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Adaptive human machine interface based on the detection of driver's cognitive state using machine learning approach

Giulio Francesco Bianchi Piccinini (Institutionen för tillämpad mekanik, Fordonssäkerhet) ; Pinotti Daniele ; Tango Fabio
Intelligenza Artificiale (1724-8035). Vol. 8 (2014), 2/2014, p. 163-179.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Lane change, driving simulator, driver state detection, adaptive HMI, agents’ cooperation



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Denna post skapades 2015-06-28. Senast ändrad 2015-07-28.
CPL Pubid: 218996

 

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Institutioner (Chalmers)

Institutionen för tillämpad mekanik, Fordonssäkerhet (2005-2017)

Ämnesområden

Transport
Datorsystem
Farkostteknik
Produktionsteknik, arbetsvetenskap och ergonomi

Chalmers infrastruktur