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D43.1 - Driver distraction and inattention D43.1 - Driver distraction and inattention

This deliverable provides better understanding of whether and how drivers manage their secondary task activities. An automated procedure has been applied to provide candidate cases of secondary tasks to manual annotators. The automatic annotation tool is based on deep learning algorithms. The focus of the research questions was on self-regulation, on how drivers manage their secondary task activity in the context of the dynamics of the traffic and road situation. That man-agement includes the determination not to engage in such tasks in the first place or only to engage in some particular activities. The deliverable finds that car drivers spent 10.2% of their driving time engaged in some kind of secondary tasks. The total time spent in all the secondary tasks for truck drivers sums up to about 20%. The duration of secondary task was affected by complexity of manoeuvre. There are thus indications of some self-regulation by drivers.

D42.1 - Risk factors, crash causation and everyday driving D42.1 - Risk factors, crash causation and everyday driving

This deliverable reports the results of normal and risky driving behaviour. The safety critical event definition section explains the procedure of creating safety critical event triggers (SCE). Actual crashes are very rare, even in a data collection of over 21 months and 200 vehicles. Thus, it is almost impossible to investigate crashes directly. Surrogate measures are used instead to identify and assess potential risk factors. Hard braking, sudden steering, and accelerations are used as surrogates for collisions. While it is reasonable to assume a connection between these surrogates and real crashes, researchers are still uncertain whether SCEs and crashes follow the same patterns. None-theless, SCEs are still the best option to investigate how crashes are caused. In addition to SCEs, episodes with a high relevance to road safety were investigated. On rural roads, more crashes occur than on highways and they are more severe than in cities. This makes them a highly relevant research area in respect to traffic safety.

D41.1 - The UDRIVE dataset and key analysis results D41.1 - The UDRIVE dataset and key analysis results

This deliverable presents key results of the analysis performed in UDRIVE SP4. It also describes the UDRIVE dataset. The analysis was facilitated by tools such as the quality assurance procedures and data tracking, the SALSA data processing tool, the UDRIVE annotation codebook and high-quality manual annotation of video. The analysis itself is described in short in this report, while details are presented in separate UDRIVE deliverables. In summary, a large variety of analyses was performed on the UDRIVE naturalistic driving data (NDD). Although the efforts and results have been significant and already impact safety measure design and devel-opment, the UDRIVE project has only scratched the surface of the analysis potential.

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.

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.