Abstract: Devices and methods for a road user are provided in this disclosure. An apparatus for determining detection of a road user may include a memory configured to store a plurality of data items received from a plurality of further road users. Each of the plurality of data items may include a detection information indicating whether an object has or has not been detected by one of the plurality of further road users. Furthermore, the apparatus may include a processor that is configured to determine a detection result indicating whether the road user has been detected by the one of the plurality of further road users based on the detection information.

Abstract: System and techniques for facility floor modeling are described herein. A scan set that includes depth data and correlated image data from various positions on a facility floor is obtained. This data includes representations of equipment in the factory. A trained model is invoked, receiving the scan set as input and producing, as output, a computer model of the equipment along with associated information. The computer model of the equipment is integrated into a larger computer model of the factory and the information regarding the equipment is stored in a data structure that corresponds to the equipment's representation within the computer model of the factory.

Abstract: Devices and methods for a vehicle are provided in this disclosure. A device for controlling an active seat of a vehicle may include a processor and a memory. The memory may be configured to store a transfer function. The processor may be configured to predict an acceleration of the active seat of the vehicle based on a first sensor data and the transfer function. The first sensor data may include information indicating an acceleration of a vibration source for the vehicle. The processor may be further configured to generate a control signal to control a movement of the active seat at a first instance of time based on the predicted acceleration. Furthermore, the processor may be configured to adjust the transfer function based on a second sensor data including information indicating a detected acceleration of the active seat at the first instance of time.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A device for blind spot determination of a second vehicle in a vicinity of a first vehicle includes a processor, configured to determine one or more context variables associated with the second vehicle; modify a model of the second vehicle based on one or more context variables; and determine a probability of the first vehicle being within an area of limited visibility in the modified model.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed herein are embodiments of systems and methods for accessible vehicles (e.g., accessible autonomous vehicles). In an embodiment, a passenger-assistance system for a vehicle includes first circuitry, second circuitry, third circuitry, and fourth circuitry. The first circuitry is configured to identify an assistance type of a passenger of the vehicle. The second circuitry is configured to control one or more passenger-comfort controls of the vehicle based on the identified assistance type. The third circuitry is configured to generate a modified route for a ride for the passenger at least in part by modifying an initial route for the ride based on the identified assistance type. The fourth circuitry is conduct a pre-ride safety check and/or a pre-exit safety check based on the identified assistance type.

Abstract: The apparatus for the autonomously acting machine comprises an interface for communicating with a computer system, wherein the computer system is separate from the autonomously acting machine. The apparatus includes a processor for executing machine-readable instructions for providing information about an internal state of the autonomously acting machine to the computer system, obtaining a feedback signal from the computer system, the feedback signal indicating whether sensor data for observing the autonomously acting machine is consistent with the internal state of the autonomously acting machine, and wherein the feedback signal is based on a comparison between a digital twin of the autonomously acting machine and the sensor data, wherein an internal state of the digital twin is based on the internal state of the autonomously acting machine, and operating the autonomously acting machine based on the feedback signal.

Abstract: 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.

Abstract: 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.

Abstract: Disclosed herein are devices, methods, and systems for providing an externally augmented camera that may utilize external light sources of a separate sensor to emit light toward a scene so as to provide accurate imaging of the scene, even in dark or low-light situations or where the scene has a high dynamic range of brightness. The externally augmented camera system may include a sensor with a light source capable of emitting light toward a scene, a camera separate from the light source, where the camera includes a detector capable of detecting emitted light from the light source. The externally augmented camera system also causes the sensor to emit light toward the scene via the light source, causes the camera to capture image data of the scene that has been illuminated by emitted light from the light source, and generates an image of the scene based on the image data.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that calibrate error aligned uncertainty for regression and continuous structured prediction tasks/optimizations. An example apparatus includes a prediction model, at least one memory, instructions, and processor circuitry to at least one of execute or instantiate the instructions to calculate a count of samples corresponding to an accuracy-certainty classification category, calculate a trainable uncertainty calibration loss value based on the calculated count, calculate a final differentiable loss value based on the trainable uncertainty calibration loss value, and calibrate the prediction model with the final differentiable loss value.