Optimal Path Planning for Connected and Automated Vehicles in Lane-free Traffic

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 sub-objectives, is designed, whose minimization leads to vehicle advancement at the desired speed, whenever 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 countless 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.

Constructing Artificial Traffic Fluids by Designing Cruise Controllers

“…(90) Due to (23), we have that for all w ∈ Ω , max (0, −Λ i (w)) ≤ r (−Λ i (w)) for all i = 1, ..., n. The previous inequality, in conjunction with (90) and definition (22) implies inequality (89).…”

“…) where ρ i,j : R + → (L i,j , λ] for i, j = 1, ..., n, j = i are the non-increasing functions involved in (17), H(w) is given by ( 16), r(x) satisfies (23), and B i,j (s), c i,j (s), for i, j = 1, ..., n, j = i are defined in (66) and (67), respectively. In order to show (91) notice first that due to definition of m i , (66), (17), and (69), we have (with similar arguments that led to the derivation of (70)) that max…”

“…Recent advances in technology have revolutionized vehicle automation with different kinds of driver support systems (see for instance [9], [16], [18]). In the era of connected and automated vehicles, new perspectives and principles have also been suggested [17], where autonomous vehicles can move on the two-dimensional surface of lane-free roads ( [11], [23]) without abiding to lane discipline, something that may improve traffic flow and increase capacity of highways and arterials.…”

“…) with γ > 0, g (v) > 0 for v ∈ R, (g is the function g 1 appearing in (31)), and r satisfying (23). The state space is Ω…”

Modeling Vehicle Flocking in Lane-Free Automated Traffic

“…Thereby, the total flow efficiency of both directions can be maximized. Control strategies and path planning algorithms for CAVs in the lane-free environment were designed by Levy and Haddad ( 26 ), Karafyllis et al ( 27 ), Yanumula et al ( 28 ), and Troullinos et al ( 29 ). These strategies avoid collisions with other vehicles, obstacles, and the road boundary and optimize vehicle speeds.…”