Real-time driver characterization during car following using stochastic evolving models

Filev, Dimitar; Lǚ, Jianbo; Tseng, Finn; Prakah-Asante, Kwaku O.

doi:10.1109/icsmc.2011.6083810

Cited by 37 publications

(24 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.

View full text Add to dashboard Cite

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.

show abstract

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.

View full text Add to dashboard Cite

show abstract

“…The ability of observing differences in driving style during the long lasting car-following maneuver have been presented in [7]. The authors conducted an experiment, where a single driver followed another car on a road and tried to exhibit different style during four consecutive runs.…”

Section: Existing Approaches To Driver Behavior Modelingmentioning

confidence: 99%

Towards Credible Driver Behavior Modeling

Błaszczyk¹,

Turek

Byrski

et al. 2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

View full text Add to dashboard Cite

In order to predict the driver's behaviour, to adapt different mechanisms of the car or to tune up the traffic controls, one has to create a reliable driver's model. Such approaches have already been made, based on the assumption, that certain similarity must be observed for one driver doing the same maneuver (e.g. braking) or even in certain situations for different drivers. The research presented in this paper shows clearly that such assumptions are wrong in general, based on a number of real-life experiments using a real car (equipped with necessary sensors) in real traffic conditions. The experiments show that the same driver performs his/hers maneuvers differently, even though they could have been assumed to be the same or at least very similar. Thus necessity of further looking for reliable models of driver's behaviour is proven.

show abstract

“…Such approaches do not take into account the common features of drivers and the differences between them. Many driver models have been developed using control theory approaches, we can find a large number of articles describing the car following model [10], [11], but it is difficult to find the analysis of individual drivers features.…”

Section: Existing Solutionsmentioning

confidence: 99%

Extensible platform for studying the behavior of drivers in urban traffic

Błaszczyk

Turek

Cetnarowicz

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

Research on traffic management and automation focuses on macro-scale problems involving large number of vehicles. It seems that the influence of individual decisions of particular drivers has not received sufficient attention over last decades. Understanding the characteristics of particular drivers is crucial for traffic optimization and for decisions making process performed by automated vehicles. In this paper we would like to present a prototype platform for studying characteristics of individual human drivers. The platform collects data during driving in urban environment and performs analysis and classification of observed behaviors. It is extensible in terms of measured parameters and classification methods. We also present results of first experiment using the created platform -classification of drivers during the left turn maneuver.

show abstract

Real-time driver characterization during car following using stochastic evolving models

Cited by 37 publications

References 15 publications

A review of fuzzy methods in automotive engineering applications

A review of fuzzy methods in automotive engineering applications

Towards Credible Driver Behavior Modeling

Extensible platform for studying the behavior of drivers in urban traffic

Contact Info

Product

Resources

About