1,721,033 research outputs found
How imitation learning and human factors can be combined in a model predictive control algorithm for adaptive motion planning and control
Full text linkInterest in autonomous vehicles (AVs) has significantly increased in recent years, but despite the huge research efforts carried out in the field of intelligent transportation systems (ITSs), several technological challenges must still be addressed before AVs can be extensively deployed in any environment. In this context, one of the key technological enablers is represented by the motion-planning and control system, with the aim of guaranteeing the occupants comfort and safety. In this paper, a trajectory-planning and control algorithm is developed based on a Model Predictive Control (MPC) approach that is able to work in different road scenarios (such as urban areas and motorways). This MPC is designed considering imitation-learning from a specific dataset (from real-world overtaking maneuver data), with the aim of getting human-like behavior. The algorithm is used to generate optimal trajectories and control the vehicle dynamics. Simulations and Hardware-In-the-Loop tests are carried out to demonstrate the effectiveness and computation efficiency of the proposed approach
A Supervisor Αgent-Based on the Markovian Decision Process Framework to Optimize the Behavior of a Highly Automated System
Full text linkIn this paper, we explore how MDP can be used as the framework to design and develop an Intelligent Decision Support System/Recommender System, in order to extend human perception and overcome human senses limitations (because covered by the ADS), by augmenting human cognition, emphasizing human judgement and intuition, as well as supporting him/her to take the proper decision in the right terms and time. Moreover, we develop Human-Machine Interaction (HMI) strategies able to make “transparent” the decision-making/recommendation process. This is strongly needed, since the adoption of partial automated systems is not only connected to the effectiveness of the decision and control processes, but also relies on how these processes are communicated and “explained” to the human driver, in order to achieve his/her trust
Edel EU Project: User requirements and customer benefit analysis in the design of a novel driver support system for night vision
No full textEDEL is a three years R&D project funded within the European Fifth Framework IST programme for Intelligent Transport Systems. The project main goal resides in the development of an advanced vision enhancement system for night driving based on: near infrared sensors, a novel illumination system, and an innovative human machine interface. EDEL's expected impact is on safety in road transport, in the prevention of accidents and consequent fatalities and injuries. Infrared imaging may be well-suited to night conditions only if the information is displayed to the driver adequately. An appropriate consideration of user requirements in the design phase will increase EDEL benefits and customer acceptance. Authors of this paper devised a focus group concept that was conceived to identify both customer benefit and initial system performances specification. Groups were defined to represent both those people who are most exposed at incidents when driving at night (young and elderly) and the majority of the driving population (middle aged). This approach allowed to come up with a set of unified requirements for the system to be designed
Saspence - Safe Speed And Safe Distance: Project Overview and Customer Benefit Analysis of a Novel Driver’s Collision Avoidance Support System
No full textIn Europe, a considerable amount of lives lost in traffic accidents is due to inappropriate vehiclespeed or inappropriate headway. Excessive speed is acknowledged as one of the major causes ofaccidents being responsible for about one-third of crashes, and contributing to the death of around1.200 people each year and more than 100.000 injuries. In addition, rear-end and chain accidents,which are mainly caused by inappropriate headways, altogether account for another 15% of all roadaccidents. In order to improve driving safety it is therefore of great importance to develop anintelligent system that helps the driver in reducing risky and dangerous situations related to theaforementioned factors. Such a system is the goal of the SAPENCE Project (part of IP PREVENT).The safety gains that are expected from systems capable to appropriately warn the driver in case ofexcessive speed and small headway look very promising
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A Decision Model for Enhancing Driving Security
No full textDriving is a complex activity which requires constant care and attention. Intelligent Advance Driver Assistance Systems (ADAS) can improve vehicle control performance and, thus, drivers and passengers safety. In particular, identification and prediction of driving intention can provide prompt information to drivers and vehicles in their vicinity that are fundamental for avoiding collisions. In this paper, we propose a lane change prediction model based on machine learning able to distinguish between left and right lane changes, a distinction that becomes particularly important when driving in a highway. Models have been trained and validated using a real dataset gathered online by using a high-tech demonstrator vehicle provided by Centro Ricerche Fiat (i.e., Fiat Research Center). Data, which refer to real driving conditions on a highway, have been collected by monitoring different drivers showing different behaviors. We address the problem of unbalanced data, typical of real data sets, and propose two prediction models based on Support Vector Machines and Random Forests. The results of our computational experiments show the validity of the approach with respect to state of the art models, both in terms of prediction accuracy and prediction time
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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