In an approach to improve mobile computation while traveling by dynamically generating one or more routes base on computing resource requirements of one or more endpoint devices. Embodiments identify, in real time, a plurality of autonomous vehicles, wherein the plurality of autonomous vehicles are traveling along a common route. Further embodiments, adjust, in real time, relative positions and speeds of the plurality of autonomous vehicles to maintain the plurality of autonomous vehicles within a predetermined geographic area while traveling along the common route, and wherein the predetermined geographic area is sufficient to collectively provide an amount of edge computing resources to satisfy one or more computing resource requirements of the one or more endpoint devices located within a first autonomous vehicle. Additionally, embodiments adjust, in real time, a route of the first autonomous vehicle based on the common route of the plurality of autonomous vehicles providing the edge computing resources.

A method and device for operating a vehicle comprising a step of recording environment data values, which represent an environment of the vehicle, the environment comprising at least one environmental feature; a step of determining a comparative value of a comparison between the at least one environmental feature and a map, the map comprising at least one map feature, the at least one environmental feature corresponding the at least one map feature; a step of determining an up-to-dateness of the map, based on a comparison of the comparative value with a threshold value; and a step of operating the vehicle, as a function of the up-to-dateness of the map.

A system and method are described for controlling a vehicle interior environmental condition. A biometric sensor senses a biometric condition of a vehicle seat occupant and generates a sensed biometric condition value. A controller receives the sensed biometric condition value, a sensed interior environmental condition value, and a sensed exterior environmental condition value. Each of multiple exterior environmental condition values has an associated biometric condition value defined as optimal for the vehicle occupant. The controller determines the optimal biometric condition value associated with the sensed exterior environmental condition value, compares the optimal biometric condition value to the sensed biometric condition value, and in response to a difference between the optimal biometric condition value and the sensed biometric condition value, generates a control signal to control an actuator to control the controllable interior environmental condition to reduce the difference between sensed biometric condition value and the optimal biometric condition value.

A method includes encapsulating a current state of a road portion into a voxel grid, a first dimension of the voxel grid being associated with a first spatial dimension of the road portion, a second dimension of the voxel grid being associated with a second spatial dimension of the road portion, and a third dimension of the voxel grid comprising a plurality of feature layers, wherein each feature layer is associated with a feature of the road portion. The voxel grid may be input into a trained neural network and a future state of the road portion may be predicted based on an output of the neural network.

Aspects and implementations of the present disclosure address challenges of the existing technology by enabling lidar-assisted identification and characterization of visibility-reducing media (VRM) such as fog, rain, snow, dust in autonomous vehicle applications, using lidar sensing. VRM can be identified and characterized using a variety of techniques, including analyzing a spatial distribution of low-intensity lidar returns, detecting pulse elongation of VRM-returns associated with reflection from VRM, determining intensity of VRM-returns, determining reduction of intensity of returns from various reference objects, and other techniques.

Autonomous driving systems and methods for a vehicle include a driver intent determination system configured to determine a driver intent for which of two different lanes the driver intends the vehicle to follow during a lane split scenario and a controller configured to operate the vehicle according to an autonomous driving feature whereby the controller automatically controls steering of the vehicle, determine which of the two different lanes are supported for the autonomous driving feature, determine which of the two different lanes that the vehicle will follow during the lane split scenario based at least on the determined driver intent and which of the two different lanes are supported for the autonomous driving feature to obtain a target lane, and automatically control at least the steering system of the vehicle to follow the target lane.

Provided is a display control apparatus that generates a display overhead image including overhead images of each of a vacant parking space and a vehicle and outputs the display overhead image to a display apparatus. The display overhead image includes at least one of an overhead image of the vacant parking space outside of a current viewing angle of a vehicle-mounted camera and an overhead image of the vacant parking space hidden behind a static object as seen from a current position of the vehicle.

Techniques for analyzing a parameter space are discussed. Techniques may include receiving policy data for evaluating a vehicle controller. The techniques may further include determining, using a Bayesian optimization and based at least in part on the vehicle controller, parameter sets associated with adverse events. The adverse events may be associated with a violation of the policy data. The techniques may associate, based on exposure data, parameter bounds of the adverse events and probabilities of the adverse events in a driving environment. A safety metric may be determined based on the Bayesian optimization. The techniques may also include weighting an impact of an adverse event based on the safety metric.

The present teaching relates to method, system, and medium, for operating a vehicle. The method includes the steps of receiving Real-time data related to the vehicle are received. A current mode of operation of the vehicle is determined. A first risk associated with the current mode of operation of the vehicle is evaluated based on the real-time data in accordance with a risk model. If the first risk satisfies a first criterion, a second risk associated with switching the current mode to a different mode of operation of the vehicle is determined. The vehicle is switched from the current mode to the different mode if the second risk satisfies a second criterion.

In some examples, a retroreflective article may include a retroreflective substrate, and an optical pattern embodied on the retroreflective substrate. The optical pattern may include a first optical sub-pattern and a second optical sub-pattern, wherein the optical pattern represents a set of information that is interpretable based on a combination of the first optical sub-pattern that is visible in a first light spectrum and the second optical sub-pattern that is visible in a second light spectrum. The first and second light spectra may be different.