Abstract:
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.
Abstract:
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.
Abstract:
An apparatus and method for managing failure in an autonomous navigation system are provided. The method includes collecting, by a controller, failure information in the autonomous navigation system and a monitoring a driver condition. The controller is configured to collect the failure information in the autonomous navigation system, and determine whether to switch control from the autonomous navigation vehicle to a manual driving mode based on the driver condition.
Abstract:
An apparatus and method for recognizing a driving field of a vehicle are provided. The apparatus includes a sensor that is configured to sense a location of a vehicle driving on a road and sense whether an object is adjacent to the vehicle. In addition, a controller is configured to detect whether the object is present and a lane of the road on which the vehicle is being driven is changed to detect a final lane candidate group on which the vehicle is positioned. The final lane candidate group is then displayed by the controller.
Abstract:
A method and system for measuring a vehicle position indoors are provided. The method includes continuously determining, by a controller, whether a vehicle is present within a range captured by a closed circuit television (CCTV). When the vehicle is present within that range, the controller is configured to calculate the vehicle position on a map using an image result of the vehicle captured the CCTV and calculate a heading angle which is a running direction of the vehicle. In addition, an absolute position of the vehicle on the map is calculated using the vehicle position on the map and the heading angle.
Abstract:
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.
Abstract:
A driving mode changing method and apparatus of an autonomous navigation vehicle that allows a driver to stably operate the autonomous navigation vehicle. The autonomous navigation vehicle may be stably operated by mounting an apparatus (a touch pad, a joystick, or the like) to operate the autonomous navigation vehicle on seats (a passenger seat and a rear seat) other than a driver seat of the autonomous navigation vehicle and providing various information (a near around view, a far around view, a critical level, vehicle information, and the like) to drive the autonomous navigation vehicle.
Abstract:
An unmanned autonomous traveling service apparatus and method based on driving information database that allows an unmanned autonomous traveling vehicle to be autonomously operated stably without performing a large scale computing process in real time by allowing the unmanned autonomous traveling vehicle to be autonomously operated based on driving information generated in a database and allowing the unmanned autonomous traveling vehicle to be autonomously operated based on an installed sensor at the time of a traffic lane change or an unexpected situation. In particular, the driving information is collected from drivers throughout the world to create the database for the driving information.
Abstract:
A deep learning-based autonomous vehicle control system includes: a processor determining an autonomous driving control based on deep learning, correcting an error in determination of the deep learning-based autonomous driving control based on determination of an autonomous driving control based on a predetermined expert rule, and controlling an autonomous vehicle; and a non-transitory computer-readable storage medium storing data for the determination of the deep learning-based autonomous driving control, data for the determination of the expert rule-based autonomous driving control, and information about the error in the determination of the deep learning-based autonomous driving control.
Abstract:
A deep learning-based autonomous vehicle control system includes: a processor determining an autonomous driving control based on deep learning, correcting an error in determination of the deep learning-based autonomous driving control based on determination of an autonomous driving control based on a predetermined expert rule, and controlling an autonomous vehicle; and a non-transitory computer-readable storage medium storing data for the determination of the deep learning-based autonomous driving control, data for the determination of the expert rule-based autonomous driving control, and information about the error in the determination of the deep learning-based autonomous driving control.