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D52.1 - The potential of naturalistic driving studies with simple Data Acquisition Systems (DAS) for monitoring driver behaviour D52.1 - The potential of naturalistic driving studies with simple Data Acquisition Systems (DAS) for monitoring driver behaviour

This deliverable concerns questions regarding the potential of simple and low-cost technologies for future Naturalistic Driving data collection in relation to the complex, tailor-made and expensive data acquisition devices that have been used in recent large scale naturalistic studies. A major strength of the elaborated DAS used in UDRIVE is its extensiveness and hence, its ability to address many research questions and provide a comprehensive view of the driving environment and circumstances. Although there is no real substitute to a car equipped with 7 cameras and ample data loggers and sensors, all integrated into a workable platform, given the rapid advancement of technology, a vast amount of interesting and relevant research questions can be addressed with much less sophisticated and costly systems.

D51.1 - Safety and sustainability D51.1 - Safety and sustainability

This deliverable formulates measures to improve road safety based on the UDRIVE findings while keeping in mind that they cannot be generalised to all car drivers or all European countries. Rec-ommendations are clustered in regulation and enforcement measures, vehicle safety, awareness campaigns and training and road infrastructure design. Recommendations are made in the areas of seat belt use, speeding, reduction of critical situations, vulnerable road user safety, eco-driving promotion and secondary task reduction. The recommendations should be completed after new analyses on the UDRIVE data, which was not analysed completely. The workshops discussions at the end of the project highlighted several stakeholders' needs that could be addressed by new analyses.

D45.1 - Potential of eco-driving D45.1 - Potential of eco-driving

This deliverable offers the analysis of possibilities of the naturalistic driving data to provide more in-sight in different (normal) driving styles and eco-driving. Unique to UDRIVE is the augmentation of the velocity data with driving circumstances, like road type, speed limits, headway, and in-vehicle information. This allows placing the driver behaviour in context and distinguishing personal driving styles from behaviour forced by traffic conditions. To assess the fuel consumption and CO2 emis-sion reduction potential associated with adopting an eco-driving style, it is crucial to separate per-sonal driving style from infrastructure and from congestion while driving.

D44.1 - Interactions with vulnerable road users D44.1 - Interactions with vulnerable road users

This deliverable analyses the interactions of pedestrians, cyclists and PTWs with passenger cars and trucks. The aim was to identify and understand the everyday behavioural patterns in these interac-tions as well as the circumstances of safety critical events (SCE). For cyclists, identified SCEs were caused by a combination of infrastructure (a curve or a too narrow road), manoeuvre (often over-taking), the presence of other traffic, and an error or unexpected behaviour of the cyclist (slowing down). For pedestrians, in around three quarters of SCEs, the driver him- or herself had spotted the pedestrian in time. In the remaining situations, a warning system could have been of help. For PTWs, the data did not show that car drivers tend to follow them closer than cars or trucks.

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.