Hyundai Motor Company

Kia Motors Corporation

Seong Su Im

Seok Youl Yang

Jin Hak Kim

Tae Seok Lee

Yoon Ho Jang

Eun Jin Choi

Byung Yong You

Kyung Ho Yoo

Hui Sung Lee

Kyoung Moo Min

Cheol Ha Lee

A method of determining a driving lane of an autonomous vehicle is provided. The method includes classifying, by an autonomous driving logic of an electronic control unit (ECU), at least one object sensed in front of the autonomous vehicle as a stationary object or a moving object. A clustering group is generated by clustering the stationary object and a boundary of an entire driving road is determined based on a position of a moving object approaching the subject vehicle among the moving objects and the clustering group. Additionally, the method includes comparing positions of a plurality of lanes based on lane widths of the lanes for travel of the subject vehicle with the boundary of the entire driving road and determining a driving lane of the subject vehicle.

ECU, autonomous vehicle including ECU, and method of determining driving lane for the same

Disclosed herein is a method of recognizing a vehicle near an autonomous vehicle. The method includes, using an autonomous driving logic of an electronic control unit (ECU) extracting nearby vehicle information by sensing at least one nearby vehicle from a distance sensed at each angle within a certain angular range. The method further includes calculating an misrecognition index indicating a degree of closeness of a shape of the nearby vehicle to a shape of an exhaust gas based on the nearby vehicle information, executing an exhaust gas removal algorithm for correcting the nearby vehicle information when the misrecognition index is greater than a threshold misrecognition index, and estimating movement of the nearby vehicle after a current frame using the nearby vehicle information or the corrected nearby vehicle information obtained through the exhaust gas removal algorithm.

ECU, autonomous vehicle including ECU, and method of recognizing nearby vehicle for the same

A method of controlling lane change of an autonomous vehicle is provided. The method includes determining a type of a command for lane change by an autonomous driving logic of an electronic control unit (ECU) in response to the command for lane change being generated. When the command for lane change is not a specific command for lane change, attributes of each of at least one region included in a change target region is determined using information regarding the change target region. The attributes of each of the at least one region are corrected using information regarding a lane in which the autonomous vehicle is driven and a lane change region is determined from the at least one region based on the corrected attributes of each of the at least one region.

ECU, autonomous vehicle including ECU, and method of controlling lane change for the same

An apparatus and a method for determining careless driving are provided and determine more reliable careless driving by generating normal driving patterns using driving performance data for a reference time at the beginning of driving. In addition, careless driving patterns greater than a predetermined number are detected using the normal driving pattern and a boundary between the normal driving and the careless driving is determined using a supervised learning method. The careless driving of the driver is then determined based on the determined boundary.

Apparatus and method for determining careless driving

The present invention provides a method and a system for producing a classifier for recognizing an obstacle, including a processor configured to: display surface data of a plurality of obstacles measured by a distance measurement sensor in a two-dimensional (2D) coordinate system; group and classify the surface data displayed in the 2D coordinate system for each obstacle; setting a plurality of feature references to analyze region based features displayed for each obstacle in the 2D coordinate system and calculate the respective feature references for each obstacle grouping; and producing the classifier by applying a weight to each of the feature references.

Method and system for producing classifier for recognizing obstacle

A driving concentration level calculating apparatus is provided and includes a controller configured to acquire an acceleration of a traveling vehicle and measure a relative velocity to a preceding vehicle. In addition, noise is removed from the acquired acceleration of the traveling vehicle and from the measured relative velocity to the preceding vehicle. A plurality of correlation values are calculated based on the acceleration of the traveling vehicle and the relative velocity to the preceding vehicle from which noise has been removed. In addition, the controller is configured to detect a time at which a maximum correlation value is calculated as a driving concentration level from among the calculated correlation values.

Driving concentration level calculating apparatus and method, and system and method for warning of vehicle collision using the same

Disclosed herein is a system and method of providing a warning to a pedestrian using a laser beam. In particular, objects present in a detection area of a front impact sensor provided on a vehicle are detected. A movement path vector of a first moving object that is moving in a direction identical to that of the vehicle, among the objects present in the detection area, is generated. When the movement path vector of the first moving object falls within an error range of a movement vector of the vehicle, the first moving object is designated as belonging to a group of laser warning target candidates, and thereafter an emission angle of a laser beam is generated. The laser beam is emitted through a laser light based on the emission angle of the laser beam.

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Gyeonggi-do, South Korea

Seong Su Im