Deliverables

Documents

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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.

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.

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.

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.

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.