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arXiv · 2110.10097

Data-Driven Predictive Control for Connected and Autonomous Vehicles in Mixed Traffic

Jiawei Wang·Yang Zheng·Qing Xu·Keqiang Li
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Abstract

Cooperative control of Connected and Autonomous Vehicles (CAVs) promises great benefits for mixed traffic. Most existing research focuses on model-based control strategies, assuming that car-following dynamics of human-driven vehicles are explicitly known. In this paper, instead of relying on a parametric car-following model, we introduce a data-driven predictive control strategy to achieve safe and optimal control for CAVs in mixed traffic. We first present a linearized dynamical model for mixed traffic systems, and investigate its controllability and observability. Based on these control-theoretic properties, we then propose a novel DeeP-LCC (Data-EnablEd Predictive Leading Cruise Control) strategy for CAVs based on measurable driving data to smooth mixed traffic. Our method is implemented in a receding horizon manner, in which input/output constraints are incorporated to achieve collision-free guarantees. Nonlinear traffic simulations reveal its saving of up to 24.96% fuel consumption during a braking scenario of Extra-Urban Driving Cycle while ensuring safety.

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Jiawei Wang, Yang Zheng, Qing Xu, Keqiang Li. 2021-10-19. Data-Driven Predictive Control for Connected and Autonomous Vehicles in Mixed Traffic. https://arxiv.org/abs/2110.10097

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