Formation and reconfiguration of tight multi-lane platoons

Firoozi, Roya; Zhang, Xiaojing; Borrelli, Francesco

doi:10.1016/j.conengprac.2020.104714

Cited by 35 publications

(12 citation statements)

References 14 publications

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“…The road region occupied by the vehicle is defined by a two-dimensional convex polygon P, L car and W car are the length and width of the vehicle, d lon and d lat are the longitudinal and lateral safe distances. The convex set P for a state vector z = [x, y, v, ψ] can be mathematically defined as [33]:…”

Section: Collision Detectionmentioning

confidence: 99%

Real-time Cooperative Vehicle Coordination at Unsignalized Road Intersections

Luo¹,

Zhang²,

Rui³

et al. 2022

Preprint

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Cooperative coordination at unsignalized road intersections, which aims to improve the driving safety and traffic throughput for connected and automated vehicles, has attracted increasing interests in recent years. However, most existing investigations either suffer from computational complexity or cannot harness the full potential of the road infrastructure. To this end, we first present a dedicated intersection coordination framework, where the involved vehicles hand over their control authorities and follow instructions from a centralized coordinator. Then a unified cooperative trajectory optimization problem will be formulated to maximize the traffic throughput while ensuring the driving safety and long-term stability of the coordination system. To address the key computational challenges in the real-world deployment, we reformulate this non-convex sequential decision problem into a model-free Markov Decision Process (MDP) and tackle it by devising a Twin Delayed Deep Deterministic Policy Gradient (TD3)-based strategy in the deep reinforcement learning (DRL) framework. Simulation and practical experiments show that the proposed strategy could achieve near-optimal performance in sub-static coordination scenarios and significantly improve the traffic throughput in the realistic continuous traffic flow. The most remarkable advantage is that our strategy could reduce the time complexity of computation to milliseconds, and is shown scalable when the road lanes increase.

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Section: Collision Detectionmentioning

confidence: 99%

Real-time Cooperative Vehicle Coordination at Unsignalized Road Intersections

Luo¹,

Zhang²,

Rui³

et al. 2022

Preprint

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“…An architecture for tight multi-lanes platoons of AVs travelling on public roads is presented in [26]. Various geometrical configurations are used to form platoons with different configurations.…”

Section: Related Workmentioning

confidence: 99%

Traffic Flow Management of Autonomous Vehicles Using Platooning and Collision Avoidance Strategies

et al. 2021

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Connected Autonomous Vehicles (AVs) promise innovative solutions for traffic flow management, especially for congestion mitigation. Vehicle-to-Vehicle (V2V) communication depends on wireless technology where vehicles can communicate with each other about obstacles and make cooperative strategies to avoid these obstacles. Vehicle-to-Infrastructure (V2I) also helps vehicles to make use of infrastructural components to navigate through different paths. This paper proposes an approach based on swarm intelligence for the formation and evolution of platoons to maintain traffic flow during congestion and collision avoidance practices using V2V and V2I communications. In this paper, we present a two level approach to improve traffic flow of AVs. At the first level, we reduce the congestion by forming platoons and study how platooning helps vehicles deal with congestion or obstacles in uncertain situations. We performed experiments based on different challenging scenarios during the platoon’s formation and evolution. At the second level, we incorporate a collision avoidance mechanism using V2V and V2I infrastructures. We used SUMO, Omnet++ with veins for simulations. The results show significant improvement in performance in maintaining traffic flow.

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“…Each J i can be designed according to the local-robot planning and control objectives. For example, the local costs can be specified to reach a goal set or to reduce the deviation from a global reference path (which is not collision-free) computed using high-level planning methods (e.g., A* or RRT*) as proposed in [28]- [31] for single-robot local motion planning.…”

Section: Preliminariesmentioning

confidence: 99%

A Distributed Multi-Robot Coordination Algorithm for Navigation in Tight Environments

Firoozi,

Ferranti,

Zhang

et al. 2020

Preprint

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This work presents a distributed method for multirobot coordination based on nonlinear model predictive control (NMPC) and dual decomposition. Our approach allows the robots to coordinate in tight spaces (e.g., highway lanes, parking lots, warehouses, canals, etc.) by using a polytopic description of each robot's shape and formulating the collision avoidance as a dual optimization problem. Our method accommodates heterogeneous teams of robots (i.e., robots with different polytopic shapes and dynamic models can be part of the same team) and can be used to avoid collisions in n-dimensional spaces. Starting from a centralized implementation of the NMPC problem, we show how to exploit the problem structure to allow the robots to cooperate (while communicating their intentions to the neighbors) and compute collision-free paths in a distributed way in real time. By relying on a bi-level optimization scheme, our design decouples the optimization of the robot states and of the collision-avoidance variables to create real time coordination strategies. Finally, we apply our method for the autonomous navigation of a platoon of connected vehicles on a simulation setting. We compare our design with the centralized NMPC design to show the computational benefits of the proposed distributed algorithm. In addition, we demonstrate our method for coordination of a heterogeneous team of robots (with different polytopic shapes).

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Formation and reconfiguration of tight multi-lane platoons

Cited by 35 publications

References 14 publications

Real-time Cooperative Vehicle Coordination at Unsignalized Road Intersections

Real-time Cooperative Vehicle Coordination at Unsignalized Road Intersections

Traffic Flow Management of Autonomous Vehicles Using Platooning and Collision Avoidance Strategies

A Distributed Multi-Robot Coordination Algorithm for Navigation in Tight Environments

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