Provided are methods for semantic annotation of sensor data using unreliable map annotation inputs, which can include training a machine learning model to accept inputs including images representing sensor data for a geographic area and unreliable semantic annotations for the geographic area. The machine learning model can be trained against validated semantic annotations for the geographic area, such that subsequent to training, additional images representing sensor data and additional unreliable semantic annotations can be passed through the neural network to provide predicted semantic annotations for the additional images. Systems and computer program products are also provided.

Stochastic nonlinear model predictive control (SNMPC) allows to directly take uncertainty of the dynamics and/or of the system's environment into account, e.g., by including probabilistic chance constraints. However, SNMPC requires the approximate computation of the probability distributions for the state variables that are propagated through the nonlinear system dynamics. This invention proposes the use of Gaussian-assumed density filters (ADF) to perform high-accuracy propagation of mean and covariance information of the state variables through the nonlinear system dynamics, resulting in a tractable SNMPC approach with improved control performance. In addition, the use of a matrix factorization for the covariance matrix variables in the constrained optimal control problem (OCP) formulation guarantees positive definiteness of the full trajectory of covariance matrices in each iteration of any optimization algorithm. Finally, a tailored adjoint-based sequential quadratic programming (SQP) algorithm is described that considerably reduces the computational cost and allows a real-time feasible implementation of the proposed ADF-based SNMPC method to control nonlinear dynamical systems under uncertainty.

A computing framework for addressing a variety of vehicle conditions includes receiving, from a first set of sensors by an edge compute node, first transportation network data associated with a transportation network region, receiving, from a second set of sensors by a cloud computing node, second transportation network data associated multiple transportation network regions, providing, by the edge compute node to one or more autonomous vehicles at the transportation network region, real-time transportation network region information based on at least the first transportation network data to facilitate control decisions by the one or more autonomous vehicles, and providing, by the cloud computing node to at least the one or more autonomous vehicles, non-real-time transportation network region information based on at least the second transportation network data to facilitate the control decisions by the at least one or more autonomous vehicles.

A mounting device for selectively positioning an optical element within a field-of-view of an optical sensor of a vehicle includes: a housing defining an opening sized to fit over an aperture of the optical sensor; a holder for the optical element connected to the housing and positioned such that, when the holder is in a first position, the optical element is at least partially within the field-of-view of the optical sensor; and a motorized actuator. The motorized actuator can be configured to move the holder to adjust the position of the optical element relative to the field-of-view of the optical sensor.

A vehicle system, including: a steering system; and a longitudinal-force application system including longitudinal-force application actuators configured to apply longitudinal forces respectively to one or more left-side wheels and one or more right-side wheels and a longitudinal-force controller configured to control the longitudinal-force application actuators to control the longitudinal forces applied respectively to the one or more left-side wheels and the one or more right-side wheels;, and an onboard power source device including a main power source and a secondary power source. When the main power source fails to supply electric power to the longitudinal-force application system and the steering system, the longitudinal-force controller controls the longitudinal-force application actuators to control a difference between the longitudinal force applied to the one or more left-side wheels and the longitudinal force applied to the one or more right-side wheels, thereby turning the vehicle.

A system for user interaction with an automated device includes a control system configured to operate the device during an operating mode corresponding to a first state in which the control system automatically controls the device operation, and the operating mode prescribes that a user monitor the device operation during automated control. The control system is configured to allocate a time period for the device to transition to a temporary state in which automated control is maintained and the user is permitted to stop monitoring and perform a task unrelated to device operation. The system includes a user interaction system including a visual display configured to present trajectory information, an indication as to whether an area is conducive to putting the device in the temporary state, and time period allocation information, the user interaction system including an interface engageable by the user to manage scheduling of allocated time period(s).

A carbon dioxide recovery system that recovers carbon dioxide from a hybrid vehicle traveling in a CO.sub.2 recovery area including a CO.sub.2 recovery road in which a stationary CO.sub.2 recovery device collecting and recovering carbon dioxide from the atmosphere is provided acquires a residual charging capacity of the battery and position information of the hybrid vehicle and guides the hybrid vehicle such that the hybrid vehicle travels on the CO.sub.2 recovery road using a notification device when the residual charging capacity is equal to or less than a predetermined SOC threshold value while the hybrid vehicle is traveling in the CO.sub.2 recovery area.

Provided are systems and methods for detecting a vehicle with sensors that are not calibrated properly and calibrating such sensor in real-time. In one example, a method may include iteratively capturing sensor data of a road while the vehicle is travelling on the road; monitoring a calibration of the sensors of the vehicle based on the sensor data, determining that the sensors of the vehicle are not calibrated properly based on the monitoring, generating a calibration target of an object on the road based on the sensor data, and adjusting a calibration parameter of the one or more sensors of the vehicle based on the generated calibration target.

A vehicle information DB holds information on whether or not a vehicle falls under a welfare vehicle provided with an assistance apparatus for a driver who has a lower limb impairment. A power feed facility DB holds position information of a power feed facility and information indicating whether the power feed facility falls under a contact type power feed facility or a wireless power feed facility. A processing apparatus creates a travel route such that a larger number of wireless power feed facilities are included along the travel route when the vehicle falls under the welfare vehicle than when the vehicle does not fall under the welfare vehicle.

The present disclosure relates to a method for the fully automated guidance of a motor vehicle by a vehicle system in a driving situation of at least one driving situation class, wherein the vehicle system has a control device and accesses position data of a position sensor. The method starts with providing a traffic rule database in which traffic rule sets for a plurality of geographic regions are stored in a machine-readable formal language and which can be accessed by the control device, and determining a geographical region currently traveled by the motor vehicle based on the current position data and retrieving a traffic rule set for the currently traveled geographical region from the traffic rule database via the control device. After or upon determination of a trajectory currently to be traveled by the control device, the vehicle system will verify the trajectory for compliance with traffic rules using the retrieved traffic rule set for the geographic region currently traveled by the control device, wherein the trajectory currently to be traveled is adjusted in the event of non-compliance, and guide the motor vehicle based on the trajectory currently to be traveled.