Intel Corporation

Frederik Pasch

Cornelius Buerkle

Fabian Oboril

Kay-ulrich Scholl

Bernd Gassmann

Wei Seng Yeap

Say Chuan Tan

Ignacio Javier Alvarez

Rita Chattopadhyay

Michael Paulitsch

Rafael Rosales

Florian Geissler

Ophir Shabtay

Yang Peng

Ruchika Singh

Rony Ferzli

Ying Wei Liew

Chien Chern Yew

Tze Ming Hau

Florian Mirus

Thierry Beaumont

Kay-Ulrich Dr Scholl

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

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

Techniques are disclosed to facilitate path planning safety guiding robots (SGR) for safely navigating and guiding humans through autonomous environments having other autonomous agents such as stationary and/or mobile robots.

System and safety guide robot for accessing automated environments

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

Disclosed herein are systems, devices, and methods of a safety system for analyzing and improving the safety of collaborative environments in which a robot may interact a human. The safety system may determine a monitored attribute of a person within an operating environment of a robot, where the monitored attribute may be based on received sensor information about the person in the operating environment. In addition, the safety system may determine a risk score for the person based on the monitored attribute. The risk score may be defined by (1) a collision probability that the person will cause an interference during a planned operation of the robot and (2) a severity level associated with the interference. The safety system may also generate a mitigating instruction for the robot if the risk score exceeds a threshold level.

Situation-aware safety assessment of robot-human activities

Disclosed herein is an active cyclist and/or pedestrian safety system for identifying, warning, and reacting to dangerous situations that may be experienced by cyclists and/or pedestrians. The safety system may receive first information indicative of a head position of a cyclist and determine, based on the first information, a field of view of the cyclist. The safety system may receive second information indicative of an environment of the cyclist and determine, based on the field of view and the environment, an expected trajectory of the cyclist in a next road segment. The safety system may also determine, based on the field of view and the environment, a risk probability for the expected trajectory of the cyclist in the next road segment. The safety system may also generate an instruction to transmit a warning to the cyclist if the risk probability exceeds a threshold value.

Active vehicle safety system for cyclists and pedestrians

Disclosed herein is a vehicle handover system that monitors an environment of a vehicle. The vehicle handover system receives a transition request to change control of the vehicle from an automated driving mode to a passenger of the vehicle. The vehicle handover system detects a key event that may be relevant to the transition request and the detection of the key event is based on the monitored environment. The vehicle handover system may generate a handover scene that includes images associated with the key event, and the images include an image sequence over a time-period of the key event. Before the vehicle handover system changes control of the vehicle from the automated driving mode to the passenger, the handover scene is displayed to the passenger.

Handover assistant for machine to driver transitions

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.

Growing community of inventors

Karlsruhe, Germany

Frederik Pasch