Abstract: The technology relates to partially redundant equipment architectures for vehicles able to operate in an autonomous driving mode. Aspects of the technology employ fallback configurations, such as two or more fallback sensor configurations that provide some minimum amount of field of view (FOV) around the vehicle. For instance, different sensor arrangements are logically associated with different operating domains of the vehicle. Fallback configurations for computing resources and/or power resources are also provided. Each fallback configuration may have different reasons for being triggered, and may result in different types of fallback modes of operation. Triggering conditions may relate, e.g., to a type of failure, fault or other reduction in component capability, the current driving mode, environmental conditions in the vicinity of vehicle or along a planned route, or other factors.

Abstract: The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wetness and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules, external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth.

Abstract: The solution provides a system remote from edge devices at sites of an electricity grid. The system can have a processor to trigger, based on a characteristic of electricity related to the electricity distribution grid, a control function. The processor can ping, using a natural language-based protocol compatible with a generative machine learning model, edge devices for data on electricity consumption at the grid and receive, from the edge devices, information having data structures generated via the generative machine learning model executed by the edge devices. The processor can generate, based on the information and a grid layout, responsive to the trigger of the control function, guidance to impact the electricity characteristic at one or more sites. The system can transmit, to the edge devices, the guidance to adjust the electricity characteristic.

Abstract: The technology relates to partially redundant equipment architectures for vehicles able to operate in an autonomous driving mode. Aspects of the technology employ fallback configurations, such as two or more fallback sensor configurations that provide some minimum amount of field of view (FOV) around the vehicle. For instance, different sensor arrangements are logically associated with different operating domains of the vehicle. Fallback configurations for computing resources and/or power resources are also provided. Each fallback configuration may have different reasons for being triggered, and may result in different types of fallback modes of operation. Triggering conditions may relate, e.g., to a type of failure, fault or other reduction in component capability, the current driving mode, environmental conditions in the vicinity of vehicle or along a planned route, or other factors.

Abstract: The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wetness and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules, external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth.

Abstract: Technical solutions provide a system with a device having a processor coupled with memory to receive, via an interface, a query related to a characteristic of electricity and retrieve data for the grid edge device. The data can include any combination of contextual data, operational data and metering data related to the device or the site. The device can construct a prompt data structure using generative artificial intelligence based on the input query and the data for the device. The device can generate, using the generative artificial intelligence on the device, code responsive to the prompt data structure. The device can execute the processor-executable code to generate output responsive to the query and perform an action in accordance with the output.

Abstract: Cybersecurity reconnaissance, analysis, and scoring uses distributed, cloud or edge-based pools of computing services to provide sufficient scalability for analysis of IT/OT networks using only publicly available characterizations. An in-memory associative array manages a queue of configuration and vulnerability search tasks through at least one public-facing proxy network which uses configurable search nodes to approach the target network with search tools in a desired manner to control certain aspects of the search in order to obtain the desired results, especially when target network behavior adjusts based on counterparty characteristics. A data packet modifier reveals IP addresses of threat actors behind port scans and subsequently block the threat actors.

Abstract: Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode. The system includes a plurality of sensors configured to generate sensor data. The system also includes a first computing system configured to generate trajectories using the sensor data and send the generated trajectories to a second computing system. The second computing system is configured to cause the vehicle to follow a receive trajectory. The system also includes a third computing system configured to, when there is a failure of the first computer system, generate and send trajectories to the second computing system based on whether a vehicle is located on a highway or a surface street.

Abstract: The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wetness and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules, external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth.

Abstract: A system and method for cybersecurity reconnaissance, analysis, and scoring that uses distributed, cloud-based computing services to provide sufficient scalability for analysis of enterprise IT networks using only publicly available characterizations. The system and method comprise an in-memory associative array which manages a queue of vulnerability search tasks through a public-facing proxy network. The public-facing proxy network has search nodes configurable to present the network to search tools in a desired manner to control certain aspects of the search to obtain the desired results. A distributed data processing engine and cloud-based storage are used to provide scalable computing power and storage. Each of the cloud-based computing services is containerized and orchestrated for management and efficient scaling purposes.

Abstract: Cybersecurity reconnaissance, analysis, and scoring uses distributed, cloud or edge-based pools of computing services to provide sufficient scalability for analysis of IT/OT networks using only publicly available characterizations. An in-memory associative array manages a queue of configuration and vulnerability search tasks through at least one public-facing proxy network which uses configurable search nodes to approach the target network with search tools in a desired manner to control certain aspects of the search in order to obtain the desired results, especially when target network behavior adjusts based on counterparty characteristics. A data packet modifier reveals IP addresses of threat actors behind port scans and subsequently block the threat actors.

Abstract: The technology relates to using on-board sensor data, off-board information and a deep learning model to classify road wetness and/or to perform a regression analysis on road wetness based on a set of input information. Such information includes on-board and/or off-board signals obtained from one or more sources including on-board perception sensors, other on-board modules, external weather measurement, external weather services, etc. The ground truth includes measurements of water film thickness and/or ice coverage on road surfaces. The ground truth, on-board and off-board signals are used to build the model. The constructed model can be deployed in autonomous vehicles for classifying/regressing the road wetness with on-board and/or off-board signals as the input, without referring to the ground truth.

Abstract: Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode. The system includes a plurality of sensors configured to generate sensor data. The system also includes a first computing system configured to generate trajectories using the sensor data and send the generated trajectories to a second computing system. The second computing system is configured to cause the vehicle to follow a receive trajectory. The system also includes a third computing system configured to, when there is a failure of the first computer system, generate and send trajectories to the second computing system based on whether a vehicle is located on a highway or a surface street.

Abstract: A storage system includes one or more data storage devices, a PCIe switch coupled to the one or more data storage devices, and a controller unit coupled to the PCIe switch. The one or more data storage devices are DRAM-less. The controller unit includes a dynamic random access memory (DRAM) host memory buffer (HMB) controller and a DRAM pool or a controller memory buffer (CMB) controller, a root complex/port, and the DRAM pool. The DRAM pool includes one or more DRAM devices. The one or more data storage devices are configured to interact with the controller unit and store data to a DRAM of the DRAM pool of the controller unit.

Abstract: The technology relates to determining general weather conditions affecting the roadway around a vehicle, and how such conditions may impact driving and route planning for the vehicle when operating in an autonomous mode. For instance, the on-board sensor system may detect whether the road is generally icy as opposed to a small ice patch on a specific portion of the road surface. The system may also evaluate specific driving actions taken by the vehicle and/or other nearby vehicles. Based on such information, the vehicle's control system is able to use the resultant information to select an appropriate braking level or braking strategy. As a result, the system can detect and respond to different levels of adverse weather conditions. The on-board computer system may share road condition information with nearby vehicles and with remote assistance, so that it may be employed with broader fleet planning operations.

Abstract: An example method involves identifying one or more potential route segments that collectively connect at least two geographical points, receiving spatiotemporal weather information that predicts future weather conditions along each of the potential segments, and, for each potential segment, evaluating a partial cost function that comprises a summation of a set of segment-weighted cost factors, where at least one segment-weighted cost factor comprises an adverse weather risk factor based on the future weather conditions along the potential segment. The method also involves selecting, based on a minimization of a total cost function, a set of selected segments and corresponding segment target speeds for the vehicle to utilize while traversing between the at least two geographical points so as to avoid adverse weather conditions, the total cost function being the sum of partial cost functions associated with a set of segments that collectively connect the at least two geographical points.

Abstract: The technology relates to determining general weather conditions affecting the roadway around a vehicle, and how such conditions may impact driving and route planning for the vehicle when operating in an autonomous mode. For instance, the on-board sensor system may detect whether the road is generally icy as opposed to a small ice patch on a specific portion of the road surface. The system may also evaluate specific driving actions taken by the vehicle and/or other nearby vehicles. Based on such information, the vehicle's control system is able to use the resultant information to select an appropriate braking level or braking strategy. As a result, the system can detect and respond to different levels of adverse weather conditions. The on-board computer system may share road condition information with nearby vehicles and with remote assistance, so that it may be employed with broader fleet planning operations.

Abstract: Aspects of the disclosure relate to generating a puddle occupancy grid including a plurality of cells. For instance, a first probability value for a puddle being located at a first location generated using sensor data from a first sensor may be received. A second probability value for a puddle being located at a second location generating using sensor data from a second sensor different from the first sensor may be received. A first cell may be identified from the plurality of cells using the first location. The first cell may also be identified using the second location. A value for the cell may be generated using the first probability value and the second probability value.

Abstract: A hose connection for an arrangement for collection of liquid from a patient is disclosed which connection comprises a first tubular member (1) to be in fluid connection with the patient and having one or more protrusions (3) arranged on an outer surface, and an outlet (2), and further comprises a collection bag (24) having an inlet to be connected to the outlet for collection of liquid coming through the first tubular member. The inlet comprises a second tubular member (5) being connected to the bag and adapted to receive and surround at least a part of said first tubular member. The second tubular member has one or more receiving means (6, 11) being engageable with the protrusions for providing a locked, but separable connection between the first and second tubular member.

Abstract: A system and method for cybersecurity reconnaissance, analysis, and scoring that uses distributed, cloud-based computing services to provide sufficient scalability for analysis of enterprise IT networks using only publicly available characterizations. The system and method comprise an in-memory associative array which manages a queue of vulnerability search tasks through a public-facing proxy network. The public-facing proxy network has search nodes configurable to present the network to search tools in a desired manner to control certain aspects of the search to obtain the desired results. A distributed data processing engine and cloud-based storage are used to provide scalable computing power and storage. Each of the cloud-based computing services is containerized and orchestrated for management and efficient scaling purposes.