Intel Corporation

Mobileye Vision Technologies Ltd.

Realsense Ltd.

Karlsruher Institut Fur Technologie

Fabian Oboril

Cornelius Buerkle

Frederik Pasch

Bernd Gassmann

Kay-ulrich Scholl

Ignacio Javier Alvarez

Florian Geissler

Yang Peng

Julio Jarquin Arroyo

Daniel Pohl

Kay-Ulrich Scholl

Ralf Graefe

Yiwen Guo

Michael Paulitsch

Xiangbin Wu

Rafael Rosales

Yuqing Hou

Maik Sven Fox

Norbert Stoeffler

Ophir Shabtay

Wei Seng Yeap

Ying Wei Liew

Alexander Carl Unnervik

Say Chuan Tan

Chien Chern Yew

Tze Ming Hau

Rajendra Bishnoi

Mehdi B Tahoori

Mehdi Tahoori

An apparatus, including an interface configured to receive images with measured distance information of an environment in which an autonomous agent is designed to operate; and processing circuitry that is configured to: generate distance histogram images over time, wherein the distance histogram images include the measured distance information in corresponding picture elements; perform a distribution-based outlier analysis on the distance histogram images to classify each picture element of each of the received images as either an outlier picture element representing a dynamic object or a non-outlier picture element representing a static portion of the environment; and track a dynamic object over time and cause an action by the autonomous agent if it is determined, based on a result of the distribution-based outlier analysis, a distance between the dynamic object and the autonomous agent is less than a predefined distance.

Autonomous agent operation using histogram images

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 is a device for filtering a point cloud. The device may include processor configured to receive a plurality of sensed points representing distance measurements to points in an area around an entity. The processor may also be configured to determine, for each sensed point of the plurality of sensed points, one or more probabilities that the sensed point is associated with an obstacle in the area around the entity, wherein each probability of the one or more probabilities is based on a corresponding filter of one or more filters. The processor may also be configured to generate a filtered point cloud from the plurality of sensed points based on, for each sensed point, a hierarchical combination of the one or more probabilities.

Hierarchical perception monitor for vehicle safety

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

The automated driving perception systems described herein provide technical solutions for technical problems facing navigation sensors for autonomous vehicle navigation. These systems may be used to combine inputs from multiple navigation sensors to provide a multimodal perception system. These multimodal perception systems may augment raw data within a development framework to improve performance of object detection, classification, tracking, and sensor fusion under varying external conditions, such as adverse weather and light, as well as possible sensor errors or malfunctions like miss-calibration, noise, and dirty or faulty sensors. This augmentation may include injection of noise, occlusions, and misalignments from raw sensor data, and may include ground-truth labeling to match the augmented data. This augmentation provides improved robustness of the trained perception algorithms against calibration, noise, occlusion, and faults that may exist in real-world scenarios.

Autonomous vehicle perception multimodal sensor data management

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

An occupancy verification including one or more processors, configured to receive occupancy grid data representing a plurality of cells of an occupancy grid and for each cell of the plurality of cells, a probability of whether the cell is occupied; receive object location data, representing one or more locations of one or more objects identified using a model; map the object location data to one or more cells of the plurality of cells based on the one or more locations of the one or more objects; determine comparison grid data, representing the plurality of cells of the occupancy grid and for each cell of the plurality of cells, a probability of whether the cell is occupied based on whether the object location data are mapped to the cell; and compare the occupancy grid data to the comparison grid data using at least one comparing rule.

Occupancy verification device and method

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 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.

Systems, devices, and methods for predictive risk-aware driving

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.

Distributed in-vehicle realtime sensor data processing as a service

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.

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Karlsruhe, Germany

Fabian Oboril