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

Assessing safety functionalities in the design and validation of driving automation

This paper aims to contribute to the comprehensive and systematic safety assessment of Automated Driving Systems (ADSs) by identifying unknown hazardous areas of operation. The current methodologies employed in this domain typically involve estimating the distributions of situational variables based on human-centered field test, crash databases, or expert knowledge of critical values. However, due to the lack of a-priori knowledge regarding the influential factors, their critical ranges, and their distributions, these approaches may not be entirely suitable for the assessment of emerging automated driving technologies. To deal with this challenging problem, here we propose a testing methodology incorporating realistic yet unobserved driving conditions, distinguished by numerous situational variables, so to encompass unknown unsafe conditions comprehensively. Our methodology utilizes stochastic simulation and uncertainty modeling techniques to account for the variability of realistic driving conditions and their impact on ADSs' performances. By doing so, we aim to identify unsafe operational regions and triggering conditions that can lead to hazardous behaviors, thus improving the development and safety of automated driving functions. For our purposes, the Latin Hypercube Sampling technique and the recently proposed PAWN density-based sensitivity analysis method are employed. We apply this methodology for the first time in the specific field of ADSs design and validation, using an exemplificative use case. We discuss and compare the results obtained from our approach with those obtained from a traditional approach

Using Driver Control Models to Understand and Evaluate Behavioral Validity of Driving Simulators

For a driving simulator to be a valid tool for research, vehicle development, or driver training, it is crucial that it elicits similar driver behavior as the corresponding real vehicle. To assess such behavioral validity, the use of quantitative driver models has been suggested but not previously reported. Here, a task-general conceptual driver model is proposed, along with a taxonomy defining levels of behavioral validity. Based on these theoretical concepts, it is argued that driver models without explicit representations of sensory or neuromuscular dynamics should be sufficient for a model-based assessment of driving simulators in most contexts. As a task-specific example, two parsimonious driver steering models of this nature are developed and tested on a dataset of real and simulated driving in near-limit, low-friction circumstances, indicating a clear preference of one model over the other. By means of closed-loop simulations, it is demonstrated that the parameters of this preferred model can generally be accurately estimated from unperturbed driver steering data, using a simple, open-loop fitting method, as long as the vehicle positioning data are reliable. Some recurring patterns between the two studied tasks are noted in how the model’s parameters, fitted to human steering, are affected by the presence or absence of steering torques and motion cues in the simulator

Data Collection for Traffic and Drivers’ Behaviour Studies: A Large-scale Survey

AbstractStudies of driving behaviour are of great help for different tasks in transportation engineering. These include data collection both for statistical analysis and for identification of driving models and estimation of modelling parameters (calibration). The data and models may be applied to different areas: i) road safety analysis; ii) microscopic models for traffic simulation, forecast and control; iii) control logics aimed at ADAS (Advanced Driving Assistance Systems). In this paper we present a large survey based on the naturalistic (on-the-road) observation of driving behaviour with a view to obtaining microscopic data for single vehicles on long road segments and for long time periods. Data are collected by means of an instrumented vehicle (IV), equipped with GPS, radar, cameras and other sensors. The behaviour of more than 100 drivers was observed by using the IV in active mode, that is by observing the kinematics imposed on the vehicle by the driver, as well as the kinematics with respect to neighbouring vehicles. Sensors were also mounted backwards on the IV, allowing the behaviour of the driver behind to be observed in passive mode. As the vehicle behind changes, the next is observed and within a short period of time the behaviour of several drivers can be examined, without the observed driver being aware. The paper presents the experiment by describing the road context, aims and experimental procedure. Statistics and initial insights are also presented based on the large amount of data collected (more than 8000km of observed trajectories and 120hours of driving in active mode). As an example of how to use the data directly, apart from calibration of driving behaviour models, indexes based on aggregate measures of safety are computed, presented and discussed

Modelling components for the fuel consumption investigation in Model in the Loop environment: Parameter tuning for an ecological fully-adaptive cruise control system

The research presented here is framed in the area of the design of Advanced Driving Assistance Systems (ADAS), carried out in the so-called automotive V-Cycle. In particular we concentrated our effort at the Model In the Loop (MIL) level. Indeed we developed an additional component for the fuel consumption evaluation at the MIL stage. The developed component has been based on the combination of a simplified model of the vehicle dynamics, and a fuel consumption model calibrated in previous experiments. The developed module is used for tuning the parameters of an Ecological fully Adaptive Cruise Control System

Impacts of Connected Automated Vehicles on Large Urban Road Network

As an essential component of the Cooperative Intelligent Transportation System (C-ITS), Connected Automated Vehicles (CAVs) are anticipated to play a significant role in the development of the future mobility service. This paper investigates the impacts of different penetration of CAVs on the urban road network. The investigation is carried out in a vast urban network with Simulation of Urban MObility (SUMO), a microscopic traffic simulator. The estimated factors of the network are network maximum flow, critical density, average speed, congestion duration, and roadway over-saturation degree. The Macroscopic Fundamental Diagram (MFD) has been used to estimate the maximum flow and critical density. In a simulation way, it substantiated that a road network could have less scattered MFDs, even if the traffic flow is distributed heterogeneously. The congestion duration and over-saturation degree are used to check traffic congestion. The simulation results show that applying 100% CAVs can contribute about a 13.55% increase in maximum flow. A similar trend can be found in the critical density for different CAV penetration rates. In a similar congestion situation, the network with 100% CAV driving in can carry more than 130% of the original travel demand. In terms of congestion level, even a low CAV penetration rate may significantly improve the traffic condition

Simulation Experiments for an Approximate Definition of the Macroscopic Fundamental Diagram

The paper presents some exploratory experiments for defining a Macroscopic Fundamental Diagram starting from data collected in some specific sensor network layouts, that is by just monitoring the cordon of a study area. Variables defined in the original proposition of the MFD where here re-defined by just considering the number of vehicles estimated to be present in the study area (N) by means of this layout. We found that in some cases a strong correlation among defined variables can be found, and also similar patterns in the depicted MFD are evidenced. Findings of the paper are limited, given the limited amount of simulation performed, and also considering the limited number of factors varied in the simulations; as expected, results seem to be strongly affected by the traffic demand. Apart that, the approach is worth to be investigated, because this kind of layout is becoming very common in some urban contexts (e.g. in Italy)

C-ITS communication: An insight on the current research activities in the European Union

Cooperative-Intelligent Transportation Systems aim at connecting vehicles, among them and/with road infrastructures, so as to increase traffic safety and efficiency. The paper focuses on the European framework for supporting the development of Cooperative, Connected and Automated Mobility, in order to provide an overview about the current status of testing and deployment activities in the field, in view of the milestone of 2019 which has been identified as the start time for the actual deployment of mature services. Therefore, firstly, the European strategy is described and communication (collectively known as vehicle-to-everything) services, as well as related technologies, are discussed. Then, funded research projects across the Union are recalled and, finally, a critical discussion on the resulting picture is provided

L’impiego di strumenti open-source per la definizione del piano spostamenti casa-lavoro: Il caso dell’Ente Autonomo Volturno

Il lavoro presenta un sistema di supporto alle decisioni per la redazione del Piano Spostamenti Casa-Lavoro (PSCL). In particolare, è stata sviluppata una metodologia innovativa di stima aggregata della domanda di mobilità, opportunamente integrata dal ricorso a strumenti di tipo open-source. Al fine di mostrare la bontà dell’approccio proposto, esso è stato applicato al caso di una azienda realmente esistente: l’Ente Autonomo Volturno (EAV)