Optimization-Based Path-Planning for Connected and non-Connected Automated Vehicles

TL;DR

This paper presents an optimization-based path-planning algorithm for connected and non-connected automated vehicles on multilane motorways, improving traffic flow efficiency and vehicle performance through real-time information exchange and model predictive control.

Contribution

It introduces a novel MPC-based path-planning method that incorporates connectivity and optimal control, with efficient algorithms for real-time implementation within a microsimulation platform.

Findings

Connected vehicles improve traffic flow efficiency.

Automated vehicles drive closer to desired speeds.

Higher penetration of automated vehicles benefits overall traffic.

Abstract

A path-planning algorithm for connected and non-connected automated road vehicles on multilane motorways is derived from the opportune formulation of an optimal control problem. In this framework, the objective function to be minimized contains appropriate respective terms to reflect: the goals of vehicle advancement; passenger comfort; and avoidance of collisions with other vehicles, of road departures and of negative speeds. Connectivity implies that connected vehicles are able to exchange with each other (V2V) or the infrastructure (V2I), real-time information about their last generated path. For the numerical solution of the optimal control problem, an efficient feasible direction algorithm is used. To ensure high-quality local minima, a simplified Dynamic Programming algorithm is also conceived to deliver the initial guess trajectory for the feasible direction algorithm. Thanks to low computation times, the approach is readily executable within a model predictive control (MPC) framework. The proposed MPC-based approach is embedded within the Aimsun microsimulation platform, which enables the evaluation of a plethora of realistic vehicle driving and advancement scenarios. Results obtained on a multilane motorway stretch indicate higher efficiency of the optimally controlled vehicles in driving closer to their desired speed, compared to ordinary Aimsun vehicles. Increased penetration rates of automated vehicles are found to increase the efficiency of the overall traffic flow, benefiting manual vehicles as well. Moreover, connected controlled vehicles appear to be more efficient compared to the corresponding non-connected controlled vehicles, due to the improved real-time information and short-term prediction.