Making Transport Safer: V2v-Based Automated Emergency Braking System

Milanés, Vicente; Onieva, Enrique; Pérez, Joshue; Simó, Javier; González, Carlos; Pedro, Teresa de

doi:10.3846/16484142.2011.622359

Cited by 17 publications

(6 citation statements)

References 26 publications

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“…Such components take into account vehicles' negotiation, which can be provided by vehicle-to-vehicle communication [83,84,85], or through the estimation of the intention and trajectory of other traffic participants to find the best time to cross an intersection [86,87,88,89]. Similarly, the latter situation can also be avoided by vehicle-to-vehicle communication [90], i.e., the vehicle ahead advises other vehicles on its action, or by predicting its intention according to its actions over a period of time, which provides the ego-vehicle with enough time to react [91,92,93,94].…”

Section: Traffic Infractionsmentioning

confidence: 99%

A Software Architecture for Autonomous Vehicles: Team LRM-B Entry in the First CARLA Autonomous Driving Challenge

Rosero,

Gomes,

da Silva

et al. 2020

Preprint

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The objective of the first CARLA autonomous driving challenge was to deploy autonomous driving systems to lead with complex traffic scenarios where all participants faced the same challenging traffic situations. According to the organizers, this competition emerges as a way to democratize and to accelerate the research and development of autonomous vehicles around the world using the CARLA simulator contributing to the development of the autonomous vehicle area. Therefore, this paper presents the architecture design for the navigation of an autonomous vehicle in a simulated urban environment that attempts to commit the least number of traffic infractions, which used as the baseline the original architecture of the platform for autonomous navigation CaRINA 2. Our agent traveled in simulated scenarios for several hours, demonstrating his capabilities, winning three out of the four tracks of the challenge, and being ranked second in the remaining track. Our architecture was made towards meeting the requirements of CARLA Autonomous Driving Challenge and has components for obstacle detection using 3D point clouds, traffic signs detection and classification which employs Convolutional Neural Networks (CNN) and depth information, risk assessment with collision detection using short-term motion prediction, decision-making with Markov Decision Process (MDP), and control using Model Predictive Control (MPC).

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Section: Traffic Infractionsmentioning

confidence: 99%

A Software Architecture for Autonomous Vehicles: Team LRM-B Entry in the First CARLA Autonomous Driving Challenge

Rosero,

Gomes,

da Silva

et al. 2020

Preprint

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“…The paper (Milanés et al 2011b) deals with the necessity of a communication between vehicles reliable and fast. Many urban accidents are because driver reactions are not as fast as sudden stops need.…”

Section: Cooperative Controlmentioning

confidence: 99%

Fuzzy Modeling and Control: Theory and Applications

Mata¹,

Marichal²,

Jimnez³

2014

Atlantis Computational Intelligence Systems

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Aims and scope of the seriesThe series 'Atlantis Computational Intelligence Systems' aims at covering state-of-the-art research and development in all fields where computational intelligence is investigated and applied. The series seeks to publish monographs and edited volumes on foundations and new developments in the field of computational intelligence, including fundamental and applied research as well as work describing new, emerging technologies originating from computational intelligence research. Applied CI research may range from CI applications in the industry to research projects in the life sciences, including research in biology, physics, chemistry and the neurosciences.All books in this series are co-published with Springer.For more information on this series and our other book series, please visit our website at: www.atlantis-press.com/publications/books. This book is published under the Creative Commons Attribution-Non-commercial license, meaning that copying, distribution, transmitting and adapting the book is permitted, provided that this is done for non-commercial purposes and that the book is attributed. This book, or any parts thereof, may not be reproduced for commercial purposes in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system known or to be invented, without prior permission from the Publisher.Printed on acid-free paper ForewordAlmost half a century has passed since the publication of the seminal paper by Prof. Lotfi A. Zadeh. Fifty years that have witnessed an impressive growth of fuzzy systems from both the theoretical and applied points of view. Impressive growth and at the same time following an unforeseen way, since as Prof. Zadeh has said many times, his expectations when writing his first paper on fuzzy sets in 1965 were that fuzzy sets would find its main applications in the realm of humanistic systems (economics, linguistics, psychology, etc.). Now the reality is that most of the success of fuzzy systems research (at least in its initial decades) was related to its application to mechanistic instead of humanistic systems. This book concentrates on modeling and control of mechanistic systems, as can be seen when reading the Table of Contents, first considering important theoretical aspects and then showing its application to quite interesting real-world problems.Any book on fuzzy modeling and control is somehow a tribute to those who paved the way for the topic. In this case, we need to consider three key persons and three related milestones in the history of fuzzy sets. First, the already mentioned Prof. Zadeh and his 1973 paper ''Outline of a New Approach to the Analysis of Complex Systems and Decision Processes,'' IEEE Transactions on Systems, Man, and Cybernetics 3: 28-44 (1973), the paper where concepts as linguistic variable and fuzzy if-then rules took form, and at the same time, the paper that triggered the second hint in the road to build up the theory of fuzzy modeling and control. Ab...

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“…Milanes et al. [10] analyze V2V communication system in which the activation of braking is based on the measurement of two variables: the distance between the vehicles and acceleration of the leading vehicle. Failure probability is checked during driving with adaptive cruise control system.…”

Section: Introductionmentioning

confidence: 99%