Human Factors of Automated Driving (HFAUTO)

Forskningsprojekt, 2013 – 2017

Road transport is an essential part of society but the burden of traffic crashes, congestion, and pollution is enormous. Highly automated driving (HAD) has the potential to resolve these problems and major car makers foresee that HAD will be technically ready for commercialisation within one decade from now. However, industry is ill-prepared to deal with imminent human-error and legal consequences. This ITN will answer crucial human-factors questions, such as: how should human-machine-interfaces (HMI) be designed to support transitions between automated and manual control?, how can the automation understand the drivers state and intentions? what are the effects of HAD on accident risk and transport efficiency?, and who is legally responsible for accidents?Current human-factors expertise of HAD is scattered across disciplines (psychologists vs. engineers) and sectors (academia vs. industry) calling for a multidisciplinary approach. This ITN will train 14 young researchers in HMI design, behavioural research, driver modelling, and traffic flow theory, mentored by leading human-factors scientists in Europe. Through secondments in automotive industry, road safety institutes, and academia, the researchers will gain transferable knowledge of human factors, technology and legal and marketing aspects of HAD.The ITN will investigate human behaviour in HAD using extensive driving simulator studies with a newly developed driver state monitor. A multimodal human-machine interface (HMI) will be developed supporting the driver in HAD and in transitions between automated and manual driving. Driver cognition and behaviour will be captured in mathematical driver and traffic flow models. Using this methodology we will predict benefits of HAD in terms of enhanced safety, traffic efficiency and eco-driving before system introduction. Taking into account, legal, human and technical requirements a roadmap for market introduction of HAD will be proposed.

Deltagare

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Finansiering

Relaterade styrkeområden och infrastruktur

Hållbar utveckling

Drivkrafter

Publikationer

Users’ response to critical situations in automated driving: rear-ends, sideswipes, and false warnings

A Bayesian reference model for visual time-sharing behaviour in manual and automated naturalistic driving

Drivers’ response to attentional demand in automated driving

A Reference Model for Driver Attention in Automation: Glance Behavior Changes During Lateral and Longitudinal Assistance

Mer information

Projektets webbplats

http://hf-auto.eu

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