Abstract: Disclosed herein is a passenger monitoring system for monitoring an observed attribute of a passenger in a vehicle. The observed attribute may include a gaze of the passenger, a head track of the passenger, and other observations about the passenger in the vehicle. Based on the observed attribute(s), a field of view of the passenger may be determined. Based on the field of view, a focus point of the passenger may be determined, where the focus point is estimated to be within the field of view. If a sign (e.g., a road sign, a billboard, etc.) is within the field of view of the passenger, record an attention score for the sign based on a duration of time during which the sign is within the field of view and estimated to be the focus point of the passenger.

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: A controller for an autonomous vehicle may include: one or more processors configured to: determine a maneuver planned for the vehicle based on a safety driving model and based on a first message from a network component external to the vehicle, the first message including a respective assessment for each proposed maneuver of at least two maneuvers proposed for the vehicle, and provide an in-vehicle instruction to perform the maneuver planned for the vehicle.

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: Systems, methods, and computer-readable media are provided for wireless sensor networks (WSNs), including vehicle-based WSNs. A road side unit (RSU) includes one or more fixed sensors covering different sectors of a designated coverage area. The RSU uses the sensors to capture sensor data that is representative of objects in the coverage area, tracks objects (e.g., vehicles) in the coverage area, and determines regions in the coverage area that are not adequately covered by the sensors (e.g., “perception gaps”). When the RSU identifies an object that is in or at a perception gap, then the RSU sends a request to that object for sensor data captured by the object's on-board sensors. The RSU obtains the sensor data from the object, and uses the obtained sensor data to complement the knowledge that the RSU (i.e., “filling the perception gaps”). Other embodiments are disclosed and/or claimed.

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: A controller for an autonomous vehicle may include: one or more processors configured to: determine a maneuver planned for the vehicle based on a safety driving model and based on a first message from a network component external to the vehicle, the first message including a respective assessment for each proposed maneuver of at least two maneuvers proposed for the vehicle, and provide an in-vehicle instruction to perform the maneuver planned for the vehicle.

Abstract: 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.

Abstract: Aspects concern a method for controlling a braking of a vehicle. The method including detecting a braking situation, determining a classification of the braking situation, selecting a braking profile based on the determined classification, and applying a deceleration based on the selected braking profile to maintain a safety distance based on the selected braking profile.

Abstract: A controller for an autonomous vehicle may include: one or more processors configured to: determine a maneuver planned for the vehicle based on a safety driving model and based on a first message from a network component external to the vehicle, the first message including a respective assessment for each proposed maneuver of at least two maneuvers proposed for the vehicle, and provide an in-vehicle instruction to perform the maneuver planned for the vehicle.

Abstract: A safety system (200) for a vehicle (100) is provided. The safety system (200) may include one or more processors (102). The one or more processors (102) may be configured to control a vehicle (100) to operate in accordance with the predefined stored driving model parameters, to detect vehicle operation data during the operation of the vehicle (100), to determine whether to change predefined driving model parameters based on the detected vehicle operation data and the driving model parameters, to change the driving model parameters to changed driving model parameters, and to control the vehicle (100) to operate in accordance with the changed driving model parameters.

Abstract: A controller for an autonomous vehicle may include: one or more processors configured to: determine a maneuver planned for the vehicle based on a safety driving model and based on a first message from a network component external to the vehicle, the first message including a respective assessment for each proposed maneuver of at least two maneuvers proposed for the vehicle, and provide an in-vehicle instruction to perform the maneuver planned for the vehicle.

Abstract: An exemplary method includes obtaining vehicle data comprising environmental perception data indicating a risk assessment regarding one or more perceived elements of an environment surrounding a vehicle; obtaining driver perception data regarding a driver inside the vehicle; determining an integrated risk assessment based on the vehicle data and the driver perception data; and determining an Operational Design Doman (ODD) compliance assessment of the vehicle at least based on the determined integrated risk assessment.

Abstract: Systems, methods, and computer-readable media are provided for wireless sensor networks (WSNs), including vehicle-based WSNs. A road side unit (RSU) includes one or more fixed sensors covering different sectors of a designated coverage area. The RSU uses the sensors to capture sensor data that is representative of objects in the coverage area, tracks objects (e.g., vehicles) in the coverage area, and determines regions in the coverage area that are not adequately covered by the sensors (e.g., “perception gaps”). When the RSU identifies an object that is in or at a perception gap, then the RSU sends a request to that object for sensor data captured by the object's on-board sensors. The RSU obtains the sensor data from the object, and uses the obtained sensor data to complement the knowledge that the RSU (i.e., “filling the peception gaps”). Other embodiments are disclosed and/or claimed.

Abstract: Techniques are disclosed for the exploration of environments for the estimation and detection of hazards or near hazards within the environment and the mitigation of hazards therein. The exploration of the environment and mitigation of hazards therein may use one or more autonomous agents, including a hazard response robot. The estimation of the hazards may use a policy learning engine, and the hazards may be detected, and the associated risks therefrom, may be determined using a hazard estimation system.

Abstract: Disclosed herein are systems, devices, and methods for improving the safety of a robot. The safety system may determine a safety envelope of a robot based on a planned movement of the robot and based on state information about a load carried by a robot. The state information may include a dynamic status of the load. The safety system may also determine a safety risk based on a detected object with respect to the safety envelope. The safety system may also generate a mitigating action to the planned movement if the safety risk exceeds a threshold value.