehicle to improve control of an autonomous vehicle. A perception engine may detect a stationary vehicle in an environment of the autonomous vehicle from sensor data received by the autonomous vehicle. Responsive to this detection, the perception engine may determine feature values of the environment of the vehicle from sensor data (e.g., features of the stationary vehicle, other object(s), the environment itself). The autonomous vehicle may input these feature values into a machine-learning model to determine a probability that the stationary vehicle is a blocking vehicle and use the probability to generate a trajectory to control motion of the autonomous vehicle.

A method for training a neural network. The neural network comprises a first layer which includes a plurality of filters to provide a first layer output comprising a plurality of feature maps. Training of the classifier includes: receiving, by a preceding layer, a first layer input in the first layer, wherein the first layer input is based on the input signal; determining the first layer output based on the first layer input and a plurality of parameters of the first layer; determining a first layer loss value based on the first layer output, wherein the first layer loss value characterizes a degree of dependency between the feature maps, the first layer loss value being obtained in an unsupervised fashion; and training the neural network. The training includes an adaption of the parameters of the first layer, the adaption being based on the first layer loss value.