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.
Original languageEnglish
Title of host publicationTRB 95th Annual Meeting Compendium of Papers
Place of PublicationWashington, DC, USA
PublisherTransporation Research Board (TRB)
Number of pages21
Publication statusPublished - 2016
EventTransportation Research Board 95th annual meeting - Washington, United States
Duration: 10 Jan 201614 Jan 2016
Conference number: 95

Conference

ConferenceTransportation Research Board 95th annual meeting
Abbreviated titleTRB 95
CountryUnited States
CityWashington
Period10/01/1614/01/16

    Research areas

  • Acceleration (Mechanics), Algorithms, Decision making, Intelligent vehicles, Lane changing, Optimization

ID: 30169988