Hyundai Motor Company

Kia Motors Corporation

Kia Corporation

Myung Seon Heo

Young Chul Oh

Byung Yong You

Ki Cheol Shin

Hyoung Geun Kwon

Seung Wook Park

In Yong Jung

Min Byeong Lee

Jun Sik Hwang

Yoon Soo Kim

Kyung Ho Yoo

Su Lyun Sung

Dong Gyu Noh

Jong Rok Park

Hahk Rel Noh

Dae Sung Hwang

Cho Rong Ryu

Wan Jae Lee

Chang Jae Lee

An apparatus and method for controlling a vehicle speed based on information about forward vehicles that travel in the same lane may be acquired using Vehicle to Everything (V2X) communications in a cooperative adaptive cruise control (CACC) system. The CACC system includes a communication unit receiving vehicle information from neighboring vehicles using V2V communications; an information collection unit collecting vehicle information of the neighboring vehicles and the subject vehicle using sensors; and a control unit determining a forward vehicle and a far-forward vehicle using the sensors, selecting first and second target vehicles for being followed by the subject vehicle based on the vehicle information of the forward vehicle and the far-forward vehicle and the vehicle information of the neighboring vehicles, and controlling the driving speed of the subject vehicle based on speed information of the first and second target vehicles.

Apparatus and method for controlling speed in cooperative adaptive cruise control system

An apparatus for preventing a pedestrian collision accident, a system having the same, and a method thereof are provided. The apparatus includes a pedestrian sensing unit that senses a pedestrian moving into a dangerous area and calculates velocity and direction information of the pedestrian. A communication unit transmits the velocity and direction information of the pedestrian to a surrounding vehicle. A time-to-collision (TTC) calculating unit calculates a TTC using velocity and direction information of a subject vehicle and second velocity and direction information of the pedestrian, when the TTC calculating unit receives the second velocity and direction information of the pedestrian from another vehicle. A controller outputs based on the TTC a warning to a driver of the subject vehicle or the pedestrian.

Apparatus for preventing pedestrian collision accident, system having the same, and method thereof

An adaptive cruise control apparatus includes a sensor device for acquiring information on vehicles around a subject vehicle including information on a distance between a forward vehicle and the subject vehicle, and a controller for calculating an acceleration of the subject vehicle based on the information on vehicles around the subject vehicle, determining a traffic condition around the subject vehicle based on the information on vehicles around the subject vehicle, limiting the acceleration of the subject vehicle according to the determined traffic condition, and controlling a power train of the subject vehicle according to the limited acceleration.

Adaptive cruise control apparatus and method of operating adaptive cruise control in consideration of traffic condition

A method and an apparatus for detecting a pedestrian by a vehicle during night driving are provided, in which the apparatus includes: a first camera configured to take a first image including color information of a vicinity of the vehicle during night driving; a second camera configured to take a second image including thermal distribution information of the vicinity of the vehicle; a pedestrian detector configured to detect a non-pedestrian area by using the color information from the first image and detect a pedestrian area by excluding the non-pedestrian area from the second image; and a display configured to match and display the pedestrian area on the second image.

Method and apparatus for detecting a pedestrian by a vehicle during night driving

The present disclosure provides a lane estimating apparatus and method. The apparatus includes: a lane determiner, an obstacle position calculator, a vehicle position corrector, and a lane estimator. The lane determiner compares a first lane detected by a first sensor with a lane on an actual road or a second lane on a local map to determine reliability of the first lane. The obstacle position calculator detects, when the reliability of the detected first lane is less than a preset reference, a first obstacle in the vicinity of a vehicle and a second obstacle on the local map, and calculates a difference between slopes and positions of straight lines extracted from the first obstacle and the second obstacle. The vehicle position corrector corrects a heading direction and a position of the vehicle based on the difference between the slopes and positions of the straight lines. In addition, the lane estimator estimates a driving lane on the local map.

Lane estimating apparatus and method

A steering control method and device for autonomous vehicles is provided. The steering control method includes sensing traffic lanes on a road on which a vehicle is being driven and deriving a vanishing point positioned on lines extending from the traffic lanes. A sensitivity of a steering angle that corresponds to a vertical coordinate of the vanishing point in a matrix and an initial steering angle that corresponds to a horizontal coordinate are then determined. Further, a steering control value that corresponds to the initial steering angle and the sensitivity of the steering angle are determined.

Apparatus and method for automatic steering control in vehicle

A system and method for filtering LiDAR data is provided. The system includes a LiDAR data collector that is configured to collect the LiDAR data from a LiDAR and store the LiDAR data in a matrix structure. A noise point determiner is configured to determine whether a first filtering condition for determining whether a point within a predetermined reference distance in the LiDAR data is present, a second filtering condition for determining whether a present point adjacent to a left and right by a reference of a reference point in the matrix structure is a first reference value or less, and a third filtering condition for determining whether a present point adjacent to a top and bottom by the reference of the reference point is a second reference value or less are satisfied.

System for filtering LiDAR data in vehicle and method thereof

An apparatus and method for identifying surrounding vehicles is provided. The apparatus includes a sensor that measures position coordinates of a first surrounding vehicle and a yaw rate and a speed of a subject vehicle and a V2V communication unit that receives a coordinate history and a speed from the plurality of surrounding vehicles. Additionally, a controller generates a traveling trajectory and a speed pattern as first identification information by applying accumulated behaviors of the subject vehicle to the position coordinates of the first surrounding vehicle and calculate each traveling trajectory and speed pattern as n-th identification information based on the coordinate history and the speed received via the V2V communication unit. The controller then compares the first identification information with the n-th identification information to recognize the surrounding vehicle corresponding to identification information most similar to the first identification information as the first surrounding vehicle.

Apparatus and method for identifying surrounding vehicles

A vehicle positioning apparatus and method are provided that are capable of more accurately measuring the position of a traveling vehicle without interoperating with a DGPS. The method includes calculating a position of the traveling vehicle based on coordinates of the respective GPS satellites shared by a surrounding vehicle and the traveling vehicle. In addition, a position coordinate is calculated of the surrounding vehicle measured by the reference of the traveling vehicle.

Vehicle positioning apparatus and method

A vehicle for supporting an object processing includes a lidar sensor configured to collect multi-layer data corresponding to sensor information for a lateral surface for each vertical interval. A controller is configured to classify objects by clustering each layer for the multi-layer data, extract contours and shapes of the objects, and then control a convergence of the objects based on a calculated value of a Mahalanobis distance between the clustered objects, and a method for an object processing.

Method for object processing and vehicle supporting the same

An apparatus for compensating for a beam angle of a multi-layer LiDAR includes: a beam angle calculation unit configured to calculate a distance d calculated by using ground data detected by a ground data detection unit and a beam angle of the multi-layer LiDAR by using a mounting height of the multi-layer LiDAR stored in a storage unit. When the beam angle calculated by a beam angle calculation unit is in a threshold range, a beam angle selection unit selects the calculated beam angle as the beam angle, when a beam angle compensation unit compensates for the initial beam angle by using the beam angle selected by the beam angle selection unit.

Apparatus and method for compensating for beam angle of multi-layer LiDAR

An apparatus and a method are provided for recognizing driving environment for an autonomous vehicle. The apparatus includes a controller configured to receive navigation information from a satellite navigation receiver. The controller is further configured to receive map data from a map storage and image data from an image sensor regarding captured images from around a vehicle and distance information from a distance sensor regarding sensed objects positioned around the vehicle. The controller is also configured to determine a fusion method for information measured by the image sensor and the distance sensor based on a receiving state of the satellite navigation receiver and precision of the map data to recognize the driving environment.

Apparatus and method for recognizing driving environment for autonomous vehicle

An apparatus and method for recognizing a vehicle. The apparatus includes a sensor configured to provide point cloud data corresponding to a front object detected by using a LiDAR; a shape determiner configured to determines a shape corresponding to the front object by analyzing the point cloud data provided by the sensor; a receiving sensitivity analyzer configured to analyze a receiving sensitivity change corresponding to each point of the point cloud data provided by the sensor based on the shape of the vehicle; a decider configured to decide whether the front object is a vehicle according to the shape corresponding to the front object and the receiving sensitivity change corresponding to points of the point cloud; and a detector configured to recognize and detect the front object as the vehicle based on the result decided by the decider.

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Seoul, South Korea

Myung Seon Heo