Real-time control of connected vehicles in signalized corridors using pseudospectral convex optimization

Shi, Yang; Wang, Zhenbo; Laclair, Tim; Wang, Ross; Shao, Yunli

doi:10.1002/oca.2978

Real-time control of connected vehicles in signalized corridors using pseudospectral convex optimization

Journal Article · Thu Jan 26 00:00:00 EST 2023 · Optimal Control Applications and Methods

DOI:https://doi.org/10.1002/oca.2978· OSTI ID:1923235

Shi, Yang ^[1]; ^[1]; ^[2]; ^[2]; Shao, Yunli ^[2]

Univ. of Tennessee, Knoxville, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Recent advances in Connected and Automated Vehicle (CAV) technologies have opened up new opportunities to enable safe, efficient, and sustainable transportation systems. However, developing reliable and rapid speed control algorithms in highly dynamic environments with complex inter-vehicle interactions and nonlinear vehicle dynamics is still a daunting task. In this paper, we develop a novel speed control method for CAVs to produce optimal speed profiles that minimize the fuel consumption and avoid idling at signalized intersections. To this end, an optimal control problem is formulated using the information of the upcoming traffic signal to adapt vehicles' speeds to avoid frequent stop-and-go driving patterns. Here, by applying the pseudospectral discretization method and the sequential convex programming method, the computational efficiency is greatly improved, enabling potential real-time on-vehicle applications. In addition, the algorithm is implemented under a model predictive control framework to ensure online control with instant response for collision avoidance and robust vehicle coordination. The proposed algorithm is verified through numerical simulations of three different traffic scenarios. The convergence and accuracy of the proposed approach are demonstrated by comparing with a popular nonlinear solver. Furthermore, the benefit of the proposed method in both traffic mobility and fuel efficiency is validated using the speed profile determined from a traffic following model in a simulation software as the baseline.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1923235

Journal Information:: Optimal Control Applications and Methods, Journal Name: Optimal Control Applications and Methods Journal Issue: 4 Vol. 44; ISSN 0143-2087

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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