TY - JOUR
T1 - Optimal Control for Speed Harmonization of Automated Vehicles
AU - Malikopoulos, Andreas A.
AU - Hong, Seongah
AU - Park, B. Brian
AU - Lee, Joyoung
AU - Ryu, Seunghan
N1 - Funding Information:
Manuscript received October 15, 2017; revised March 31, 2018 and May 30, 2018; accepted August 12, 2018. Date of publication September 13, 2018; date of current version June 26, 2019. This work was supported in part by the ARPAE’s NEXTCAR Program under Award DE-AR0000796, in part by the SMART Mobility Initiative of the Department of Energy, and in part by the Global Research Laboratory Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning under Grant 2013K1A1A2A02078326. The Associate Editor for this paper was D. Cao. (Corresponding author: Andreas A. Malikopoulos.) A. A. Malikopoulos is with the Department of Mechanical Engineering, University of Delaware, Newark, DE 19716 USA (e-mail: andreas@udel.edu).
Publisher Copyright:
© 2000-2011 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - This paper addresses the problem of controlling the speed of a number of automated vehicles before they enter a speed reduction zone on a freeway. We formulate the control problem and provide an analytical, closed-form solution that can be implemented in real time. The solution yields the optimal acceleration/deceleration of each vehicle under the hard safety constraint of rear-end collision avoidance. The effectiveness of the solution is evaluated through a microscopic simulation testbed and it is shown that the proposed approach significantly reduces both fuel consumption and travel time. In particular, for three different traffic volume levels, fuel consumption for each vehicle is reduced by 19-22% compared to the baseline scenario, in which human-driven vehicles are considered, by 12-17% compared to the variable speed limit algorithm, and by 18-34% compared to the vehicular-based speed harmonization (SPD-HARM) algorithm. Similarly, travel time is improved by 26-30% compared to the baseline scenario, by 3-19% compared to the VSL algorithm, and by 31-39% compared to the vehicular-based SPD-HARM algorithm.
AB - This paper addresses the problem of controlling the speed of a number of automated vehicles before they enter a speed reduction zone on a freeway. We formulate the control problem and provide an analytical, closed-form solution that can be implemented in real time. The solution yields the optimal acceleration/deceleration of each vehicle under the hard safety constraint of rear-end collision avoidance. The effectiveness of the solution is evaluated through a microscopic simulation testbed and it is shown that the proposed approach significantly reduces both fuel consumption and travel time. In particular, for three different traffic volume levels, fuel consumption for each vehicle is reduced by 19-22% compared to the baseline scenario, in which human-driven vehicles are considered, by 12-17% compared to the variable speed limit algorithm, and by 18-34% compared to the vehicular-based speed harmonization (SPD-HARM) algorithm. Similarly, travel time is improved by 26-30% compared to the baseline scenario, by 3-19% compared to the VSL algorithm, and by 31-39% compared to the vehicular-based SPD-HARM algorithm.
KW - Vehicle speed control
KW - automated vehicles
KW - energy usage
KW - optimal control
KW - speed harmonization
UR - http://www.scopus.com/inward/record.url?scp=85053310052&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053310052&partnerID=8YFLogxK
U2 - 10.1109/TITS.2018.2865561
DO - 10.1109/TITS.2018.2865561
M3 - Article
AN - SCOPUS:85053310052
SN - 1524-9050
VL - 20
SP - 2405
EP - 2417
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 7
M1 - 8464283
ER -