Optimal Lane Change Times and Accelerations of Autonomous and Connected Vehicles

This contribution puts forward a flexible approach to model the decision-making or design controller for automated driving systems, where tactical-level lane change decisions and control-level accelerations are jointly evaluated based on iteratively solving an online optimization problem. The key idea is that automated vehicles determine lane change times and accelerations in the predicted future to minimize an objective function representing multiple criteria of driving safety, efficiency and comfort. The interactions between controlled vehicles and surrounding vehicles are captured in the objective function. The approach can be applied to model non-cooperative decision-making of autonomous vehicles with optimization of own cost and cooperative behavior of connected vehicles with joint optimization of the collective cost. The problem is formulated as a differential game where automated vehicles make decisions based on the expected behavior of surrounding vehicles. An efficient numerical solution algorithm is used to solve problem. The proposed model performance is demonstrated via numerical examples. The results show that the proposed approach can produce efficient lane-changing maneuvers while obeying safety and comfort requirements. Particularly, the approach generates optimal lane change times and accelerations in the predicted future, including strategic overtaking and cooperative merging scenarios.

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

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Wang, Meng
  • Hoogendoorn, Serge P
  • Daamen, Winnie
  • van Arem, Bart
  • Happee, Riender
• Conference:
  • Transportation Research Board 95th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2016-1-10 to 2016-1-14
• Date: 2016

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 21p
• Monograph Title: TRB 95th Annual Meeting Compendium of Papers

Subject/Index Terms

• TRT Terms: Acceleration (Mechanics); Algorithms; Decision making; Intelligent vehicles; Lane changing; Optimization
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01592763
• Record Type: Publication
• Report/Paper Numbers: 16-2914
• Files: TRIS, TRB, ATRI
• Created Date: Mar 4 2016 5:05PM