Intel Corporation

Intel IP Corporation

Airbus Operations Gmbh

Ralf Graefe

Florian Geissler

Rainer Makowitz

Michael Paulitsch

Rafael Rosales

Mark Y Kelly

Keith Nolan

Neslihan Kose Cihangir

Manoj R Sastry

Marcio Rogerio Juliato

Liuyang L Yang

Shabbir Ahmed

Christopher N Gutierrez

Xiruo Liu

Yang Peng

Damian Kelly

Rita H Wouhaybi

Ignacio Javier Alvarez

Hassnaa Moustafa

Daniel Pohl

Cornelius Buerkle

Fabian Oboril

Yiwen Guo

Xiangbin Wu

Sangeeta Ghangam Manepalli

Siew Wen Chin

Norbert Stoeffler

Yuqing Hou

Maik Sven Fox

Tobias Dahms

David Israel González Aguirre

Alexander Carl Unnervik

Franz Angerer

Arne Anhalt

Stephan Beier

Martin Dobler

Ulf Glaser

Juergen Hairbucher

Fabian Knaul

Disclosed herein are systems, devices, and methods of a robot safety system for improving the safety of human-robot collaborations. The robot safety system may include a processor that receives a collaboration policy for task collaboration between a robot and a collaborator. The task collaboration may include a movement plan of the robot to move a manipulator to a collaboration position. The robot safety system may also determine a new collaboration position based on the collaboration policy. The robot safety system may also update the collaboration position of the movement plan to the new collaboration position.

Repetitive task and contextual risk analytics for human-robot collaboration

Disclosed embodiments prioritize gaps in V2X coverage and then selectively route traffic based on the prioritized gaps. Some embodiments combine historical vehicle presence along a route with predicted prospective vehicle traffic along the route to generate a map of regions that have a high confidence of a need for V2X coverage. This high confidence map is compared to a historical V2X coverage in those regions. From this comparison, a set of high priority V2X gaps is identified. Vehicles are then selectively routed either around or into the gaps.

Dynamic V2X networks and vehicle routing

Various systems and methods for providing autonomous driving within a restricted area are discussed. In an examples, an autonomous vehicle control system can include an interface for receiving data from multiple sensors for detecting an environment about the vehicle, a security processor coupled to the configured to receive sensor information from the sensor interface, and autonomous driving system including one or more virtual machines configured to selectively receive information from the security processor based on a security request from infrastructure of the restricted area.

Restricted area autonomous vehicle control techniques

A pedestrian route can be segmented into at least one pedestrian walking segment using location information of transportation resources. An estimated transit time for the pedestrian route can be determined as a function of an estimated transit time of the at least one pedestrian walking segment, an estimated wait time for the transportation resource to arrive at the user determined using received status real-time location and movement information of the transportation resource and the determined estimated transit time for the at least one pedestrian walking segment, and an estimated transit time for the transportation resource to transport the user.

Pedestrian traffic management

A vehicle data relation device includes an internal audio/image data analyzer, configured to receive first data representing at least one of audio from within the vehicle or an image from within the vehicle; identify within the first data second data representing an audio indicator or an image indicator, wherein the audio indicator is human speech associated with a significance of an object external to the vehicle, and wherein the image indicator is an action of a human within the vehicle associated with a significance of an object external to the vehicle; an external image analyzer, configured to receive third data representing an image of a vicinity external to the vehicle; identify within the third data an object corresponding to at least one of the audio indicator or the video indicator; and an object data generator, configured to generate data corresponding to the object.

Vehicle data relation device and methods therefor

A computer-implemented method may include obtaining, from a system using a middleware component of the system, run-time evidence of the system; applying the obtained run-time evidence to a Directed Acyclic Graph (DAG) Bayesian network to determine marginal probabilities for one or more nodes of the DAG Bayesian network, wherein the DAG Bayesian network comprises a plurality of nodes each representing states and faults of the system, wherein each node includes a parameterized conditional probability distribution, and wherein one or more of the nodes of the plurality of nodes specify a list of one or more safety goals and a safety value; determining which nodes representing faults have probabilities exceeding their specified safety value; and determining one or more risk mitigation techniques to activate for the determined nodes representing faults with probabilities exceeding their respective safety value.

Systems, methods, and devices for reducing systemic risks

Systems, methods, and computer-readable media are provided for wireless sensor networks (WSNs), including vehicle-based WSNs. A road side unit (RSU) includes one or more fixed sensors covering different sectors of a designated coverage area. The RSU uses the sensors to capture sensor data that is representative of objects in the coverage area, tracks objects (e.g., vehicles) in the coverage area, and determines regions in the coverage area that are not adequately covered by the sensors (e.g., 'perception gaps'). When the RSU identifies an object that is in or at a perception gap, then the RSU sends a request to that object for sensor data captured by the object's on-board sensors. The RSU obtains the sensor data from the object, and uses the obtained sensor data to complement the knowledge that the RSU (i.e., “filling the perception gaps”). Other embodiments are disclosed and/or claimed.

Sensor network enhancement mechanisms

Described herein is a high confidence ground truth information service executing on a network of edge computing devices. A variety of participating devices obtain high confidence ground truth information relating to objects in a local environment. This information is communicated to the ground truth information service, where it may be verified and aggregated with similar information before being communicated as part of an acquired ground truth dataset to one or more subscribing devices. The subscribing devices use the ground truth information, as included in the ground truth dataset, to both validate and improve their supervised learning systems.

Validation and training service for dynamic environment perception based on local high confidence information

Various embodiments are generally directed to techniques for providing improved privacy protection against vehicle tracking for connected vehicles of a vehicular network. For example, at least one road side unit may: identify a set of vehicles that require pseudonym changes and send an invitation for a pseudonym change event to each of the vehicles, determine at least a total number of the acceptances, determine whether the total number meets or exceeds a predetermined threshold number, send acknowledgement messages to the accepting vehicles if the threshold number is met, and form a vehicle group to coordinate the pseudonym change event during a privacy period. During the privacy period, the RSU and the vehicles may communicate with each other in a confidential and private manner via key-session-based unicast transmission, and coordinate transmission power and vehicle trajectory adjustments to maximize the benefits for safety and obfuscation for privacy.

Privacy protection mechanisms for connected vehicles

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.

Sensor calibration and sensor calibration detection

Disclosed in some examples are methods, systems, and machine readable mediums which automatically generate standardized interfaces to sensor data consumers, provide sensor data search functionality, automatically determine data quality, and cache previously used sensor data to minimize the burden on application developers and minimize API call costs.

Sensor data search platform

Systems, methods, and computer-readable media are provided for wireless sensor networks (WSNs), including vehicle-based WSNs. A road side unit (RSU) includes one or more fixed sensors covering different sectors of a designated coverage area. The RSU uses the sensors to capture sensor data that is representative of objects in the coverage area, tracks objects (e.g., vehicles) in the coverage area, and determines regions in the coverage area that are not adequately covered by the sensors (e.g., 'perception gaps'). When the RSU identifies an object that is in or at a perception gap, then the RSU sends a request to that object for sensor data captured by the object's on-board sensors. The RSU obtains the sensor data from the object, and uses the obtained sensor data to complement the knowledge that the RSU (i.e., 'filling the perception gaps'). Other embodiments are disclosed and/or claimed.

Various systems and methods for a reconfigurable roadside network. Current traffic data of a road segment is received from sensors. A traffic scenario is identified based on current the traffic data. Key performance indicators are determined for the traffic scenario. The roadside network is modified based on the key performance indicators.

Reconfigurable network infrastructure for collaborative automated driving

Methods, systems, and apparatus, including computer programs encoded on non-transitory computer storage medium(s), are directed to improving completeness of map information and data related to maps created through sensor data. Map completeness can be improved by determining object completeness and coverage completeness of a generated map and reducing amount of unknown areas of the generated map.

Growing community of inventors

Haar, Germany

Ralf Graefe