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- Bianchi Piccinini, Giulio, 1982, et al.
(author)
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Adaptive human machine interface based on the detection of driver's cognitive state using machine learning approach
- 2014
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In: Intelligenza Artificiale. - 2211-0097. ; 8:2/2014, s. 163-179
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Journal article (peer-reviewed)abstract
- Cognitive distraction during the driving task might cause impairment of detection performance and of the recognition and/or response selection, increasing the risk of road crashes. In order to avoid or mitigate the negative effects related to cognitive distraction, this paper describes the development and testing of a Cooperative Lane Change Assistant (C-LCA) system: it takes into account the real-time driver's cognitive state by means of a cognitive distraction classifier expressly designed and it implements road cooperation between the vehicles thanks to a cooperative driver model. Three different test sessions were conducted on a static driving simulator and, in each test session, the participants carried out several analogous runs of a reference protocol test, derived from the Lane Change Task. Using the data collected during the first test session, the cognitive distraction classifier was developed using Machine Learning techniques. In the remaining two sessions, a specific C-LCA HMI prototype with visual and acoustic interfaces has been evaluated. The results show that the C-LCA reduced the workload during the lane change manoeuvres compared both with the baseline and with the assistance of a non-cooperative warning system. As well, the users expressed satisfaction about the Visual Interface and Acoustic Interface designed for the C-LCA.
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| 3. |
- Rösener, Christian, et al.
(author)
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A Comprehensive Evaluation Approach for Highly Automated Driving
- 2017
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In: Enhanced Safety of Vehicles. ; , s. 1-13
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Conference paper (peer-reviewed)abstract
- Since the last decade, development efforts by academia and industry for automated driving functions have increased significantly. Also, the European research project AdaptIVe is looking into this topic. Due to the large operation spaces and various complex situations that are covered by these functions, efforts for evaluation increase also significantly. Within AdaptIVe, a comprehensive evaluation approach for automated driving functions ranging from SAE level 2-4 has been developed [1]. The approach splits the evaluation into technical, user-related, in-traffic and impact assessment addressing safety and environmental effects of automated driving. For each evaluation type appropriate test tools and methods are selected e.g. field test for technical assessment, trials on test track and in real traffic for the user-related assessments and simulations for the in-traffic and impact assessment. Next to the assessment type also the characteristics of the function must be considered when deciding for specific test tools. Hence, besides to the level of automation [8] the automated driving functions are classified into continuous and event-based operating functions. Whereas event-based operating functions are only operating for a short period in time (e.g. automated parking), continuous operating functions are, once they are active, operating for longer time periods (e.g. highway automation). Based on the classification the aspects to be evaluated and test methods are selected for all assessment types. The developed methodology has been applied to several automated driving functions developed within AdaptIVe. As an example, for the technical assessment of continuous operating functions it has been assessed whether the driving behavior of the developed functions is similar to human driving behavior and therefore not disturbing human traffic. In the user-related assessment, issues related to driver behavior, understanding of automation, trust, mental workload, resuming control, vigilance, usability and acceptance has been looked at. In this paper the key aspects of the AdaptIVe evaluation methodology for technical, user-related, in-traffic and impact assessment are presented as well as the key results of the application of this methodology on the within AdaptIVe developed automated driving functions.
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| 4. |
- Tango, Fabio, et al.
(author)
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Workshop on human machine interaction in autonomous vehicles : The perspective of the two current HORIZON 2020 Projects ADAS&ME and AUTOMATE
- 2017
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In: AutomotiveUI 2017. - New York, NY, USA : Association for Computing Machinery, Inc. - 9781450351515 ; , s. 33-38
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Conference paper (peer-reviewed)abstract
- The challenges related to the development of automated vehicles are not only of a technical nature, but also affect the quality of on-board interaction. The role of the HMI is crucial to manage unexpected events and ensure a safe and smooth transition of control. The workshop will promote and discuss the approach used in the HMI design for autonomous vehicles realized from the European projects ADAS&ME and AutoMate. ADAS&ME is dedicated to the creation of new driver state adaptive ADAS that incorporate driver/rider state, the situational and environmental context, as well as the adaptive interaction to automatically transfer control. AutoMate will enhance safety by using the strength of both the automation and driver in a dynamic situation dependent way. In the workshop the main challenges emerged in the projects will be discussed with the audience, to obtain feedbacks and enhancements, and to highlight themes and strategies not yet emerged.
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