A vehicle includes a cabin having a first room and a second room that are capable of accommodating at least one passenger, and configured to isolate one or more passengers accommodated in the first room from one or more passengers accommodated in the second room, a guidance apparatus configured to guide the at least one passenger to be accommodated in either the first room or the second room, and a control apparatus configured to control the guidance apparatus. When a user boards as the at least one passenger, the control apparatus determines which of the first room and the second room the user is to board, based on information regarding the user. The guidance apparatus is configured to guide and board the user to whichever of the first room and the second room determined by the control apparatus.

A bad weather judgment apparatus and a bad weather judgment method thereof are disclosed. The apparatus includes a target recognizer configured to recognize targets in detection areas of a plurality of heterogeneous sensors based on sensor recognition information received from the heterogeneous sensors, a counter configured to count the number of cases based on detection states of the heterogeneous sensors about a same target among the targets, and a bad weather judger configured to determine whether the same target is present in bad weather judgment zones of the detection areas of the heterogeneous sensors, control the counter to increment or decrement the number of the cases based on detection states of the heterogeneous sensors about whether the same target is present in the bad weather judgment zones, and judge current weather to be bad weather when the number of the cases is greater than a threshold value.

Provided are a lane separation line detection correcting device/method and an automatic driving system for stabilizing the behavior of a vehicle by correcting overestimated curvature information resulting from an erroneous detection of a curvature of a lane separation line. A travel speed detecting circuit detects, for example, a target travel speed as vehicle sensor information. A maximum curvature estimating circuit estimates, based on the target travel speed, a maximum curvature of a road along which an own vehicle is traveling. A curvature correcting circuit corrects a curvature of a lane separation line input thereto based on the maximum curvature. A control unit controls steering of the own vehicle based on the lane separation line having a corrected curvature. As a result, vehicle steering can be automatically controlled so as to prevent the own vehicle while traveling from departing from a driving lane.

Detecting out-of-model scenarios for an autonomous vehicle including: determining, based on first sensor data from one or more sensors, an environmental state relative to the autonomous vehicle, wherein operational commands for the autonomous vehicle are based on a selected machine learning model, wherein the selected machine learning model comprises a first machine learning model; comparing the environmental state to a predicted environmental state relative to the autonomous vehicle; and determining, based on a differential between the environmental state and the predicted environmental state, whether to select a second machine learning model as the selected machine learning model.

Detecting out-of-model scenarios for an autonomous vehicle including: determining, based on first sensor data from one or more sensors, an environmental state relative to the autonomous vehicle, wherein operational commands for the autonomous vehicle are based on a selected machine learning model, wherein the selected machine learning model comprises a first machine learning model; comparing the environmental state to a predicted environmental state relative to the autonomous vehicle; and determining, based on a differential between the environmental state and the predicted environmental state, whether to select a second machine learning model as the selected machine learning model.

A method for detecting an ice on a road surface includes: providing a spectral imaging camera; recording a first reflectance (R1) of the surface at 0.545 to 0.565 μm using the spectral imaging camera; recording a second reflectance (R2) of the surface at 0.620 to 0.670 μm using the spectral imaging camera; recording a third reflectance (R3) of the surface at 0.841 to 0.876 μm using the spectral imaging camera; calculating an ice index based on the first reflectance, the second reflectance, and the third reflectance; providing a thermometer; recording a surface temperature of the surface using the thermometer; and detecting a presence of the ice on the surface based on the ice index and the surface temperature. A system for detecting an ice on a surface is also disclosed.

A method for detecting an ice on a road surface includes: providing a spectral imaging camera; recording a first reflectance (R1) of the surface at 0.545 to 0.565 μm using the spectral imaging camera; recording a second reflectance (R2) of the surface at 0.620 to 0.670 μm using the spectral imaging camera; recording a third reflectance (R3) of the surface at 0.841 to 0.876 μm using the spectral imaging camera; calculating an ice index based on the first reflectance, the second reflectance, and the third reflectance; providing a thermometer; recording a surface temperature of the surface using the thermometer; and detecting a presence of the ice on the surface based on the ice index and the surface temperature. A system for detecting an ice on a surface is also disclosed.

A system and method for large-scale lane marking detection using multimodal sensor data are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on a vehicle; receiving point cloud data from a distance and intensity measuring device mounted on the vehicle; fusing the image data and the point cloud data to produce a set of lane marking points in three-dimensional (3D) space that correlate to the image data and the point cloud data; and generating a lane marking map from the set of lane marking points.

An approach is provided for an asymmetric evaluation of polygon similarity. The approach, for instance, involves receiving a first polygon representing an object depicted in an image. The approach also involves generating a transformation of the image comprising image elements whose values are based on a respective distance that each image element is from a nearest image element located on a first boundary of the first polygon. The approach further involves determining a subset of the plurality of image elements of the transformation that intersect with a second boundary of a second polygon. The approach further involves calculating a polygon similarity of the second polygon with respect the first polygon based on the values of the subset of image elements normalized to a length of the second boundary of the second polygon.

An approach is provided for an asymmetric evaluation of polygon similarity. The approach, for instance, involves receiving a first polygon representing an object depicted in an image. The approach also involves generating a transformation of the image comprising image elements whose values are based on a respective distance that each image element is from a nearest image element located on a first boundary of the first polygon. The approach further involves determining a subset of the plurality of image elements of the transformation that intersect with a second boundary of a second polygon. The approach further involves calculating a polygon similarity of the second polygon with respect the first polygon based on the values of the subset of image elements normalized to a length of the second boundary of the second polygon.