Ecological Driving System for Connected Automated Vehicles: A New Model Predictive Control Framework

The growing importance of sustainable and eco-friendly transportation has spurred the need for effective solutions to optimize the operational efficiency of connected automated vehicles (CAVs) in such complex environments. The trajectory planning problem (TTP) for CAVs is complicated by non-linear constraints, especially when dealing with the Eco-trajectory Planning Problem (EPP), characterized by its nonlinear, high-order, and non convex objective function. To tackle this challenge, the research team proposes a novel heuristic explicit predictive model control (heMPC) framework and aim to develop an innovative multi-objective ecological driving system that optimizes CAVs' trajectories along signalized arterial roads. Three key objectives are targeted: minimizing travel time, reducing fuel consumption, and improving traffic safety. The proposed framework comprises two interlinked modules: an offline module and an online module. In the offline module, the team will construct an optimal eco-trajectory batch by optimizing a series of simplified EPPs, considering diverse system initial states and terminal states. This process can be likened to a lookup table in the general eMPC framework, with the intention of precomputing all necessary optimizations and calculations to eliminate online optimization in the subsequent stage. In the online module, the team will employ both static and dynamic trajectory planning algorithms to efficiently handle trajectory planning for CAVs. After proving the effectiveness of the proposed algorithms in the simulation environment, the team will test the entire system with field studies, leveraging the in-house CAV in the lab.

Language

• English

Project

• Status: Active
• Funding: $150000
• Contract Numbers:

  CMMM-UMDXY-2023-C0004

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  University of Maryland, College Park

  College Park, MD  United States  20742
• Project Managers:

  Yang, Xianfeng

• Performing Organizations:

  University of Maryland, College Park

  College Park, MD  United States  20742
• Principal Investigators:

  Yang, Xianfeng

• Start Date: 20230901
• Expected Completion Date: 20240831
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Ecodriving; Fuel consumption; Predictive models; Traffic safety; Travel time; Vehicle trajectories
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01909258
• Record Type: Research project
• Source Agency: Center for Multi-Modal Mobility in Urban, Rural, and Tribal Areas (CMMM)
• Contract Numbers: CMMM-UMDXY-2023-C0004
• Files: UTC, RIP
• Created Date: Feb 22 2024 3:57PM