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

Abstract

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. 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.

Alignment of Unsupervised Machine Learning with Human Understanding: A Case Study of Connected Vehicle Patents

“…Alanazi (2023) conducted a systematic literature review of how autonomous vehicles manage traffic at junctions, exploring various methodologies that include ML [11]. Shi et al (2023) developed a real-time control algorithm for CVs to optimize fuel use in signalized corridors [12]. Gholamhosseinian and Seitz (2022) surveyed cooperative intersection management strategies for CVs [13].…”

A Systematic Patent Review of Connected Vehicle Technology Trends

“…Alanazi (2023) performed an SLR focusing on how autonomous vehicles manage traffic at junctions, categorizing approaches into rule-based, optimization, hybrid, and machine-learning procedures [14]. Shi et al (2023) devised a real-time control algorithm for CAVs in signalized corridors, aiming to minimize fuel consumption and avoid idling [15]. Gholamhosseinian and Seitz (2022) completed a comprehensive survey on cooperative intersection management for CVs [16].…”