Driving behaviour models enabling the simulation of Advanced Driving Assistance Systems: revisiting the Action Point paradigm

Bifulco, Gennaro Nicola; Pariota, Luigi; Brackstione, Mark; McDonald, Michael E.

doi:10.1016/j.trc.2013.09.009

Cited by 40 publications

(25 citation statements)

References 32 publications

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“…The discussion of the results allows some appropriate conclusions to be drawn, the most important being that that in the great part of the cases (at least among those observed in the DriveIN 2 campaign) it is worth adopting a simpler approach. This results confirm, by using a different kind of analyses (actions on pedals) and a different dataset, the ones obtained in Bifulco et al (2013), where only kinematic observations were adopted. Another main contribution of this paper is to propose to ascertain from candidate APs and actual ones.…”

Section: Introductionsupporting

confidence: 80%

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Experimental evidence supporting simpler Action Point paradigms for car-following

Pariota

Bifulco

2015

Transportation Research Part F: Traffic Psychology and Behaviou

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ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. IntroductionModelling driving behaviour represents a crucial task for many applications in transportation. Three main areas can particularly benefit from an enhanced knowledge of driving behaviour: accident analysis and prevention, microscopic simulation of traffic, and Intelligent Transportation Systems (ITS). Benefits for ITS are mainly expected in the field of Advanced Driver Assistance Systems (ADAS), where some assistance/control logics interact with drivers (and their behaviour) and where both drivers' expectations, and impacts of the innovations on drivers' behaviour have to be considered in order to improve: a) the effectiveness of the solutions; b) driving (and traffic) safety and c) acceptance of technological solutions.Modelling of driving behaviour is based on two fundamental requirements. On the one hand, theoretical frameworks and paradigms are needed. On the other, observation tools and data are required in order both to develop/validate theories and to identify modelling parameters for practical applications. If the research focus is on disaggregate driving behaviour rather than aggregate traffic behaviour, the best source of information is based on individual vehicle data (IVD), as typically obtained by instrumented vehicles (IVs). An IV can be described as a standard vehicle where the kinematics, the interaction with surrounding vehicles and the vehicle-driver interaction are recorded for subsequent analysis. The possibility of observing only the kinematics of IVs, as allowed by some camera-based microscopic roadside observation systems like in the NGSIM project (Alexiadis et al., 2004), can lead to a reduced understanding of driving behaviour. Indeed, the possibility of observing the kinematics of an IV is just a prerequisite and IVs are usually equipped with a large number of sensors. Multisensing approaches not only enhance the estimation of the ego-kinematics of the controlled vehicle (Bifulco et al., 2011), but also allow detection of the surrounding traffic conditions and direct monitoring of on-board interaction between the driver and the vehicle, generally via the controlled area network (CAN). The overall result is a more comprehensive observation and enhanced understand...

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Section: Introductionsupporting

confidence: 80%

“…VISSIM), inspiring several researchers (Hoogendoorn et al, 2011). Recently, Bifulco et al (2013) took a step forward in exploiting AP theory in the field of ADAS with the introduction of the car-following waves concept.…”

Section: Introductionmentioning

confidence: 99%

Experimental evidence supporting simpler Action Point paradigms for car-following

Pariota

Bifulco

2015

Transportation Research Part F: Traffic Psychology and Behaviou

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“…Examples of ITS applications range from driving assistance [26,27] to traffic regulation [1], from electronic toll collection to travellers' information [28]. A comprehensive list of all kinds of ITS applications is far beyond our scope.…”

Section: Using Itsmentioning

confidence: 99%

Advanced travel demand management policies

Galante

Spena

2014

The Sustainable City IX

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European urban and metropolitan regions are characterized by the concentration of activities in a limited area and by transit systems which coexist with individual transportation. Traditionally, travel demand management (TDM) policies have been applied to enhance the efficiency, sustainability and safety of the transportation systems, often via an increased use of transit modes. This strategy is one of the most effective to ensure: i) lower road-space occupation; ii) lower pollution; iii) lower energy consumption; iv) higher safety. Moreover, an increased transit use also has positive effects on the mobility still remaining on individual modes. Many TDM policies have been proposed in the past, based on push (say, disincentives of the car-mode) and pull (say, incentives of the transit mode) strategies, impacting at different levels on the mobility and on the activity patterns of travellers. Within the push strategies, measures can be based on regulation byquantity (limitation of use, as in restricted access areas) or by-pricing (e.g. road/area pricing). A more recent concept involves the use of tradable-drivingrights. The approach has already been claimed to be feasible; however, it has not reached a significant popularity among the transportation analysts nor within the decision-makers, perhaps because of difficult technical and administrative implementation, as well as some residual equity/redistribution issues. In this paper the potential of a new-generation of tradable driving rights, based on ITS (Intelligent Transportation System) is analysed. Driving rights are intended here as allowance/permits, acquired by travellers in exchange for virtuous mobility. Virtuosity is related to the contribution toward the efficiency, sustainability and safety of the mobility behaviour. Permits can be spent in exchange for nonvirtuous behaviour. The proposed approach is tested on a real network and with real data; two scenarios with different strategies in assigning permits are The Sustainable City IX, Vol. 1 703 assessed in order to show the ability of the model to forecast the effects of innovative travel demand policies.

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“…The reaction of a driver on the road situations can be different depending on the circumstances: it can be logical, illogical or irrational [1,[5][6][7][8]. During the logical behaviour his reactions can be overseen.…”

Section: Introductionmentioning

confidence: 99%

Remarks to the Reliability Assessment and to Human Actions – Especially Car Driver

et al. 2017

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Abstract. The description attempt of the system person-vehicleenvironment as an element of the system person-technical objectenvironment has been made. In the system analysis the reliability of the machine exploiter -a car driver has been taken into account. It has been pointed out that technical system operators (in this case -the drivers) have to be considered important in the structure of reliability of these systems. The operational and biological reliability characteristic of a human beingtechnical system operator has been carried out. The necessity for conducting different psychological tests for assessment of the quality of the human being -operator of each technical system -has been indicated.

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Driving behaviour models enabling the simulation of Advanced Driving Assistance Systems: revisiting the Action Point paradigm

Cited by 40 publications

References 32 publications

Experimental evidence supporting simpler Action Point paradigms for car-following

Experimental evidence supporting simpler Action Point paradigms for car-following

Advanced travel demand management policies

Remarks to the Reliability Assessment and to Human Actions – Especially Car Driver

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