Distributed model predictive control of automated vehicle entities in lane-free traffic involving human driven vehicles

This paper presents an event-triggered distributed model predictive control (MPC) scheme for diverse automated vehicle entities populating a lane-free traffic environment with vehicle nudging. The vehicle entities comprise, beyond individual automated vehicles, 1-D snake-like interruptible vehicle platoons and 2-D flexible-shape vehicle flocks. Each entity is driven independently by use of a proper movement strategy for one or multiple automated vehicles deriving from a generic single-vehicle or joint optimal control problem, respectively. Human-driven vehicles may also be present and are considered as obstacles. A two-dimensional double-integrator model is considered for the longitudinal and lateral movements of each vehicle, considering constant and state-dependent bounds on control inputs, including road boundary constraints. A multi-objective function, comprising various weighted sub-objectives, is designed for all vehicles of each entity, considering minimization of fuel consumption, intra-entity and inter-entity collision avoidance, entity-specific desired speed, prevention of infeasible maneuvers and, for multi-vehicle entities, operation of a flexible platoon or deformable flock. A computationally efficient feasible direction algorithm is called, on an event-triggered basis, to compute in real time the numerical solution of each entity’s optimal control problem for finite time-horizons within an MPC framework. Testing and demonstration scenarios are examined on two setups: one on a straight, lane-free motorway and the other on a similar motorway that transitions into a narrowed road after passing through a funnel.