Korea Electronics Technology Institute

Kyoung Won Min

Young Bo Shim

Seon Young Lee

Haeng Seon Son

Chang Gue Park

Ganzorig Gankhuyag

Jin Man Park

Gi Ho Sung

Yeong Kwon Choe

There are provided a scenario similarity retrieval-based automatic scenario generation system and method. According to an embodiment, a scenario retrieval-based automatic scenario generation method includes: retrieving scenarios similar to a query scenario from a scenario DB for an autonomous driving test; filtering only scenarios that meet a selection condition from the retrieved scenarios; and converting components of the filtered scenarios to suit a target condition. Accordingly, a desired scenario may be automatically generated by retrieving a scenario similar to a targeted scenario, converting the retrieved scenario, and concretizing the scenario.

Scenario similarity retrieval-based automatic scenario generation system and method

There are provided a behavior recognition method for efficient recognition of hand signals and gesture and a behavior recognition AI network system using the same. The behavior recognition method for efficient recognition of hand signals and gestures according to an embodiment includes: an input feature extraction step of extracting, by a behavior recognition AI network system, key points Ffrom bounding box data of an object which makes hand signals to be inputted by sequence, and generating skeleton data of the object; and a spatial feature extraction step of calculating, by the behavior recognition AI network system, a length Fand an angle Fof a bone vector based on the key points Fand the skeleton data, and extracting a spatial feature. Accordingly, performance of recognition of hand signals and gestures may be enhanced.

Behavior recognition artificial intelligence network system and method for efficient recognition of hand signals and gestures

Provided is an air housing apparatus for protecting, from contamination, scratches, damage and the like, a lens and a cover glass which affect the performance of an optical device installed on a vehicle such as a camera and LIDAR. The air housing apparatus according to an embodiment of the present invention comprises: a cover into which high pressure air is fed; an air guide for injecting the supplied high pressure air toward the front surface of a lens or a sensor; and an air housing provided so as to connect the cover and the air guide and transferring the high pressure air supplied from the cover to the air guide. Therefore, a lens, sensor, or cover glass of a vehicle-installed optical device such as a camera and LIDAR can be continually protected from contamination and scratches by means of the injected air. In addition, energy efficiency can be enhanced by controlling the discharge pressure of air provided by an air pressure generation apparatus on the basis of information relating to the running speed and RPM of the vehicle or the air flow direction and air flow velocity.

Air housing apparatus for protecting lens of vehicle-installed optical device

Provided is a method for filtering driving paths in order to stably track a path which is generated for various purposes, such as recognizing a lane and keeping or changing the lane, or avoiding an obstacle around a road. A driving path filtering system according to an embodiment of the present disclosure includes: a position recognition unit configured to recognize a position of a vehicle; a lane recognition unit configured to recognize a lane of a road; and a path generator configured to generate a path for the vehicle to travel on, based on a result of recognizing the position and a result of recognizing the lane, and to perform path noise filtering with respect to the generated path. Accordingly, a driving path can be stabilized by removing a path noise (error) by complementally filtering a lane recognition-based path generation method and a map-based path generation method according to a driving environment.

Method for filtering the driving route based on distributed Kalman filter for stabilization of route tracking of autonomous vehicles

Provided is a deep learning-based panoptic segmentation accelerated processing technique using a complexity-based RPN skip method. An image segmentation system includes: a first processing unit configured to extract dynamic objects in an instance segmentation method by using an extracted feature; a calculation unit configured to control to skip some areas of the feature extracted at the network by the first processing unit, on the basis of complexity of the input image; a second processing unit configured to extract static objects in a semantic segmentation method by using the feature extracted at the network; and a fusion unit configured to fuse a result of extracting by the first processing unit and a result of extracting by the second processing unit. Accordingly, the panoptic segmentation method can be easily performed even in an embedded environment by reducing complexity for panoptic segmentation processing by reducing a calculation burden.

Growing community of inventors

Seongnam-si, South Korea

Kyoung Won Min