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This paper analyses driving behaviour in car-following conditions, based on extensive individual vehicle data collected during experimental field surveys carried out in Italy and the UK. The aim is to contribute to identify simple evidence to be exploited in the ongoing process of driving assistance and automation which, in turn, would reduce rear-end crashes. In particular, identification of differences and similarities in observed car-following behaviours for different samples of drivers could justify common tuning, at a European or worldwide level, of a technological solution aimed at active safety, or, in the event of differences, could suggest the most critical aspects to be taken into account for localisation or customisation of driving assistance solutions. Without intending to be exhaustive, this paper moves one step in this direction. Indeed, driving behaviour and human errors are considered to be among the main crash contributory factors, and a promising approach for safety improvement is the progressive introduction of increasing levels of driving automation in next-generation vehicles, according to the active/preventive safety approach. However, the more advanced the system, the more complex will be the integration in the vehicle, and the interaction with the driver may sometimes become unproductive, or risky, should the driver be removed from the driving control loop. Thus, implementation of these systems will require the interaction of human driving logics with automation logics and then an enhanced ability in modelling drivers' behaviour. This will allow both higher active-safety levels and higher user acceptance to be achieved, thus ensuring that the driver is always in the control loop, even if his/her role is limited to supervising the automatic logic. Currently, the driving mode most targeted by driving assistance systems is longitudinal driving. This is required in various driving conditions, among which car-following assumes key importance because of the huge number of rear-end crashes. The increased availability of lower-cost information and communication technologies (ICTs) has enhanced the possibility of collecting copious and reliable car-following individual vehicle data. In this work, data collected from three different experiments, two carried out in Italy and one in the UK, are analysed and compared. The experiments involved 146 drivers (105 Italian drivers and 41 UK drivers). Data were collected by two instrumented vehicles. Our analysis focused on inter-vehicular spacing in equilibrium car-following conditions. We observed that (i) the adopted equilibrium spacing can be fitted using lognormal distributions, (ii) the adopted equilibrium spacing increases with speed, and (iii) the dispersion between drivers increases with speed. In addition, according to different headway thresholds (up to 1 second) a significant number of potentially dangerous behaviours is observed. Three different car-following paradigms are also applied to each of the experiments, and modelling parameters are calibrated and compared to obtain indirect confirmation about the observed similarities and differences in driving behaviour.

Modelling car-following in an effective and accurate way is of great importance for several areas of application, such as microscopic traffic simulation and ADAS (Advanced Driving Assistance Systems). Heterogeneity can be observed in driving behaviors if car-following data are analyzed. Part of this dispersion depends on the inherent heterogeneity across drivers, that could react in different ways to very similar stimuli. However, another source of dispersion could be related to a misleading identification of the stimuli or to an improper identification of the context in which the stimuli are evaluated. This work is oriented to analyze if a non-negligible part of the observed heterogeneity can be explained by considering the type of the leading vehicle. Observation of car-following trajectories has been carried out in a large experiment involving one hundred drivers, with different leading vehicles. These observation have been interpreted by means of behavioral models and the parameters of these models have been separately identified for different types of leading vehicles. Comparison of modelling parameters, as well as directly observed variables as speed and spacing, allows for testing if the type of leading vehicle actually influences the car-following behavior.

This paper investigated, by means of a dynamic driving-simulator experiment, drivers' behavior at curves on rural two-lane highways in relation to different advance warning signs, perceptual measures, and delineation treatments. The tested treatments were intended to alert drivers to the presence of curves, prompting a reduction in curve approach speeds, and to affect drivers' speed as they drove through curves. A benefit–cost analysis was performed to determine the economic feasibility of applying the countermeasures to speeding tested in the driving simulator. Study results showed that all the treatments produced significant effects on drivers' speed behavior at curves on rural two-lane highways. The perceptual treatments (i.e., colored transverse strips, dragon-teeth markings, and colored median island) were the most effective. The safety benefits measured in lower crash costs arising from speed reductions were much greater than the costs of the alternatives. The most cost-effective treatment was the curve warning sign because of the lower cost. However, colored transverse strips and dragon-teeth markings produced higher benefits and were highly cost-effective for average daily traffic greater than 1,000 vehicles per day. The study results strongly support real-world implementation of colored transverse strips, dragon-teeth markings, and the colored median island.

Identification of driving behavior is a crucial task in several Intelligent Transportation Systems applications, both to increase safety and assist drivers. Here we identify driving behaviors by means of an analytical model. In order to estimate the model parameters, data are collected with an instrumented vehicle. The paper presents the model, the procedure for the estimation of the parameters and the results of the proposed framework with respect to a pilot experiment to assess the feasibility and potential of the approach. Some practical implementations of the proposed model are presented. In particular, road safety assessment is introduced in greater depth to show the potential of the approach. For this purpose, a modified (and original) version of some surrogate measures of safety is introduced.

In the field of Intelligent Transportation Systems (ITS), one of the most promising sub-functions is that of Advanced Driver Assistance Systems (ADAS). Development of an effective ADAS, and one that is able to gain drivers’ acceptance, hinges on the development of a human-like car-following model, and this is particularly important in order to ensure the driver is always ‘in the (vehicle control) loop’ and is able to recover control safely in any situation where the ADAS may release control. One of the most commonly used models of car-following is that of the Action Point (AP) (psychophysical) paradigm. However, while this is widely used in both micro-simulation models and behavioural research, the approach is not without its weaknesses. One of these, the potential redundancy of some of the identified APs, is examined in this paper and its basic structure validated using microscopic driving behaviour collected on thirteen subjects in Italy. Another weakness in practical application of the Action Point theory is the identification of appropriate thresholds, accounting for the perception, reaction and adjustment of relative speed (or spacing) from the leading vehicle. This article shows that this identification is problematic if the Action Point paradigm is analysed in a traditional way (car-following spirals), while it is easier if the phenomenon is analysed in terms of car-following ‘waves’, related to Time To Collision (TTC) or the inverse of TTC. Within this new interpretative framework, the observed action points can be observed to follow a characteristically linear pattern. The identification of the most significant variables to be taken into account, and their characterisation by means of a simple linear pattern, allows for the formulation of more efficient real-time applications, thereby contributing to the development and diffusion of emerging on-board technologies in the field of vehicle control and driver’s assistance.

DRIVE IN2 is an automotive research project within the field of Intelligent Transportation Systems, especially Advanced Driving Assistance Systems (ADAS). The project originates from the idea that the development of new ADAS and evaluation of their effect have to take drivers into account, as well as their behavior while driving: the benefits of adopting new in-vehicle technologies depend also on their adoption and usage by drivers. To this aim, the project develops a Driver-In-the-Loop framework to position observation of the drivers at the center of the research activities. Observations are carried out by coupling different research tools, namely instrumented vehicle and driving simulators. The premise and methodological framework of the research project are presented and discussed. Some preliminary activities with particular reference to validating the driving simulation environment are also described.