TY - GEN
T1 - Automated Lane Changing Control in Mixed Traffic
T2 - 25th IEEE International Conference on Intelligent Transportation Systems, ITSC 2022
AU - Chakraborty, Sayan
AU - Cui, Leilei
AU - Ozbay, Kaan
AU - Jiang, Zhong Ping
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this work, we have introduced an optimal data-driven control algorithm to solve the lane changing problem of Autonomous Vehicles (AVs), where the input-state data of the AV is utilized to generate a near-optimal lane-changing controller by approximate/adaptive dynamic programming (ADP) technique. The dynamics of the AV is assumed to be a parameter varying linear system, which induces two challenges for the lane-changing controller design: parameter varying and unknown dynamics. With the help of both gain scheduling and ADP techniques, a data-driven control algorithm is proposed which can generate a near-optimal lane-changing controller in the absence of the accurate model of the AV. Then, a data-driven lane-changing decision-making algorithm is used that can make the AV perform a lane abortion if safety conditions are violated during a lane change. Finally, the data-driven gain scheduling controller and lane-change decision-making algorithm are validated by SUMO and MATLAB simulations.
AB - In this work, we have introduced an optimal data-driven control algorithm to solve the lane changing problem of Autonomous Vehicles (AVs), where the input-state data of the AV is utilized to generate a near-optimal lane-changing controller by approximate/adaptive dynamic programming (ADP) technique. The dynamics of the AV is assumed to be a parameter varying linear system, which induces two challenges for the lane-changing controller design: parameter varying and unknown dynamics. With the help of both gain scheduling and ADP techniques, a data-driven control algorithm is proposed which can generate a near-optimal lane-changing controller in the absence of the accurate model of the AV. Then, a data-driven lane-changing decision-making algorithm is used that can make the AV perform a lane abortion if safety conditions are violated during a lane change. Finally, the data-driven gain scheduling controller and lane-change decision-making algorithm are validated by SUMO and MATLAB simulations.
UR - http://www.scopus.com/inward/record.url?scp=85141882854&partnerID=8YFLogxK
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U2 - 10.1109/ITSC55140.2022.9922596
DO - 10.1109/ITSC55140.2022.9922596
M3 - Conference contribution
AN - SCOPUS:85141882854
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 1823
EP - 1828
BT - 2022 IEEE 25th International Conference on Intelligent Transportation Systems, ITSC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 October 2022 through 12 October 2022
ER -