Authors:
Changhyeon Park
1
and
Seok-Cheol Kee
2
Affiliations:
1
Department of Smart Car Engineering, Chungbuk National University, Seowon-gu Chungdae-ro 1, Cheongju-si and Korea
;
2
Smart Car Research Center, Chungbuk National University, Seowon-gu Chungdae-ro 1, Cheongju-si and Korea
Keyword(s):
Autonomous Driving Vehicle, Crossing/Stop Decision, Traffic Light Recognition (TLR), Coordinates Map, Convolutional Neural Network (CNN).
Abstract:
We implemented autonomous driving vehicle system at an intersection equipped with traffic lights. This system was consisted of a traffic light recognition, crossing/stop decision algorithm, vehicle localization, vehicle longitudinal/lateral control, and coordinate map generation. The traffic light recognition was implemented by using camera-based CNN data processing. The crossing/stop decision algorithm decides vehicle longitudinal control whether drive or not depending on recognized traffic light signal. The vehicle localization was implemented by using RTK GNSS and dead reckoning. The longitudinal control was designed by planned path data and the lateral control was designed by processed planned path data and traffic light position/signal recognition results. The overall vehicle control system was implemented based on an embedded control board. Coordinate map was made by saving vehicle’s position data received from RTK GNSS. To evaluate the performance of proposed system, we remode
led a commercial vehicle into autonomous driving vehicle and drove the vehicle on our proving ground. Our own proving ground for the test vehicle driving performance was located in Ochang Campus, Chungbuk National University. As a result, the proposed vehicle successfully drove at intersection equipped with traffic lights with a maximum speed of 40 kph on a straight course and a maximum speed of 10 kph on a 90c̊corner course.
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