Real-time Determination of Driver's Driving Behavior during Car Following

Lǚ, Jianbo; Filev, Dimitar; Tseng, Finn

doi:10.4271/2015-01-0297

Cited by 14 publications

(7 citation statements)

References 9 publications

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“…To conclude the model identification discussion, in Figure 5 a comparison between the performances of the identified TDNN model and a second order linear one (as suggested in [14]) are shown, on the training set of Driver 2. In particular, it can be noticed how the linear model is not capable, by construction, of differentiating the acceleration from the braking manoeuvre, leading to poor RMS fits.…”

Section: Driver Modelling Resultsmentioning

confidence: 99%

“…Following the suggestions of [8]- [10], [14] in this section a model of the driver behaviour is introduced, capable of reproducing his/her dynamics in the car following context. The FV driver is modelled as a dynamic system which produces as output the vehicle acceleration a f (t) in response to the available measures.…”

Section: Driver Model Identificationmentioning

confidence: 99%

“…This idea is partially inspired by the work presented in [14], where a second order linear model is used to describe the driver's behaviour. However, as shown in the following of the paper and testified by the significant amount of works related to driver behaviour modelling, this turns to be a too simplistic assumption.…”

Section: Introductionmentioning

confidence: 99%

“…Fig.5: Modelling performances of the identified TDNN and a linear II order dynamic model, as suggested in[14].…”

mentioning

confidence: 99%

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A personalized Adaptive Cruise Control driving style characterization based on a learning approach

Nava

Panzani

Zampieri

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Advanced Assistance Driver Systems (ADAS) adaptation with respect to driver driving style is a research field of major interest, given the additional benefits that could be obtained in terms of comfort and safety perceived by the user. In this work, a personalized Adaptive Cruise Control (ACC) oriented driving style features extraction method is proposed, meant to be used to choose an ACC tuning which better fits the driver on road behaviour. The method exploits an Artificial Neural Network driver model, capable of capturing the driver behaviour in a car following scenario, trained and validated over real data. From a closed-loop model analysis in a simulation environment driving style features are then extracted, looking at the system response to variations of the preceding vehicle speed. Finally, the effectiveness of the extracted features for a non-trivial characterization of the driver behaviour is assessed, comparing the results obtained considering three different drivers.

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Section: Driver Modelling Resultsmentioning

confidence: 99%

Section: Driver Model Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Fig.5: Modelling performances of the identified TDNN and a linear II order dynamic model, as suggested in[14].…”

mentioning

confidence: 99%

See 2 more Smart Citations

A personalized Adaptive Cruise Control driving style characterization based on a learning approach

Nava

Panzani

Zampieri

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

View full text Add to dashboard Cite

show abstract

“…This is particularly true for on-board systems like the cruise control, lane change assistant et al, which require identification of the driver behaviour. 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].…”

Section: Fuzzy Methods In Applied and Industrial Researchmentioning

confidence: 99%

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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Fleet analysis of headway distance for autonomous driving

Ivančo

2017

Journal of Safety Research

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Real-time Determination of Driver's Driving Behavior during Car Following

Cited by 14 publications

References 9 publications

A personalized Adaptive Cruise Control driving style characterization based on a learning approach

A personalized Adaptive Cruise Control driving style characterization based on a learning approach

A review of fuzzy methods in automotive engineering applications

Fleet analysis of headway distance for autonomous driving

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