Pariota, Luigi (2013) Driving behaviour for ADAS: theoretical and experimental analyses. [Tesi di dottorato]

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Tipologia del documento: Tesi di dottorato
Lingua: English
Titolo: Driving behaviour for ADAS: theoretical and experimental analyses
Autori:
AutoreEmail
Pariota, Luigiluigi.pariota@unina.it
Data: 17 Marzo 2013
Numero di pagine: 132
Istituzione: Università degli Studi di Napoli Federico II
Dipartimento: Ingegneria dei trasporti "Luigi Tocchetti"
Scuola di dottorato: Ingegneria civile
Dottorato: Ingegneria dei sistemi idraulici, di trasporto e territoriali
Ciclo di dottorato: 25
Coordinatore del Corso di dottorato:
nomeemail
Petroncelli, Elvirapelvira@unina.it
Tutor:
nomeemail
Bifulco, Gennaro Nicolagennaro.bifulco@unina.it
Data: 17 Marzo 2013
Numero di pagine: 132
Parole chiave: Car-following; Advanced Driving Assistance Systems; Driving Behaviour; Intelligent Transportation Systems; Instrumented Vehicle
Settori scientifico-disciplinari del MIUR: Area 08 - Ingegneria civile e Architettura > ICAR/05 - Trasporti
Aree tematiche (7° programma Quadro): TRASPORTI (INCLUSO AERONAUTICA) > Trasporti di superficie sostenibili
Depositato il: 03 Apr 2013 13:06
Ultima modifica: 22 Lug 2014 10:17
URI: http://www.fedoa.unina.it/id/eprint/9074
DOI: 10.6092/UNINA/FEDOA/9074

Abstract

This thesis deals with the analysis and understanding of drivers’ behaviours under car-following. The aim is to enhance the modelling tools toward the development of new ADAS (Advanced Driving Assistance System) logics, characterized by a more human-like behaviour. After having introduced the argument of the thesis (and motivated the work) and having recalled the state of the art most relevant in the field of car-following (as well as in the instruments for observing car-following in the real world), the thesis evolves toward three main sections: actual observation of real-world data and collection of the datasets to be employed for theoretical analysis; theoretical enhancements and propositions; applications to ACC (Adaptive Cruise Control), as a relevant field for ADAS. The data employed in this work have been collected in three different field surveys, two of them carried out in Italy and the other in the United Kingdom. In all cases data have been collected by instrumented vehicles, equipped in such a way to observe and record car-following trajectories. Data have been framed into different theoretical paradigms in order to both validate each theory and to establish the links between these theories. Links have been established both in a formal way (through theoretical investigation) and in a data-driven way. The considered theoretical paradigm for modelling car-following follows different approaches: one is based on the psycho-physical approach and two others are based on an engineering-inspired approach. In particular, the considered psycho-physical approach has been the Action Point theory (Wiedemann, 1974); a revised version of the paradigm, more compliant with the original version of Barbosa (1961) and Todosoiev (1963) has been proposed and justified with reference to the collected data. The first engineering paradigm has been based on a state-space approach. The proposed approach has been shown to be consistent with the Action Point theory. The parameters of the model have been estimated by means of the collected data and the obtained results have been discussed; they are consistent with observations and justify the adopted model. The other engineering model is based on a linear approximation (at any time t, in a discrete-time approach) of the response of the follower to the leader’s stimuli. Also the linear model is shown to be a very good approximation of the observed data; moreover, it has been shown to lead to an harmonic oscillation around the desired spacing at steady-state. This oscillation is consistent with both the Action Point theory and (partially) with the proposed state-space approach. The linear model is particularly suitable for real-time ACC-oriented application; thus it is the model employed in section 4 of this work, where a fully-adaptive ACC system is developed, able to actuate a driving-style actually consistent with driver’s expectations and preferences.

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