Fully Distributed Model Predictive Control of Connected Automated Vehicles in Intersections: Theory and Vehicle Experiments

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   Distributed Model Predictive Control for Intersection Automation Using a Parallelized Optimization Approach

   Alexander Katriniok, Peter Kleibaum, Martina Josevski · 2017 · IFAC-PapersOnLine · cited 75 times · Not summarized yet

   Abstract

   Abstract Road intersections are usually a bottleneck in big cities and might lead to severe congestion during rush hour traffic. With highly automated vehicles leveraging vehicle-to-vehicle communication, traffic lights and signs might become dispensable one day. For this particular scenario, we propose a distributed model predictive control approach which enables multiple vehicles to pass the intersection simultaneously in a safe and efficient way. The resulting non-convex distributed control problem is solved in parallel without any nested iterations which is achieved by applying constraint prioritization. Constraint prioritization introduces priorities on vehicles such that a vehicle with…

2. Very close Cited by this paper Not relevant

   Real-Time Distributed Automation Of Road Intersections

   Fabio Molinari, Alexander Katriniok, J. Raisch · 2021 · IFAC-PapersOnLine · cited 6 times · Not summarized yet

   Abstract

   Abstract The topic of this paper is the design of a fully distributed and real-time capable control scheme for the automation of road intersections. State of the art Vehicle-to-Vehicle (V2V) communication technology is adopted. Vehicles distributively negotiate crossing priorities by running a Consensus-Based Auction Algorithm (CBAA-M). Then, each agent solves a nonlinear Model Predictive Control (MPC) problem that computes the optimal trajectory avoiding collisions with higher priority vehicles and deciding the crossing order. The scheme is shown to be real-time capable and able to respond to sudden priority changes, e.g. if a vehicle gets an emergency call. Simulations reinforce theoretica…

3. Very close Cited by this paper Not relevant

   Distributed control algorithm for vehicle coordination at traffic intersections

   Jia-He Shi, Yi Zheng, Yu-Ning Jiang et al. · 2018 · European Control Conference · cited 17 times · Not summarized yet

   Abstract

   This paper proposes a distributed closed-loop control algorithm for optimal coordination of autonomous vehicles at traffic intersections. The main contribution of the paper is a distributed plug-and-play closed-loop optimal control scheme with rear-end collision avoidance constraints enforced on each lane, which maintains recursive feasibility under the assumption that communication between neighboring vehicles is possible. The method is closely related to model predictive control, but at each sampling time new vehicles are allowed to enter the modeled region around the intersection while other vehicles are leaving. In contrast to human drivers, autonomous vehicles can collaboratively form t…

4. Very close Cited by this paper Not relevant

   Decentralized model predictive control for smooth coordination of automated vehicles at intersection

   Xiangjun Qian, Jean Gregoire, A. de La Fortelle et al. · 2015 · European Control Conference · cited 86 times · Not summarized yet

   Abstract

   We consider the problem of coordinating a set of automated vehicles at an intersection with no traffic light. The priority-based coordination framework is adopted to separate the problem into a priority assignment problem and a vehicle control problem under fixed priorities. This framework ensures good properties like safety (collision-free trajectories, brake-safe control) and liveness (no gridlock). We propose a decentralized Model Predictive Control (MPC) approach where vehicles solve local optimization problems in parallel, ensuring them to cross the intersection smoothly. The proposed decentralized MPC scheme considers the requirements of efficiency, comfort and fuel economy and ensures…

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

   Alexander Katriniok, P. Sopasakis, Mathijs Schuurmans et al. · 2019 · IEEE Conference on Decision and Control · cited 19 times · Not summarized yet

   Abstract

   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 f…

6. Very close Cites this paper Not relevant

   Decentralized Model Predictive Control of Connected and Automated Vehicles with Coupled Safety Constraints

   Philip Schultheis, Kimia Chavoshi, John Lygeros · 2026 · arXiv.org · preprint, not peer reviewed · Not summarized yet

   Abstract

   Connected and Automated Vehicles (CAVs) operating on lane-free highways offer substantial gains in traffic efficiency. However, their inherent nonlinear dynamics and the presence of coupled, nonconvex safety constraints present critical challenges to control design. Centralized Model Predictive Control (MPC) ensures safety, but suffers from scalability and communication limitations. To address these challenges, this paper investigates decentralized MPC (DMPC) for CAV coordination, focusing on iterative, non-cooperative algorithms, including Jacobi-type and Gauss-Seidel-type. A novel decoupling method is developed to transform nonconvex safety constraints into convex, locally enforceable cons…

7. Very close Cites this paper Not relevant

   Distributed Minimum-Time Optimal Control for Lane-Free CAVs Intersection Crossing

   Ebubekir Pinar, N. Meskin, A. Dizqah · 2026 · International Conference on Control, Decision and Information Technologies · Not summarized yet

   Abstract

   Traffic intersections are major bottlenecks in urban environments, and studies have shown that connected and autonomous vehicles (CAVs) can improve their overall throughput. While a centralised control of CAVs is effective for this application, it suffers from communication overhead, computational bottlenecks, and single-point failure vulnerabilities. This paper proposes a fully distributed minimum-time optimisation framework where individual CAVs solve local optimal control problems using only local information with neighbours. The framework incorporates robust collision avoidance constraints with minimum-time objectives and fail-safe protocols, ensuring collision-free operations. Simulatio…

8. Very close Cited by this paper Used in depth by this paper Not relevant

   Optimal Coordination of Automated Vehicles at Intersections: Theory and Experiments

   Robert Hult, Mario Zanon, S. Gros et al. · 2019 · IEEE Transactions on Control Systems Technology · cited 93 times · Not summarized yet

   Abstract

   In this paper, we present a bilevel, model predictive controller for coordination of automated vehicles at intersections. The bilevel controller consists of a coordination level, where intersection occupancy timeslots are allocated, and a vehicle level, where the control commands for the vehicles are computed. We establish persistent feasibility and stability of the bilevel controller under some mild assumptions and derive conditions under which closed-loop collision avoidance can be ensured with bounded position uncertainty. We thereafter detail an implementation of the coordination controller on a three-vehicle test bed, where the intersection-level optimization problem is solved using a d…

Prior work it builds on

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   Primal decomposition of the optimal coordination of vehicles at traffic intersections

   Robert Hult, Mario Zanon, S. Gros et al. · 2016 · IEEE Conference on Decision and Control · cited 48 times · Not summarized yet

   Abstract

   In this paper we address the problem of coordinating automated vehicles at intersections, which we state as a constrained finite horizon optimal control problem. We present and study the properties of a primal decomposition of the optimal control problem. More specifically, the decomposition consists of an upper problem that allocates occupancy time-slots in the intersection, and lower-level problems delivering control policies for each vehicle. We investigate the continuity class of the upper problem, and show that it can be efficiently tackled using a standard sequential quadratic programming and that most computations can be distributed and performed by the participating vehicles. The pap…

2. Very close Not relevant

   A Survey on Intersection Management of Connected Autonomous Vehicles

   M. Khayatian, Mohammadreza Mehrabian, E. Andert et al. · 2020 · ACM Trans. Cyber Phys. Syst. · cited 112 times · Not summarized yet

   Abstract

   Intersection management of Connected Autonomous Vehicles (CAVs) has the potential to improve safety and mobility. CAVs approaching an intersection can exchange information with the infrastructure or each other to schedule their cross times. By avoiding unnecessary stops, scheduling CAVs can increase traffic throughput, reduce energy consumption, and most importantly, minimize the number of accidents that happen in intersection areas due to human errors. We study existing intersection management approaches from following key perspectives: (1) intersection management interface, (2) scheduling policy, (3) existing wireless technologies, (4) existing vehicle models used by researchers and their …

3. Very close Not relevant

   Optimal time trajectory and coordination for connected and automated vehicles

   Andreas A. Malikopoulos, Logan E. Beaver, Ioannis Vasileios Chremos · 2020 · at - Automatisierungstechnik · cited 135 times · Not summarized yet

   Abstract

   In this paper, we provide a decentralized theoretical framework for coordination of connected and automated vehicles (CAVs) at different traffic scenarios. The framework includes: (1) an upper-level optimization that yields for each CAV its optimal time trajectory and lane to pass through a given traffic scenario while alleviating congestion; and (2) a low-level optimization that yields for each CAV its optimal control input (acceleration/deceleration). We provide a complete, analytical solution of the low-level optimization problem that includes the rear-end, speed-dependent safety constraint. Furthermore, we provide a problem formulation for the upper-level optimization in which there is n…

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   A Stochastic Model Predictive Control Approach for Driver-Aided Intersection Crossing With Uncertain Driver Time Delay

   Alexander Katriniok, Stefan Kojchev, E. Lefeber et al. · 2019 · European Control Conference · cited 5 times · Not summarized yet

   Abstract

   We investigate the problem of coordinating human-driven vehicles in road intersections without any traffic lights or signs by issuing speed advices. The vehicles in the intersection are assumed to move along an a priori known path and to be connected via vehicle-to-vehicle communication. The challenge arises with the uncertain driver reaction to a speed advice, especially in terms of the driver reaction time delay, as it might lead to unstable system dynamics. For this control problem, a distributed stochastic model predictive control concept is designed which accounts for driver uncertainties. By optimizing over scenarios, which are sequences of independent and identically distributed sampl…

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   A Feasible MPC-Based Negotiation Algorithm for Automated Intersection Crossing*

   M. Kneissl, A. Molin, H. Esen et al. · 2018 · European Control Conference · cited 31 times · Not summarized yet

   Abstract

   We propose an intersection crossing algorithm for autonomous vehicles with vehicle to infrastructure (V2I) communication capability. All vehicles attempting to cross the intersection share their expected time of entering a critical zone based on decentralized model predictive control (MPC) results. These time suggestions are collected at a central intersection management (IM) unit, which is responsible for coordinating the vehicles. A time-based negotiation process between vehicles and IM is conducted to find a safe solution. An advantage of the approach is that model-based vehicle data is kept private, while the computational burden of the intersection coordination is distributed between th…

Papers that cite it

1. Close Uses this paper in depth Not relevant

   Centralized Cooperation for Connected Autonomous Vehicles at Intersections by Safe Deep Reinforcement Learning

   Rui Zhao, Yun Li, Kui Wang et al. · 2024 · IEEE Transactions on Mobile Computing · cited 11 times · Not summarized yet

   Abstract

   Connected and automated vehicles (CAVs) have the potential to transform traffic management, especially at intersections. Traditional traffic signals might become obsolete with the implementation of autonomous intersection management (AIM) systems, which aim for efficient and safe vehicle flow. Current AIM methods often rely on optimization control algorithms, which are not computationally efficient. Some methods use reinforcement learning (RL) but compromise safety for rewards and simplify traffic scenarios by designating specific turn lanes. This paper introduces a novel approach, the risk situation-aware constrained policy optimization (RSCPO), to enhance RL training with safety assurance.…

2. Very close Not relevant

   Alternating Direction Method of Multipliers-Based Parallel Optimization for Multi-Agent Collision-Free Model Predictive Control

   Zi-Long Cheng, Jun Ma, Wen-Xin Wang et al. · 2021 · IEEE Transactions on Artificial Intelligence · cited 11 times · Not summarized yet

   Abstract

   This article investigates the collision-free control problem for multi-agent systems. For such multi-agent systems, it is the typical situation where conventional methods using either the usual centralized model predictive control (MPC), or even the distributed counterpart, would suffer from substantial difficulty in balancing optimality and computational efficiency. Additionally, the nonconvex characteristics that invariably arise in such collision-free control and optimization problems render it difficult to effectively derive a reliable solution (and also to thoroughly analyze the associated convergence properties). To overcome these challenging issues, this work establishes a suitably no…

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   Multi-Agent Constrained Policy Optimization for Conflict-Free Management of Connected Autonomous Vehicles at Unsignalized Intersections

   Rui Zhao, Yun Li, Fei Gao et al. · 2024 · IEEE transactions on intelligent transportation systems (Print) · cited 38 times · Not summarized yet

   Abstract

   Autonomous Intersection Management (AIM) systems present a new paradigm for conflict-free cooperation of connected autonomous vehicles (CAVs) at road intersections, the aim of which is to eliminate collisions and improve the traffic efficiency and ride comfort. Given the challenges of current centralized coordination methods in balancing high computational efficiency and robust safety assurance, this paper proposes an innovative conflict-free management scheme for CAVs at unsignalized intersections, leveraging safe multi-agent deep reinforcement learning (MADRL). Firstly, we formulate the safe MADRL problem as a constrained Markov game (CMG) and then transform the AIM problem into a CMG by c…

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   Iterative distributed model predictive control for nonlinear systems with coupled non‐convex constraints and costs

   Jin-Xian Wu, Li Dai, Yuan-Qing Xia · 2024 · International Journal of Robust and Nonlinear Control · cited 9 times · Not summarized yet

   Abstract

   This paper proposes a distributed model predictive control (DMPC) algorithm for dynamic decoupled discrete‐time nonlinear systems subject to nonlinear (maybe non‐convex) coupled constraints and costs. Solving the resulting nonlinear optimal control problem (OCP) using a DMPC algorithm that is fully distributed, termination‐flexible, and recursively feasible for nonlinear systems with coupled constraints and costs remains an open problem. To address this, we propose a fully distributed and globally convergence‐guaranteed framework called inexact distributed sequential quadratic programming (IDSQP) for solving the OCP at each time step. Specifically, the proposed IDSQP framework has the follow…

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   Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects

   Min Hua, Xin-Da Qi, Dong Chen et al. · 2023 · IEEE Transactions on Automation Science and Engineering · cited 99 times · Not summarized yet

   Abstract

   Connected and automated vehicles (CAVs) have emerged as a potential solution to the future challenges of developing safe, efficient, and eco-friendly transportation systems. However, CAV control presents significant challenges significant challenges due to the complexity of interconnectivity and coordination required among vehicles. Multi-agent reinforcement learning (MARL), which has shown notable advancements in addressing complex problems in autonomous driving, robotics, and human-vehicle interaction, emerges as a promising tool to enhance CAV capabilities. Despite its potential, there is a notable absence of current reviews on mainstream MARL algorithms for CAVs. To fill this gap, this p…