Abstract: A device for detecting a dynamic object, comprising a processor configured to determine a first point density of a first volume around a first point in a first image, the first image being an image of an environment of a robot, the image including image 3D data; determine one or more second point densities, wherein each second point density is a point density of a second volume about a second point, wherein the second point corresponds to the first point in each of one or more second images, the one or more second images of the environment being one or more images taken prior to the first image and including image data corresponding to the three dimensions; and classify the first point as dynamic or static based on a comparison of the first point density and the one or more second point densities.

Abstract: Various systems and methods for providing assistance at the end of an autonomous system journey are provided. A system can include an autonomous system configured to transport the user, a first sensor coupled to the autonomous system and configured to provide a sensor stream of an environment between the autonomous system and a terminus, and a terminus assistance processor mechanically coupled to the autonomous system. In an example, the terminus assistance processor can receive and process the sensor stream, detect and can track the user in the environment in response to the sensor stream, and can provide status information to a first mobile device based on the sensor stream.

Abstract: Disclosed herein are devices, systems, and methods for a predictive imaging system to determine exposure-related settings for a scene that is to be captured by an imagining device. The predictive imaging system determines a motion of the scene that is to be captured by an image sensor of the imaging device and determines an exposure-related configuration setting of the imaging device, where the exposure-related configuration setting is based on the determined motion of the scene. The predictive imaging system then generates an instruction for the imaging device to capture the scene using the determined configuration setting.

Abstract: To receive authority certification for mass deployment of autonomous vehicles (AVs), manufacturers need to justify that their AVs operate safer than human drivers. This in turn creates the need to estimate and model the collision rate (failure rate) of an AV taking all possible errors and driving situations into account. In other words, there is the strong demand for comprehensive Mean Time between Failure (MTBF) models for AVs. The disclosure describes such a generic and scalable model that creates a link between errors in the perception system to vehicle-level failures (collisions). Using this model, requirements for the perception quality may then be derived based on the desired vehicle-level MTBF, or vice versa, to obtain an MTBF value given a certain mission profile and perception quality.

Abstract: A safety device for a vehicle including a memory configured to store instructions; one or more processors coupled to the memory to execute the instructions stored in the memory, wherein the instructions implement a safety driving model for the vehicle to determine a safety driving state for the vehicle, wherein determining includes obtaining data for one or more objects in a vehicle environment of the vehicle, wherein the data comprises motion data associated with at least one object of the one or more objects; determining a predicted motion trajectory for at least one object of the one or more objects; determining whether a risk indicating a risk of a collision for the vehicle and the at least one or more objects exceeds a predefined risk threshold; and generating an information that the risk exceeds the predefined risk threshold to be considered in determining the safety driving state for the vehicle.

Abstract: A system for a distributed in-vehicle real-time sensor data processing can be adapted to receive a request for sensor data from vehicles. The system may be further adapted to generate an application for collection of the sensor data. The application may have sensor requirements for performing the collection of sensor data. The system may be further adapted to identify a set of vehicles for distribution of the application based on available sensors in each vehicle corresponding to the sensor requirements. The system may be further adapted to transmit the application to the set of vehicles and receive sensor data results from respective instances of the application executing on the set of vehicles. The system may be further adapted to transmit a command to remove the respective instances of the application from the set of vehicles.

Abstract: A self-reconfigurable controller for a robot, including: input/output (I/O) interfaces to enable communication with I/O peripheral devices coupled to the robot; and processing circuitry that is operable to: register the I/O peripheral devices and associated functionalities; receive a command for the robot to perform a task; conduct a self-awareness check to correlate functionalities to perform the task with functionalities of the I/O peripheral devices; and generate, based on a net of deep learning (DL) models and a result of the correlation, a target deep learning controller (TDLC) model to control the robot to perform the task.

Abstract: Systems and methods for dynamically controlling an infrastructure item are disclosed. The systems and methods may include receiving environmental data. The environmental data may capture behavior of a crossing user. An intent of the crossing user may be determined based on the environmental data. A setting of the infrastructure item may be changed based on the intent of the crossing user.

Abstract: Disclosed herein are devices, methods, and systems for providing an externally augmented camera that may utilize external light sources of a separate sensor to emit light toward a scene so as to provide accurate imaging of the scene, even in dark or low-light situations or where the scene has a high dynamic range of brightness. The externally augmented camera system may include a sensor with a light source capable of emitting light toward a scene, a camera separate from the light source, where the camera includes a detector capable of detecting emitted light from the light source. The externally augmented camera system also causes the sensor to emit light toward the scene via the light source, causes the camera to capture image data of the scene that has been illuminated by emitted light from the light source, and generates an image of the scene based on the image data.

Abstract: An apparatus, including: an interface operable to receive heat factor information for a geographic area; processing circuitry operable to: generate a digital twin of the geographic area, wherein the digital twin is a virtual representation of the geographic area, and based on the received heat factor information, spawns a heat distribution model that mirrors a heat distribution of the geographic area; and perform an action based on the heat distribution model.

Abstract: An apparatus, including: an interface operable to receive from traffic actors, information related to a traffic anomaly in a traffic scene; processing circuitry operable to: generate a digital twin of the traffic scene, wherein the digital twin is a virtual representation of the traffic scene; fuse the received traffic anomaly information; and reconstruct the traffic scene by the digital twin incorporating the fused traffic anomaly information.

Abstract: A system, including: a communication interface operable to receive sensor data related to a state of an object; object state estimation processor circuitry operable to estimate, based on the sensor data, a prospective probability of a degradation of the state of the object; and cobot fleet control processor circuitry operable to generate a command for either a transport cobot operable to transport the object, or another actor, to take proactive action to mitigate the prospective probability of the degradation of the state of the object.

Abstract: An apparatus including a memory and a processor configured to: identify an item located within the environment based on sensor data, wherein the sensor data represents one or more sensor detections of the environment; determine a metric representative of a likelihood of the item becoming lost the within the environment based on information about the item; and select, based on the metric, at least one monitoring method to monitor the item within the environment from a plurality of monitoring methods.

Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed that compile portable code for specific hardware are disclosed herein that include an apparatus including computer readable instructions, and programmable circuitry to at least one of execute or instantiate the instructions to receive input code, the input code written for operation on a first platform, determine a target platform, the target platform different than the first platform, and translate, via an artificial intelligence (AI) model, the input code to output code, the output code written for operation on the target platform.

Abstract: Disclosed herein are systems, devices, and methods for monitoring and improving utilization of a public transport vehicle. The transport control system determines an in-vehicle status of a passenger transport vehicle based on in-vehicle sensor data about an interior of the passenger transport vehicle. The transport control system also determines a station status at a station stop for the passenger transport vehicle based on station sensor data about the station stop. The transport control system also determines a situational status based on the in-vehicle status and the station status. The transport control system also generates a notification message with movement control information for the passenger transport vehicle or for a passenger, wherein the movement control information is based on the situational status.

Abstract: A sensor calibrator comprising one or more processors configured to receive sensor data representing a calibration pattern detected by a sensor during a period of relative motion between the sensor and the calibration pattern in which the sensor or the calibration pattern move along a linear path of travel; determine a calibration adjustment from the plurality of images; and send a calibration instruction for calibration of the sensor according to the determined calibration adjustment. Alternatively, a sensor calibration detection device, comprising one or more processors, configured to receive first sensor data detected during movement of a first sensor along a route of travel; determine a difference between the first sensor data and stored second sensor data; and if the difference is outside of a predetermined range, switch from a first operational mode to a second operational mode.

Abstract: The present disclosure is generally related to connected vehicles, computer-assisted and/or autonomous driving vehicles, Internet of Vehicles (IoV), Intelligent Transportation Systems (ITS), and Vehicle-to-Everything (V2X) technologies, and in particular, to technologies and techniques of a road usage monitoring (RUM) service for monitoring road usage of electric vehicles. The RUM service can be implemented or operated by individual electric vehicles, infrastructure nodes, edge compute nodes, cloud computing services, electric vehicle supply equipment, and/or combinations thereof. Additional RUM aspects may be described and/or claimed.

Abstract: Various systems and methods for controlling a vehicle using driving policies are described herein.

Abstract: Aspects concern a method for controlling a braking of a vehicle. The method including detecting a braking situation, determining a classification of the braking situation, selecting a braking profile based on the determined classification, and applying a deceleration based on the selected braking profile to maintain a safety distance based on the selected braking profile.

Abstract: An occupancy grid manager having a non-uniform occupancy which includes a plurality of cells, configurable in a plurality of cell sizes, each cell representing a region of an environment of a vehicle; and one or more processors, configured to determine one or more context factors; select a cell size for a cell of the plurality of cells based on the one or more context factors; process sensor data provided by one or more sensors; and determine a probability that the cell of the plurality of cells is occupied based on the sensor data.