A decentralized approach to collaborative driving coordination

Journal of Systems Architecture, 2013

Cooperative driving systems enable vehicles to adapt their motion to the surrounding trac situation by utilizing information communicated by other vehicles and infrastructure in the vicinity. How should these systems be designed and integrated into the modern automobile? What are the needed functions, key architectural elements and their relationships? We created a reference architecture that systematically answers these questions and validated it in real world usage scenarios. Key ndings concern required services and enabling them via the architecture. We present the reference architecture and discuss how it can inuence the design and implementation of such features in automotive systems.

2012

Cooperative driving aims to reduce traffic congestion, air pollution, and it increases safety. One of the applications of cooperative driving is platooning, where string of vehicles drives automatically in the same lane. The cooperative driving system consists of such components as communication box, sensor fusion, radar, and controller.

2020

Urban traffic congestion is a chronic problem faced by many cities. It is essentially inefficient infrastructure use which results in increased vehicle fuel consumption and emissions. This in turn adds extra costs to commuters and businesses. Addressing this issue is therefore of paramount interest due to the perceived dual benefit. Many technologies were and are being developed. These include adaptive traffic signals, dedicated lanes, etc. This paper presents a simple platooning algorithm that maintains relatively small distances (pre-specified time gap) between consecutive vehicles to enhance mobility, increase transportation capacity and ultimately reduce travel costs. Several dynamic and kinematic constraints governing the motion of vehicles are also accounted for. These include acceleration, velocity, and distance constraints. This developed logic was tested on highways that traverse the downtown area of Los Angeles. Depending on the market penetration rate of connected automat...

2003

Abstract Collaborative driving is an important sub-component of Intelligent Transportation Systems (ITS) and it strives to create vehicles that are able to cooperate in order to navigate through urban traffic by using communications. In this paper, we present an overview of different architecture from the field of artificial intelligence and robotic systems, related to collaborative driving, and analyze their relevance to our AUTO'21 project. The report is divided in three parts.

The long-term goal of this project is to derive systems that would allow the safe and efficient coordination of collaborating vehicles in high-density highway traffic in order to alleviate traffic congestion and reduce driving stress. The challenge is to ensure safe movements of each vehicle, inside the collaborative driving system. An architecture for the control and collaboration of vehicles is needed. We will present the importance of communication in CDS. After reviewing related work, the control scenarios for making vehicles join a pre-existing platoon, leave a platoon, join two platoons, do lane transition of a platoon or for ensuring safe emergency procedures are presented. The architecture developed to assure the safe execution of those scenarios is then exposed.

Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems

One of the key priorities of technologies is performance. In the area of transportation, performance is typically intertwined with increased mobility and reduced costs. Congestion alleviation which is a persistent challenge faced by many cities is a priority. The use of infrastructure is inherently inefficient, resulting in higher vehicle fuel consumption and pollution. This in turn burdens commuters and businesses. Therefore, solving this issue is of prime significance because of the potential benefit. Many technologies have been and are being developed. These include adaptive traffic signals and various dynamic traffic control strategies. This paper introduces a platooning controller that keeps relatively small time gaps between consecutive vehicles to increase mobility, and eventually reduce travel costs. This controller also accounts for complex dynamic and kinematic restrictions controlling vehicle motion. The controller is tested in a virtual environment on highways in downtown Los Angeles. A drop-in travel time, delay, fuel consumption was observed across the area for connected automated vehicles (CAVs) and non-connected vehicles, at various market penetration rates (MPRs). Reductions of up to 5%, 9.4%, and 8.17% in travel time, delay, and fuel consumption, respectively are observed. These observations are observed for all vehicles platooned and non-platooned. a .

2020

This is an overview on the models used to control fleets of road vehicles. In general, simplified vehicle models are used and their coupling features are introduced through the (individual) controllers. The robustness and precision of motion control needs geometric, kinematic and dynamic descriptions. We propose a modeling methodology for robots platooning to introduce specific features in the platoon behavior. This overview proposes reference models to link the fleet vehicles and assign a character to the group independent from control.

Procedia Computer Science, 2016

Nowadays, we are more and more surrounded by powerful and intelligent communicating objects. Many of these objects, as smartphones, watches, detectors and soon cars, are moving in increasingly interconnected environments, have abilities to communicate with each other and to exchange information. A collaborative approach allows these entities to exchange information and objectives and to implement rules in a structured manner in order to optimize the execution of their own mission and, therefore, the operation of the system in general. For example, collaborative behaviours and informations exchanges could improve the movement of vehicles in an urban center and avoid traffic jams. Our contribution puts a stress on a definition of collaboration in the context of mobile communicating entities. For the sake of agent-based modeling, we also list challenges raised like technical architecture and data organisation. Then we propose an hybrid architecture for collaborative exchanges with an example based on communicating vehicles in an urban context and implemented on the GAMA platform.

The platooning of autonomous ground vehicles has potential to largely benefit the road traffic, including enhancing highway safety, improving traffic utility and reducing fuel consumption. The main goal of platoon control is to ensure all the vehicles in the same group to move at consensual speed while maintaining desired spaces between adjacent vehicles. This paper presents an overview of vehicular platoon control techniques from networked control perspective, which naturally decomposes a platoon into four interrelated components, i.e., 1) node dynamics (ND), 2) information flow topology (IFT), 3) distributed controller (DC) and, 4) geometry formation (GF). Under the four-component framework, existing literature are categorized and analyzed according to their technical features. Three main performance metrics, i.e. string stability, stability margin and coherence behavior, are also discussed.

2010

An approach to increase road capacity consists in making vehicles move much closer to each other, i.e. using platoons, and to avoid the constant stop-and-go in urban traffic. This paper addresses the studies of new models to allow the research of cooperative and autonomous communication-enabled vehicles, with platooning capabilities, and the addition of new features in SUMO (Simulation for Urban MObility) to allow the simulation of those models. Simulations results of vehicle platoons using constant spacing instead of the more common constant time headway, with intervehicle communications, are reported in the paper. The platoon leaders are controlled by an external application and the simulation platform works at the subsecond level.