iDREAMS_814761_D3.2_30042020_Final.pdf (3.16 MB)
D3.2 Toolbox of recommended data collection tools and monitoring methods and a conceptual definition of the Safety Tolerance Zone
report
posted on 2020-11-05, 14:27 authored by Christos Katrakazas, Eva Michelaraki, George Yannis, Susanne Kaiser, Nina Senitschnig, Veerle Ross, Muhammad Adnan, Kris Brijs, Tom Brijs, Rachel TalbotRachel Talbot, Fran Pilkington-Cheney, Ashleigh FiltnessAshleigh Filtness, Graham Hancox, Eleonora Papadimitriou, André Lourenço, Cátia Gaspar, Carlos Carreiras, Christelle Al Haddad, Kui Yang, Constantinos Antoniou, Chiara Gruden, Petros Fortsakis, Eleni Konstantina Frantzola, Rodrigo TaveiraThe STZ is the core concept of the i-DREAMS project. This report aims to explicitly describe the practical conceptualisation of the STZ to develop the theoretical framework for operational design, presented in Deliverable 3.1, towards a fully functional methodology to be implemented in the forthcoming experimental setups (i.e. in WP4). In order to fulfil this purpose a trilateral correspondence is needed between the list of available technologies, the factors and indicators that need to be monitored (as described in Deliverable 2.1) and the translation of themeasurements into meaningful STZ levels and the triggering of interventions (Deliverable 2.2). As a result, the ultimate outcomes of this deliverable will be the provision of a toolbox, a list of viable options of the most useful data collection and monitoring tools as well as the suggestion of a mathematical framework to realize the STZ in real-world driving situations. With regards to the state-of-the-art measuring tools, several physiological and behavioral indicators, such as distraction/inattention, fatigue, emotions or forward collision warning are proposed for realtime, while performance measurements such as speeding, harsh acceleration, braking or risky hours driving are also mentioned for post-trip processing.
Furthermore, as different aspects related to the actual driving context (e.g. driver stress, time schedules, workload, frustration) can explain why drivers deviate from their “normal” way of driving, by accepting higher risks and engaging in increased risky driving behaviors (e.g. speeding, harsh accelerations, dangerous overtaking), the identification and detection of abnormal driving episodes becomes one of the most relevance to STZ estimation.
Funding
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 814761.
History
School
- Design and Creative Arts
Department
- Design
Publisher
i-DREAMSVersion
- VoR (Version of Record)
Rights holder
© i-DREAMSPublisher statement
© i-DREAMS Consortium, 2019-2022. This deliverable contains original unpublished work except where clearly indicated otherwise. Acknowledgement of previously published material and of the work of others has been made through appropriate citation, quotation or both. Reproduction is authorised provided the source is acknowledged.Publication date
2020-08-31Copyright date
2020Publisher version
Language
- en