Comparative Analysis and Modeling of Driver Behavior Characteristics (original) (raw)

Effect of Human Factors on Driver Behavior

Advances in Intelligent Vehicles, 2014

Road traffic accidents have always been a serious issue in modern society. According to statistical results, more than 90% of accidents have been caused by a driver's mistake and/or fatigue . Therefore, the human driver's behavior has been an important component in Intelligent Transportation System (ITS) research. Some results on driver behavior have been applied to the development of intelligent vehicles [2e4]. There are various aspects of this research field. Some studies focused on specific driving scenarios, including car following and lane changing . The driver's physiological characteristics during driving, such as response time [7], cognition process [8] and fatigue , have also been investigated.

A new framework for the computer modelling and simulation of car driver behavior

SIMULATION, 2018

In recent years, the simulation of personal car driver behavior has attracted increasing attention in recent research works. Such works are based on models and systems derived from social and psychological studies. The complexity of the simulation of such systems is due to the need for modeling driver behavior and the integration of psychological and physiological factors that can affect driver performance. Although there is only a limited number of models that have been proposed to simulate driver behavior, most of them suffer from limitations pertaining to the integration of some factors, an inadequacy that will be discussed in this paper. This investigation work focuses on the development of a new model for driver behavior simulation based on recent physiological and psychological theories. The model aims to reproduce the driver behavior with respect to some psychological factors. An experimental framework is also presented to build the simulation model. This article concludes by...

Review of Models of Driver Behaviour and Development of a Unified Driver Behaviour Model for Driving in Safety Critical Situations

Human Modelling in Assisted Transportation, 2011

Driver behaviour can be modelled in one of two approaches: 'Descriptive' models that describe the driving task in terms of what the driver does, and 'Functional' models that attempt to explain why the driver behaves the way he/she does, and how to predict drivers' performance in demanding and routine situations. Demanding situations elicit peak performance capabilities, and routine situations elicit typical (not necessarily best) behaviour. It seems that the optimal approach might be a hybrid of several types of models, extracting the most useful features of each. In recent years, a variety of driver support and information management systems have been designed and implemented with the objective of improving safety as well as performance of vehicles. To predict the impact of various assistance systems on driver behaviour predictive models of the interaction of the driver with the vehicle and the environment are necessary. The first step of the ITERATE project is to critically review existing Driver-Vehicle-Environment (DVE) models and identify the most relevant drivers' parameters and variables that need to be included in such models: (a) in different surface transport modes (this paper deals with road vehicles only, other transport domains are detailed in D1.1 & D1.2 of the ITERATE project), and (b) in different safety critical situations. On the basis of this review, we propose here a Unified Model of Driver behaviour (UMD), that is a hybrid model of the two approaches. The model allows for individual differences on pre-specified dimensions and includes the vehicle and environmental parameters. Within the ITERATE project this model will be used to support safety assessment of innovative technologies (based on the abilities, needs, driving style and capacity of the individual drivers). In this brief paper we describe only the behaviour of a single test driver, while the environment and vehicle are defined as parameters with fixed values (and detailed in D1.2 of the ITERATE project). The selected driver characteristics (and variables used to measure them) are culture (Country), attitudes/personality (Sensation Seeking), experience (Hazard Perception Skills), driver state (Fatigue), and task demand (Subjective workload).

A Fuzzy Model of Driver Behaviour: Computer Simulation and Experimental Results

Elsevier eBooks, 1983

A model for driver-behaviour founded on fuzzy set theory will be presented here. Our results show that this method achieves a high degree of accordance between observed and simulated eye-and steering movements, and, accordingly, it is highly suitable for heuristic modelling of complex systems. In particular, a close connection between eye-movements and steering wheel turning has been established.

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

Applied Ergonomics, 2010

This paper describes the field tests on a driving simulator carried out to validate the algorithms and the correlations of dynamic parameters, specifically driving task demand and drivers' distraction, able to predict drivers' intentions. These parameters belong to the driver's model developed by AIDE (Adaptive Integrated Driver-vehicle InterfacE) European Integrated Project. Drivers' behavioural data have been collected from the simulator tests to model and validate these parameters using machine learning techniques, specifically the adaptive neuro fuzzy inference systems (ANFIS) and the artificial neural network (ANN). Two models of task demand and distraction have been developed, one for each adopted technique. The paper provides an overview of the driver's model, the description of the task demand and distraction modelling and the tests conducted for the validation of these parameters. A test comparing predicted and expected outcomes of the modelled parameters for each machine learning technique has been carried out: for distraction, in particular, promising results (low prediction errors) have been obtained by adopting an artificial neural network.

Modeling of individual differences in car-following behaviour of drivers

2017 International Multi-topic Conference (INMIC), 2017

Car-following models microscopically express acceleration behavior of an individual driver. There are many car-following models each with its own assumptions. Among these car-following models, Intelligent Driver Model (IDM) has been used and cited extensively by research community. All the models including IDM have been developed with engineering perspective i.e. to reproduce perfect acceleration behavior. This study focuses on development of a humanistic car-following model. We have identified humanistic parameters that have been modeled in IDM from mathematical formulation of the model. In its existing form, parameters of IDM could be assigned arbitrary values from a prescribed range to define different driver profiles. This way, theoretically, infinite driver profiles could be created many of which does not exist in real. Literature of traffic psychology suggests that there are few dominant classes of drivers, which exhibit certain behavioral patterns. These dominant classes are characterized with the help of human personality. In our study, we have modeled a relationship between model of human's personality profile namely Big Five Factors (BFF) and parameters of IDM. The enhanced model let us reproduce individual differences in driving behaviors. The proposed model has been verified using computer simulation to investigate whether proposed humanistic car-following model produce desirable results or not. The proposed car-following model would be able to help in simulating driving behavior of an individual given that personality profile of that individual is known.

Heterogeneity of Driving Behaviors in Different Car-Following Conditions

Periodica Polytechnica Transportation Engineering, 2016

Many application fields in transportation engineering can benefit from an accurate modelling of car-following behavior. In particular, in recent years, an increased importance is assigned to embed behavioral abilities in ADAS (Advanced Driving Assistance Systems) and in driving automation solutions. However, accurate development of car-following models needs for accounting of the drivers' heterogeneity, which can be easily observed in car-following data. This paper contributes to analyze different sources of heterogeneity with particular focus on three factors: the dispersion overtime of the behavior of a single driver; the heterogeneous behaviors of different drivers; and the possible bias introduced by some oversimplification of the modelling framework, with particular reference to the type of leading vehicle. Our analyses are based on the observation of car-following trajectories collected in a large experiment involving one hundred drivers. Observed behaviors have been interpreted by means of several car-following models proposed in past. The comparison of the values of the parameters identified for the models (versus observed data) is adopted for the analyses. Moreover, directly observed variables (car-following speed and spacing) are adopted to complement and confirm the analyses. Results show that the greater among the sources of dispersion is the across-driver heterogeneity and that by taking into account such an inherent drivers' dispersion of car-following behaviors it is possible to better identify also the effect of the modelling oversimplifications induced by not considering the type of leading vehicle.

Evaluation Method of Driver's Behavior on Motorway

IFAC Proceedings Volumes, 1997

This paper presents a proposed method to study the driver's behavior and its awareness defects on motorway. The method is based on the calculation of the "guidance task demand" of car driving. Guidance during driving can be seen as a continuous tuning of the speed and position of the car by the driver. The formalization of guidance demand has been implemented by three time pressures representing the road anticipation and the near space collision avoidance. A preliminary validation of this method is carried out by some tests on a driving simulator.

Comprehensive driving behavior model for intelligent transportation systems

2008

This paper presents a novel approach of modeling human driving behavior in a more realistic way that can be effectively utilized in realizing intelligent transportation systems to ensure efficient, safe, secure and human-friendly vehicle control and transportations. A number of supporting systems based on individual driving behavior are identified. The proposed comprehensive driving model approximates complete behavior of individual drivers focusing not only the ideal steady and transient driving styles but also their natural variations. Simulation results and observations from real driving scenario illustrate the significance of the proposed model and its scopes.