Nonlinear Model Predictive Control for Distributed Motion Planning in Road Intersections Using PANOC

The coordination of highly automated vehicles (or agents) in road intersections is an inherently nonconvex and challenging problem. In this paper, we propose a distributed motion planning scheme under reasonable vehicle-to-vehicle communication requirements. Each agent solves a nonlinear model predictive control problem in real time and transmits its planned trajectory to other agents, which may have conflicting objectives. The problem formulation is augmented with conditional constraints that enable the agents to decide whether to wait at a stopping line, if safe crossing is not possible. The involved nonconvex problems are solved very efficiently using the proximal averaged Newton method for optimal control (PANOC). We demonstrate the efficiency of the proposed approach in a realistic intersection crossing scenario.

[1]  Panagiotis Patrinos,et al.  Forward-Backward Envelope for the Sum of Two Nonconvex Functions: Further Properties and Nonmonotone Linesearch Algorithms , 2016, SIAM J. Optim..

[2]  Rajesh Rajamani,et al.  Vehicle dynamics and control , 2005 .

[3]  Yi Zheng,et al.  Distributed control algorithm for vehicle coordination at traffic intersections , 2018, 2018 European Control Conference (ECC).

[4]  Henk Wymeersch,et al.  Cooperative receding horizon conflict resolution at traffic intersections , 2014, 53rd IEEE Conference on Decision and Control.

[5]  Pantelis Sopasakis,et al.  A simple and efficient algorithm for nonlinear model predictive control , 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC).

[6]  Panganamala Ramana Kumar,et al.  An MPC-Based Approach to Provable System-Wide Safety and Liveness of Autonomous Ground Traffic , 2014, IEEE Transactions on Automatic Control.

[7]  Alberto Bemporad,et al.  Proximal Newton methods for convex composite optimization , 2013, 52nd IEEE Conference on Decision and Control.

[8]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[9]  Martina Josevski,et al.  Distributed Model Predictive Control for Intersection Automation Using a Parallelized Optimization Approach , 2017 .

[10]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[11]  Myoungho Sunwoo,et al.  Hierarchical Trajectory Planning of an Autonomous Car Based on the Integration of a Sampling and an Optimization Method , 2018, IEEE Transactions on Intelligent Transportation Systems.

[12]  Panagiotis Patrinos,et al.  Forward–backward quasi-Newton methods for nonsmooth optimization problems , 2016, Computational Optimization and Applications.

[13]  Jörg Raisch,et al.  Automation Of Road Intersections Using Consensus-based Auction Algorithms , 2018, 2018 Annual American Control Conference (ACC).

[14]  C. R. Deboor,et al.  A practical guide to splines , 1978 .

[15]  Mario Zanon,et al.  Primal decomposition of the optimal coordination of vehicles at traffic intersections , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[16]  W. Marsden.  I and J , 2012 .

[17]  Goele Pipeleers,et al.  A Penalty Method Based Approach for Autonomous Navigation using Nonlinear Model Predictive Control , 2018, 1805.02524.

[18]  Sandra Hirche,et al.  A Feasible MPC-Based Negotiation Algorithm for Automated Intersection Crossing* , 2018, 2018 European Control Conference (ECC).

[19]  Goele Pipeleers,et al.  Embedded nonlinear model predictive control for obstacle avoidance using PANOC , 2018, 2018 European Control Conference (ECC).