Intel Corporation

Airbus Defence and Space Gmbh

Airbus Operations (s.a.s.)

Eads Deutschland Gmbh

Michael Paulitsch

Florian Geissler

Rafael Rosales

Ralf Graefe

Yang Peng

Neslihan Kose Cihangir

Ignacio Javier Alvarez

Bertrand Leconte

Cristina Simache

Kevin Mueller

Kathiravetpillai Sivanesan

S M Iftekharul Alam

Kevin Safford

Kuilin Clark Chen

Cornelius Buerkle

Peter Lachner

Fabian Oboril

Yaser M Fouad

Riccardo Mariani

Rainer Makowitz

Frederik Pasch

Norbert Stoeffler

David Israel González Aguirre

Dietmar Geiger

Daniel J Muench

Riccardo Locatelli

Pablo Munoz

Jan Nowotsch

Wolfgang Schlecker

Michael Honold

Fabian Israel Oboril

David Israel GONZÁLEZ Aguirre

Thomas Wild

Kevin Müller

Ludwig Girbinger

Daniel Buehler

Andreas Herkersdorf

For example, an apparatus may include an input to receive Machine Learning (ML) model information corresponding to an ML model to process input information; and a processor to construct a multi-model ML architecture including a plurality of ML model variants based on the ML model, wherein the processor is configured to determine the plurality of ML model variants based on an attribution-based diversity metric corresponding to a model group including a first ML model variant and a second ML model variant, wherein the attribution-based diversity metric corresponding to the model group is based on a diversity between a first attribution scheme and a second attribution scheme, the first attribution scheme representing first portions of the input information attributing to an output of the first ML model variant, the second attribution scheme representing second portions of the input information attributing to an output of the second ML model variant.

Apparatus, system, and method of generating a multi-model Machine Learning (ML) architecture

Techniques are disclosed to detect, inform, and automatically correct typical awareness-related human driver mistakes. This may include those that are caused by a misunderstanding of the current situation, a lack of focus or attention, and/or overconfidence in any currently-engaged assistance features. The disclosure is directed to the prediction of vehicle maneuvers using driver and external environment modeling. The consequence of executing a predicted maneuver is categorized based upon its risk or danger posed to the driving environment, and the vehicle may execute various actions based upon the categorization of a predicted riving maneuver to mitigate or eliminate that risk.

Driver and environment monitoring to predict human driving maneuvers and reduce human driving errors

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

Disclosed herein are systems and methods for dynamically distributing a safety, awareness task. The systems and methods may include receiving hardware resources data associated with a plurality of remote computing systems. A plurality of safety assurance profiles may be received. Each of the plurality of safety assurance profiles may be associated with a respective service. A safety assurance task may be dynamically assigned to one of the plurality of remote computing systems based on the hardware resources data and one of the plurality of safety assurance profiles.

Dynamic safety awareness task distribution

A device may include an image sensor, configured to generate image sensor data representing a vicinity of the device; an image processor, configured to generate a first code from the image sensor data using a code generation protocol; an input validity checker, configured to compare the first code to a second code; and if a similarity of the first code and the second code is within a predetermined range, operate according to a first operational mode; and if the similarity of the first code and the second code is not within the predetermined range, operate according to a second operational mode.

Sensor verifier and method

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

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 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

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

V2X trusted agents provide technical solutions for technical problems facing falsely reported locations of connected vehicles within V2X systems. These trusted agents (e.g., trusted members) may be used to detect an abrupt physical attenuation of a wireless signal and determine whether the attenuation was caused by signal occlusion caused by the presence of an untrusted vehicle or other untrusted object. When the untrusted vehicle is sending a message received by trusted agents, these temporary occlusions allow trusted members to collaboratively estimate the positions of untrusted vehicles in the shared network, and to detect misbehavior by associating the untrusted vehicle with reported positions. Trusted agents may also be used to pinpoint specific mobile targets. Information about one or more untrusted vehicles may be aggregated and distributed as a service.

Pathloss drop trusted agent misbehavior detection

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.

Methods and systems for improving maps

A system to evaluate functional safety in an integrated circuit. The system includes a first circuit to execute an operation to cause to system to perform a function, where the function associated with a specified safety integrity level. The system also includes second circuit to capture trace data at an interface to the first circuit or at internal signals without inhibiting performance of the function, where the trace data comprising information that is used to determine whether the system can perform the function with an indicated level of functional safety and transmit the trace data to a safety evaluation circuit.

Functional safety over trace-and-debug

A gateway having an architecture authorizing a bidirectional communication between applications located in different domains and presenting a high assurance level of protection. The gateway uses a virtualization platform and comprises a set of functional blocs configured to authorize secure bidirectional flow of data along two different paths between first and second domains, said set of functional blocs being decomposed into a plurality of elementarily evaluable components each one of which having a specified function and being adapted to communicate with other predefined elementarily evaluable components.

High assurance security gateway interconnecting different domains

A virtualization platform that provides a systematic, transparent and local testing of components hosted by the virtualization platform in their integrated context. The virtualization platform comprises integrated interceptor modules connected to the components via communication channels, each interceptor module being interposed in the communication channel connecting two components, and an integrated analyzing device connected to the interceptor modules and comprising a control device and a testing device. The control device is configured to put each interceptor module in an operational mode selected out of a set of predetermined operational modes including a testing mode. The testing device is configured to locally test the components connected to the interceptor modules being put in the testing mode.

Growing community of inventors

Ottobrunn, Germany

Michael Paulitsch