Luca Minin

Measuring the effects of visual demand on lateral deviation: a comparison among driver's performance indicators.

In this study we compare the efficacy of three drivers performance indicators based on lateral deviation in detecting significant on-road performance degradations while interacting with a secondary task: the High Frequency Component of steering wheel (HFC), and two indicators described in ISO/DIS 26022 (2007): the Normative and the Adapted Lane Change Test (LCT). Sixteen participants were asked to perform a simulated lane-change task while interacting, when required, with a visual search task with two levels of difficulty. According to predictions, results showed that the Adapted LCT indicator, taking into consideration individual practices in performing the LCT, succeeded in discriminating between single and dual task conditions. Furthermore, this indicator was also able to detect whether the driver was interacting with an easy or a difficult secondary task. Despite predictions, results did not confirm Normative LCT and HFC to be reliable indicators of performance degradation within the simulated LCT.

Moving attention from the road: A new methodology for the driver distraction evaluation using machine learning approaches

This work describes an approach to develop a model of drivers distraction induced by an on-board information system basing on vehicle data. Machine learning techniques have been adopted to find the model able to better predict distraction, given a target value. Results pointed in favor of a model obtained with the ANFIS (Adaptive Neuro Fuzzy Inference System) technique. Further investigations will be carried out by porting the model on real car.

Automation effects on driver's behaviour when integrating a PADAS and a distraction classifier

The FP7 EU project ISi-PADAS aims at conceiving an intelligent system, called PADAS, to support drivers, which intervenes continuously from warning up to automatic braking in the whole longitudinal control of the vehicle. However, such supporting systems can have some unwanted side-effect: due to the presence of automation in the driving task, less attention and reaction are needed by the drivers to intervene in the longitudinal control of the vehicle. Such a paper aims at investigating the effects of the level of automation on drivers, in particular on their Situation Awareness, when the user is supported by a specific PADAS application, integrated with a drivers distraction classifier.

Design of Warning Delivery Strategies in Advanced Rider Assistance Systems

European statistics show that motorbikes road accidents are extremely high and the reduction of such accidents is one of the main concern for the European community. Advanced Driver Assistance Systems are safety electronic systems used to assist the driver in avoiding risks and road accidents, by means of warnings sent before the situation becomes critical. The use of such systems in motorcycle context is currently lacking due to numerous variables that it is necessary to consider for making sure the riding. This paper presents an innovative research for the safety improvement of Powered-Two-Wheelers (PTW) by means of the development of effective and rider-friendly interfaces and interaction elements for the on-bike assistance systems. In particular, the paper presents the experimental results on comfort and safety aspects of two advanced rider assistance systems: the Frontal Collision Warning (FCW) and the Lane Change Support (LCS). The study starts from analyzing results of motorcycle simulator tests performed in 3D Virtual Reality environments which aim is to find recursive rider’s behavior patterns in FCW and LCS situations according to different multimodal type of warnings (visual, audio and haptic). Afterward, the paper presents three different machine learning models, Hidden Markov Models, Support Vector Machines and Artificial Neural Networks, that have been considered for simulating the riders’ behavior patterns according to the reaction time needful for avoiding a front collision. These simulation behavior models enabled to design a warning delivery strategy for apprising the rider of possible dangerous situations due to front collisions. Finally, the paper describes how this warning delivery strategy has been implemented in a HMI (Human Machine Interface) installed on motorbikes. This HMI is thought to offer an effective FCW system based on an understandable but, at the same time, discreet and unobtrusive rider-friendly solution.Copyright

Context-Dependent Force-Feedback Steering Wheel to Enhance Drivers' On-Road Performances

In this paper the topic of the augmented cognition applied to the driving task, and specifically to the steering maneuver, is discussed. We analyze how the presence of haptic feedback on the steering wheel could help drivers to perform a visually-guided task by providing relevant information like vehicle speed and trajectory. Starting from these considerations, a Context-Dependant Steering Wheel force feedback (CDSW) had been developed, able to provide to the driver the most suitable feeling of the vehicle dynamics according to the driven context. With a driving simulator the CSWD software had been tested twice and then compared with a traditional steering wheel.

Designing a Control and Visualization System for Off-Highway Machinery According to the Adaptive Automation Paradigm

This paper aims at describing the requirements of an off-highway human-machine system able to recognize potential risky situations and consequently prevent them. The developed methodology is based on two techniques derived from the field of human factors studies, namely the Hierarchical Task Analysis (HTA) and the Function Allocation (FA), which have been integrated and revised to suit the specific domain of off-highway machinery. The paradigms of adaptive automation and persuasive technology will be followed in the design process. After the off-highway domain analysis a system aiming at improving operator and machine safety is presented. The information system extends the human intelligence monitoring the stability of the machine.

Force feedback: new frontier as the innovative driving comfort tool

Previous Human Factors studies in the automotive field showed that drivers performance is influenced by the type of Force Feedback (FF) reproduced by the steering wheel. In the present study, six FF were compared. \ \ Results suggest that the effect of the type of FF depend on the specific driving scenario, thus suggesting the utility of an adaptive force feedback based steering wheel. \ \ In the final part of the paper, we describe how such a system could be implemented.

Effects of Distraction and Traffic Events Expectation on Drivers' Performances in a Longitudinal Control Task

Background In recent studies it has been investigated how the decrease of situation awareness is related to the level of drivers’ attention dedicated to the road and to drivers’ incorrect expectation on traffic events. This paper is aimed at investigating the effects of a distracting visual research task and drivers’ expectations on traffic behaviour on drivers’ on-road performances.

Design of an adaptive feedback based steering wheel

This paper aims at describing the architectural model of an adaptive force-feedback for a By Wire steering wheel system. This solution uses a steering wheel to replicate the reactive torque law which allows the driver to complete a precise driving scenario or a task with the higher performances. Then, the steering wheel adapts the reactive torque to the driving scenario. Since the design of this system considers the driver performances, it is called Ergonomic Steer-By-Wire. Now a prototype version of the ESBW is connected on a professional driving simulator and several tests are going to be conducted in order to tune the system components. Adapting the force feedback to the driving scenario could be a solution for improving drivers safety and vehicle control.