Abstract: 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.

Abstract: Systems, methods, and computer-readable media are provided for wireless sensor networks (WSNs), including sensor deployment mechanisms for road surveillance. Disclosed embodiments are applied to design roadside infrastructure with optimal perception for a given geographic area. The deployment mechanisms account for the presence of static and dynamic obstacles, as well as symmetry aspects of the underlying environment. The deployment mechanisms minimize the number of required sensors to reduce costs and conserve compute and network resources, and extended infrastructure the sensing capabilities of sensor networks. Other embodiments are disclosed and/or claimed.

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: Disclosure herein are systems and methods for deploying an autonomous vehicle during an idle time. As disclosed herein, a request for a mobility service may be received. The request may include constraints for usage of the autonomous vehicle. An optimal mobility service strategy may be determined based on the constraints. The optimal mobility service strategy may be selected from a plurality of mobility service strategies. A notification may be transmitted to a user device. The notification may include details of the optimal mobility service strategy.

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: 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: 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: 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.

Abstract: Systems, methods, and computer-readable media are provided for wireless sensor networks (WSNs), including sensor deployment mechanisms for road surveillance. Disclosed embodiments are applied to design roadside infrastructure with optimal perception for a given geographic area. The deployment mechanisms account for the presence of static and dynamic obstacles, as well as symmetry aspects of the underlying environment. The deployment mechanisms minimize the number of required sensors to reduce costs and conserve compute and network resources, and extended infrastructure the sensing capabilities of sensor networks. Other embodiments are disclosed and/or claimed.

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.