Deliverables

Documents

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D34.1 - Summary of OS operations D34.1 - Summary of OS operations

This deliverable provides an overview of the OS operations during the data collection phase. It contains the vehicles instrumentation towards the start of full-scale operations, the operational tasks involved during the data collection to monitor the vehicles, the drivers and the data quality, the final stage including the de-installation and the exit questionnaire collection and an overview of the final sample and total data collected per OS and per vehicle type. The deliverable concludes with the summary of the learned lessons, in regards to recruitment, instrumentation and data collection.

D30.1 - Overview of the Data Collection D30.1 - Overview of the Data Collection

This deliverable details the steps and activities necessary to organise, prepare and carry out data collection at each OS. It clarifies the objectives and responsibilities with regards to SP3 organisation and execution, describes the organisation and planning tasks to prepare for the data collection, the channels for recruitment and its legal, ethical, technical and operational challenges. The last chapter details the actual data collection phase. With regards to recruitment, the deliverable indicates that the duration of the study was one of the main barriers for recruitment.

D53.1 - Driver models and risk functions on safety and sustainability D53.1 - Driver models and risk functions on safety and sustainability

This deliverable investigates how Naturalistic Driving Data (NDD) can be used to improve existing driver models, particularly those related to safety and sustainability. The analysis builds on the specific case of driving in horizontal curves on rural and motorways roads. Single-vehicle crashes, often occurring on curves, account for approximately one-third of fatalities across Europe and the speed choice on curve approach has significant environmental implications. The results show that problematic steering by the UDRIVE participants could be related to curve radius: the tighter the curve, the higher was the incidence of high-amplitude steering input. There is a far higher risk for small-radius curves. The insight obtained can not only enhance driver-vehicle simulation models, but also suggests the development of new countermeasures to stimulate greater safety margins in curve driving. Suggested further work would be to investigate the correlation between the safety and eco-related.

D54.1 - Feasibility of commercial applications D54.1 - Feasibility of commercial applications

This deliverable addresses how NDD can be used for the development of driver support functions and driver coaching. It also outlines business models for continued and open access to UDRIVE data after the project. For function development, NDD can be used in combination with statistical crash data. NDD can also be used to provide the relevant data to develop, test and validate the behaviour models. For coaching programs, it is necessary to track reliable NDD to provide timely and effective feedback to the driver. For data access, survey respondents favour a one-stop European approach, sharing many different data sets. The main challenges for this are the costs to host the data, the financial model for access and privacy barriers.

D35.1 - Lessons learnt from OS operations D35.1 - Lessons learnt from OS operations

This deliverable focuses on the specific lessons learnt from the data collection in UDRIVE. During the project, the operation sites provided three feedback rounds and and gathered 71 lessons. Participants filled out a final questionnaire. A frequent problem across the Operation Sites (OS) was the participants’ drop-outs. The main lesson learnt was that it is important to maintain a set of replacement participants until the end of the project. From the participants’ questionnaires, it was noted that even if they felt comfortable, their driving behaviour was affected. The roles and responsibilities of every supplier should be defined in detail to avoid misunderstandings, delays or ambiguities. Developing as much early as possible a very detailed and realistic plan of action allows to avoid delays, overspending, save resources and to achieve the project objectives.