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:
The present disclosure provides a system for recognizing a position of a vehicle including: a lane-based position recognition device configured to extract correction information about a heading angle and a lateral position of the vehicle by comparing measured lane information with lane information on an accurate map; a LiDAR-based position recognition device that extracts correction information about a position of the vehicle by detecting an area in consideration of surrounding vehicles and obstacles measured through a LiDAR sensor; and a position assemble device configured to assemble a position based on the correction information about the heading angle and the lateral position of the vehicle, correction information about a heading angle, a longitudinal position and a lateral position of the vehicle from the LiDAR sensor, and correction information about a heading angle, a longitudinal position and a lateral position of the vehicle from GPS.
Abstract:
A lane change control apparatus includes a lane information extractor configured to obtain lane information for a driving lane by using image information for a lane. A lane changeable time calculator is configured to calculate a lane changeable time by using speed information of an own vehicle and information for peripheral vehicles obtained from sensing apparatuses installed in the vehicle. A reference yaw rate generator is configured to determine a lane change time by using the lane changeable time and speed information and generate a reference yaw rate symmetrically changed on a time axis during the lane change time by using the lane change time and lane information. A reference yaw rate tracker is configured to control an operation of the own vehicle so as to track the reference yaw rate.
Abstract:
A lane changing apparatus of an autonomous vehicle includes a lane recognizer, a vehicle information collector, control information, calculator, a controller and a steering apparatus. The lane recognizer is configured to recognize a lane of a road on which the vehicle is driving and extract road information from the recognized lane. The vehicle information collector is configured to collect vehicle information by a variety of sensors installed in the vehicle. The control information calculator is configured to calculate control information for changing the lane by using the vehicle information and the road information. The controller is configured to control a yaw rate of the vehicle based on the control information upon changing the lane. The steering apparatus is configured to change a moving direction of the vehicle according to a control of the controller.
Abstract:
There are provided an apparatus and method for generating a global path for an autonomous vehicle. The apparatus for generating a global path for an autonomous vehicle includes a sensor module including one or more sensors installed in the vehicle, a traffic information receiver configured to receive traffic information through wireless communication, a path generator configured to generate one or more candidate paths based on the traffic information, a difficulty evaluator configured to evaluate a difficulty of driving in the one or more candidate paths in each section of the one or more candidate paths using recognition rates of the one or more sensors and the traffic information, and an autonomous driving path selector configured to finally select an autonomous driving path by evaluating the one or more candidate paths based on the evaluation of the difficulty of driving.
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:
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.
Abstract:
A lane change control apparatus includes a lane information extractor configured to obtain lane information for a driving lane by using image information for a lane. A lane changeable time calculator is configured to calculate a lane changeable time by using speed information of an own vehicle and information for peripheral vehicles obtained from sensing apparatuses installed in the vehicle. A reference yaw rate generator is configured to determine a lane change time by using the lane changeable time and speed information and generate a reference yaw rate symmetrically changed on a time axis during the lane change time by using the lane change time and lane information. A reference yaw rate tracker is configured to control an operation of the own vehicle so as to track the reference yaw rate.
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:
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.