Field tests and machine learning approaches for refining algorithms and correlations of driver’s model parameters

Tango, Fabio; Minin, Luca; Tesauri, Francesco; Montanari, Roberto

doi:10.1016/j.apergo.2009.01.010

Cited by 23 publications

(7 citation statements)

References 9 publications

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“…The W value for each classification was calculated as: W (1) ¼ 161.92, W(2) ¼ 154.59, W(3) ¼ 145.14, W(4) ¼ 137.04 and W (5) ¼ 135.68. Since the clustering method aims to put participants into clusters according to "closest similarity" rules, and no a priori hypotheses were made, statistical significance testing is not appropriate (Tango et al, 2010). Consequently, intra-cluster dissimilarity (low values when the partition is good) is used to determine the appropriate number of clusters.…”

Section: Q1 Opening Questionmentioning

confidence: 99%

Lane-changing behavior on urban streets: A focus group-based study

Sun

Elefteriadou

2011

Applied Ergonomics

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Section: Q1 Opening Questionmentioning

confidence: 99%

Lane-changing behavior on urban streets: A focus group-based study

Sun

Elefteriadou

2011

Applied Ergonomics

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“…Depending on the measurements under investigation, previous studies have considered various machine learning techniques, including decision tree [27], artificial neural network (ANN) [28], Adaboost [29], support vector machine (SVM) [2], [29] and hidden Markov models (HMMs) [30]. Tseng et al [27] used decision tree to explore the relationship between driver inattention and accidents.…”

Section: Detecting Driver Distractionsmentioning

confidence: 99%

“…Lee et al [30] studied driver distraction using two-state HMMs, where the number of states was determined by a neural network analysis. Tango et al [28] used an ANN to model driver distraction using drivers' behavioral data collected from simulated recordings.…”

Section: Detecting Driver Distractionsmentioning

confidence: 99%

Predicting Perceived Visual and Cognitive Distractions of Drivers With Multimodal Features

Busso

2015

IEEE Trans. Intell. Transport. Syst.

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A driver's behaviors can be affected by visual, cognitive, auditory, and manual distractions. While it is important to identify the patterns associated with particular secondary tasks, it is more general and useful to define distraction modes that capture the general behaviors induced by various sources of distractions. By explicitly modeling the distinction between types of distractions, we can assess the detrimental effects induced by new in-vehicle technology. This study investigates drivers' behaviors associated with visual and cognitive distractions, both separately and jointly. External observers assessed the perceived cognitive and visual distractions from real-world driving recordings, showing high interevaluator agreement in both dimensions. The scores from the perceptual evaluation are used to define regression models with elastic net regularization and binary classifiers to separately estimate the cognitive and visual distraction levels. The analysis reveals multimodal features that are discriminative of cognitive and visual distractions. Furthermore, the study proposes a novel joint visual-cognitive distraction space to characterize driver behaviors. A data-driven clustering approach identifies four distraction modes that provide insights to better understand the deviation in driving behaviors induced by secondary tasks. Binary and multiclass recognition problems demonstrate the effectiveness of the proposed multimodal features to infer these distraction modes defined in the visual-cognitive space.

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“…The research of Ford Motor on the driver characterization for the car following [131,132] and for the vehicle destination prediction [133] can be mentioned in this regard. A reasonable applicability of fuzzy methods to this topic has been also demonstrated in studies with the participation of Centro Richerce Fiat for the driver distraction modelling [134]. Other work [135], performed under coordination of researchers from Renault, has indicated an efficient implementation of fuzzy sets and fuzzy space windowing for the psychophysiological characterization of drivers within the context of the car following.…”

Section: Fuzzy Methods In Applied and Industrial Researchmentioning

confidence: 91%

A review of fuzzy methods in automotive engineering applications

Ivanov

2015

Eur. Transp. Res. Rev.

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Purpose The paper addresses evolution of fuzzy systems for core applications of automotive engineering. Methods The presented study is based on the analysis of bibliography dedicated to fuzzy sets and fuzzy control for ground vehicles. A special attention is given to fuzzy approaches used in the following domains of automotive engineering: vehicle dynamics control systems, driver and driving environment identification, ride comfort control, and energy management of electric vehicles. Results The bibliographical analysis, supplemented with statistics of relevant research publications, has allowed to allocate the most important fuzzy application cases for each domain. In particular, it concerns driver identification, humanmachine interface, recognition of road conditions, and controllers of vehicle chassis and powertrain systems. It is found out that fuzzy methods have the primary use most of all for tasks requiring identification and forecasting procedures, especially in conditions of limited informational space. Additional observation that can be also derived from the presented survey points to reasonable integration of fuzzy technique with other control engineering methods to improve the performance of automotive control systems. Conclusions In the aggregate the performed review indicates that fuzzy computing can be considered as a versatile tool for automotive engineering applications of different nature.

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Field tests and machine learning approaches for refining algorithms and correlations of driver’s model parameters

Cited by 23 publications

References 9 publications

Lane-changing behavior on urban streets: A focus group-based study

Lane-changing behavior on urban streets: A focus group-based study

Predicting Perceived Visual and Cognitive Distractions of Drivers With Multimodal Features

A review of fuzzy methods in automotive engineering applications

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