Optimal Trajectory Planning for Connected and Automated Vehicles in Lane-free Traffic with Vehicle Nudging

TL;DR

This paper introduces an optimal control-based movement strategy for connected and automated vehicles in lane-free traffic, ensuring safety and efficiency through real-time trajectory optimization within a Model Predictive Control framework.

Contribution

It develops a nonlinear optimal control formulation with a feasible direction algorithm for real-time trajectory planning in lane-free environments, advancing CAV traffic management.

Findings

High traffic flow efficiency observed in simulations

Safe and comfortable vehicle trajectories achieved

Effective real-time control in diverse traffic densities

Abstract

The paper presents a movement strategy for Connected and Automated Vehicles (CAVs) in a lane-free traffic environment with vehicle nudging by use of an optimal control approach. State-dependent constraints on control inputs are considered to ensure that the vehicle moves within the road boundaries and to prevent collisions. An objective function, comprising various weighted sub-objectives, is designed, whose minimization leads to vehicle advancement at the desired speed, when possible, while avoiding obstacles. A nonlinear optimal control problem (OCP) is formulated for the minimization of the objective function subject to constraints for each vehicle. A computationally efficient Feasible Direction Algorithm (FDA) is called, on event-triggered basis, to compute in real-time the numerical solution for finite time-horizons within a Model Predictive Control (MPC) framework. The approach is applied to each vehicle on the road, while running simulations on a lane-free ring-road, for a wide range of vehicle densities and different types of vehicles. From the simulations, which create myriads of driving episodes for each involved vehicle, it is observed that the proposed approach is highly efficient in delivering safe, comfortable and efficient vehicle trajectories, as well as high traffic flow outcomes. The approach is under investigation for further use in various lane-free road infrastructures for CAV traffic.