Distributed Cooperative MPC for Autonomous Driving in Different Traffic Scenarios

A cooperative control approach for autonomous vehicles is developed in order to perform different complex traffic maneuvers, e.g., double lane-switching or intersection situations. The problem is formulated as a distributed optimal control problem for a system of multiple autonomous vehicles, and then solved using a nonlinear Model Predictive Control (MPC) technique, where the distributed approach is used to make the problem computationally feasible in real-time. To provide safety, a collision avoidance constraint is introduced, also in a distributed way. In the proposed method, each vehicle computes its own control inputs using estimated states of neighboring vehicles. In addition, a compatibility constraint is defined that takes collision avoidance into account but also ensures that each vehicle does not deviate significantly from what is expected by neighboring vehicles. The method allows us to construct a cost function for several different traffic scenarios. The asymptotic convergence of the system to the desired destination is proven, in the absence of uncertainty, and disturbances, for a sufficiently small MPC control horizon. Simulation results show that the distributed algorithm scales well with increasing number of vehicles.

• Record URL:
  http://dx.doi.org/10.1109/TIV.2020.3025484
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
• Supplemental Notes:
  • Copyright © 2021, IEEE.
• Authors:
  • Mohseni, Fatemeh
  • Frisk, Erik
  • Nielsen, Lars
• Publication Date: 2021-6

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 299-309
• Serial:
  • IEEE Transactions on Intelligent Vehicles
  • Volume: 6
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2379-8858
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7274857

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Driving; Optimization; Process control; Traffic safety
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01779865
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 26 2021 4:54PM