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The goal of this paper is to emphasize and present briefly the nanotechnology science and its potential impact on the automotive industry in order to improve the production of recent models with an optimization of the safety performance and a reduction in the environmental impacts. Nanomaterials can be applied in car bodies as light weight constructions without compromising the stiffness and crashwortiness, which means less material and less fuel consumption. This paper outlines the progress of nanotechnology applications into the safety features of more recent vehicle models and fuel efficiency, but also emphasis the importance of sustainable development on the application of these technologies and life cycle analysis of the considered materials, in order to meet the society trends and customers demands to improve ecology, safety and comfort.

While extracting meaningful information from big data is getting relevance, literature lacks information on how to handle sensitive data by different project partners in order to collectively answer research questions (RQs), especially on impact assessment of new automated driving technologies. This paper presents the application of an established reference piloting methodology and the consequent development of a coherent, robust workflow. Key challenges include ensuring methodological soundness and data validity while protecting partners’ intellectual property. The authors draw on their experiences in a 34-partner project aimed at assessing the impact of advanced automated driving functions, across 10 European countries. In the first step of the workflow, we captured the quantitative requirements of each RQ in terms of the relevant data needed from the tests. Most of the data come from vehicular sensors, but subjective data from questionnaires are processed as well. Next, we set up a data management process involving several partners (vehicle manufacturers, research institutions, suppliers and developers), with different perspectives and requirements. Finally, we deployed the system so that it is fully integrated within the project big data toolchain and usable by all the partners. Based on our experience, we highlight the importance of the reference methodology to theoretically inform and coherently manage all the steps of the project and the need for effective and efficient tools, in order to support the everyday work of all the involved research teams, from vehicle manufacturers to data analysts.

The main goal of this research was to develop models for crash severity prediction in a subject vehicle while taking into account the impact of both vehicles involved (Vehicles V1 and V2). Three binary targets were modeled: FatalSIK (to predict overall severity), FatalSIKV1 (to predict the probability of a serious injury, fatality, or both in Vehicle V1), and FatalSIKV2 (to predict the probability of a serious injury, fatality, or both in Vehicle V2). For the period from 2006 to 2010, 874 collisions involving injuries, fatalities, or both were analyzed. However, the crash sample included few severe events. Because imbalanced data introduce a bias toward the majority class (nonsevere crashes), in predictive modeling, they would result in less accurate predictions of the minority class (severe crashes). For the challenge imposed by small sample size and imbalanced data to be overcome, an important methodology was developed on the basis of a resampling strategy by using 10 stratified random samples for model evaluation. The effect of vehicle characteristics such as weight, engine size, wheelbase, and registration year (age of vehicle) were explored. Logistic regression analysis for FatalSIK suggested that the age of the vehicle and the type of collision were significant predictors (p < .0084 and .0346, respectively). Models FatalSIKV1 and FatalSIKV2 showed that the engine size of the opponent vehicle was statistically significant in predicting severity (p < .0762 and p < .03875, respectively). Models for FatalSIKV1 and FatalSIKV2 yielded satisfactory results when evaluated with the 10 stratified random samples: 61.2% [standard deviation (SD) = 2.4] and 61.4% (SD = 3.1), respectively.

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