D3.2 Toolbox of recommended data collection tools and monitoring methods and a conceptual definition of the Safety Tolerance Zone
reportposted on 05.11.2020, 14:27 by Christos Katrakazas, Eva Michelaraki, George Yannis, Susanne Kaiser, Nina Senitschnig, Veerle Ross, Muhammad Adnan, Kris Brijs, Tom Brijs, Rachel TalbotRachel Talbot, Fran Pilkington-CheneyFran Pilkington-Cheney, Ashleigh FiltnessAshleigh Filtness, Graham HancoxGraham Hancox, Eleonora Papadimitriou, André Lourenço, Cátia Gaspar, Carlos Carreiras, Christelle Al Haddad, Kui Yang, Constantinos Antoniou, Chiara Gruden, Petros Fortsakis, Eleni Konstantina Frantzola, Rodrigo Taveira
The 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.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 814761.
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