TY - GEN
T1 - Autonomous vehicles lane detection using particle filters
AU - Vechet, Stanislav
AU - Krejsa, Jiri
AU - Chen, Kuo Shen
N1 - Funding Information:
The results were obtained with support of mobility project plus MPP with Ministry of Science and Technology MOST-20-06.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Lane detection belongs among many others crucial tasks which an autonomous vehicles traveling in urban roads need to take care of. We present a lane detection method which uses particle filters combined with visual information from onboard camera to control the vehicles direction. Our initial experiments shows promising results as applied and tested in urban roads with focus on real-time data processing.
AB - Lane detection belongs among many others crucial tasks which an autonomous vehicles traveling in urban roads need to take care of. We present a lane detection method which uses particle filters combined with visual information from onboard camera to control the vehicles direction. Our initial experiments shows promising results as applied and tested in urban roads with focus on real-time data processing.
UR - http://www.scopus.com/inward/record.url?scp=85146312025&partnerID=8YFLogxK
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U2 - 10.1109/ME54704.2022.9983129
DO - 10.1109/ME54704.2022.9983129
M3 - Conference contribution
AN - SCOPUS:85146312025
T3 - Proceedings of the 2022 20th International Conference on Mechatronics - Mechatronika, ME 2022
BT - Proceedings of the 2022 20th International Conference on Mechatronics - Mechatronika, ME 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th International Conference on Mechatronics - Mechatronika, ME 2022
Y2 - 7 December 2022 through 9 December 2022
ER -