3 research outputs found
Control and communication systems for automated vehicles cooperation and coordination
Menci贸n Internacional en el t铆tulo de doctorThe technological advances in the Intelligent Transportation Systems (ITS) are exponentially
improving over the last century. The objective is to provide intelligent and innovative services
for the different modes of transportation, towards a better, safer, coordinated and smarter
transport networks. The Intelligent Transportation Systems (ITS) focus is divided into two
main categories; the first is to improve existing components of the transport networks, while
the second is to develop intelligent vehicles which facilitate the transportation process. Different
research efforts have been exerted to tackle various aspects in the fields of the automated
vehicles. Accordingly, this thesis is addressing the problem of multiple automated vehicles
cooperation and coordination. At first, 3DCoAutoSim driving simulator was developed
in Unity game engine and connected to Robot Operating System (ROS) framework and
Simulation of Urban Mobility (SUMO). 3DCoAutoSim is an abbreviation for "3D Simulator
for Cooperative Advanced Driver Assistance Systems (ADAS) and Automated Vehicles
Simulator". 3DCoAutoSim was tested under different circumstances and conditions, afterward,
it was validated through carrying-out several controlled experiments and compare
the results against their counter reality experiments. The obtained results showed the efficiency
of the simulator to handle different situations, emulating real world vehicles. Next
is the development of the iCab platforms, which is an abbreviation for "Intelligent Campus
Automobile". The platforms are two electric golf-carts that were modified mechanically, electronically
and electrically towards the goal of automated driving. Each iCab was equipped
with several on-board embedded computers, perception sensors and auxiliary devices, in
order to execute the necessary actions for self-driving. Moreover, the platforms are capable
of several Vehicle-to-Everything (V2X) communication schemes, applying three layers of
control, utilizing cooperation architecture for platooning, executing localization systems,
mapping systems, perception systems, and finally several planning systems. Hundreds of
experiments were carried-out for the validation of each system in the iCab platform. Results
proved the functionality of the platform to self-drive from one point to another with minimal
human intervention.Los avances tecnol贸gicos en Sistemas Inteligentes de Transporte (ITS) han crecido de forma
exponencial durante el 煤ltimo siglo. El objetivo de estos avances es el de proveer de sistemas
innovadores e inteligentes para ser aplicados a los diferentes medios de transporte, con el fin
de conseguir un transporte mas eficiente, seguro, coordinado e inteligente. El foco de los ITS
se divide principalmente en dos categor铆as; la primera es la mejora de los componentes ya
existentes en las redes de transporte, mientras que la segunda es la de desarrollar veh铆culos
inteligentes que hagan m谩s f谩cil y eficiente el transporte. Diferentes esfuerzos de investigaci贸n
se han llevado a cabo con el fin de solucionar los numerosos aspectos asociados con
la conducci贸n aut贸noma. Esta tesis propone una soluci贸n para la cooperaci贸n y coordinaci贸n
de m煤ltiples veh铆culos. Para ello, en primer lugar se desarroll贸 un simulador (3DCoAutoSim)
de conducci贸n basado en el motor de juegos Unity, conectado al framework Robot Operating
System (ROS) y al simulador Simulation of Urban Mobility (SUMO). 3DCoAutoSim ha
sido probado en diferentes condiciones y circunstancias, para posteriormente validarlo con
resultados a trav茅s de varios experimentos reales controlados. Los resultados obtenidos
mostraron la eficiencia del simulador para manejar diferentes situaciones, emulando los
veh铆culos en el mundo real. En segundo lugar, se desarroll贸 la plataforma de investigaci贸n
Intelligent Campus Automobile (iCab), que consiste en dos carritos el茅ctricos de golf, que
fueron modificados el茅ctrica, mec谩nica y electr贸nicamente para darle capacidades aut贸nomas.
Cada iCab se equip贸 con diferentes computadoras embebidas, sensores de percepci贸n y
unidades auxiliares, con la finalidad de transformarlos en veh铆culos aut贸nomos. Adem谩s,
se les han dado capacidad de comunicaci贸n multimodal (V2X), se les han aplicado tres
capas de control, incorporando una arquitectura de cooperaci贸n para operaci贸n en modo
tren, diferentes esquemas de localizaci贸n, mapeado, percepci贸n y planificaci贸n de rutas.
Innumerables experimentos han sido realizados para validar cada uno de los diferentes sistemas
incorporados. Los resultados prueban la funcionalidad de esta plataforma para realizar
conducci贸n aut贸noma y cooperativa con m铆nima intervenci贸n humana.Programa Oficial de Doctorado en Ingenier铆a El茅ctrica, Electr贸nica y Autom谩ticaPresidente: Francisco Javier Otamendi Fern谩ndez de la Puebla.- Secretario: Hanno Hildmann.- Vocal: Pietro Cerr
A game theory-based approach for modeling autonomous vehicle behavior in congested, urban lane-changing scenarios
Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green. 漏 2021 by the authors. Licensee MDPI, Basel, Switzerland.This work was supported by the Austrian Ministry for Climate Action, Environment, Energy, Mobility, Innovation, and Technology (BMK) Endowed Professorship for Sustainable Transport Logistics 4.0., IAV France S.A.S.U., IAV GmbH, Austrian Post AG, and the UAS Technikum Wien. It was additionally supported by the Zero Emission Roll-Out?Cold Chain Distribution_877493