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Träfflista för sökning "WFRF:(Forcolin M.) "

Search: WFRF:(Forcolin M.)

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1.
  • Baalsrud Hauge, Jannicke, 1969-, et al. (author)
  • Development of a framework and an online tool for measuring the innovation capabilities among small companies in the logistics and transport industry
  • 2016
  • In: 2016 International Conference on Engineering, Technology and Innovation/IEEE lnternational Technology Management Conference, ICE/ITMC 2016 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781509029358
  • Conference paper (peer-reviewed)abstract
    • This paper presents an online tool developed in order to support the undertaking among small and medium sized enterprises in measuring and monitoring their innovation capabilities. The tool is based on a framework which can be seen as an adaption of the existing Innovation Scoreboard, an instrument used by the European commission in order to measure the innovation among its member states. This framework has been adapted to better fit the needs of the SMEs in logistics and transport. It is a part of a set of actions and tools developed within an European project. 
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2.
  • Bianchi Piccinini, Giulio, 1982, et al. (author)
  • How Do Drivers Respond to Silent Automation Failures? Driving Simulator Study and Comparison of Computational Driver Braking Models
  • 2020
  • In: Human Factors. - Chalmers University of Technology, Gothenburg, Sweden.; Volvo Group Trucks Technology, Gothenburg, Sweden.; Virginia Tech Transportation Institute, Blacksburg, USA.; University of Leeds, UK.; VTI, Gothenburg, Sweden. : SAGE Publications. - 1547-8181 .- 0018-7208. ; 62:7, s. 1212-1229
  • Journal article (peer-reviewed)abstract
    • Objective: This paper aims to describe and test novel computational driver models, predicting drivers’ brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC). Background: Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving. Method: Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers’ arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study. Results: The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study. Conclusion: Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data. Application: Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving.
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