Intel Corporation

Harman Becker Automotive Systems Gmbh

Kay-ulrich Scholl

Koba Natroshvili

Fabian Oboril

Cornelius Buerkle

Frederik Pasch

Bernd Gassmann

Norbert Stoeffler

Rita Chattopadhyay

Rafael Rosales

Yang Peng

Johannes Quast

Ruchika Singh

Rony Ferzli

Wei Seng Yeap

Say Chuan Tan

Fabio Cesari

Florian Mirus

Thierry Beaumont

Systems and techniques for reliable real-time deployment of robot safety updates are described herein. Condition data may be collected for an environment in which a robot is operating. The robot may be classified with a condition type. Condition type data selected from the condition data may be analyzed based on the condition type to calculate a safety risk level for the robot. A microservice may be identified to provide a robot safety rule for the robot based on the safety risk level. The microservice may be identified using a safety prediction model generated from the condition type data.

Reliable real-time deployment of robot safety updates

A device including a processor configured to: receive sensor measurements; determine a viewer position based on the received sensor measurements; generate a control signal based on the viewer position; and determine a view based on the viewer position.

Strapless autonomous extended reality viewer

Various systems and methods for detecting risk conditions in a physical workspace. An apparatus can include an interface to receive smart sensor signals from at least one autonomous mobile entity (AME) in the physical workspace. The apparatus can also include processing circuitry coupled to the interface to detect a risk condition associated with the at least one AME, based on the smart sensor signals, relative to a user device associated with a human present in the physical workspace. The processing circuitry can also detect a direction of the risk condition relative to the user device and cause a notification to the first user device. The notification can indicate the direction of the risk condition relative to the user device. Other systems, methods and apparatuses are described.

Autonomous mobile entity (AME) risk monitoring and maneuvering systems

Disclosed herein are systems, devices, and methods for efficiently checking the integrity of a robot system. The integrity-checking system may generate a predefined motion instruction for a robot, where the predefined motion instruction instructs the robot to perform a random movement at a test time. The random movement may be associated with an expected observation at the test time. The integrity-checking system may also determine a systematic failure based on a difference between the expected observation and a current observation of the robot at the test time. The current observation may be determined from received sensor data, and if the integrity-checking system detects a failure, it may stop the robot's motion or other mitigating instructions.

Integrity and safety checking for robots

Disclosed herein are systems, devices, and methods of a safety system for monitoring the in-vehicle safety of internal objects within a vehicle. The safety system generates a digital twin of the interior objects from vehicle configuration data indicating a configuration of an interior environment of the vehicle, from interior object data associated with the interior object within the interior environment of the vehicle, and from vehicle situation data that indicates an operating status of the vehicle. The digital twin is an abstract model of the interior objects within the interior environment of the vehicle, and the safety system generates, based on the digital twin, a safety score associated with an operating behavior of the vehicle. Based on the safety score, the safety system determines a target level for the operating behavior.

Analyzing in-vehicle safety based on a digital twin

A monitoring system may include a memory having computer-readable instructions stored thereon and a processor operatively coupled to the memory. The processor may read and execute the computer-readable instructions to perform or control performance of operations. The operations may include receive, prior to a collision involving a vehicle, sensor data representative of a feature of an internal environment and determine the collision has occurred. The operations may include automatically instruct, based on the collision, a sensor to generate another sensor data representative of another feature of the internal environment. The operations may include receive the another sensor data from the sensor and compare the sensor data and the another sensor data to accident data corresponding to previous accidents. The accident data may include a diagnosed injury and an accident severity of each of the previous accidents. The operations may include determine a severity of the collision based on the comparison.

Injury severity estimation by using in-vehicle perception

Mobility-as-a-Service (MaaS) provides technical solutions for technical problems facing this shift toward public and private vehicles for hire, including providing a platform for users to identify and select public transportation and private vehicles for hire. Users may plan and book transportation services through a MaaS platform, such as a smartphone application. Technical solutions described herein include improved identification and selection of a vehicle based on wireless communication, such as using Near-Field Communication (NFC), Bluetooth, Wi-Fi, and other wireless communication.

Transmission limited beacon for transportation device selection

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.

Autonomous system terminus assistance techniques

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.

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Malsch, Germany

Kay-ulrich Scholl