Luigi Pariota

A Linear Dynamic Model for Driving Behavior in Car Following

In this paper a car-following model is formulated as a time-continuous dynamic process, depending on two parameters and two inputs. One of these inputs is the followers desired equilibrium spacing, assumed to exist and to be known. Another input is the speed of the lead vehicle. Given the formulation of the model, the contribution of these two inputs is separable from an analytical point of view. The proposed model is simple enough whereas not being simplistic to support real-time applications in the field of advanced driving assistance systems. Starting from the equilibrium spacing, it is possible to estimate the parameters of the model, allowing for a full identification procedure. The modeling framework was prevalidated against observed data from two different data sets, collected by means of two instrumented vehicles in independent experiments, carried out in Italy and the United Kingdom. The validation proved that the proposed car-following model gives good results not only around the desired equilibrium spacing but also in general car-following conditions. The experimental data sets are discussed in terms of parameter values as well as performance of the dynamic process against observed data.

Prediction of Drivers' Speed Behavior on Rural Motorways Based on an Instrumented Vehicle Study

Several studies have developed operating speed prediction models. Most of the models are based on spot speed data, collected by radar guns, pavement sensors, and similar mechanisms. Unfortunately, these data collection methods force the users to assume some invalid assumptions in driver behavior modeling: constant operating speed throughout horizontal curves and occurrence of acceleration and deceleration only on tangents. In this study, an instrumented vehicle with GPS continuous speed tracking was used to analyze driver behavior in terms of speed choice and deceleration or acceleration performance and to develop operating speed prediction models. The data used in the study were from a field experiment conducted in Italy on the rural motorway A16 (Naples–Avellino). Models were developed to predict operating speed in curves and tangents, deceleration and acceleration rates to be used in the operating speed profiles, starting and ending points of constant operating speed in a curve, 85th percentile of the deceleration and acceleration rates of individual drivers, and 85th percentile of the individual drivers’ maximum speed reduction in the tangent-to-curve transition. The study results showed that (a) the drivers’ speed was not constant along curves, (b) the individual drivers’ maximum speed reduction was greater than the operating speed difference in the tangent-to-curve transition, and (c) the deceleration and acceleration rates experienced by individual drivers were greater than the deceleration and acceleration rates used to draw operating speed profiles.

Coupling instrumented vehicles and driving simulators: Opportunities from the DRIVE IN2 project

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.

Identification of Driving Behaviors with Computer-Aided Tools

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.

Effects of traffic control devices on rural curve driving behavior

This study investigated, by means of a dynamic driving simulator experiment, driver 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 low-radius curves and to affect their behavior in the approach to the curve as well as along the curve itself. The study results showed that the advance warning signs, perceptual measures, and delineation treatments tested in the driving simulator experiment produced significant effects on driver behavior. The perceptual treatments (i.e., colored transverse strips, dragon teeth markings, colored median island) were the most effective treatments because they produced significant speed reductions in the approach tangent as well as inside the curve. Deceleration behavior in the approach to the curve was affected significantly by the presence of treatments that helped drivers to detect the curve earlier; early detection provided more time to perform deceleration maneuvers at lower rates. The study results strongly supported the real-world implementation of colored transverse strips, dragon teeth markings, and the colored median island. Implementation of the tested measures should be conducted on similar rural highways to validate general application of the results of this study to other regions.

Driving behaviour for ADAS: theoretical and experimental analyses

This thesis deals with the analysis and understanding of drivers’ behaviours under car-following. The aim is to enhance the modelling tools toward the development of new ADAS (Advanced Driving Assistance System) logics, characterized by a more human-like behaviour. After having introduced the argument of the thesis (and motivated the work) and having recalled the state of the art most relevant in the field of car-following (as well as in the instruments for observing car-following in the real world), the thesis evolves toward three main sections: actual observation of real-world data and collection of the datasets to be employed for theoretical analysis; theoretical enhancements and propositions; applications to ACC (Adaptive Cruise Control), as a relevant field for ADAS. The data employed in this work have been collected in three different field surveys, two of them carried out in Italy and the other in the United Kingdom. In all cases data have been collected by instrumented vehicles, equipped in such a way to observe and record car-following trajectories. Data have been framed into different theoretical paradigms in order to both validate each theory and to establish the links between these theories. Links have been established both in a formal way (through theoretical investigation) and in a data-driven way. The considered theoretical paradigm for modelling car-following follows different approaches: one is based on the psycho-physical approach and two others are based on an engineering-inspired approach. In particular, the considered psycho-physical approach has been the Action Point theory (Wiedemann, 1974); a revised version of the paradigm, more compliant with the original version of Barbosa (1961) and Todosoiev (1963) has been proposed and justified with reference to the collected data. The first engineering paradigm has been based on a state-space approach. The proposed approach has been shown to be consistent with the Action Point theory. The parameters of the model have been estimated by means of the collected data and the obtained results have been discussed; they are consistent with observations and justify the adopted model. The other engineering model is based on a linear approximation (at any time t, in a discrete-time approach) of the response of the follower to the leader’s stimuli. Also the linear model is shown to be a very good approximation of the observed data; moreover, it has been shown to lead to an harmonic oscillation around the desired spacing at steady-state. This oscillation is consistent with both the Action Point theory and (partially) with the proposed state-space approach. The linear model is particularly suitable for real-time ACC-oriented application; thus it is the model employed in section 4 of this work, where a fully-adaptive ACC system is developed, able to actuate a driving-style actually consistent with driver’s expectations and preferences.

Comparing Signal Setting Design Methods Through Emission and Fuel Consumption Performance Indicators

In order to address the Signal Setting Design at urban level two main approaches may be pursued: the coordination and the synchronisation approaches depending on the steps considered for the optimisation of decision variables (two steps vs. one step). Furthermore, in terms of objective functions mono-criterion or multi-criteria may be adopted. In this paper the coordination approach is implemented considering the multi-criteria optimisation at single junctions and mono-criterion optimisation at network level whereas the synchronisation is implemented considering the mono-criterion optimisation.

Validity of Mental Workload Measures in a Driving Simulation Environment

Automated in-vehicle systems and related human-machine interfaces can contribute to alleviating the workload of drivers. However, each new functionality can also introduce a new source of workload, due to the need to attend to new tasks and thus requires careful testing before being implemented in vehicles. Driving simulators have become a viable alternative to on-the-road tests, since they allow optimal experimental control and high safety. However, for each driving simulator to be a useful research tool, for each specific task an adequate correspondence must be established between the behavior in the simulator and the behavior on the road, namely, the simulator absolute and relative validity. In this study we investigated the validity of a driving-simulator-based experimental environment for research on mental workload measures by comparing behavioral and subjective measures of workload of the same large group of participants in a simulated and on-road driving task on the same route. Consistent with previous studies, mixed support was found for both types of validity, although results suggest that allowing more and/or longer familiarization sessions with the simulator may be needed to increase its validity. Simulator sickness also emerged as a critical issue for the generalizability of the results.

Matching macro- and micro-scopic approaches for the evaluation of traffic management impacts

The paper focuses on the evaluation of the combined effect of Traffic Signal Control Strategy (TSC) and Variable Message Sign (VMS). With reference to the TSC a dynamic selection strategy based on macroscopic flow variables was considered for off-line traffic signal plans design. The combination of two ITS solutions, TSC and VMS, was tested through microscopic approach by SUMO traffic simulator which allows to directly reproduce the pollutant emissions and fuel consumptions.

Validation of driving behaviour as a step towards the investigation of Connected and Automated Vehicles by means of driving simulators

Connected and Automated Vehicles (CAVs) are likely to become an integral part of the traffic stream within the next few years. Their presence is expected to greatly modify mobility behaviours, travel demands and habits, traffic flow characteristics, traffic safety and related external impacts. Tools and methodologies are needed to evaluate the effects of CAVs on traffic streams, as well as the impact on traffic externalities. This is particularly relevant under mixed traffic conditions, where human-driven vehicles and CAVs will interact. Understanding technological aspects (e.g. communication protocols, control algorithms, etc.) is crucial for analysing the impact of CAVs, but the modification induced in human driving behaviours by the presence of CAVs is also of paramount importance. For this reason, the definition of appropriate CAV investigations methods and tools represents a key (and open) issue. One of the most promising approaches for assessing the impact of CAVs is operator in the loop simulators, since having a real driver involved in the simulation represents an advantageous approach. However, the behaviour of the driver in the simulator must be validated and this paper discusses the results of some experiments concerning car-following behaviour. These experiments have included both driving simulators and an instrumented vehicle, and have observed the behaviours of a large sample of drivers, in similar conditions, in different experimental environments. Similarities and differences in driver behaviour will be presented and discussed with respect to the observation of one important quantity of car-following, the maintained spacing.