A Data-Driven Optimization Based Model Predictive Control for Real-Time ECO Approach and Departure at Signalized Intersections under Uncertain Traffic Conditions

Motivated by Connected and Automated Vehicle (CAV) technologies, this paper proposes a data-driven optimization based Model Predictive Control (MPC) modeling framework for real-time automatically controlling ECO Approach and Departure (ECO-AND) under uncertain traffic conditions. The proposed data-driven optimization based MPC modeling framework aims to improve the safety, energy efficiency, driving comfort, and robustness of the ECO-AND longitudinal automated driving under uncertain traffic conditions by using Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) data. Through an online learning-based driving dynamics prediction model, the authors predict a set of uncertain driving states of the preceding vehicles in front of the controlled CAV. With the predicted driving states of the preceding vehicles, the authors solve a constrained Finite-Horizon Optimal Control problem considering the trade-off between energy consumption and driving efficiency to predict a set of uncertain driving states of the controlled CAV as ECO-driving references. To obtain the optimal acceleration or deceleration commands for the CAV with the set of ECO-driving references, the authors formulate a Distributionally Robust Stochastic Optimization (DRSO) model (i.e. a special case of data-driven optimization models under moment bounds) with Distributionally Robust Chance Constraints (DRCC) that explicitly counts for location-based intersection traffic signal control constraints as well as safe driving constraints. To solve the minimax program of the DRSO-DRCC model, the authors reformulate a relaxed dual problem as a Semidefinite Program (SDP) based on the strong duality theory and the Semidefinite Relaxation technique. In addition, the authors propose a solution algorithm to solve the relaxed SDP problem. The authors design experiments to demonstrate that the proposed model and conduct computational analyses to validate the efficiency of the proposed algorithm for solving the DRSO-DRCC model for real-time automated Eco-driving applications.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AHB45 Standing Committee on Traffic Flow Theory and Characteristics.
  • Corporate Authors:

    Transportation Research Board

  • Authors:
    • Zhao, Shuaidong
    • Zhang, Kuilin
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References;
  • Pagination: 10p

Subject/Index Terms

Filing Info

  • Accession Number: 01698044
  • Record Type: Publication
  • Report/Paper Numbers: 19-06035
  • Files: TRIS, TRB, ATRI
  • Created Date: Mar 1 2019 3:51PM